Traditional DoE vs. Bayesian Optimization

The first and second videos from Taylor Sparks’ Optimization Tutorial YouTube playlist are based on the results from this notebook. I suggest watching these two videos prior to working through this notebook for better context.

This notebook first describes uninformed sampling methods including random, grid, and quasi-random (e.g., Latin hypercube, Sobol sequences) and shows how quasi-random achieves a much more even distribution of points. Finally, we compare the efficiency of these traditional methods for design of experiments (DoE) against Bayesian optimization. We show that, on average, Bayesian optimization is much more efficient than traditional DoE.

try:
  import google.colab
  IN_COLAB = True
  %pip install git+https://github.com/sparks-baird/self-driving-lab-demo.git # latest
  # %pip install self-driving-lab-demo # uncomment for stable release
except:
  IN_COLAB = False

import numpy as np
import pandas as pd
import plotly.express as px
from scipy.stats import qmc
from os import path
from self_driving_lab_demo.utils.plotting import plot_and_save

bounds = {"x1": [0, 1], "x2": [0, 1]}
num_samples = 10

Uninformed Sampling Methods

These are sampling methods that do not incorporate information about the objective function to be optimized. We will cover grid search, random search, and two quasi-random methods: Latin hypercube and Sobol sequences.

Grid

Grid sampling is a structured approach to sampling from a search space. It involves creating a grid of points over the space, and then selecting points from the grid. When grids are used for certain types of problems (e.g., finite element methods), the algorithms are often more straightforward than for non-lattice point sets. This can be an effective way to ensure that the entire search space is explored, but it can also lead to a lot of unnecessary points being generated, especially for high-dimensional search spaces, due to large, systematic “pockets” in the search space.

from sklearn.model_selection import ParameterGrid

def get_grid_samples(bounds, num_samples = 10, seed=None):
    # seed is unused, for compatibility only
    param_grid = {}
    num_pts_per_dim = max(1, np.floor(num_samples ** (1 / len(bounds))).astype(int))
    for name, bnd in bounds.items():
        param_grid[name] = np.linspace(bnd[0], bnd[1], num=num_pts_per_dim)
    print(num_pts_per_dim)
    return pd.DataFrame(list(ParameterGrid(param_grid)))

grid_samples = get_grid_samples(bounds, num_samples=num_samples)
grid_samples

3

	x1	x2
0	0.0	0.0
1	0.0	0.5
2	0.0	1.0
3	0.5	0.0
4	0.5	0.5
5	0.5	1.0
6	1.0	0.0
7	1.0	0.5
8	1.0	1.0

grid_fig = px.scatter(grid_samples, x="x1", y="x2", width=400, height=400)
grid_fig

Data type cannot be displayed: application/vnd.plotly.v1+json

Random

Random sampling is a simple and straightforward method for generating samples from a search space. It involves randomly selecting points from the space, without any regard for their distribution. This can be an effective method for exploring search spaces, and it is often more effective than grid search. While it doesn’t have the large, systematic pockets characteristic of grid search, it has large, occasional gaps due to the random nature of the search.

from numpy.random import default_rng

def get_random_samples(bounds, num_samples=9, seed=None):
    rng = default_rng(seed)
    samples = {}
    for parameter, bound in bounds.items():
        samples[parameter] = rng.uniform(bound[0], bound[1], num_samples)
    return pd.DataFrame(samples)

random_samples = get_random_samples(bounds, seed=0)
random_samples

	x1	x2
0	0.636962	0.935072
1	0.269787	0.815854
2	0.040974	0.002739
3	0.016528	0.857404
4	0.813270	0.033586
5	0.912756	0.729655
6	0.606636	0.175656
7	0.729497	0.863179
8	0.543625	0.541461

random_fig = px.scatter(random_samples, x="x1", y="x2", width=400, height=400)
random_fig

Data type cannot be displayed: application/vnd.plotly.v1+json

Quasi-Random

Quasi-random sampling methods, also known as low-discrepancy or deterministic sampling methods, are a family of sampling techniques that are designed to produce samples that are more evenly distributed than random samples. Unlike random sampling, which selects points randomly and independently, quasi-random sampling methods aim to achieve a more uniform coverage of the parameter space by reducing the discrepancy between the generated points and the desired distribution.

We’ll discuss two common quasi-random sampling methods: Latin hypercube and Sobol sequences.

Latin Hypercube

Latin hypercube sampling (LHS) is a variation of grid sampling that aims to improve the uniformity of the samples. It does this by ensuring that each dimension of the search space is represented equally in the sample set. It involves dividing the parameter space into equally spaced intervals and randomly selecting one point within each interval. LHS ensures a more even coverage of the parameter space compared to random or grid sampling methods (i.e., lower discrepancy).

def get_latin_hypercube_samples(bounds, num_samples=10, seed=None):
    sampler = qmc.LatinHypercube(d=len(bounds), optimization="random-cd", seed=seed)
    samples = sampler.random(num_samples)
    l_bounds = [bound[0] for bound in bounds.values()]
    u_bounds = [bound[1] for bound in bounds.values()]
    samples = qmc.scale(samples, l_bounds, u_bounds)
    return pd.DataFrame(samples, columns=list(bounds.keys()))

latin_hypercube_samples = get_latin_hypercube_samples(bounds, seed=0)
latin_hypercube_samples

	x1	x2
0	0.245638	0.173021
1	0.436304	0.298347
2	0.927034	0.708724
3	0.114260	0.827050
4	0.718673	0.006493
5	0.013682	0.499726
6	0.595903	0.996641
7	0.639336	0.582434
8	0.318415	0.645854
9	0.870029	0.357731

latin_hypercube_fig = px.scatter(
    latin_hypercube_samples, x="x1", y="x2", width=400, height=400
)
latin_hypercube_fig

Data type cannot be displayed: application/vnd.plotly.v1+json

Sobol Sequences

Sobol sequences are another type of quasi-random sampling with good space-filling (i.e., low discrepancy) properties. Sobol sequences “use a base of two to form successively finer uniform partitions of the unit interval and then reorder the coordinates in each dimension” (source). In other words, to obtain optimal space-filling properties, sample sizes that are powers of 2 (i.e., \(n=2^m\)) should be used. For each dimension, Sobol sequences utilize a set of direction numbers, also known as primitive polynomials, that are converted to a binary representation and undergo bitwise operations to arrive at the final sequence.

from scipy.stats.qmc import Sobol

def get_sobol_samples(bounds, num_samples=10, seed=None):
    sampler = Sobol(len(bounds), seed=seed)
    samples = sampler.random(num_samples)

    l_bounds = [bound[0] for bound in bounds.values()]
    u_bounds = [bound[1] for bound in bounds.values()]
    samples = qmc.scale(samples, l_bounds, u_bounds)

    return pd.DataFrame(samples, columns=list(bounds.keys()))

sobol_samples = get_sobol_samples(bounds, num_samples=num_samples, seed=0)
sobol_samples

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

	x1	x2
0	0.850585	0.931366
1	0.451565	0.166937
2	0.248736	0.591645
3	0.584153	0.326728
4	0.663688	0.711389
5	0.014668	0.448486
6	0.312342	0.808678
7	0.897760	0.046263
8	0.987552	0.509826
9	0.339692	0.274682

sobol_fig = px.scatter(sobol_samples, x="x1", y="x2", width=400, height=400)
sobol_fig

Data type cannot be displayed: application/vnd.plotly.v1+json

Comparison between sampling methods

The quasi-random methods tend to have better space-filling properties than random or grid search. Note the large systematic gaps in grid, the large occasional gaps in random, and the more even distribution of points in LHS and Sobol.

sampling_fns = dict(
    grid=get_grid_samples,
    random=get_random_samples,
    latin_hypercube=get_latin_hypercube_samples,
    sobol=get_sobol_samples,
)

sample_nums = [5, 10, 50, 100]
sample_nums.reverse()

sample_dfs = []
for name, sampling_fn in sampling_fns.items():
    for num_samples in sample_nums:
        sample_df = sampling_fn(bounds, num_samples)
        sample_df["name"] = name
        sample_df["num_samples"] = num_samples
        sample_dfs.append(sample_df)

compare_df = pd.concat(sample_dfs, axis=0)

10
7
3
2

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

compare_df

	x1	x2	name	num_samples
0	0.000000	0.000000	grid	100
1	0.000000	0.111111	grid	100
2	0.000000	0.222222	grid	100
3	0.000000	0.333333	grid	100
4	0.000000	0.444444	grid	100
...	...	...	...	...
0	0.924471	0.379841	sobol	5
1	0.321745	0.816658	sobol	5
2	0.119724	0.066057	sobol	5
3	0.626250	0.628000	sobol	5
4	0.557449	0.146910	sobol	5

657 rows × 4 columns

fig = px.scatter(
    compare_df,
    x="x1",
    y="x2",
    facet_row="num_samples",
    facet_col="name",
    width=800,
    height=800,
)
plot_and_save(
    "traditional-doe-compare",
    fig,
    show=True,
    mpl_kwargs=dict(width_inches=7.5, height_inches=8.0),
)

Data type cannot be displayed: application/vnd.plotly.v1+json

Worsening performance in higher dimensions

As we observe the discrepancy associated with sampling methods in higher dimensions, we notice that the gap between the quasi-random methods and the random and grid methods widens. In other words, quasi-random methods increasingly outperform random and grid as the dimensionality increases.

one = get_grid_samples(dict(x1=bounds["x1"]), num_samples=3**1)
two = get_grid_samples(dict(x1=bounds["x1"], x2=bounds["x2"]), num_samples=3**2)
three = get_grid_samples(
    dict(x1=bounds["x1"], x2=bounds["x2"], x3=[0.0, 1.0]), num_samples=3**3
)

3
3
3

# https://community.plotly.com/t/plotting-a-simple-1d-number-line/39169/4
import plotly.graph_objects as go
fig = go.Figure()
x = one["x1"]
fig.add_trace(go.Scatter(
    x=x, y=[0] * len(x), mode='markers', marker_size=20,
))
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False,
                 zeroline=True, zerolinecolor='black', zerolinewidth=3,
                 showticklabels=False)
fig.update_layout(height=200, plot_bgcolor='white')
fig.show()

# px.scatter(one, x="x1", y=[0]*3)

Data type cannot be displayed: application/vnd.plotly.v1+json

px.scatter(two, x="x1", y="x2", width=400, height=400)

Data type cannot be displayed: application/vnd.plotly.v1+json

# https://community.plotly.com/t/rotating-3d-plots-with-plotly/34776/2
# https://community.plotly.com/t/how-to-export-animation-and-save-it-in-a-video-format-like-mp4-mpeg-or/64621/2
import plotly.graph_objects as go
import numpy as np
import plotly.io as pio

x, y, z = three["x1"], three["x2"], three["x3"]

fig = go.Figure(go.Scatter3d(x=x, y=y, z=z, mode="markers"))

x_eye = -1.25
y_eye = 2
z_eye = 1.0

fig.update_layout(
    title="Animation Test",
    width=600,
    height=600,
    scene_camera_eye=dict(x=x_eye, y=y_eye, z=z_eye),
    updatemenus=[
        dict(
            type="buttons",
            showactive=False,
            y=1,
            x=0.8,
            xanchor="left",
            yanchor="bottom",
            pad=dict(t=45, r=10),
            buttons=[
                dict(
                    label="Play",
                    method="animate",
                    args=[
                        None,
                        dict(
                            frame=dict(duration=5, redraw=True),
                            transition=dict(duration=0),
                            fromcurrent=True,
                            mode="immediate",
                        ),
                    ],
                )
            ],
        )
    ],
)


def rotate_z(x, y, z, theta):
    w = x + 1j * y
    return np.real(np.exp(1j * theta) * w), np.imag(np.exp(1j * theta) * w), z


frames = []
pil_frames = []
for t in np.arange(0, 3.14, 0.025):
    xe, ye, ze = rotate_z(x_eye, y_eye, z_eye, -t)
    frames.append(go.Frame(layout=dict(scene_camera_eye=dict(x=xe, y=ye, z=ze))))
fig.frames = frames

fig.show()

Data type cannot be displayed: application/vnd.plotly.v1+json

[qmc.discrepancy(df.values) for df in [one, two, three]]

[0.0277777777777779, 0.060956790123456894, 0.10033007544581585]

discrepancies = []
for name, sampling_fn in sampling_fns.items():
    for num_samples in sample_nums:
        sample_df = compare_df.query("name == @name and num_samples == @num_samples")
        discrepancies.append(
            dict(
                name=name,
                num_samples=num_samples,
                discrepancy=qmc.discrepancy(sample_df[["x1", "x2"]].values),
            )
        )

discrepancy_df = pd.DataFrame(discrepancies)
discrepancy_df

	name	num_samples	discrepancy
0	grid	100	0.004093
1	grid	50	0.008708
2	grid	10	0.060957
3	grid	5	0.204861
4	random	100	0.002874
5	random	50	0.011180
6	random	10	0.033466
7	random	5	0.098165
8	latin_hypercube	100	0.000058
9	latin_hypercube	50	0.000224
10	latin_hypercube	10	0.005263
11	latin_hypercube	5	0.019957
12	sobol	100	0.000106
13	sobol	50	0.000440
14	sobol	10	0.007685
15	sobol	5	0.024782

fig = px.scatter(
    compare_df,
    x="x1",
    y="x2",
    facet_row="num_samples",
    facet_col="name",
    width=800,
    height=800,
)

for col, (name, sampling_fn) in enumerate(sampling_fns.items()):
    col = col+1
    for row, num_samples in enumerate(sample_nums):
        row = 4 - row
        fig.add_annotation(
            xref="x domain",
            yref="y domain",
            x=0.5,
            y=-0.1,
            text=f' Discrepancy = {discrepancy_df.query("name == @name and num_samples == @num_samples").iloc[0]["discrepancy"]:.3g} ',
            # text = f"row={row}, col={col}",
            showarrow=False,
            bgcolor="white",
            row=row,
            col=col,
        )

fig_path = "traditional-doe-compare-discrepancy"
fig.update_layout(
    margin=dict(r=40, t=30, b=30),
)
fig.write_html(fig_path + ".html")
fig.write_image(fig_path + ".png")
fig.show()

Data type cannot be displayed: application/vnd.plotly.v1+json

dim_discrepancies = []
# sample_dfs = []
dim_nums = [2, 3, 10, 20]
num_samples = 100
for name, sampling_fn in sampling_fns.items():
    for num_dims in dim_nums:
        bounds = {f"x{i+1}": [0, 1] for i in range(num_dims)}
        sample_df = sampling_fn(bounds, num_samples, seed=0)
        discrepancy = qmc.discrepancy(sample_df.values)
        dim_discrepancies.append(dict(name=name, num_samples=sample_df.shape[0], discrepancy=discrepancy, num_dims=num_dims))
        # sample_dfs.append(sample_df)

dim_discrepancy_df = pd.DataFrame(dim_discrepancies)
pd.pivot_table(
    dim_discrepancy_df.drop("num_samples", axis=1),
    index=["num_dims", "discrepancy", "name"],
)

