Optimizing KB Mirrors with Bayesian Optimization#
In this tutorial, you will learn how to use Blop to optimize a Kirkpatrick-Baez (KB) mirror system. By the end, you will understand:
How degrees of freedom (DOFs) represent the parameters you can adjust in an experiment
How objectives define what you’re trying to optimize
How to write an evaluation function that extracts results from experimental data
How the Agent coordinates the optimization loop
How to check optimization health mid-run and continue
We’ll work with a simulated KB mirror beamline, but the concepts apply directly to real experimental setups.
What are KB Mirrors?#
KB mirror systems use two curved mirrors to focus X-ray beams. Each mirror has adjustable curvature—getting both just right produces a tight, intense focal spot. This is a multi-objective optimization problem: we want to maximize beam intensity while minimizing the spot size in both X and Y directions.
The image below shows our simulated setup: a beam from a geometric source propagates through a pair of toroidal mirrors that focus it onto a screen.
Setting Up the Environment#
Before we can optimize, we need to set up the data infrastructure. Blop uses Bluesky to run experiments and Tiled to store and retrieve data.
import logging
from pathlib import PurePath
import cv2
import numpy as np
import matplotlib.pyplot as plt
from tiled.client.container import Container
from bluesky_tiled_plugins import TiledWriter
from bluesky.run_engine import RunEngine
from tiled.client import from_uri # type: ignore[import-untyped]
from tiled.server import SimpleTiledServer
from ophyd_async.core import StaticPathProvider, UUIDFilenameProvider
from blop.ax import Agent, RangeDOF, Objective
from blop.protocols import EvaluationFunction
# Import simulation devices (requires: pip install -e sim/)
from blop_sim.backends.xrt import XRTBackend
from blop_sim.devices.xrt import KBMirror
from blop_sim.devices import DetectorDevice
# Suppress noisy logs from httpx
logging.getLogger("httpx").setLevel(logging.WARNING)
# Enable interactive plotting
plt.ion()
DETECTOR_STORAGE = "/tmp/blop/sim"
[WARNING 04-19 07:11:00] ax.storage.sqa_store.with_db_settings_base: Ax currently requires a sqlalchemy version below 2.0. This will be addressed in a future release. Disabling SQL storage in Ax for now, if you would like to use SQL storage please install Ax with mysql extras via `pip install ax-platform[mysql]`.
Next, we create a local Tiled server. The TiledWriter callback will save experimental data to this server, and our evaluation function will read from it.
tiled_server = SimpleTiledServer(readable_storage=[DETECTOR_STORAGE])
tiled_client = from_uri(tiled_server.uri)
tiled_writer = TiledWriter(tiled_client)
RE = RunEngine({})
RE.subscribe(tiled_writer)
Tiled version 0.2.9
0
Defining Degrees of Freedom#
Degrees of freedom (DOFs) are the parameters the optimizer can adjust. In our KB system, we control the curvature radius of each mirror. Let’s define the search space:
# Define search ranges for each mirror's curvature radius
# The optimal values (~38000 and ~21000) are intentionally placed
# away from the center to make the optimization more realistic
VERTICAL_BOUNDS = (25000, 45000) # Optimal ~38000 is in upper portion
HORIZONTAL_BOUNDS = (15000, 35000) # Optimal ~21000 is in lower portion
Now we create the simulation backend and individual devices. Each RangeDOF wraps an actuator (something we can move) with bounds that constrain the search space:
# Create XRT simulation backend
backend = XRTBackend()
# Create individual KB mirror devices
kbv = KBMirror(backend, mirror_index=0, initial_radius=38000, name="kbv")
kbh = KBMirror(backend, mirror_index=1, initial_radius=21000, name="kbh")
# Create detector device
det = DetectorDevice(backend, StaticPathProvider(UUIDFilenameProvider(), PurePath(DETECTOR_STORAGE)), name="det")
# Define DOFs using mirror radius signals
dofs = [
RangeDOF(actuator=kbv.radius, bounds=VERTICAL_BOUNDS, parameter_type="float"),
RangeDOF(actuator=kbh.radius, bounds=HORIZONTAL_BOUNDS, parameter_type="float"),
]
The actuator is the device that physically changes the parameter. The bounds tell the optimizer what range of values to explore. Think of DOFs as the “knobs” the optimizer can turn.