10
4
1
1

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

num_dims	discrepancy	name
2	0.000064	latin_hypercube
	0.000134	sobol
	0.004093	grid
	0.006478	random
3	0.000210	latin_hypercube
	0.000324	sobol
	0.010809	random
	0.053356	grid
10	0.018654	latin_hypercube
	0.022224	sobol
	0.068428	random
	53.396889	grid
20	0.454235	latin_hypercube
	0.571777	sobol
	0.840589	random
	3309.123807	grid

dim_discrepancies = []
# sample_dfs = []
dim_nums = [2, 3, 10, 20]
num_samples = 10
for name, sampling_fn in sampling_fns.items():
    for num_dims in dim_nums:
        bounds = {f"x{i+1}": [0, 1] for i in range(num_dims)}
        sample_df = sampling_fn(bounds, num_samples, seed=0)
        discrepancy = qmc.discrepancy(sample_df.values)
        dim_discrepancies.append(dict(name=name, num_samples=sample_df.shape[0], discrepancy=discrepancy, num_dims=num_dims))
        # sample_dfs.append(sample_df)

dim_discrepancy_df = pd.DataFrame(dim_discrepancies)
pd.pivot_table(
    dim_discrepancy_df.drop("num_samples", axis=1),
    index=["num_dims", "discrepancy", "name"],
)

3
2
1
1

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

num_dims	discrepancy	name
2	0.005237	latin_hypercube
	0.007647	sobol
	0.019628	random
	0.060957	grid
3	0.011434	latin_hypercube
	0.014515	sobol
	0.045862	random
	0.376881	grid
10	0.304143	latin_hypercube
	0.437433	sobol
	0.997640	random
	53.396889	grid
20	6.131461	latin_hypercube
	7.369506	sobol
	11.771547	random
	3309.123807	grid

dim_discrepancies = []
# sample_dfs = []
dim_nums = [2, 3, 10, 20]
num_samples = 1000
for name, sampling_fn in sampling_fns.items():
    for num_dims in dim_nums:
        bounds = {f"x{i+1}": [0, 1] for i in range(num_dims)}
        sample_df = sampling_fn(bounds, num_samples, seed=0)
        discrepancy = qmc.discrepancy(sample_df.values)
        dim_discrepancies.append(dict(name=name, num_samples=sample_df.shape[0], discrepancy=discrepancy, num_dims=num_dims))
        # sample_dfs.append(sample_df)

dim_discrepancy_df = pd.DataFrame(dim_discrepancies)
pd.pivot_table(
    dim_discrepancy_df.drop("num_samples", axis=1),
    index=["num_dims", "discrepancy", "name"],
)

31
9
1
1

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

num_dims	discrepancy	name
2	0.000001	latin_hypercube
	0.000002	sobol
	0.000392	grid
	0.001073	random
3	0.000005	sobol
	0.000009	latin_hypercube
	0.001335	random
	0.008351	grid
10	0.000865	sobol
	0.001360	latin_hypercube
	0.007363	random
	53.396889	grid
20	0.043370	sobol
	0.044576	latin_hypercube
	0.079098	random
	3309.123807	grid

Informed Sampling via Bayesian Optimization

Informed sampling algorithms are algorithms that use information about the objective function to be optimized. Bayesian optimization is a highly efficient example of informed sampling that uses Bayesian statistics to guide the search process. It does this by building a probabilistic model of the objective function, and then using this model to select points that are likely to lead to improvements in the objective function. This allows the method to balance the exploration-exploitation trade-off and focus on promising regions of the parameter space Bayesian optimization can be an effective way to find the global optimum of an objective function, but it can also be computationally expensive.

Evaluating Performance

Discrepancy is no longer a satisfactory measure of performance, since we’re now moving to a situation where we can leverage information about the function we’re trying to optimize; however, we still need to decide on an objective function.

For simplicity, we’ll start by using an analytic 2D function, called the “Branin function”, with a limited evaluation budget. In chemistry and materials science, an objective might be maximizing the yield of a reaction or the efficiency of a solar cell. “Objective function” refers to the objective (yield, efficiency, etc.) being a function of tunable parameters (temperature, pressure, composition, etc.).

from ax.utils.measurement.synthetic_functions import branin
branin(2.0, 3.0)

6.115426298669772

total_trials = 20

Let’s peak at the solution space to get an idea of how complex the function is. Note that there are three global minima in the Branin function.

bounds = {"x1": [-5.0, 10.0], "x2": [0.0, 10.0]}
branin_samples = get_sobol_samples(bounds, num_samples=10000, seed=None)
branin_values = branin(branin_samples.values)
px.scatter(
    branin_samples,
    x="x1",
    y="x2",
    color=branin_values,
    width=400,
    height=400,
    labels=dict(color="branin"),
)

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

Data type cannot be displayed: application/vnd.plotly.v1+json

It becomes more obvious where the three minima are if we use logarithmic scaling for the Branin function values.

fig = px.scatter(
    branin_samples,
    x="x1",
    y="x2",
    color=np.log(branin_values),
    width=400,
    height=400,
    labels=dict(color="log_branin"),
)
fig

Data type cannot be displayed: application/vnd.plotly.v1+json

Next, we’ll perform closed-loop optimization in Meta’s Adaptive Experimentation (Ax) platform using their simplest API, the “Loop API” via the optimize function.

from ax import optimize

objective_name = "branin"

def evaluate(parameters):
    return {objective_name: branin(parameters["x1"], parameters["x2"])}

best_parameters, values, experiment, model = optimize(
    parameters=[
        {
            "name": "x1",
            "type": "range",
            "bounds": [-5.0, 10.0],
        },
        {
            "name": "x2",
            "type": "range",
            "bounds": [0.0, 10.0],
        },
    ],
    evaluation_function=evaluate,
    objective_name = objective_name,
    minimize=True,
    total_trials=total_trials,
    random_seed=0,
)

[INFO 06-17 23:15:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:15:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:15:47] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.0, 10.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 10.0])], parameter_constraints=[]).
[INFO 06-17 23:15:47] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:15:47] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:15:47] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:15:47] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:15:47] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:15:47] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:15:47] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:15:47] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:15:47] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:15:47] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:15:47] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:15:47] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:15:48] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:15:49] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:15:50] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:15:50] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:15:51] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:15:51] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:15:52] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:15:53] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:15:54] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:15:56] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:15:58] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:16:00] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:16:02] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:16:03] ax.service.managed_loop: Running optimization trial 20...

Let’s take a look at how well we did in optimizing the function.

print(best_parameters, values[0])

{'x1': 9.486598027537017, 'x2': 2.6789992399381175} {'branin': 0.4508448724217615}

fig.add_trace(
    go.Scatter(
        x=[best_parameters["x1"]],
        y=[best_parameters["x2"]],
        mode="markers",
        marker=dict(
            color="red",
            size=10,
        ),
    )
)
d = fig.to_dict()
d["data"][0]["type"] = "scatter"
fig2 = go.Figure(d)
fig2.update_layout(showlegend=False)
fig2

Data type cannot be displayed: application/vnd.plotly.v1+json

Looks like Bayesian optimization did a pretty good job at finding one of the global minima in a limited number of evaluations. We can also have a look at the actual “experiments” that were performed, as well as the model predictions (mean and uncertainty).

from ax.plot.contour import plot_contour
from ax.utils.notebook.plotting import render, init_notebook_plotting
init_notebook_plotting()
render(plot_contour(model, "x1", "x2", metric_name=objective_name))

[INFO 06-17 23:16:06] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.

Comparison between sampling methods

To make a fair comparison between methods, it’s best if we can look at average behavior across multiple repeat runs, or “campaigns”.

Helper Functions

Since the Bayesian Optimization API is different from the functions we’ve defined previously, we’ll create a function for extracting the parameters and their objective values.

def extract_data(experiment):
    trials = experiment.trials
    params = [experiment.trials[key].arm.parameters for key in trials.keys()]
    bayes_df = pd.DataFrame(params)
    bayes_df["objective"] = experiment.fetch_data().df["mean"]
    return bayes_df

bayes_df = extract_data(experiment)
bayes_df

	x1	x2	objective
0	2.126608	5.925240	12.362557
1	3.681450	0.371219	4.075152
2	9.260048	8.623440	40.015122
3	-3.193132	2.614422	96.152604
4	-1.775822	9.741822	8.303279
5	7.870957	0.786271	10.313032
6	5.662621	3.473325	23.302991
7	2.006520	10.000000	50.481044
8	-5.000000	10.000000	64.381898
9	5.721417	0.000000	19.387280
10	2.923004	2.789356	0.740426
11	1.908665	0.723161	14.159732
12	10.000000	0.000000	10.960889
13	10.000000	3.085075	1.949884
14	8.996744	2.650281	1.526983
15	-0.367826	7.467447	19.707294
16	9.520028	1.974204	0.780503
17	3.515847	2.218133	1.109587
18	3.227902	3.780020	2.902821
19	9.486598	2.678999	0.439139

For visualization purposes, it will also help to visualize the best objective so far as a function of the iteration number.

def add_best_obj_so_far(df):
    df["iteration"] = range(1, len(df) + 1)
    df["best_obj"] = df["objective"].cummin()
    return df

bayes_df = add_best_obj_so_far(bayes_df)
bayes_df

	x1	x2	objective	iteration	best_obj
0	2.126608	5.925240	12.362557	1	12.362557
1	3.681450	0.371219	4.075152	2	4.075152
2	9.260048	8.623440	40.015122	3	4.075152
3	-3.193132	2.614422	96.152604	4	4.075152
4	-1.775822	9.741822	8.303279	5	4.075152
5	7.870957	0.786271	10.313032	6	4.075152
6	5.662621	3.473325	23.302991	7	4.075152
7	2.006520	10.000000	50.481044	8	4.075152
8	-5.000000	10.000000	64.381898	9	4.075152
9	5.721417	0.000000	19.387280	10	4.075152
10	2.923004	2.789356	0.740426	11	0.740426
11	1.908665	0.723161	14.159732	12	0.740426
12	10.000000	0.000000	10.960889	13	0.740426
13	10.000000	3.085075	1.949884	14	0.740426
14	8.996744	2.650281	1.526983	15	0.740426
15	-0.367826	7.467447	19.707294	16	0.740426
16	9.520028	1.974204	0.780503	17	0.740426
17	3.515847	2.218133	1.109587	18	0.740426
18	3.227902	3.780020	2.902821	19	0.740426
19	9.486598	2.678999	0.439139	20	0.439139

px.line(bayes_df, x="iteration", y="best_obj")

Bayesian Campaigns

We’ll add some jitter to the bounds of the objective function so that when we compare with methods that tend to do systematic sampling (in particular, grid search), we are making a fair comparison.

def get_noisy_bounds(bounds, seed=None, noise_scale=2):
    rng = default_rng(seed)
    return {
        key: np.add(bound, rng.uniform(-1 * noise_scale, noise_scale, 2)).tolist()
        for key, bound in bounds.items()
    }

get_noisy_bounds(bounds, seed=0)

{'x1': [-4.452153250714183, 9.07914685505548],
 'x2': [-1.8361059042552212, 8.066110542114117]}

num_repeats = 50
SEEDS = list(range(num_repeats))
bayes_results = []
for seed in SEEDS:
    noisy_bounds = get_noisy_bounds(bounds, seed=seed)
    results = optimize(
        parameters=[
            {
                "name": "x1",
                "type": "range",
                "bounds": noisy_bounds["x1"],
            },
            {
                "name": "x2",
                "type": "range",
                "bounds": noisy_bounds["x2"],
            },
        ],
        evaluation_function=evaluate,
        objective_name=objective_name,
        minimize=True,
        total_trials=total_trials,
        random_seed=seed,
    )

    bayes_results.append(
        dict(
            best_parameters=results[0],
            values=results[1],
            experiment=results[2],
            model=results[3],
        )
    )

[INFO 06-17 23:16:08] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:16:08] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:16:08] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.452153250714183, 9.07914685505548]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.8361059042552212, 8.066110542114117])], parameter_constraints=[]).
[INFO 06-17 23:16:08] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:16:08] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:16:08] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:16:08] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:16:08] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:16:08] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:16:08] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:16:08] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:16:08] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:16:08] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:16:08] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:16:08] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:16:09] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:16:10] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:16:10] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:16:11] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:16:12] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:16:13] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:16:14] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:16:15] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:16:16] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:16:17] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:16:19] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:16:20] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:16:20] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:16:21] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:16:24] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:16:24] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:16:24] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.952713501198973, 11.80185478530374]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.423361549121465, 11.794597788548975])], parameter_constraints=[]).
[INFO 06-17 23:16:24] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:16:24] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:16:24] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:16:24] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:16:24] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:16:24] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:16:24] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:16:24] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:16:24] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:16:24] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:16:24] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:16:24] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:16:24] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:16:25] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:16:26] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:16:26] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:16:27] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:16:28] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:16:29] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:16:29] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:16:30] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:16:31] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:16:33] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:16:34] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:16:35] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:16:36] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:16:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:16:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:16:39] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.953551463002734, 9.193964573656494]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.2569029623771213, 8.367663768540387])], parameter_constraints=[]).
[INFO 06-17 23:16:39] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:16:39] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:16:39] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:16:39] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:16:39] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:16:39] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:16:39] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:16:39] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:16:39] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:16:39] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:16:39] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:16:39] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:16:40] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:16:41] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:16:42] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:16:43] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:16:44] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:16:44] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:16:45] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:16:47] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:16:48] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:16:50] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:16:50] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:16:51] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:16:52] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:16:54] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:16:56] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:16:56] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:16:56] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-6.657403331425503, 8.947242026384398]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.2050978608255876, 10.328648144257471])], parameter_constraints=[]).
[INFO 06-17 23:16:56] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:16:56] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:16:56] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:16:56] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:16:56] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:16:56] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:16:56] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:16:56] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:16:56] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:16:56] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:16:56] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:16:56] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:16:56] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:16:57] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:16:57] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:16:58] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:16:59] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:16:59] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:17:00] ax.service.managed_loop: Running optimization trial 13...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\optimize.py:369: RuntimeWarning:

Optimization failed in `gen_candidates_scipy` with the following warning(s):
[NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal'), OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.'), NumericalWarning('A not p.d., added jitter of 1.0e-08 to the diagonal')]
Trying again with a new set of initial conditions.

[INFO 06-17 23:17:02] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:17:03] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:17:04] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:17:05] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:17:06] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:17:07] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:17:08] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:17:09] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:17:09] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:17:09] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.2277755777105295, 10.045310211257446]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.9049748228308165, 8.323344095582408])], parameter_constraints=[]).
[INFO 06-17 23:17:09] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:17:09] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:17:09] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:17:09] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:17:09] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:17:09] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:17:09] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:17:09] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:17:09] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:17:10] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:17:10] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:17:10] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:17:10] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:17:11] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:17:11] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:17:12] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:17:13] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:17:14] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:17:15] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:17:16] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:17:17] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:17:18] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:17:19] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:17:20] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:17:25] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:17:28] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:17:31] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:17:31] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:17:31] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.7799883050184793, 11.231763158945975]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.061302244168568, 9.143205520352566])], parameter_constraints=[]).
[INFO 06-17 23:17:31] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:17:31] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:17:31] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:17:31] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:17:31] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:17:31] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:17:31] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:17:31] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:17:31] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:17:31] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:17:31] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:17:31] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:17:32] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:17:32] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:17:33] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:17:34] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:17:34] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:17:36] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:17:37] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:17:38] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:17:39] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:17:40] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:17:42] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:17:43] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:17:44] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:17:46] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:17:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:17:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:17:47] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.847342594112227, 9.373083479253353]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.523731040818487, 9.49798706235153])], parameter_constraints=[]).
[INFO 06-17 23:17:47] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:17:47] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:17:47] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:17:47] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:17:47] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:17:47] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:17:47] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:17:47] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:17:47] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:17:47] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:17:47] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:17:47] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:17:48] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:17:49] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:17:50] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:17:50] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:17:51] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:17:52] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:17:53] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:17:53] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:17:54] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:17:55] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:17:56] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:17:57] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:17:58] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:17:59] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:18:01] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:18:01] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:18:01] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.499618133581333, 11.588855203878301]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.102742760980774, 8.900828759962367])], parameter_constraints=[]).
[INFO 06-17 23:18:01] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:18:01] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:18:01] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:18:01] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:18:01] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:18:01] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:18:01] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:18:01] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:18:01] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:18:01] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:18:01] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:18:01] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:18:02] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:18:02] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:18:03] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:18:03] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:18:04] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:18:04] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:18:05] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:18:06] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:18:07] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:18:08] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:18:10] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:18:11] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:18:13] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:18:15] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:18:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:18:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:18:17] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.692110893577757, 11.949107373351701]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.7251566460579331, 11.154195743280116])], parameter_constraints=[]).
[INFO 06-17 23:18:17] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:18:17] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:18:17] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:18:17] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:18:17] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:18:17] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:18:17] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:18:17] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:18:17] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:18:18] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:18:18] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:18:18] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:18:18] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:18:19] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:18:20] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:18:21] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:18:21] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:18:22] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:18:23] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:18:24] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:18:26] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:18:27] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:18:29] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:18:30] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:18:32] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:18:34] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:18:36] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:18:36] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:18:36] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.5190031841196614, 9.147268836350221]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.41259260020624744, 11.110136331680716])], parameter_constraints=[]).
[INFO 06-17 23:18:36] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:18:36] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:18:36] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:18:36] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:18:36] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:18:36] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:18:36] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:18:36] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:18:36] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:18:36] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:18:36] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:18:36] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:18:37] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:18:37] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:18:38] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:18:39] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:18:40] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:18:41] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:18:42] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:18:43] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:18:44] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:18:45] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:18:46] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:18:48] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:18:50] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:18:52] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:18:54] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:18:54] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:18:54] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.1759931614840986, 8.830727240316588]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.313779541098123, 8.597128492328082])], parameter_constraints=[]).
[INFO 06-17 23:18:54] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:18:54] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:18:54] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:18:54] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:18:54] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:18:54] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:18:54] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:18:54] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:18:54] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:18:54] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:18:55] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:18:55] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:18:55] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:18:56] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:18:57] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:18:58] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:18:59] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:18:59] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:19:00] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:19:01] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:19:03] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:19:04] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:19:05] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:19:07] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:19:07] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:19:09] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:19:11] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:19:11] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:19:11] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-6.485719188923202, 9.99711144976046]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.40599343049342984, 8.114756033487778])], parameter_constraints=[]).
[INFO 06-17 23:19:11] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:19:11] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:19:11] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:19:11] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:19:11] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:19:11] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:19:11] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:19:11] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:19:11] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:19:11] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:19:11] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:19:11] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:19:12] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:19:12] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:19:13] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:19:14] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:19:14] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:19:15] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:19:16] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:19:18] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:19:19] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:19:21] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:19:21] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:19:24] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:19:26] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:19:27] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:19:29] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:19:29] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:19:29] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.996702167566216, 11.787011771437697]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.2427184618409548, 8.71716564167243])], parameter_constraints=[]).
[INFO 06-17 23:19:29] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:19:29] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:19:29] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:19:29] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:19:29] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:19:29] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:19:29] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:19:29] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:19:29] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:19:29] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:19:29] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:19:29] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:19:30] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:19:31] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:19:31] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:19:32] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:19:33] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:19:33] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:19:34] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:19:35] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:19:36] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:19:37] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:19:38] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:19:39] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:19:41] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:19:42] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:19:44] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:19:44] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:19:44] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.540809651933654, 11.421210059728235]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.2440935951373686, 9.045785445665906])], parameter_constraints=[]).
[INFO 06-17 23:19:44] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:19:44] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:19:44] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:19:44] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:19:44] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:19:44] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:19:44] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:19:44] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:19:44] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:19:44] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:19:44] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:19:44] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:19:45] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:19:46] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:19:47] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:19:48] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:19:49] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:19:49] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:19:50] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:19:52] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:19:54] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:19:55] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:19:56] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:19:57] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:19:59] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:20:01] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:20:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:20:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:20:03] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.676066724443554, 9.443786667337026]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.8109572223282195, 11.440475146610378])], parameter_constraints=[]).
[INFO 06-17 23:20:03] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:20:03] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:20:03] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:20:03] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:20:03] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:20:03] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:20:03] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:20:03] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:20:03] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:20:03] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:20:03] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:20:03] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:20:03] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:20:04] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:20:05] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:20:05] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:20:06] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:20:07] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:20:07] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:20:08] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:20:09] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:20:09] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:20:10] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:20:11] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:20:12] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:20:14] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:20:16] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:20:16] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:20:16] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.229026528139391, 11.26326844534423]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.6223729688285733, 8.179352702761676])], parameter_constraints=[]).
[INFO 06-17 23:20:16] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:20:16] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:20:16] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:20:16] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:20:16] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:20:16] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:20:16] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:20:16] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:20:16] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:20:16] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:20:16] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:20:16] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:20:16] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:20:17] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:20:18] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:20:18] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:20:19] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:20:20] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:20:20] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:20:21] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:20:22] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:20:23] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:20:24] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:20:25] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:20:26] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:20:27] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:20:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:20:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:20:28] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.73233264448254, 9.722976581960742]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.6237046981539018, 9.392318201743983])], parameter_constraints=[]).
[INFO 06-17 23:20:28] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:20:28] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:20:28] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:20:28] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:20:28] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:20:28] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:20:28] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:20:28] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:20:28] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:20:28] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:20:28] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:20:28] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:20:29] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:20:30] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:20:30] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:20:31] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:20:32] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:20:32] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:20:33] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:20:35] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:20:36] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:20:36] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:20:37] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:20:39] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:20:40] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:20:41] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:20:44] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:20:44] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:20:44] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.619700828808394, 8.643892364676429]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.2309781894627685, 9.472319766657085])], parameter_constraints=[]).
[INFO 06-17 23:20:44] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:20:44] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:20:44] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:20:44] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:20:44] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:20:44] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:20:44] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:20:44] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:20:44] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:20:44] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:20:44] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:20:44] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:20:44] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:20:45] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:20:46] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:20:46] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:20:47] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:20:48] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:20:49] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:20:50] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:20:51] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:20:52] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:20:53] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:20:55] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:20:56] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:20:57] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:21:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:00] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.402776923129709, 10.869658717152864]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.8767066878559864, 8.330899671992466])], parameter_constraints=[]).
[INFO 06-17 23:21:00] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:21:00] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:21:00] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:21:00] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:21:00] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:21:00] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:21:00] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:21:00] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:21:00] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:21:00] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:21:00] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:21:00] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:21:00] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:21:01] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:21:02] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:21:03] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:21:04] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:21:04] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:21:05] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:21:06] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:21:07] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:21:08] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:21:09] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:21:10] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:21:11] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:21:12] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:21:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:14] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.318484817082957, 11.703477379266815]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.9045200484068716, 8.240194416490107])], parameter_constraints=[]).
[INFO 06-17 23:21:14] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:21:14] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:21:14] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:21:14] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:21:14] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:21:14] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:21:14] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:21:14] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:21:14] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:21:14] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:21:14] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:21:14] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:21:15] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:21:16] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:21:16] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:21:17] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:21:17] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:21:18] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:21:19] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:21:19] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:21:21] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:21:22] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:21:23] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:21:23] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:21:24] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:21:26] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:21:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:27] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.879696149479363, 9.844586839211768]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.5131212296942325, 10.09043340687573])], parameter_constraints=[]).
[INFO 06-17 23:21:27] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:21:27] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:21:27] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:21:27] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:21:27] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:21:27] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:21:27] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:21:27] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:21:27] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:21:27] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:21:27] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:21:27] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:21:28] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:21:28] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:21:29] ax.modelbridge.base: Untransformed parameter 10.090433406875732 greater than upper bound 10.09043340687573, clamping
[INFO 06-17 23:21:29] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:21:30] ax.modelbridge.base: Untransformed parameter 10.090433406875732 greater than upper bound 10.09043340687573, clamping
[INFO 06-17 23:21:30] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:21:30] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:21:31] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:21:32] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:21:33] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:21:34] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:21:35] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:21:37] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:21:38] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:21:39] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:21:41] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:21:42] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:42] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:42] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.875529644730116, 10.423388118283873]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.8392047616337006, 8.356391491688836])], parameter_constraints=[]).
[INFO 06-17 23:21:42] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:21:42] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:21:42] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:21:42] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:21:42] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:21:42] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:21:42] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:21:42] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:21:42] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:21:42] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:21:42] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:21:42] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:21:43] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:21:44] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:21:44] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:21:45] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:21:46] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:21:46] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:21:47] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:21:48] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:21:49] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:21:49] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:21:50] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:21:51] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:21:52] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:21:53] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:21:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:21:55] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.534612338271696, 8.79718151750033]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.6457665069548844, 10.61276675040368])], parameter_constraints=[]).
[INFO 06-17 23:21:55] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:21:55] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:21:55] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:21:55] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:21:55] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:21:55] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:21:55] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:21:55] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:21:55] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:21:55] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:21:55] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:21:55] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:21:55] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:21:56] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:21:56] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:21:57] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:21:57] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:21:58] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:21:59] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:22:00] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:22:01] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:22:01] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:22:02] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:22:03] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:22:04] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:22:05] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:22:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:22:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:22:07] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.224267677370543, 10.565832883512922]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.485423102729455, 8.454832200525317])], parameter_constraints=[]).
[INFO 06-17 23:22:07] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:22:07] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:22:07] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:22:07] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:22:07] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:22:07] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:22:07] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:22:07] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:22:07] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:22:07] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:22:07] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:22:07] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:22:08] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:22:08] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:22:09] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:22:09] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:22:10] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:22:11] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:22:12] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:22:12] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:22:13] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:22:14] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:22:15] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:22:16] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:22:17] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:22:18] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:22:19] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:22:19] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:22:19] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.678924653322738, 9.620709262556575]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.29895127730876814, 10.025599069080592])], parameter_constraints=[]).
[INFO 06-17 23:22:19] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:22:19] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:22:19] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:22:19] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:22:19] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:22:19] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:22:19] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:22:19] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:22:19] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:22:19] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:22:19] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:22:19] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:22:20] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:22:20] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:22:21] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:22:22] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:22:23] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:22:24] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:22:25] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:22:26] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:22:27] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:22:28] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:22:29] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:22:30] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:22:31] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:22:32] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:22:34] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:22:34] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:22:34] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-6.357115067071969, 8.001248140684014]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.1336061421487322, 9.472117187960661])], parameter_constraints=[]).
[INFO 06-17 23:22:34] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:22:34] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:22:34] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:22:34] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:22:34] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:22:34] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:22:34] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:22:34] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:22:34] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:22:34] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:22:34] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:22:34] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:22:35] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:22:35] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:22:36] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:22:36] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:22:37] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:22:38] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:22:39] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:22:40] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:22:41] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:22:42] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:22:43] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:22:45] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:22:46] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:22:47] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:22:49] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:22:49] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:22:49] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.033604329580382, 8.937598095791666]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.7295689672306955, 10.851432815528455])], parameter_constraints=[]).
[INFO 06-17 23:22:49] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:22:49] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:22:49] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:22:49] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:22:49] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:22:49] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:22:49] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:22:49] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:22:49] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:22:49] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:22:49] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:22:50] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:22:50] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:22:51] ax.modelbridge.base: Untransformed parameter 8.937598095791667 greater than upper bound 8.937598095791666, clamping
[INFO 06-17 23:22:51] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:22:52] ax.modelbridge.base: Untransformed parameter 8.937598095791667 greater than upper bound 8.937598095791666, clamping
[INFO 06-17 23:22:52] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:22:52] ax.modelbridge.base: Untransformed parameter 8.937598095791667 greater than upper bound 8.937598095791666, clamping
[INFO 06-17 23:22:52] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:22:53] ax.modelbridge.base: Untransformed parameter 8.937598095791667 greater than upper bound 8.937598095791666, clamping
[INFO 06-17 23:22:53] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:22:54] ax.modelbridge.base: Untransformed parameter 8.937598095791667 greater than upper bound 8.937598095791666, clamping
[INFO 06-17 23:22:54] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:22:55] ax.modelbridge.base: Untransformed parameter 8.937598095791667 greater than upper bound 8.937598095791666, clamping
[INFO 06-17 23:22:55] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:22:55] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:22:56] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:22:57] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:22:58] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:22:59] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:23:00] ax.modelbridge.base: Untransformed parameter 8.937598095791667 greater than upper bound 8.937598095791666, clamping
[INFO 06-17 23:23:01] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:23:02] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:23:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:23:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:23:03] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.209055134092859, 9.255257088889726]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.5152116045534907, 9.29436608645651])], parameter_constraints=[]).
[INFO 06-17 23:23:03] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:23:03] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:23:03] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:23:03] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:23:03] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:23:03] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:23:03] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:23:03] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:23:03] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:23:03] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:23:03] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:23:03] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:23:04] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:23:05] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:23:05] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:23:06] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:23:06] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:23:07] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:23:08] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:23:09] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:23:10] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:23:11] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:23:12] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:23:13] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:23:14] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:23:15] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:23:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:23:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:23:17] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.592664256071369, 11.539571816307454]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.0628341062302624, 8.128794403532622])], parameter_constraints=[]).
[INFO 06-17 23:23:17] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:23:17] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:23:17] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:23:17] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:23:17] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:23:17] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:23:17] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:23:17] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:23:17] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:23:17] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:23:17] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:23:17] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:23:17] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:23:18] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:23:19] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:23:19] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:23:20] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:23:21] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:23:23] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:23:24] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:23:25] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:23:26] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:23:27] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:23:28] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:23:30] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:23:31] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:23:33] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:23:33] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:23:33] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-6.799812118583724, 10.025289194285797]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.07693611851884619, 9.060812855748845])], parameter_constraints=[]).
[INFO 06-17 23:23:33] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:23:33] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:23:33] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:23:33] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:23:33] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:23:33] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:23:33] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:23:33] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:23:33] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:23:33] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:23:33] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:23:33] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:23:33] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:23:34] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:23:35] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:23:35] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:23:36] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:23:37] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:23:37] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:23:38] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:23:40] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:23:40] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:23:41] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:23:43] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:23:44] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:23:45] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:23:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:23:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:23:47] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-6.056804780749869, 9.716427994663245]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.6309079337868315, 10.370411027818841])], parameter_constraints=[]).
[INFO 06-17 23:23:47] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:23:47] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:23:47] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:23:47] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:23:47] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:23:47] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:23:47] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:23:47] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:23:47] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:23:47] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:23:47] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:23:47] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:23:47] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:23:48] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:23:49] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:23:49] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:23:50] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:23:51] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:23:52] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:23:52] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:23:53] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:23:54] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:23:55] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:23:56] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:23:57] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:23:59] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:24:01] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:24:01] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:24:01] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.3873127563405583, 8.270713049446533]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.6909232564014567, 9.890178823916946])], parameter_constraints=[]).
[INFO 06-17 23:24:01] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:24:01] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:24:01] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:24:01] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:24:01] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:24:01] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:24:01] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:24:01] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:24:01] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:24:01] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:24:01] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:24:01] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:24:02] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:24:02] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:24:03] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:24:03] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:24:04] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:24:05] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:24:06] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:24:08] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:24:09] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:24:10] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:24:11] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:24:12] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:24:12] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:24:13] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:24:15] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:24:15] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:24:15] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-6.359028678500928, 10.288917981888336]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.4914577841541199, 9.29174071949257])], parameter_constraints=[]).
[INFO 06-17 23:24:15] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:24:15] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:24:15] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:24:15] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:24:15] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:24:15] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:24:15] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:24:15] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:24:15] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:24:15] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:24:15] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:24:15] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:24:16] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:24:16] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:24:17] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:24:18] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:24:19] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:24:20] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:24:21] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:24:22] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:24:23] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:24:24] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:24:25] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:24:26] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:24:27] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:24:29] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:24:31] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:24:31] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:24:31] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.2254310521493315, 10.273964767698226]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.6324150998432785, 9.016998213995208])], parameter_constraints=[]).
[INFO 06-17 23:24:31] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:24:31] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:24:31] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:24:31] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:24:31] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:24:31] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:24:31] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:24:31] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:24:31] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:24:31] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:24:31] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:24:31] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:24:32] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:24:33] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:24:33] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:24:34] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:24:35] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:24:37] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:24:38] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:24:39] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:24:40] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:24:42] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:24:43] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:24:44] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:24:45] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:24:47] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:24:49] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:24:49] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:24:49] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-6.983887026027826, 11.48870765708217]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.029030449582343, 10.60424519144674])], parameter_constraints=[]).
[INFO 06-17 23:24:49] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:24:49] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:24:49] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:24:49] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:24:49] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:24:49] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:24:49] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:24:49] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:24:49] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:24:49] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:24:49] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:24:49] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:24:50] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:24:51] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:24:51] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:24:52] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:24:52] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:24:53] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:24:54] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:24:55] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:24:57] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:24:58] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:24:59] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:25:00] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:25:02] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:25:04] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:25:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:25:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:25:06] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.648201764465101, 9.833982224413411]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.7400274033992509, 11.787302782987569])], parameter_constraints=[]).
[INFO 06-17 23:25:06] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:25:06] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:25:06] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:25:06] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:25:06] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:25:06] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:25:06] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:25:06] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:25:06] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:25:06] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:25:06] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:25:06] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:25:07] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:25:08] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:25:08] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:25:09] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:25:10] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:25:10] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:25:11] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:25:13] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:25:14] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:25:15] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:25:16] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:25:18] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:25:20] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:25:21] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:25:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:25:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:25:23] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-6.2774676351033225, 9.592947042942674]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.5753296142795117, 9.613544030058668])], parameter_constraints=[]).
[INFO 06-17 23:25:23] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:25:23] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:25:23] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:25:23] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:25:23] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:25:23] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:25:23] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:25:23] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:25:23] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:25:23] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:25:23] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:25:23] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:25:24] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:25:24] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:25:25] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:25:25] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:25:26] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:25:26] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:25:27] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:25:28] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:25:28] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:25:29] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:25:31] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:25:32] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:25:33] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:25:34] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:25:36] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:25:36] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:25:36] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.183387873545722, 10.646674883209897]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.7240270550719101, 10.811206588438875])], parameter_constraints=[]).
[INFO 06-17 23:25:36] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:25:36] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:25:36] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:25:36] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:25:36] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:25:36] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:25:36] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:25:36] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:25:36] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:25:36] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:25:36] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:25:36] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:25:36] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:25:37] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:25:38] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:25:39] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:25:40] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:25:42] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:25:43] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:25:44] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:25:45] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:25:46] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:25:47] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:25:48] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:25:49] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:25:50] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:25:51] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:25:51] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:25:51] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.05695582064853, 8.99894343626031]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.8815246656444029, 10.538456025047441])], parameter_constraints=[]).
[INFO 06-17 23:25:51] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:25:51] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:25:51] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:25:51] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:25:51] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:25:51] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:25:51] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:25:51] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:25:51] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:25:52] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:25:52] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:25:52] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:25:52] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:25:53] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:25:54] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:25:55] ax.modelbridge.base: Untransformed parameter 8.998943436260312 greater than upper bound 8.99894343626031, clamping
[INFO 06-17 23:25:55] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:25:55] ax.modelbridge.base: Untransformed parameter 8.998943436260312 greater than upper bound 8.99894343626031, clamping
[INFO 06-17 23:25:55] ax.modelbridge.base: Untransformed parameter 8.998943436260312 greater than upper bound 8.99894343626031, clamping
[INFO 06-17 23:25:55] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:25:56] ax.modelbridge.base: Untransformed parameter 8.998943436260312 greater than upper bound 8.99894343626031, clamping
[INFO 06-17 23:25:56] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:25:57] ax.modelbridge.base: Untransformed parameter 8.998943436260312 greater than upper bound 8.99894343626031, clamping
[INFO 06-17 23:25:57] ax.modelbridge.base: Untransformed parameter 8.998943436260312 greater than upper bound 8.99894343626031, clamping
[INFO 06-17 23:25:57] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:25:58] ax.modelbridge.base: Untransformed parameter 8.998943436260312 greater than upper bound 8.99894343626031, clamping
[INFO 06-17 23:25:58] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:25:59] ax.modelbridge.base: Untransformed parameter 8.998943436260312 greater than upper bound 8.99894343626031, clamping
[INFO 06-17 23:25:59] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:26:01] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:26:03] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:26:05] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:26:06] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:26:08] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:26:10] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:26:10] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:26:10] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.993152171394367, 10.157341624825953]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.4565031921788463, 9.225546273686636])], parameter_constraints=[]).
[INFO 06-17 23:26:10] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:26:10] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:26:10] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:26:10] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:26:10] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:26:10] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:26:10] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:26:10] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:26:10] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:26:10] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:26:10] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:26:10] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:26:11] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:26:11] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:26:12] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:26:13] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:26:14] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:26:15] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:26:15] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:26:16] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:26:17] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:26:18] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:26:19] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:26:20] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:26:21] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:26:22] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:26:24] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:26:24] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:26:24] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.0804060189411295, 10.77365982720752]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.7676840689383306, 8.238608240179097])], parameter_constraints=[]).
[INFO 06-17 23:26:24] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:26:24] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:26:24] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:26:24] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:26:24] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:26:24] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:26:24] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:26:24] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:26:24] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:26:24] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:26:24] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:26:24] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:26:25] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:26:26] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:26:26] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:26:27] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:26:28] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:26:29] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:26:30] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:26:31] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:26:32] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:26:34] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:26:35] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:26:36] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:26:37] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:26:38] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:26:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:26:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:26:41] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.1833955881519818, 11.071728362542398]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.4961170128471917, 11.30795234372298])], parameter_constraints=[]).
[INFO 06-17 23:26:41] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:26:41] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:26:41] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:26:41] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:26:41] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:26:41] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:26:41] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:26:41] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:26:41] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:26:41] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:26:41] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:26:41] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:26:42] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:26:43] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:26:43] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:26:44] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:26:45] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:26:45] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:26:46] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:26:47] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:26:48] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:26:48] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:26:49] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:26:50] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:26:52] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:26:53] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:26:54] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:26:54] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:26:54] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.9041758057761466, 9.75551375900821]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.4343916796455298, 10.789472116237455])], parameter_constraints=[]).
[INFO 06-17 23:26:54] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:26:54] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:26:54] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:26:54] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:26:54] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:26:54] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:26:54] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:26:54] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:26:54] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:26:54] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:26:54] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:26:54] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:26:55] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:26:56] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:26:56] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:26:57] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:26:58] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:26:58] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:26:59] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:27:00] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:27:01] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:27:02] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:27:03] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:27:04] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:27:05] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:27:06] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:27:08] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:27:08] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:27:08] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.390802949196358, 8.175101294555986]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-1.9198816525031326, 11.35685033004412])], parameter_constraints=[]).
[INFO 06-17 23:27:08] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:27:08] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:27:08] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:27:08] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:27:08] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:27:08] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:27:08] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:27:08] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:27:08] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:27:08] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:27:08] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:27:08] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:27:09] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:27:09] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:27:10] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:27:11] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:27:11] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:27:12] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:27:13] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:27:14] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:27:15] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:27:16] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:27:17] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:27:17] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:27:18] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:27:20] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:27:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:27:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:27:22] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-6.509737961304514, 9.032452299091092]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.3769170887154756, 11.876735792577687])], parameter_constraints=[]).
[INFO 06-17 23:27:22] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:27:22] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:27:22] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:27:22] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:27:22] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:27:22] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:27:22] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:27:22] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:27:22] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:27:22] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:27:22] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:27:22] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:27:22] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:27:23] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:27:23] ax.modelbridge.base: Untransformed parameter 9.032452299091094 greater than upper bound 9.032452299091092, clamping
[INFO 06-17 23:27:23] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:27:24] ax.modelbridge.base: Untransformed parameter 9.032452299091094 greater than upper bound 9.032452299091092, clamping
[INFO 06-17 23:27:24] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:27:25] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:27:27] ax.modelbridge.base: Untransformed parameter 9.032452299091094 greater than upper bound 9.032452299091092, clamping
[INFO 06-17 23:27:27] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:27:27] ax.modelbridge.base: Untransformed parameter 9.032452299091094 greater than upper bound 9.032452299091092, clamping
[INFO 06-17 23:27:27] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:27:29] ax.modelbridge.base: Untransformed parameter 9.032452299091094 greater than upper bound 9.032452299091092, clamping
[INFO 06-17 23:27:29] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:27:30] ax.modelbridge.base: Untransformed parameter 9.032452299091094 greater than upper bound 9.032452299091092, clamping
[INFO 06-17 23:27:30] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:27:31] ax.modelbridge.base: Untransformed parameter 9.032452299091094 greater than upper bound 9.032452299091092, clamping
[INFO 06-17 23:27:31] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:27:32] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:27:33] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:27:34] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:27:35] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:27:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:27:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:27:37] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.707477372209977, 10.113964580680811]), RangeParameter(name='x2', parameter_type=FLOAT, range=[1.0546009592077041, 11.246771068038427])], parameter_constraints=[]).
[INFO 06-17 23:27:37] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:27:37] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:27:37] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:27:37] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:27:37] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:27:37] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:27:37] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:27:37] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:27:37] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:27:37] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:27:37] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:27:37] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:27:37] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:27:38] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:27:38] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:27:39] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:27:40] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:27:41] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:27:41] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:27:43] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:27:44] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:27:45] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:27:46] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:27:47] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:27:48] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:27:49] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:27:52] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:27:52] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:27:52] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-3.3775828932387517, 8.308907172846972]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.909720976325449, 10.487401928984532])], parameter_constraints=[]).
[INFO 06-17 23:27:52] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:27:52] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:27:52] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:27:52] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:27:52] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:27:52] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:27:52] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:27:52] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:27:52] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:27:52] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:27:52] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:27:52] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:27:53] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:27:54] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:27:55] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:27:55] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:27:56] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:27:57] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:27:58] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:27:59] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:28:01] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:28:01] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:28:02] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:28:03] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:28:04] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:28:05] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:28:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:07] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-4.032792021429758, 11.014675268478431]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.13927784708206614, 8.414900118591568])], parameter_constraints=[]).
[INFO 06-17 23:28:07] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:28:07] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:28:07] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:28:07] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:28:07] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:28:07] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:28:07] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:28:07] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:28:07] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:28:07] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:28:07] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:28:07] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:28:08] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:28:08] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:28:09] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:28:10] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:28:10] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:28:11] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:28:12] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:28:13] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:28:14] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:28:15] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:28:16] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:28:17] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:28:18] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:28:19] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:28:21] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:21] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:21] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.449200510857622, 10.38309396011736]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.055202180216040286, 10.77796761602961])], parameter_constraints=[]).
[INFO 06-17 23:28:21] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:28:21] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:28:21] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:28:21] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:28:21] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:28:21] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:28:21] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:28:21] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:28:21] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:28:21] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:28:21] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:28:21] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:28:22] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:28:22] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:28:23] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:28:23] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:28:24] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:28:25] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:28:26] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:28:26] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:28:27] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:28:28] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:28:28] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:28:30] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:28:31] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:28:32] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:28:33] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:33] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:33] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.548583230895455, 10.372869040678928]), RangeParameter(name='x2', parameter_type=FLOAT, range=[-0.432199152143252, 10.494797142706453])], parameter_constraints=[]).
[INFO 06-17 23:28:33] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:28:33] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False
[INFO 06-17 23:28:33] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 06-17 23:28:33] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 06-17 23:28:33] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[INFO 06-17 23:28:33] ax.service.managed_loop: Started full optimization with 20 steps.
[INFO 06-17 23:28:33] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:28:34] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:28:34] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:28:34] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:28:34] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:28:34] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:28:34] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:28:35] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:28:35] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:28:36] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:28:36] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:28:37] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:28:38] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:28:38] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:28:39] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:28:40] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:28:41] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:28:42] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:28:43] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:28:44] ax.service.managed_loop: Running optimization trial 20...