Defining Objectives#
Objectives specify what you want to optimize. Each objective has a name (matching a value your evaluation function will return) and a direction: minimize=True for things you want smaller, minimize=False for things you want larger.
For our KB mirrors, we have three objectives:
Intensity (
intensity): We want more signal →minimize=FalseSpot width (
width): We want a tighter spot →minimize=TrueSpot height (
height): We want a tighter spot →minimize=True
objectives = [
Objective(name="intensity", minimize=False),
Objective(name="width", minimize=True),
Objective(name="height", minimize=True),
]
With multiple objectives that can conflict (maximizing intensity might increase spot size), the optimizer finds the Pareto frontier—the set of solutions where you can’t improve one objective without sacrificing another.
Writing an Evaluation Function#
The evaluation function is the bridge between raw experimental data and the optimizer. After each measurement, the optimizer needs to know how well that configuration performed. Your evaluation function:
Receives a run UID and the suggestions that were tested
Reads the beam images from Tiled
Computes statistics (intensity, width, centroid, etc.) from the images
Returns outcome values for each suggestion
class DetectorEvaluation(EvaluationFunction):
def __init__(self, tiled_client: Container):
self.tiled_client = tiled_client
def _compute_stats(self, image: np.array) -> tuple[str, str, str]:
"""Compute integrated intensity and beam width/height from a beam image."""
# Convert to grayscale
gray = image.squeeze()
if gray.ndim == 3:
gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY)
# Convert data type for numerical stability
gray = gray.astype(np.float32)
# Smooth w/ (5, 5) kernel and threshold
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
max_val = np.max(blurred)
if max_val == 0:
return 0.0, 0.0, 0.0
thresh_value = 0.2 * max_val
_, thresh = cv2.threshold(blurred, thresh_value, 255, cv2.THRESH_TOZERO)
# Total integrated intensity
total_intensity = np.sum(thresh)
# Beam width/height from intensity-weighted second moment (σ)
total_weight = np.sum(thresh)
if total_weight <= 0:
return total_intensity, 0.0, 0.0
h, w = thresh.shape
y_coords = np.arange(h, dtype=np.float32)
x_coords = np.arange(w, dtype=np.float32)
x_bar = np.sum(x_coords * np.sum(thresh, axis=0)) / total_weight
y_bar = np.sum(y_coords * np.sum(thresh, axis=1)) / total_weight
x_var = np.sum((x_coords - x_bar) ** 2 * np.sum(thresh, axis=0)) / total_weight
y_var = np.sum((y_coords - y_bar) ** 2 * np.sum(thresh, axis=1)) / total_weight
width = 2 * np.sqrt(x_var) # ~2σ width
height = 2 * np.sqrt(y_var) # ~2σ height
return total_intensity, width, height
def __call__(self, uid: str, suggestions: list[dict]) -> list[dict]:
outcomes = []
run = self.tiled_client[uid]
# Read beam images from detector
images = run["primary/det_image"].read()
# Suggestions are stored in the start document's metadata when
# using the `blop.plans.default_acquire` plan.
# You may want to store them differently in your experiment when writing
# a custom acquisition plan.
suggestion_ids = [suggestion["_id"] for suggestion in run.metadata["start"]["blop_suggestions"]]
# Compute statistics from each image
for idx, sid in enumerate(suggestion_ids):
image = images[idx]
intensity, width, height = self._compute_stats(image)
outcome = {
"_id": sid,
"intensity": intensity,
"width": width,
"height": height,
}
outcomes.append(outcome)
return outcomes
Note how we:
Read the image data from the stored detector data
Use image processing techniques to compute beam metrics from the raw detector images
Link each outcome back to its suggestion via the
_idfield
Creating and Running the Agent#
The Agent brings everything together. It:
Uses DOFs to know what parameters to adjust
Uses objectives to know what to optimize
Calls the evaluation function to assess each configuration
Builds a surrogate model to predict outcomes across the parameter space
Suggests the next configurations to try
agent = Agent(
sensors=[det],
dofs=dofs,
objectives=objectives,
evaluation_function=DetectorEvaluation(tiled_client),
name="xrt-blop-demo",
description="A demo of the Blop agent with XRT simulated beamline",
experiment_type="demo",
)
The sensors list contains any devices that produce data during acquisition. Here, det is our detector device.