bayes_results_df = pd.DataFrame(bayes_results)
bayes_results_df

	best_parameters	values	experiment	model
0	{'x1': 3.0680120336398433, 'x2': 2.42394177397...	({'branin': 0.3908059975557059}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
1	{'x1': 3.0963502711038853, 'x2': 1.78547251414...	({'branin': 0.7838779711543786}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
2	{'x1': 3.077416999175419, 'x2': 2.479596897973...	({'branin': 0.4438113116492435}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
3	{'x1': 8.947242026384398, 'x2': 2.087424711997...	({'branin': 1.470220380208879}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
4	{'x1': 3.148098792316719, 'x2': 2.512923549765...	({'branin': 0.4412631520017065}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
5	{'x1': 3.13137476931262, 'x2': 2.004979698174613}	({'branin': 0.422869203629638}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
6	{'x1': 9.373083479253353, 'x2': 2.436358460404...	({'branin': 0.382957758813685}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
7	{'x1': 3.128737378175823, 'x2': 2.231945134896...	({'branin': 0.42421732050160443}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
8	{'x1': 9.357828329748807, 'x2': 2.81459614083349}	({'branin': 0.4995165880749042}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
9	{'x1': 3.103555991287304, 'x2': 2.136100509746...	({'branin': 0.4202894865369231}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
10	{'x1': 3.1187088125542783, 'x2': 2.26104840656...	({'branin': 0.41647845367493375}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
11	{'x1': 9.355773458817819, 'x2': 2.729475533280...	({'branin': 0.5122529866475425}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
12	{'x1': 3.1212753856102164, 'x2': 1.52587609306...	({'branin': 1.0809419746613678}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
13	{'x1': 9.362551522816542, 'x2': 2.361412663709...	({'branin': 0.41719794678623323}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
14	{'x1': 9.443786667337026, 'x2': 2.362262683168...	({'branin': 0.36810837447282907}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
15	{'x1': 9.474798148929633, 'x2': 2.313744979894...	({'branin': 0.46293236340182453}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
16	{'x1': 9.401117205065539, 'x2': 2.416175341998...	({'branin': 0.4143731341337933}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
17	{'x1': 3.1305794850084, 'x2': 2.333756704471489}	({'branin': 0.40129254269447223}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
18	{'x1': 9.293559335091802, 'x2': 2.372122822278...	({'branin': 0.543414558177183}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
19	{'x1': 9.453306657533979, 'x2': 2.159698878502...	({'branin': 0.5764481271492059}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
20	{'x1': 3.0518596706342063, 'x2': 2.42419981960...	({'branin': 0.459443918913399}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
21	{'x1': 3.1360438137426603, 'x2': 2.33843501143...	({'branin': 0.3943690862493163}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
22	{'x1': 3.3162889667361064, 'x2': 2.06841533093...	({'branin': 0.6005187730437207}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
23	{'x1': 9.475044498126888, 'x2': 2.852792438646...	({'branin': 0.49146628225304667}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
24	{'x1': 9.620709262556575, 'x2': 2.886407437730...	({'branin': 0.5194499063963498}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
25	{'x1': 3.0639653931732225, 'x2': 2.44331232221...	({'branin': 0.4251906406783412}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
26	{'x1': 3.384004599659707, 'x2': 2.384576862906...	({'branin': 0.7586709703753911}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
27	{'x1': 3.200142675805, 'x2': 2.1791260461002304}	({'branin': 0.4412258119624326}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
28	{'x1': 3.1459737408142474, 'x2': 2.02277127740...	({'branin': 0.44651470504150303}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
29	{'x1': 3.180512037102896, 'x2': 2.277404717263...	({'branin': 0.411734070657662}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
30	{'x1': 3.098895700055883, 'x2': 2.58860437231378}	({'branin': 0.49576387856480864}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
31	{'x1': 3.1977558388217315, 'x2': 2.16121462220...	({'branin': 0.4330669627424957}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
32	{'x1': 9.276788527732023, 'x2': 2.587713947714...	({'branin': 0.5600101797866408}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
33	{'x1': 3.1484740874807757, 'x2': 2.18129007504...	({'branin': 0.417992985983469}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
34	{'x1': 9.523497341096608, 'x2': 2.793883718468...	({'branin': 0.4705996485571724}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
35	{'x1': 3.1756075402790813, 'x2': 2.21853727365...	({'branin': 0.398139115874919}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
36	{'x1': 3.00262298048845, 'x2': 2.269953137333639}	({'branin': 0.49386804925261707}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
37	{'x1': 9.48540604290357, 'x2': 2.4924828269817...	({'branin': 0.3825031395410612}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
38	{'x1': 3.124277797099201, 'x2': 2.252480610950...	({'branin': 0.3987095717520681}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
39	{'x1': 3.3005862973172224, 'x2': 2.40761663399...	({'branin': 0.5917154573189407}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
40	{'x1': 3.088233793351904, 'x2': 2.345316331460...	({'branin': 0.40674471363118947}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
41	{'x1': 9.438902805757907, 'x2': 2.698535406512...	({'branin': 0.4732159976702697}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
42	{'x1': 3.169523975370913, 'x2': 2.321128857676...	({'branin': 0.420873043104951}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
43	{'x1': 3.0463918842135174, 'x2': 2.06452949347...	({'branin': 0.49737392694669325}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
44	{'x1': -3.1015562366159055, 'x2': 11.876735792...	({'branin': 0.567088890979857}, {'branin': {'b...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
45	{'x1': 3.2843810407225433, 'x2': 2.28213991591...	({'branin': 0.5114902399043011}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
46	{'x1': 3.101319287806481, 'x2': 2.311587763242...	({'branin': 0.37464623993620805}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
47	{'x1': 9.286137862583253, 'x2': 2.118978796917...	({'branin': 0.43345133858430174}, {'branin': {...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
48	{'x1': 9.701515388823882, 'x2': 3.066318967310...	({'branin': 0.9861078517226005}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...
49	{'x1': 2.978277435163278, 'x2': 2.476612170964...	({'branin': 0.7156109869902387}, {'branin': {'...	Experiment(None)	<ax.modelbridge.torch.TorchModelBridge object ...

bayes_dfs = [
    add_best_obj_so_far(extract_data(result["experiment"])) for result in bayes_results
]
bayes_dfs[0]

	x1	x2	objective	iteration	best_obj
0	1.976665	4.031195	6.661052	1	6.661052
1	3.379267	-1.468516	13.380335	2	6.661052
2	8.411646	6.703012	29.421102	3	6.661052
3	-2.822202	0.752751	116.830170	4	6.661052
4	-1.543665	7.810457	11.170977	5	6.661052
5	2.270610	7.444633	23.108594	6	6.661052
6	4.919456	1.970024	12.427275	7	6.661052
7	7.280802	-1.836106	24.795167	8	6.661052
8	2.845761	1.943520	1.143896	9	1.143896
9	1.883771	1.779229	9.869627	10	1.143896
10	3.186603	3.034088	1.037934	11	1.037934
11	-4.452153	8.066111	64.991214	12	1.037934
12	-0.201343	8.066111	22.437219	13	1.037934
13	3.450935	1.872040	0.883964	14	0.883964
14	3.068012	2.423942	0.432125	15	0.432125
15	5.446620	8.066111	64.075736	16	0.432125
16	9.079147	1.588244	1.338621	17	0.432125
17	9.079147	3.023820	1.646255	18	0.432125
18	8.382787	2.188023	5.359747	19	0.432125
19	9.079147	-1.836106	17.246893	20	0.432125

Now that we’ve performed repeat campaigns for Bayesian optimization, we can take a look at the average behavior across the campaigns.

from self_driving_lab_demo.utils.plotting import line

bayes_best_objs = [bayes_df["best_obj"] for bayes_df in bayes_dfs]
bayes_mean = np.mean(bayes_best_objs, axis=0)
bayes_std = np.std(bayes_best_objs, axis=0)

bayes_line_df = pd.DataFrame(dict(iteration=bayes_dfs[0]["iteration"], mean=bayes_mean, std=bayes_std))
bayes_line_df["name"] = "bayes"

line(data_frame=bayes_line_df, x="iteration", y="mean", error_y="std", error_y_mode="band", range_y=[0, 60])

Now that we have our Bayesian optimization results, we can compute and compare with the other search types.

from numpy.random import default_rng

rng = default_rng(0)
sample_dfs = []
for name, sampling_fn in sampling_fns.items():
    for seed in SEEDS:
        noisy_bounds = get_noisy_bounds(bounds, rng)
        sample_df = sampling_fn(
            noisy_bounds, num_samples=total_trials, seed=seed
        ).sample(frac=1.0)
        sample_df["name"] = name
        sample_df["total_trials"] = total_trials
        sample_df["seed"] = seed
        sample_df["iteration"] = range(1, len(sample_df) + 1)
        sample_dfs.append(sample_df)

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C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

doe_df = pd.concat(sample_dfs, axis=0)
doe_df["objective"] = branin(doe_df[["x1", "x2"]].values)
doe_df

	x1	x2	name	total_trials	seed	iteration	objective
3	-4.452153	8.066111	grid	20	0	1	64.991214
0	-4.452153	-1.836106	grid	20	0	2	313.169914
5	0.058280	1.464633	grid	20	0	3	39.326505
2	-4.452153	4.765372	grid	20	0	4	125.927694
4	0.058280	-1.836106	grid	20	0	5	79.552078
...	...	...	...	...	...	...	...
7	1.876880	6.435670	sobol	20	49	16	15.914119
15	4.392660	10.594331	sobol	20	49	17	89.660830
8	3.361274	3.124848	sobol	20	49	18	1.658748
17	-5.886917	10.956959	sobol	20	49	19	97.878598
4	6.527614	4.941615	sobol	20	49	20	33.955866

3800 rows × 7 columns

name = "grid"
doe_df.query("name == @name and total_trials == @total_trials")

	x1	x2	name	total_trials	seed	iteration	objective
3	-4.452153	8.066111	grid	20	0	1	64.991214
0	-4.452153	-1.836106	grid	20	0	2	313.169914
5	0.058280	1.464633	grid	20	0	3	39.326505
2	-4.452153	4.765372	grid	20	0	4	125.927694
4	0.058280	-1.836106	grid	20	0	5	79.552078
...	...	...	...	...	...	...	...
12	11.090597	1.913063	grid	20	49	12	16.319327
7	-0.952711	10.359480	grid	20	49	13	22.994935
6	-0.952711	7.544008	grid	20	49	14	15.572210
10	5.068943	7.544008	grid	20	49	15	52.943252
2	-6.974364	7.544008	grid	20	49	16	268.297642

800 rows × 7 columns

sub_dfs = []
for name, sampling_fn in sampling_fns.items():
    for seed in range(num_repeats):
        sub_df = doe_df.query(
            "name == @name and total_trials == @total_trials and seed == @seed",
        )
        sub_df.loc[:, "best_obj"] = sub_df["objective"].cummin()
        sub_dfs.append(sub_df)

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1933624879.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

doe_best_df = pd.concat(sub_dfs, axis=0)
grp = doe_best_df.groupby(["name", "total_trials", "iteration"], as_index=False)
doe_result_df = grp.mean().drop("seed", axis=1)
doe_result_df = doe_result_df.rename(columns=dict(best_obj="mean"))
doe_result_df.loc[:, "std"] = grp.std()["best_obj"]

doe_result_df

	name	total_trials	iteration	x1	x2	objective	mean	std
0	grid	20	1	1.469975	6.227899	111.073604	111.073604	118.357514
1	grid	20	2	0.883308	5.413341	94.041615	43.022933	49.524717
2	grid	20	3	2.636184	4.444537	75.072845	26.105970	19.795899
3	grid	20	4	2.497031	5.256449	73.256810	21.350273	18.004786
4	grid	20	5	3.247982	5.363575	48.050921	15.204124	7.517051
...	...	...	...	...	...	...	...	...
71	sobol	20	16	1.504064	5.073849	46.167809	3.770998	2.442256
72	sobol	20	17	3.606755	4.511856	33.549178	3.655296	2.384255
73	sobol	20	18	1.796636	5.142396	47.487885	3.391802	2.159060
74	sobol	20	19	1.787223	4.887649	54.701454	3.226560	2.040018
75	sobol	20	20	2.797726	4.721839	31.378234	3.121778	1.988530

76 rows × 8 columns

line_df = pd.concat([bayes_line_df, doe_result_df[["name", "iteration", "mean", "std"]]])
line_df

	iteration	mean	std	name
0	1	52.210913	66.523704	bayes
1	2	25.569411	19.484752	bayes
2	3	13.806625	9.609292	bayes
3	4	11.142287	6.591441	bayes
4	5	9.307427	6.159751	bayes
...	...	...	...	...
71	16	3.770998	2.442256	sobol
72	17	3.655296	2.384255	sobol
73	18	3.391802	2.159060	sobol
74	19	3.226560	2.040018	sobol
75	20	3.121778	1.988530	sobol

96 rows × 4 columns

fig = line(
    data_frame=line_df,
    x="iteration",
    y="mean",
    error_y="std",
    color="name",
    error_y_mode="band",
    range_y=[0, 30],
    labels=dict(mean="branin function value")
)
fig.update_layout(hovermode="x")
fig

Higher Dimensions

We observed trends in the 2D case, but what about higher dimensions? Let’s try a 6D example. Since we can’t easily visualize the full solution space in advance, instead we’ll provide the analytic formula here as well as a description of the function. The Hartmann6 function is a 6D function with 6 local minima, in contrast to the Branin function which was 2D and had 3 global minima. The function is defined as follows:

\(f(\mathbf{x})=-\sum_{i=1}^4 \alpha_i \exp \left(-\sum_{j=1}^6 A_{i j}\left(x_j-P_{i j}\right)^2\right)\), where

\(\alpha=(1.0,1.2,3.0,3.2)^T\)

\(\mathbf{A}=\left(\begin{array}{cccccc}10 & 3 & 17 & 3.50 & 1.7 & 8 \\ 0.05 & 10 & 17 & 0.1 & 8 & 14 \\ 3 & 3.5 & 1.7 & 10 & 17 & 8 \\ 17 & 8 & 0.05 & 10 & 0.1 & 14\end{array}\right)\)

\(\mathbf{P}=10^{-4}\left(\begin{array}{cccccc}1312 & 1696 & 5569 & 124 & 8283 & 5886 \\ 2329 & 4135 & 8307 & 3736 & 1004 & 9991 \\ 2348 & 1451 & 3522 & 2883 & 3047 & 6650 \\ 4047 & 8828 & 8732 & 5743 & 1091 & 381\end{array}\right)\)

The inputs are \(x_j, j \in {1,2,3,4,5,6}\) which are typically in the bounds of \([0,1]\).

Finally, the global minimum is given by:

\(f\left(\mathbf{x}^*\right)=-3.32237\), at \(\mathbf{x}^*=(0.20169,0.150011,0.476874,0.275332,0.311652,0.6573)\)

Compared with the previous experiment, we’ll also allow for more total trials while keeping in mind that materials optimization tasks are typically both high-dimensional and subject to a limited number of evaluations.

from ax.utils.measurement.synthetic_functions import hartmann6
from ax import optimize


total_trials = 50
objective_name = "hartmann6"


def evaluate(parameters):
    return {
        objective_name: hartmann6(
            parameters["x1"],
            parameters["x2"],
            parameters["x3"],
            parameters["x4"],
            parameters["x5"],
            parameters["x6"],
        )
    }


best_parameters, values, experiment, model = optimize(
    parameters=[
        {
            "name": "x1",
            "type": "range",
            "bounds": [0.0, 1.0],
        },
        {
            "name": "x2",
            "type": "range",
            "bounds": [0.0, 1.0],
        },
        {
            "name": "x3",
            "type": "range",
            "bounds": [0.0, 1.0],
        },
        {
            "name": "x4",
            "type": "range",
            "bounds": [0.0, 1.0],
        },
        {
            "name": "x5",
            "type": "range",
            "bounds": [0.0, 1.0],
        },
        {
            "name": "x6",
            "type": "range",
            "bounds": [0.0, 1.0],
        },
    ],
    evaluation_function=evaluate,
    objective_name=objective_name,
    minimize=True,
    total_trials=total_trials,
    random_seed=0,
)

[INFO 06-17 23:28:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:28:55] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:28:55] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:28:55] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:28:55] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:28:55] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:28:55] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:28:55] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:28:55] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:28:56] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:28:56] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:28:56] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:28:58] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:29:00] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:29:06] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:29:07] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:29:12] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:29:13] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:29:15] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:29:16] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:29:20] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:29:21] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:29:24] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:29:25] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:29:26] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:29:27] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:29:36] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:29:37] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:29:39] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:29:42] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:29:45] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:29:46] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:29:48] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:29:49] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:29:52] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:29:52] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:29:53] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:29:55] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:29:57] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:29:59] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:30:01] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:30:04] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:30:05] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:30:06] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:30:08] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:30:10] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:30:11] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:30:12] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:30:13] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

num_repeats = 50
SEEDS = list(range(num_repeats))

bounds = {
    "x1": [0, 1],
    "x2": [0, 1],
    "x3": [0, 1],
    "x4": [0, 1],
    "x5": [0, 1],
    "x6": [0, 1],
}

bayes_results = []
for seed in SEEDS:
    noisy_bounds = get_noisy_bounds(bounds, seed=seed)
    results = optimize(
        parameters=[
            {
                "name": "x1",
                "type": "range",
                "bounds": [0.0, 1.0],
            },
            {
                "name": "x2",
                "type": "range",
                "bounds": [0.0, 1.0],
            },
            {
                "name": "x3",
                "type": "range",
                "bounds": [0.0, 1.0],
            },
            {
                "name": "x4",
                "type": "range",
                "bounds": [0.0, 1.0],
            },
            {
                "name": "x5",
                "type": "range",
                "bounds": [0.0, 1.0],
            },
            {
                "name": "x6",
                "type": "range",
                "bounds": [0.0, 1.0],
            },
        ],
        evaluation_function=evaluate,
        objective_name=objective_name,
        minimize=True,
        total_trials=total_trials,
        random_seed=seed,
    )

    bayes_results.append(
        dict(
            best_parameters=results[0],
            values=results[1],
            experiment=results[2],
            model=results[3],
        )
    )

bayes_results_df = pd.DataFrame(bayes_results)