Running the Optimization#
Let’s start the optimization. Rather than running all iterations at once, we’ll pause partway through to check the optimization’s health—a practical workflow you’ll use in real experiments.
# Run first 10 iterations
RE(agent.optimize(10))
╭───────────────────────────────────────────────── Optimization ──────────────────────────────────────────────────╮ │ Optimizer AxOptimizer │ │ Actuators kbv-radius, kbh-radius │ │ Sensors det │ │ Iterations 10 │ │ Run UID 3de7fa77-0bf3-4edf-9a37-46acba2cd653 │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
ToroidMirror2
center:
[0, 11000, np.float64(176.32899989366115)]
[INFO 04-19 07:11:04] ax.api.client: GenerationStrategy(name='Center+Sobol+MBM:fast', nodes=[CenterGenerationNode(next_node_name='Sobol'), GenerationNode(name='Sobol', generator_specs=[GeneratorSpec(generator_enum=Sobol, generator_key_override=None)], transition_criteria=[MinTrials(transition_to='MBM'), MinTrials(transition_to='MBM')], suggested_experiment_status=ExperimentStatus.INITIALIZATION, pausing_criteria=[MaxTrialsAwaitingData(threshold=5)]), GenerationNode(name='MBM', generator_specs=[GeneratorSpec(generator_enum=BoTorch, generator_key_override=None)], transition_criteria=None, suggested_experiment_status=ExperimentStatus.OPTIMIZATION, pausing_criteria=None)]) chosen based on user input and problem structure.
[INFO 04-19 07:11:04] ax.api.client: Generated new trial 0 with parameters {'kbv-radius': 35000.0, 'kbh-radius': 25000.0} using GenerationNode CenterOfSearchSpace.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:05] ax.api.client: Trial 0 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:05] ax.api.client: Generated new trial 1 with parameters {'kbv-radius': 35528.516769, 'kbh-radius': 29308.345318} using GenerationNode Sobol.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:05] ax.api.client: Trial 1 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:05] ax.api.client: Generated new trial 2 with parameters {'kbv-radius': 27542.769965, 'kbh-radius': 20028.450154} using GenerationNode Sobol.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:06] ax.api.client: Trial 2 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:06] ax.api.client: Generated new trial 3 with parameters {'kbv-radius': 31476.355288, 'kbh-radius': 31937.575676} using GenerationNode Sobol.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:06] ax.api.client: Trial 3 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:06] ax.api.client: Generated new trial 4 with parameters {'kbv-radius': 44114.464223, 'kbh-radius': 18727.302644} using GenerationNode Sobol.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:06] ax.api.client: Trial 4 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:09] ax.api.client: Generated new trial 5 with parameters {'kbv-radius': 37684.518752, 'kbh-radius': 17302.231826} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:09] ax.api.client: Trial 5 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:11] ax.api.client: Generated new trial 6 with parameters {'kbv-radius': 40522.428456, 'kbh-radius': 22657.060851} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:12] ax.api.client: Trial 6 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:14] ax.api.client: Generated new trial 7 with parameters {'kbv-radius': 38615.92055, 'kbh-radius': 21656.046132} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:14] ax.api.client: Trial 7 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:17] ax.api.client: Generated new trial 8 with parameters {'kbv-radius': 37873.970853, 'kbh-radius': 20617.303811} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:17] ax.api.client: Trial 8 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:20] ax.api.client: Generated new trial 9 with parameters {'kbv-radius': 38158.3474, 'kbh-radius': 18640.476644} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:21] ax.api.client: Trial 9 marked COMPLETED.