[INFO 06-17 23:30:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:30:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:30:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:30:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:30:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:30:17] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:30:17] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:30:17] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:30:17] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:30:17] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:30:17] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:30:17] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:30:17] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:30:17] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:30:21] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:30:24] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:30:26] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:30:30] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:30:32] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:30:33] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:30:36] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:30:39] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:30:41] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:30:42] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:30:45] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:30:46] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:30:48] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:30:49] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:30:51] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:30:52] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:30:56] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:30:57] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:30:58] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:30:59] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:31:00] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:31:01] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:31:02] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:31:03] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:31:05] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:31:06] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:31:07] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:31:09] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:31:11] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:31:13] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:31:15] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:31:16] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:31:18] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:31:20] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:31:22] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:31:23] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:31:25] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:31:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:31:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:31:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:31:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:31:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:31:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:31:28] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:31:28] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:31:28] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:31:28] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:31:28] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:31:28] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:31:28] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:31:28] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:31:30] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:31:32] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:31:34] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:31:36] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:31:40] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:31:44] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:31:49] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:31:54] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:31:57] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:32:01] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:32:03] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:32:05] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:32:07] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:32:08] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:32:09] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:32:11] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:32:12] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:32:13] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:32:14] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:32:15] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:32:16] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:32:17] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:32:18] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:32:19] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:32:20] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:32:21] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:32:22] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:32:23] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:32:26] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:32:27] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:32:28] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:32:31] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:32:33] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:32:34] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:32:36] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:32:37] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:32:38] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:32:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:32:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:32:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:32:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:32:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:32:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:32:41] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:32:41] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:32:41] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:32:41] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:32:41] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:32:41] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:32:41] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:32:41] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:32:44] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:32:51] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:32:56] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:32:59] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:33:01] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:33:04] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:33:06] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:33:08] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:33:10] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:33:13] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:33:15] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:33:17] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:33:19] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:33:21] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:33:23] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:33:25] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:33:27] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:33:28] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:33:30] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:33:32] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:33:32] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:33:34] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:33:35] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:33:37] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:33:37] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:33:38] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:33:39] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:33:40] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:33:41] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:33:42] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:33:43] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:33:45] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:33:46] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:33:47] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:33:49] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:33:49] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:33:50] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:33:52] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:33:52] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:33:52] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:33:52] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:33:52] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:33:52] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:33:52] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:33:52] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:33:52] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:33:52] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:33:52] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:33:52] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:33:52] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:33:52] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:33:56] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:34:01] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:34:03] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:34:07] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:34:09] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:34:12] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:34:14] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:34:16] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:34:19] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:34:21] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:34:23] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:34:24] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:34:26] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:34:28] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:34:29] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:34:31] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:34:32] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:34:32] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:34:34] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:34:35] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:34:36] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:34:37] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:34:39] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:34:40] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:34:40] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:34:42] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:34:44] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:34:46] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:34:48] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:34:50] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:34:52] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:34:54] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:34:56] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:34:58] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:34:59] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:35:02] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:35:03] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:35:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:35:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:35:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:35:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:35:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:35:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:35:07] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:35:07] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:35:07] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:35:07] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:35:07] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:35:07] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:35:07] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:35:07] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:35:13] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:35:16] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:35:19] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:35:21] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:35:23] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:35:26] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:35:28] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:35:30] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:35:32] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:35:34] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:35:37] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:35:38] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:35:40] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:35:42] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:35:43] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:35:46] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:35:47] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:35:48] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:35:50] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:35:51] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:35:52] ax.service.managed_loop: Running optimization trial 34...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:35:54] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:35:55] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:35:56] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:35:58] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:35:59] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:36:00] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:36:01] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:36:03] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:36:04] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:36:06] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:36:07] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:36:10] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:36:11] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:36:13] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:36:15] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:36:17] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:36:19] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:36:19] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:36:19] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:36:19] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:36:19] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:36:19] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:36:19] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:36:19] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:36:19] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:36:19] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:36:19] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:36:19] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:36:19] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:36:19] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:36:22] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:36:28] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:36:31] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:36:35] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:36:37] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:36:38] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:36:41] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:36:42] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:36:44] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:36:47] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:36:49] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:36:50] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:36:52] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:36:53] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:36:54] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:36:56] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:36:58] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:36:59] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:37:01] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:37:02] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:37:04] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:37:05] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:37:07] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:37:07] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:09] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:10] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:12] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:37:13] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:14] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:16] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:18] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:19] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:21] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:37:22] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:23] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:25] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:27] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:37:29] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:37:29] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:37:29] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:37:29] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:37:29] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:37:29] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:37:29] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:37:29] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:37:29] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:37:29] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:37:29] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:37:29] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:37:29] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:37:29] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:37:30] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:37:30] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:37:30] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:37:32] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:37:35] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:37:37] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:37:39] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:37:42] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:37:44] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:37:47] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:37:49] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:37:51] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:37:53] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:37:55] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:37:57] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:37:59] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:38:01] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:38:02] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:38:03] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:38:04] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:38:05] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:38:06] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:38:07] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:38:07] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:38:08] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:38:09] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:38:10] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:38:11] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:38:13] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:38:14] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:38:15] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:38:16] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:38:18] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:38:19] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:38:20] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:38:21] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:38:22] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:38:23] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:38:24] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:38:25] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:38:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:38:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:38:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:38:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:38:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:38:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:38:27] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:38:27] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:38:27] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:38:27] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:38:27] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:38:27] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:38:27] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:38:27] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:38:29] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:38:31] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:38:34] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:38:36] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:38:38] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:38:41] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:38:42] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:38:44] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:38:47] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:38:49] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:38:51] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:38:53] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:38:55] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:38:58] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:39:02] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:39:03] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:39:04] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:39:06] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:39:07] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:39:08] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:39:10] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:39:11] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:13] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:14] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:16] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:20] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:23] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:26] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:28] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:30] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:32] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:34] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:36] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:38] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:40] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:41] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:44] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:39:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:39:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:39:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:39:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:39:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:39:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:39:48] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:39:48] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:39:48] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:39:48] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:39:48] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:39:48] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:39:48] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:39:48] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:39:48] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:39:48] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:39:48] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:39:48] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:39:48] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:39:48] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:39:48] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:39:48] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:39:49] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:39:49] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:39:49] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:39:49] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:39:51] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:39:56] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:40:01] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:40:08] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:40:11] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:40:15] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:40:17] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:40:19] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:40:20] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:40:23] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:40:24] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:40:26] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:40:28] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:40:30] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:40:32] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:40:34] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:40:35] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:40:36] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:40:37] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:40:38] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:40:39] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:40:40] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:40:42] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:40:44] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:40:45] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:40:46] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:40:48] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:40:49] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:40:50] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:40:52] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:40:53] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:40:54] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:40:56] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:40:57] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:40:58] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:40:59] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:41:01] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:41:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:41:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:41:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:41:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:41:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:41:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:41:04] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:41:04] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:41:04] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:41:04] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:41:04] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:41:04] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:41:04] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:41:04] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:41:06] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:41:10] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:41:14] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:41:19] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:41:22] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:41:23] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:41:25] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:41:27] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:41:29] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:41:31] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:41:34] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:41:36] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:41:37] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:41:38] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:41:39] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:41:40] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:41:41] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:41:42] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:41:44] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:41:45] ax.service.managed_loop: Running optimization trial 33...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:41:46] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:41:47] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:41:48] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:41:49] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:41:51] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:41:52] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:41:53] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:41:55] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:41:56] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:41:58] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:42:00] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:42:01] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:42:03] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:42:04] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:42:06] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:42:07] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:42:09] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:42:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:42:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:42:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:42:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:42:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:42:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:42:12] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:42:12] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:42:12] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:42:12] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:42:12] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:42:12] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:42:12] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:42:12] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:42:13] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:42:15] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:42:19] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:42:22] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:42:26] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:42:28] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:42:29] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:42:33] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:42:35] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:42:36] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:42:38] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:42:40] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:42:42] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:42:44] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:42:45] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:42:47] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:42:49] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:42:52] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:42:54] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:42:56] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:42:57] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:42:58] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:42:59] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:43:00] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:43:01] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:43:02] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:43:03] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:43:05] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:43:07] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:43:08] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:43:08] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:43:10] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:43:11] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:43:12] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:43:14] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:43:16] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:43:18] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:43:19] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:43:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:43:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:43:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:43:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:43:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:43:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:43:23] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:43:23] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:43:23] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:43:23] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:43:23] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:43:23] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:43:23] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:43:23] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:43:25] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:43:29] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:43:32] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:43:35] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:43:41] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:43:41] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:43:42] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:43:43] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:43:45] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:43:46] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:43:47] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:43:50] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:43:51] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:43:52] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:43:53] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:43:54] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:43:55] ax.service.managed_loop: Running optimization trial 30...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:43:57] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:43:58] ax.service.managed_loop: Running optimization trial 32...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:43:59] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:44:01] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:44:02] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:44:03] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:44:05] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:44:07] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:44:09] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:44:11] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:44:12] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:44:14] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:44:15] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:44:17] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:44:18] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:44:20] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:44:22] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:44:23] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:44:24] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:44:24] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:44:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:44:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:44:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:44:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:44:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:44:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:44:27] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:44:27] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:44:27] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:44:27] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:44:27] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:44:27] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:44:27] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:44:27] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:44:29] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:44:31] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:44:36] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:44:40] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:44:46] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:44:50] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:44:52] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:44:54] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:44:58] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:44:59] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:45:02] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:45:04] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:45:06] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:45:08] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:45:10] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:45:14] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:45:16] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:45:17] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:45:19] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:45:20] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:45:22] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:45:23] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:45:24] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:45:26] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:45:27] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:45:28] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:30] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:31] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:33] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:45:35] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:37] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:39] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:40] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:42] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:43] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:45] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:47] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:45:50] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:45:50] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:45:50] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:45:50] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:45:50] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:45:50] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:45:50] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:45:50] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:45:50] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:45:50] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:45:50] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:45:50] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:45:50] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:45:50] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:45:52] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:45:57] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:46:00] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:46:01] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:46:03] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:46:05] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:46:08] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:46:11] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:46:13] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:46:14] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:46:16] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:46:17] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:46:18] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:46:19] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:46:21] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:46:22] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:46:24] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:46:26] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:46:27] ax.service.managed_loop: Running optimization trial 32...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:28] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:46:29] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:46:30] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:46:31] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:46:32] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:34] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:35] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:37] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:38] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:40] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:42] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:43] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:45] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:47] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:46:48] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:50] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:51] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:53] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:46:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:46:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:46:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:46:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:46:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:46:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:46:55] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:46:55] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:46:55] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:46:55] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:46:55] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:46:55] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:46:55] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:46:55] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:46:58] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:47:03] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:47:07] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:47:09] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:47:11] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:47:13] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:47:15] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:47:17] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:47:17] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:47:18] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:47:20] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:47:21] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:47:22] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:47:23] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:47:23] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:47:24] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:47:26] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:47:27] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:47:28] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:47:35] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:47:37] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:47:38] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:47:40] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:47:42] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:47:43] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:47:44] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:47:46] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:47:47] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:47:48] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:47:49] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:47:50] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:47:51] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:47:52] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:47:54] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:47:55] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:47:56] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:47:57] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:48:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:48:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:48:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:48:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:48:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:48:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:48:00] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:48:00] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:48:00] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:48:00] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:48:00] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:48:00] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:48:00] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:48:00] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:48:01] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:48:02] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:48:04] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:48:07] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:48:13] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:48:15] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:48:18] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:48:21] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:48:23] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:48:24] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:48:25] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:48:27] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:48:28] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:48:29] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:48:33] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:48:35] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:48:37] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:48:41] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:48:41] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:48:42] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:48:43] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:48:44] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:48:45] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:48:46] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:48:47] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:48:50] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:48:51] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:48:52] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:48:52] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:48:54] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:48:55] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:48:56] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:48:57] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:48:58] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:48:59] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:49:01] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:49:03] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:49:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:49:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:49:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:49:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:49:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:49:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:49:06] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:49:06] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:49:06] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:49:06] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:49:06] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:49:06] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:49:06] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:49:06] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:49:08] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:49:10] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:49:12] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:49:14] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:49:18] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:49:21] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:49:24] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:49:27] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:49:29] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:49:30] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:49:32] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:49:33] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:49:34] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:49:36] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:49:37] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:49:38] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:49:41] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:49:42] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:49:43] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:49:44] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:49:46] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:49:47] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:49:49] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:49:50] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:49:51] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:49:53] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:49:54] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:49:56] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:49:57] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:49:58] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:50:00] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:50:02] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:50:04] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:50:06] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:50:07] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:50:08] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:50:09] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:50:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:50:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:50:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:50:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:50:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:50:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:50:12] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:50:12] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:50:12] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:50:12] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:50:12] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:50:12] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:50:12] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:50:12] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:50:13] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:50:13] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:50:14] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:50:17] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:50:21] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:50:23] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:50:25] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:50:28] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:50:30] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:50:31] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:50:34] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:50:37] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:50:38] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:50:39] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:50:41] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:50:42] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:50:43] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:50:44] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:50:45] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:50:46] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:50:47] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:50:48] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:50:50] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:50:51] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:50:53] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:50:54] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:50:56] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:50:57] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:50:59] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:51:00] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:51:02] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:51:03] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:51:04] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:51:05] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:51:06] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:51:08] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:51:09] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:51:10] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:51:12] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:51:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:51:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:51:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:51:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:51:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:51:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:51:14] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:51:14] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:51:14] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:51:14] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:51:14] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:51:14] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:51:14] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:51:14] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:51:16] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:51:18] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:51:22] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:51:25] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:51:29] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:51:34] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:51:37] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:51:39] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:51:44] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:51:45] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:51:48] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:51:49] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:51:50] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:51:54] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:51:55] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:51:56] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:51:57] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:52:02] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:52:03] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:52:07] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:52:08] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:52:09] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:52:10] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:52:12] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:52:13] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:52:14] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:52:14] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:52:15] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:52:16] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:52:18] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:52:19] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:52:20] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:52:22] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:52:22] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:52:23] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:52:24] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:52:25] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:52:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:52:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:52:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:52:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:52:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:52:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:52:28] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:52:28] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:52:28] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:52:28] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:52:28] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:52:28] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:52:28] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:52:28] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:52:29] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:52:32] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:52:34] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:52:36] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:52:38] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:52:40] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:52:43] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:52:44] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:52:46] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:52:47] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:52:49] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:52:50] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:52:51] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:52:52] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:52:53] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:52:55] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:52:56] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:52:57] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:52:58] ax.service.managed_loop: Running optimization trial 32...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:00] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:53:01] ax.service.managed_loop: Running optimization trial 34...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:04] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:09] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:12] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:53:14] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:53:16] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:18] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:21] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:22] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:25] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:26] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:28] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:30] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:32] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:53:33] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:53:35] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:53:37] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:53:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:53:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:53:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:53:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:53:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:53:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:53:40] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:53:40] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:53:40] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:53:40] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:53:40] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:53:40] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:53:40] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:53:40] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:53:44] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:53:46] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:53:52] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:53:55] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:53:59] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:54:01] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:54:03] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:54:05] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:54:07] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:54:09] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:54:11] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:54:13] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:54:15] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:54:17] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:54:19] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:54:21] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:54:22] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:54:23] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:54:24] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:54:25] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:54:26] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:54:27] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:54:28] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:54:30] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:54:32] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:54:33] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:54:35] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:54:37] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:54:38] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:54:40] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:54:42] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:54:42] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:54:44] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:54:46] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:54:49] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:54:50] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:54:52] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:54:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:54:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:54:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:54:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:54:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:54:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:54:55] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:54:55] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:54:55] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:54:55] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:54:55] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:54:55] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:54:55] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:54:55] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:54:58] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:55:00] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:55:07] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:55:19] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:55:32] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:55:39] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:55:43] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:55:45] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:55:47] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:55:50] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:55:51] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:55:53] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:55:54] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:55:56] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:56:00] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:56:01] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:56:02] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:56:03] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:56:05] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:56:06] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:56:13] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:56:15] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:56:16] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:56:17] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:56:19] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:56:20] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:56:21] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:56:23] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:56:25] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:56:27] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:56:28] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:56:30] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:56:31] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-17 23:56:33] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:56:35] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:56:36] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:56:38] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:56:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:56:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:56:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:56:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:56:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:56:41] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:56:41] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:56:41] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:56:41] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:56:41] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:56:41] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:56:41] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:56:41] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:56:41] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:56:44] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:56:49] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:57:00] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:57:07] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:57:10] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:57:14] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:57:17] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:57:20] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:57:23] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:57:24] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:57:26] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:57:27] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:57:29] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:57:30] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:57:33] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:57:34] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:57:36] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:57:38] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:57:40] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:57:41] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:57:43] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:57:44] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-17 23:57:47] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:57:48] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:57:50] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-17 23:57:51] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-17 23:57:52] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-17 23:57:52] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-17 23:57:54] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-17 23:57:55] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-17 23:57:56] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-17 23:57:59] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-17 23:58:01] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:58:03] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:58:04] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-17 23:58:06] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-17 23:58:08] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-17 23:58:10] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:58:10] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:58:10] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:58:10] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:58:10] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:58:10] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:58:10] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:58:10] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:58:10] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:58:10] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:58:10] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:58:10] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:58:10] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:58:10] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:58:13] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-17 23:58:18] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-17 23:58:21] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-17 23:58:26] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-17 23:58:30] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-17 23:58:34] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-17 23:58:36] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-17 23:58:40] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-17 23:58:43] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-17 23:58:45] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-17 23:58:48] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-17 23:58:50] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-17 23:58:53] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-17 23:58:56] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-17 23:58:58] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-17 23:58:59] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-17 23:59:00] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-17 23:59:01] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-17 23:59:02] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-17 23:59:04] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-17 23:59:06] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-17 23:59:09] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:10] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-17 23:59:11] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-17 23:59:14] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:16] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:17] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:19] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:21] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:26] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:29] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:32] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:36] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:39] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-17 23:59:42] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:45] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:47] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-17 23:59:51] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:59:51] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:59:51] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:59:51] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:59:51] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:59:51] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-17 23:59:51] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-17 23:59:51] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-17 23:59:51] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-17 23:59:51] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-17 23:59:51] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-17 23:59:51] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-17 23:59:51] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-17 23:59:51] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-17 23:59:52] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-17 23:59:52] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-17 23:59:52] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-17 23:59:55] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:00:01] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:00:04] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:00:09] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:00:12] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:00:16] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:00:19] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:00:21] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:00:24] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:00:26] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:00:28] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:00:30] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:00:32] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:00:33] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:00:35] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:00:37] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:00:38] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:00:40] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:00:41] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:00:42] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:00:45] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:00:48] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:00:50] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:00:52] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:00:53] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:00:55] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:00:56] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:00:58] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:01:01] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:01:04] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:01:06] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:01:07] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:01:09] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:01:11] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:01:14] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:01:16] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:01:18] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:01:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:01:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:01:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:01:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:01:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:01:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:01:22] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:01:22] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:01:22] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:01:22] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:01:22] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:01:22] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:01:22] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:01:22] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:01:22] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:01:22] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:01:22] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:01:22] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:01:22] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:01:22] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:01:22] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:01:22] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:01:23] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:01:23] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:01:23] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:01:23] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:01:25] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:01:28] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:01:32] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:01:36] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:01:42] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:01:46] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:01:50] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:01:51] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:01:54] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:01:57] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:01:59] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:02:01] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:02:03] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:02:05] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:02:09] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:02:10] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:02:12] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:02:14] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:02:16] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:02:19] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:02:21] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:02:22] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:02:24] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:26] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:02:27] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:29] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:31] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:02:33] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:35] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:02:38] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:40] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:42] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:02:44] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:47] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:49] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:51] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:54] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:02:59] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:02:59] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:02:59] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:02:59] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:02:59] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:02:59] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:02:59] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:02:59] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:02:59] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:02:59] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:02:59] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:02:59] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:02:59] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:02:59] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:03:06] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:03:11] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:03:15] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:03:17] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:03:21] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:03:24] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:03:26] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:03:29] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:03:31] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:03:32] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:03:35] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:03:37] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:03:38] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:03:40] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:03:42] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:03:44] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:03:45] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:03:46] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:03:48] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:03:49] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:03:50] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:03:53] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:03:54] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:03:55] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:03:57] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:03:57] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:03:59] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:04:00] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:04:02] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:04:04] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:04:06] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:04:08] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:04:10] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:04:12] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:04:14] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:04:16] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:04:18] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:04:21] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:04:21] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:04:21] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:04:21] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:04:21] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:04:21] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:04:21] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:04:21] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:04:21] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:04:21] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:04:21] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:04:21] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:04:21] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:04:21] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:04:22] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:04:25] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:04:28] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:04:32] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:04:35] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:04:37] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:04:39] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:04:41] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:04:42] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:04:44] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:04:47] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:04:51] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:04:53] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:04:56] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:04:59] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:05:00] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:05:01] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:05:02] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:05:03] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:05:04] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:05:05] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:05:07] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:05:09] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:05:10] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:05:12] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:05:13] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:05:15] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:05:17] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:05:18] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:05:20] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:05:22] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:05:23] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:05:25] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:05:28] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:05:30] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:05:33] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:05:35] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:05:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:05:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:05:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:05:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:05:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:05:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:05:39] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:05:39] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:05:39] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:05:39] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:05:39] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:05:39] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:05:39] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:05:39] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:05:40] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:05:40] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:05:42] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:05:45] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:05:48] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:05:52] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:05:57] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:06:03] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:06:14] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:06:24] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:06:32] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:06:40] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:06:48] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:06:54] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:06:58] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:07:01] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:07:05] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:07:08] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:07:10] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:07:13] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:07:14] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:07:15] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:07:17] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:07:21] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:07:23] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:07:24] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:07:25] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:07:26] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:07:29] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:07:30] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:07:33] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:07:35] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:07:37] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:07:39] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:07:41] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:07:42] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:07:43] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-18 00:07:45] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:07:46] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-18 00:07:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:07:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:07:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:07:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:07:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:07:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:07:48] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:07:48] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:07:48] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:07:48] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:07:48] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:07:48] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:07:48] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:07:48] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:07:48] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:07:48] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:07:48] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:07:48] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:07:48] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:07:48] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:07:48] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:07:49] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:07:49] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:07:49] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:07:49] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:07:49] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:07:51] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:07:55] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:07:59] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:08:01] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:08:04] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:08:06] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:08:08] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:08:10] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:08:12] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:08:14] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:08:16] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:08:19] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:08:22] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:08:24] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:08:26] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:08:29] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:08:30] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:08:31] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:08:32] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:08:34] ax.service.managed_loop: Running optimization trial 33...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:35] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:08:36] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:08:37] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:39] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:40] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:42] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:08:44] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:08:45] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:47] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:48] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:50] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:51] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:53] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:55] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:56] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:08:58] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:09:00] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:09:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:09:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:09:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:09:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:09:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:09:03] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:09:03] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:09:03] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:09:03] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:09:03] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:09:03] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:09:03] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:09:03] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:09:03] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:09:05] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:09:06] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:09:09] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:09:16] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:09:23] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:09:28] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:09:34] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:09:35] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:09:38] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:09:42] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:09:42] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:09:44] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:09:45] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:09:46] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:09:48] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:09:50] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:09:51] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:09:52] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:09:53] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:09:54] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:09:56] ax.service.managed_loop: Running optimization trial 34...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:09:58] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:10:00] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:10:02] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:10:04] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:10:06] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:10:07] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:10:09] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:10:10] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:10:12] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:10:14] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:10:15] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:10:17] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:10:19] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:10:20] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:10:22] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:10:25] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:10:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:10:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:10:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:10:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:10:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:10:28] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:10:28] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:10:28] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:10:28] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:10:28] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:10:28] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:10:28] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:10:28] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:10:28] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:10:31] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:10:37] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:10:43] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:10:49] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:10:54] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:10:56] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:10:59] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:11:01] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:11:04] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:11:07] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:11:09] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:11:11] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:11:14] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:11:16] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:11:18] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:11:20] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:11:22] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:11:24] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:11:26] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:11:27] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:11:28] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:11:29] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:11:30] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:11:31] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:11:32] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:11:33] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:35] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:37] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:11:38] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:40] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:11:42] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:43] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:45] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:47] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:48] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:50] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:52] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:11:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:11:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:11:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:11:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:11:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:11:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:11:55] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:11:55] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:11:55] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:11:55] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:11:55] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:11:55] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:11:55] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:11:55] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:11:57] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:11:59] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:12:03] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:12:10] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:12:14] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:12:15] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:12:17] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:12:19] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:12:21] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:12:24] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:12:27] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:12:28] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:12:30] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:12:30] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:12:31] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:12:32] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:12:33] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:12:35] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:12:36] ax.service.managed_loop: Running optimization trial 32...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:12:38] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:12:39] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:12:40] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:12:42] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:12:43] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:12:45] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:12:47] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:12:49] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:12:50] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:12:51] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:12:52] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:12:52] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:12:53] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:12:55] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:12:56] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:12:57] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:12:59] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:13:01] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:13:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:13:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:13:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:13:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:13:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:13:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:13:04] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:13:04] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:13:04] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:13:04] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:13:04] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:13:04] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:13:04] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:13:04] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:13:08] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:13:12] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:13:17] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:13:22] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:13:32] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:13:42] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:13:48] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:13:53] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:13:56] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:13:59] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:14:01] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:14:04] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:14:06] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:14:08] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:14:09] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:14:11] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:14:13] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:14:15] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:14:16] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:14:17] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:14:18] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:14:19] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:14:22] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:14:23] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:14:25] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:14:27] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:14:28] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:14:31] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:14:32] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:14:34] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:14:36] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:14:37] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:14:39] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:14:41] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:14:42] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-18 00:14:43] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:14:44] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-18 00:14:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:14:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:14:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:14:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:14:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:14:47] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:14:47] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:14:47] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:14:47] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:14:47] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:14:47] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:14:47] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:14:47] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:14:47] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:14:50] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:14:53] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:14:55] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:14:59] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:15:04] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:15:08] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:15:10] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:15:12] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:15:15] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:15:17] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:15:21] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:15:23] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:15:25] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:15:26] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:15:28] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:15:30] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:15:32] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:15:32] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:15:33] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:15:34] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:15:36] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:15:38] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:39] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:15:41] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:43] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:45] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:46] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:48] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:15:49] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:51] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:52] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:54] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:56] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:57] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:15:59] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:16:01] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:16:02] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-18 00:16:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:16:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:16:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:16:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:16:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:16:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:16:04] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:16:04] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:16:04] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:16:04] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:16:04] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:16:04] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:16:04] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:16:04] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:16:04] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:16:04] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:16:04] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:16:04] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:16:04] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:16:05] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:16:05] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:16:05] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:16:05] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:16:05] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:16:05] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:16:05] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:16:06] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:16:11] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:16:14] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:16:17] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:16:20] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:16:22] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:16:25] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:16:27] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:16:29] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:16:31] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:16:33] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:16:35] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:16:37] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:16:38] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:16:42] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:16:46] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:16:47] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:16:50] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:16:51] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:16:52] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:16:53] ax.service.managed_loop: Running optimization trial 34...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:16:55] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:16:56] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:16:58] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:17:00] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:17:01] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:17:03] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:17:05] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:17:07] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:17:09] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:17:11] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:17:12] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:17:14] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:17:16] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:17:17] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:17:19] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:17:20] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:17:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:17:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:17:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:17:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:17:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:17:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:17:23] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:17:23] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:17:23] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:17:23] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:17:23] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:17:23] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:17:23] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:17:23] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:17:25] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:17:29] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:17:34] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:17:37] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:17:40] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:17:42] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:17:43] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:17:46] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:17:47] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:17:51] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:17:52] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:17:53] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:17:56] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:17:58] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:17:59] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:18:00] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:18:02] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:18:03] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:18:07] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:18:09] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:18:10] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:18:13] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:18:14] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:18:16] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:18:17] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:18:19] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:18:21] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:18:22] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:18:23] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:18:25] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:18:26] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:18:29] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:18:30] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:18:32] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:18:33] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:18:35] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:18:37] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-18 00:18:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:18:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:18:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:18:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:18:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:18:40] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:18:40] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:18:40] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:18:40] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:18:40] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:18:40] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:18:40] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:18:40] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:18:40] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:18:43] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:18:48] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:18:53] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:18:57] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:19:02] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:19:06] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:19:12] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:19:14] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:19:17] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:19:19] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:19:21] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:19:24] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:19:27] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:19:28] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:19:30] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:19:31] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:19:34] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:19:36] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:19:38] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:19:40] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:19:43] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:19:45] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:19:47] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:19:48] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:19:49] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:19:51] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:19:52] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:19:55] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:19:57] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:19:59] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:20:01] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:20:02] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:20:03] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:20:04] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:20:05] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-18 00:20:07] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:20:09] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-18 00:20:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:20:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:20:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:20:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:20:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:20:12] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:20:12] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:20:12] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:20:12] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:20:12] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:20:12] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:20:12] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:20:12] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:20:12] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:20:15] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:20:20] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:20:24] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:20:27] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:20:29] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:20:31] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:20:33] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:20:35] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:20:37] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:20:39] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:20:41] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:20:43] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:20:46] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:20:48] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:20:49] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:20:50] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:20:52] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:20:53] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:20:55] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:20:56] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:21:07] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:21:08] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:21:09] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:21:10] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:21:11] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:21:13] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:21:14] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:21:16] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:21:17] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:21:24] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:21:25] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:21:26] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:21:28] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:21:30] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:21:31] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-18 00:21:32] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:21:34] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:21:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:21:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:21:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:21:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:21:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:21:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:21:37] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:21:37] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:21:37] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:21:37] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:21:37] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:21:37] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:21:37] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:21:37] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:21:37] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:21:37] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:21:37] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:21:37] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:21:37] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:21:37] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:21:37] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:21:38] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:21:38] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:21:38] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:21:38] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:21:38] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:21:40] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:21:44] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:21:51] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:21:55] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:21:59] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:22:02] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:22:04] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:22:06] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:22:07] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:22:09] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:22:11] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:22:13] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:22:16] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:22:17] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:22:18] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:22:19] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:22:20] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:22:23] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:22:25] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:22:26] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:22:26] ax.service.managed_loop: Running optimization trial 34...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:28] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:22:29] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:31] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:33] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:22:35] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:36] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:38] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:40] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:41] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:44] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:46] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:47] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:49] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:51] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:53] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:54] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:22:58] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:22:58] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:22:58] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:22:58] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:22:58] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:22:58] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:22:58] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:22:58] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:22:58] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:22:58] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:22:58] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:22:58] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:22:58] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:22:58] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:23:01] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:23:04] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:23:08] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:23:16] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:23:27] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:23:34] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:23:37] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:23:40] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:23:43] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:23:45] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:23:49] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:23:53] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:24:01] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:24:05] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:24:06] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:24:07] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:24:11] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:24:12] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:24:13] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:24:15] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:24:16] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:24:20] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:24:21] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:24:22] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:24:23] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:24:25] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:24:26] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:24:28] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:24:29] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:24:31] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:24:32] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:24:34] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:24:35] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:24:37] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:24:39] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-18 00:24:40] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:24:42] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-18 00:24:45] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:24:45] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:24:45] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:24:45] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:24:45] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:24:45] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:24:45] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:24:45] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:24:45] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:24:45] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:24:45] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:24:45] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:24:45] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:24:45] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:24:47] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:24:50] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:24:53] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:24:57] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:25:00] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:25:02] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:25:05] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:25:07] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:25:09] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:25:11] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:25:13] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:25:15] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:25:16] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:25:17] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:25:19] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:25:21] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:25:22] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:25:23] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:25:24] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:25:26] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:25:28] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:25:29] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:25:29] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:25:31] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:25:32] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:25:33] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:25:35] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:37] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:39] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:40] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:42] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:44] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:46] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:47] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:49] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:50] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:52] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:25:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:25:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:25:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:25:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:25:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:25:55] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:25:55] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:25:55] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:25:55] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:25:55] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:25:55] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:25:55] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:25:55] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:25:55] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:25:57] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:26:00] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:26:04] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:26:09] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:26:13] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:26:19] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:26:22] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:26:25] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:26:28] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:26:32] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:26:37] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:26:39] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:26:42] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:26:43] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:26:44] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:26:46] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:26:47] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:26:48] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:26:50] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:26:51] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:26:53] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:26:54] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:26:56] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:26:57] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:27:03] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:27:04] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:27:05] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:27:06] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:27:08] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:27:10] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:27:11] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:27:13] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:27:15] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:27:16] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:27:18] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-18 00:27:20] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:27:23] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-18 00:27:26] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:27:26] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:27:26] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:27:26] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:27:26] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:27:26] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:27:26] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:27:26] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:27:26] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:27:26] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:27:26] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:27:26] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:27:26] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:27:26] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:27:30] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:27:37] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:27:39] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:27:42] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:27:45] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:27:47] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:27:50] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:27:52] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:27:54] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:27:56] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:27:58] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:27:59] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:28:01] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:28:02] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:28:04] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:28:06] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:28:07] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:28:07] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:28:09] ax.service.managed_loop: Running optimization trial 32...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:11] ax.service.managed_loop: Running optimization trial 33...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:12] ax.service.managed_loop: Running optimization trial 34...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:14] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:28:15] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:17] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:18] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:20] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:22] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:23] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:25] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:28:27] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:28] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:28:30] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:28:32] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:33] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:35] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:37] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:39] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:28:42] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:28:42] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:28:42] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:28:42] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:28:42] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:28:42] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:28:42] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:28:42] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:28:42] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:28:42] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:28:42] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:28:42] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:28:42] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:28:42] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:28:45] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:28:48] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:28:50] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:28:53] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:28:55] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:28:57] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:29:00] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:29:02] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:29:04] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:29:06] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:29:08] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:29:13] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:29:15] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:29:17] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:29:19] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:29:21] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:29:23] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:29:24] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:29:26] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:29:27] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:29:28] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:29:30] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:29:31] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:29:34] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:29:36] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:29:37] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:29:38] ax.service.managed_loop: Running optimization trial 40...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:29:39] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:29:41] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:29:43] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:29:44] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:29:46] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:29:47] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:29:49] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:29:51] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-18 00:29:52] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:29:53] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-18 00:29:56] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:29:56] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:29:56] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:29:56] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:29:56] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:29:56] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:29:56] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:29:56] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:29:56] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:29:56] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:29:56] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:29:56] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:29:56] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:29:56] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:30:00] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:30:04] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:30:08] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:30:11] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:30:14] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:30:16] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:30:19] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:30:21] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:30:24] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:30:27] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:30:30] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:30:32] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:30:34] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:30:36] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:30:37] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:30:38] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:30:40] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:30:41] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:30:43] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:30:45] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:30:46] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:30:47] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:30:49] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:30:50] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:30:51] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:30:52] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:30:54] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:30:55] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:30:57] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:30:58] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:31:00] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:31:02] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:31:03] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:31:05] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:31:07] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:31:08] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:31:11] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:31:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:31:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:31:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:31:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:31:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:31:14] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:31:14] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:31:14] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:31:14] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:31:14] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:31:14] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:31:14] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:31:14] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:31:14] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:31:20] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:31:28] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:31:32] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:31:35] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:31:37] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:31:39] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:31:41] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:31:44] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:31:47] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:31:50] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:31:52] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:31:54] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:31:56] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:31:59] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:32:00] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:32:02] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:32:04] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:32:05] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:32:07] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:32:08] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:32:11] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:32:12] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:32:13] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:32:14] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:32:16] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:32:17] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:32:18] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:32:20] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:32:21] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:32:23] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:32:24] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:32:26] ax.service.managed_loop: Running optimization trial 45...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:32:28] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:32:30] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:32:30] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:32:32] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:32:34] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:32:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:32:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:32:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:32:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:32:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:32:37] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:32:37] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:32:37] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:32:37] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:32:37] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:32:37] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:32:37] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:32:37] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:32:37] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:32:39] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:32:41] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:32:46] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:32:51] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:32:56] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:33:03] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:33:09] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:33:13] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:33:15] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:33:19] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:33:23] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:33:26] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:33:28] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:33:31] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:33:32] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:33:35] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:33:37] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:33:39] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:33:40] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:33:42] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:33:44] ax.service.managed_loop: Running optimization trial 34...
[INFO 06-18 00:33:45] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:33:47] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:33:48] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:33:50] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:33:51] ax.service.managed_loop: Running optimization trial 39...
[INFO 06-18 00:33:52] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:33:54] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:33:55] ax.service.managed_loop: Running optimization trial 42...
[INFO 06-18 00:33:56] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:33:57] ax.service.managed_loop: Running optimization trial 44...
[INFO 06-18 00:33:58] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:33:59] ax.service.managed_loop: Running optimization trial 46...
[INFO 06-18 00:33:59] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:34:01] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-18 00:34:02] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:34:03] ax.service.managed_loop: Running optimization trial 50...
[INFO 06-18 00:34:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:34:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:34:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:34:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:34:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:34:06] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:34:06] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:34:06] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:34:06] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:34:06] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:34:06] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:34:06] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:34:06] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:34:06] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:34:09] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:34:13] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:34:17] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:34:22] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:34:27] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:34:28] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:34:30] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:34:34] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:34:36] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:34:38] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:34:40] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:34:42] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:34:44] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:34:47] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:34:49] ax.service.managed_loop: Running optimization trial 28...
[INFO 06-18 00:34:49] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:34:51] ax.service.managed_loop: Running optimization trial 30...
[INFO 06-18 00:34:52] ax.service.managed_loop: Running optimization trial 31...
[INFO 06-18 00:34:54] ax.service.managed_loop: Running optimization trial 32...
[INFO 06-18 00:34:55] ax.service.managed_loop: Running optimization trial 33...
[INFO 06-18 00:34:56] ax.service.managed_loop: Running optimization trial 34...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:34:57] ax.service.managed_loop: Running optimization trial 35...
[INFO 06-18 00:34:59] ax.service.managed_loop: Running optimization trial 36...
[INFO 06-18 00:35:01] ax.service.managed_loop: Running optimization trial 37...
[INFO 06-18 00:35:02] ax.service.managed_loop: Running optimization trial 38...
[INFO 06-18 00:35:04] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:35:06] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:35:08] ax.service.managed_loop: Running optimization trial 41...
[INFO 06-18 00:35:10] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:35:12] ax.service.managed_loop: Running optimization trial 43...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:35:14] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:35:16] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:35:18] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:35:19] ax.service.managed_loop: Running optimization trial 47...
[INFO 06-18 00:35:20] ax.service.managed_loop: Running optimization trial 48...
[INFO 06-18 00:35:22] ax.service.managed_loop: Running optimization trial 49...
[INFO 06-18 00:35:23] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:35:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:35:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:35:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:35:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:35:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:35:27] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.
[INFO 06-18 00:35:27] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).
[INFO 06-18 00:35:27] ax.modelbridge.dispatch_utils: Using Models.GPEI since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 06-18 00:35:27] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False
[INFO 06-18 00:35:27] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12
[INFO 06-18 00:35:27] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12
[INFO 06-18 00:35:27] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 12 trials, GPEI for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.
[INFO 06-18 00:35:27] ax.service.managed_loop: Started full optimization with 50 steps.
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 1...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 2...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 3...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 4...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 5...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 6...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 7...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 8...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 9...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 10...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 11...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 12...
[INFO 06-18 00:35:27] ax.service.managed_loop: Running optimization trial 13...
[INFO 06-18 00:35:33] ax.service.managed_loop: Running optimization trial 14...
[INFO 06-18 00:35:37] ax.service.managed_loop: Running optimization trial 15...
[INFO 06-18 00:35:42] ax.service.managed_loop: Running optimization trial 16...
[INFO 06-18 00:35:45] ax.service.managed_loop: Running optimization trial 17...
[INFO 06-18 00:35:47] ax.service.managed_loop: Running optimization trial 18...
[INFO 06-18 00:35:49] ax.service.managed_loop: Running optimization trial 19...
[INFO 06-18 00:35:52] ax.service.managed_loop: Running optimization trial 20...
[INFO 06-18 00:35:54] ax.service.managed_loop: Running optimization trial 21...
[INFO 06-18 00:35:56] ax.service.managed_loop: Running optimization trial 22...
[INFO 06-18 00:35:58] ax.service.managed_loop: Running optimization trial 23...
[INFO 06-18 00:36:01] ax.service.managed_loop: Running optimization trial 24...
[INFO 06-18 00:36:06] ax.service.managed_loop: Running optimization trial 25...
[INFO 06-18 00:36:07] ax.service.managed_loop: Running optimization trial 26...
[INFO 06-18 00:36:09] ax.service.managed_loop: Running optimization trial 27...
[INFO 06-18 00:36:10] ax.service.managed_loop: Running optimization trial 28...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:12] ax.service.managed_loop: Running optimization trial 29...
[INFO 06-18 00:36:14] ax.service.managed_loop: Running optimization trial 30...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:16] ax.service.managed_loop: Running optimization trial 31...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:18] ax.service.managed_loop: Running optimization trial 32...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:19] ax.service.managed_loop: Running optimization trial 33...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:21] ax.service.managed_loop: Running optimization trial 34...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:23] ax.service.managed_loop: Running optimization trial 35...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:25] ax.service.managed_loop: Running optimization trial 36...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:26] ax.service.managed_loop: Running optimization trial 37...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:28] ax.service.managed_loop: Running optimization trial 38...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:30] ax.service.managed_loop: Running optimization trial 39...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:32] ax.service.managed_loop: Running optimization trial 40...
[INFO 06-18 00:36:34] ax.service.managed_loop: Running optimization trial 41...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:36] ax.service.managed_loop: Running optimization trial 42...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:38] ax.service.managed_loop: Running optimization trial 43...
[INFO 06-18 00:36:38] ax.service.managed_loop: Running optimization trial 44...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:40] ax.service.managed_loop: Running optimization trial 45...
[INFO 06-18 00:36:42] ax.service.managed_loop: Running optimization trial 46...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:43] ax.service.managed_loop: Running optimization trial 47...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:44] ax.service.managed_loop: Running optimization trial 48...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:46] ax.service.managed_loop: Running optimization trial 49...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