──────────────────────────────────────────────── Iteration 1 / 10 ─────────────────────────────────────────────────
Acquire UID 4980e741-7903-4b7b-835a-3fc73db9b7c1
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 0 │ 25000 │ 35000 │ 51.7303 │ 16411 │ 149.344 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 51.7303 max: 51.7303 mean: 51.7303 intensity min: 16411 max: 16411 mean: 16411 width min: 149.344 max: 149.344 mean: 149.344 (1 pts sampled)
──────────────────────────────────────────────── Iteration 2 / 10 ─────────────────────────────────────────────────
Acquire UID d4a8f09b-4a3a-4f75-9da4-0da493635284
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 1 │ 29308.3 │ 35528.5 │ 42.1793 │ 13512.1 │ 201.431 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 42.1793 max: 51.7303 mean: 46.9548 intensity min: 13512.1 max: 16411 mean: 14961.5 width min: 149.344 max: 201.431 mean: 175.387 (2 pts sampled)
──────────────────────────────────────────────── Iteration 3 / 10 ─────────────────────────────────────────────────
Acquire UID 58dd6279-4ee5-4cb1-8c13-bd39e04df63e
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 2 │ 20028.5 │ 27542.8 │ 157.145 │ 12027.5 │ 20.9125 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 42.1793 max: 157.145 mean: 83.6849 intensity min: 12027.5 max: 16411 mean: 13983.5 width min: 20.9125 max: 201.431 mean: 123.896 (3 pts sampled)
──────────────────────────────────────────────── Iteration 4 / 10 ─────────────────────────────────────────────────
Acquire UID fd6d7297-d786-460b-9267-d5e3f84b1413
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩ │ 0 │ 3 │ 31937.6 │ 31476.4 │ 116.764 │ 8454.65 │ 208.19 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴────────┘
height min: 42.1793 max: 157.145 mean: 91.9548 intensity min: 8454.65 max: 16411 mean: 12601.3 width min: 20.9125 max: 208.19 mean: 144.969 (4 pts sampled)
──────────────────────────────────────────────── Iteration 5 / 10 ─────────────────────────────────────────────────
Acquire UID 965a0963-e0e7-4918-b86d-d3b1e3655769
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩ │ 0 │ 4 │ 18727.3 │ 44114.5 │ 81.0443 │ 16573.8 │ 87.833 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴────────┘
height min: 42.1793 max: 157.145 mean: 89.7727 intensity min: 8454.65 max: 16573.8 mean: 13395.8 width min: 20.9125 max: 208.19 mean: 133.542 (5 pts sampled)
──────────────────────────────────────────────── Iteration 6 / 10 ─────────────────────────────────────────────────
Acquire UID a84cad23-345e-4d04-b1bb-199c20f98c33
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 5 │ 17302.2 │ 37684.5 │ 15.5608 │ 18190.7 │ 169.667 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 15.5608 max: 157.145 mean: 77.404 intensity min: 8454.65 max: 18190.7 mean: 14195 width min: 20.9125 max: 208.19 mean: 139.563 (6 pts sampled)
──────────────────────────────────────────────── Iteration 7 / 10 ─────────────────────────────────────────────────
Acquire UID eb22ec90-ca09-4e3a-83ba-d66d84aed224
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 6 │ 22657.1 │ 40522.4 │ 40.2347 │ 17933.3 │ 75.8053 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 15.5608 max: 157.145 mean: 72.0941 intensity min: 8454.65 max: 18190.7 mean: 14729 width min: 20.9125 max: 208.19 mean: 130.455 (7 pts sampled)
──────────────────────────────────────────────── Iteration 8 / 10 ─────────────────────────────────────────────────
Acquire UID d8de51e0-233b-4602-9bfa-fe045ecb5959
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 7 │ 21656 │ 38615.9 │ 20.0893 │ 18318.8 │ 36.4633 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 15.5608 max: 157.145 mean: 65.5935 intensity min: 8454.65 max: 18318.8 mean: 15177.7 width min: 20.9125 max: 208.19 mean: 118.706 (8 pts sampled)