[INFO 06-18 00:36:48] ax.service.managed_loop: Running optimization trial 50...
c:\Users\sterg\Miniconda3\envs\sdl-demo\lib\site-packages\botorch\optim\initializers.py:403: BadInitialCandidatesWarning:

Unable to find non-zero acquisition function values - initial conditions are being selected randomly.

bayes_dfs = [
    add_best_obj_so_far(extract_data(result["experiment"])) for result in bayes_results
]

bayes_best_objs = [bayes_df["best_obj"] for bayes_df in bayes_dfs]
bayes_mean = np.mean(bayes_best_objs, axis=0)
bayes_std = np.std(bayes_best_objs, axis=0)

bayes_line_df = pd.DataFrame(
    dict(iteration=bayes_dfs[0]["iteration"], mean=bayes_mean, std=bayes_std)
)
bayes_line_df["name"] = "bayes"

from numpy.random import default_rng

rng = default_rng(0)
sample_dfs = []
for name, sampling_fn in sampling_fns.items():
    for seed in SEEDS:
        noisy_bounds = get_noisy_bounds(bounds, rng, noise_scale=0.1)
        sample_df = sampling_fn(
            noisy_bounds, num_samples=total_trials, seed=seed
        ).sample(frac=1.0)
        sample_df["name"] = name
        sample_df["total_trials"] = total_trials
        sample_df["seed"] = seed
        sample_df["iteration"] = range(1, len(sample_df) + 1)
        sample_dfs.append(sample_df)

doe_df = pd.concat(sample_dfs, axis=0)
doe_df["objective"] = hartmann6(doe_df[["x1", "x2", "x3", "x4", "x5", "x6"]].values)

sub_dfs = []
for name, sampling_fn in sampling_fns.items():
    for seed in range(num_repeats):
        sub_df = doe_df.query(
            "name == @name and total_trials == @total_trials and seed == @seed",
        )
        sub_df.loc[:, "best_obj"] = sub_df["objective"].cummin()
        sub_dfs.append(sub_df)

doe_best_df = pd.concat(sub_dfs, axis=0)
grp = doe_best_df.groupby(["name", "total_trials", "iteration"], as_index=False)
doe_result_df = grp.mean().drop("seed", axis=1)
doe_result_df = doe_result_df.rename(columns=dict(best_obj="mean"))
doe_result_df.loc[:, "std"] = grp.std()["best_obj"]

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C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\2746881076.py:5: UserWarning:

The balance properties of Sobol' points require n to be a power of 2.

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\sterg\AppData\Local\Temp\ipykernel_29488\1511024706.py:26: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

line_df = pd.concat(
    [bayes_line_df, doe_result_df[["name", "iteration", "mean", "std"]]]
)

fig = line(
    data_frame=line_df,
    x="iteration",
    y="mean",
    error_y="std",
    color="name",
    error_y_mode="band",
    # range_y=[0, 30],
    labels=dict(mean="hartmann6 function value")
)
fig.update_layout(margin=dict(r=40, t=30, b=30), hovermode="x")

fig_path = "hartmann6-comparison"
fig.write_html(fig_path + ".html")
fig.write_image(fig_path + ".png")
fig.show()

# For notebook gallery thumbnail
from IPython.display import Image
Image(filename=fig_path + ".png")

Code Graveyard

# from ax.modelbridge.factory import get_sobol
# from ax.service.ax_client import AxClient

# def get_sobol_samples(bounds, num_samples=10):
#     parameters = [
#             {
#                 "name": "x1",
#                 "type": "range",
#                 "bounds": bounds["x1"],
#             },
#             {
#                 "name": "x2",
#                 "type": "range",
#                 "bounds": bounds["x2"],
#             },
#         ]

#     client = AxClient()
#     client.create_experiment(
#         name="experiment",
#         parameters=parameters,  # type: ignore
#     )

#     m = get_sobol(client.experiment.search_space)
#     gr = m.gen(n=num_samples)
#     gr

# compare_samples = {}
# for name, sampling_fn in sampling_fns.items():
#     compare_samples[name] = {}
#     for num_samples in sample_nums:
#         compare_samples[name][num_samples] = sampling_fn(bounds, num_samples)

# df = pd.DataFrame(compare_samples)
# df.index.name = "num_samples"
# df

# three_plot_df = three.copy()
# three_plot_df["step"] = (np.arange(0, three_plot_df.shape[0]))
# three_plot_df["group"] = 1

# px.scatter_3d(three_plot_df, x="x1", y="x2", z="x3", animation_frame="step", animation_group="group", width=400, height=400)

# discrepancies = []
# for name, sampling_fn in sampling_fns.items():
#     for num_samples in sample_nums:
#         sample_df = compare_df.query("name == @name and num_samples == @num_samples")
#         sample_df["discrepancy"] = qmc.discrepancy(sample_df[["x1", "x2"]].values)
#         sample_dfs.append(sample_df)

# compare_df_2 = pd.concat(sample_dfs, axis=0)
# compare_df_2