──────────────────────────────────────────────── Iteration 9 / 10 ─────────────────────────────────────────────────
Acquire UID c5abcefb-1f3b-4e71-8222-37ba97cb5f4c
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 8 │ 20617.3 │ 37874 │ 15.2716 │ 17309.5 │ 12.4174 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 15.2716 max: 157.145 mean: 60.0022 intensity min: 8454.65 max: 18318.8 mean: 15414.6 width min: 12.4174 max: 208.19 mean: 106.896 (9 pts sampled)
──────────────────────────────────────────────── Iteration 10 / 10 ────────────────────────────────────────────────
Acquire UID 065933bd-4121-449b-a816-c5b116c04c41
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩ │ 0 │ 9 │ 18640.5 │ 38158.3 │ 16.372 │ 18735.6 │ 97.11 │ └───────┴───────────────┴────────────┴────────────┴────────┴───────────┴───────┘
height min: 15.2716 max: 157.145 mean: 55.6392 intensity min: 8454.65 max: 18735.6 mean: 15746.7 width min: 12.4174 max: 208.19 mean: 105.917 (10 pts sampled)
Summary Statistics ┏━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┓ ┃ Name ┃ Type ┃ Min ┃ Max ┃ Mean ┃ Std ┃ Count ┃ ┡━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━┩ │ kbh-radius │ param │ 17302.2 │ 31937.6 │ 22587.5 │ 4808.91 │ 10 │ │ kbv-radius │ param │ 27542.8 │ 44114.5 │ 36651.7 │ 4632.14 │ 10 │ │ height │ outcome │ 15.2716 │ 157.145 │ 55.6392 │ 48.4888 │ 10 │ │ intensity │ outcome │ 8454.65 │ 18735.6 │ 15746.7 │ 3364.39 │ 10 │ │ width │ outcome │ 12.4174 │ 208.19 │ 105.917 │ 72.752 │ 10 │ └────────────┴─────────┴─────────┴─────────┴─────────┴─────────┴───────┘
────────────────────────────────────────────── Optimization Complete ──────────────────────────────────────────────
('3de7fa77-0bf3-4edf-9a37-46acba2cd653',
'4980e741-7903-4b7b-835a-3fc73db9b7c1',
'd4a8f09b-4a3a-4f75-9da4-0da493635284',
'58dd6279-4ee5-4cb1-8c13-bd39e04df63e',
'fd6d7297-d786-460b-9267-d5e3f84b1413',
'965a0963-e0e7-4918-b86d-d3b1e3655769',
'a84cad23-345e-4d04-b1bb-199c20f98c33',
'eb22ec90-ca09-4e3a-83ba-d66d84aed224',
'd8de51e0-233b-4602-9bfa-fe045ecb5959',
'c5abcefb-1f3b-4e71-8222-37ba97cb5f4c',
'065933bd-4121-449b-a816-c5b116c04c41')
Checking Optimization Health#
After running some iterations, it’s good practice to check how the optimization is progressing. Ax provides built-in health checks and diagnostics through compute_analyses():
_ = agent.ax_client.compute_analyses()
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:23] ax.service.utils.best_point: Using inferred objective thresholds: [ObjectiveThreshold(intensity >= 18088.035733558558), ObjectiveThreshold(width <= 185.40684198221163), ObjectiveThreshold(height <= 16.97024999382038)], as objective thresholds were not specified as part of the optimization configuration on the experiment.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:27] ax.service.utils.best_point: Using inferred objective thresholds: [ObjectiveThreshold(intensity >= 18088.035733558558), ObjectiveThreshold(width <= 185.40684198221163), ObjectiveThreshold(height <= 16.97024999382038)], as objective thresholds were not specified as part of the optimization configuration on the experiment.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:27] ax.service.utils.best_point: Using inferred objective thresholds: [ObjectiveThreshold(intensity >= 18088.035733558558), ObjectiveThreshold(width <= 185.40684198221163), ObjectiveThreshold(height <= 16.97024999382038)], as objective thresholds were not specified as part of the optimization configuration on the experiment.
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:76, in run_check(cmd, input_lines, encoding, quiet, **kwargs)
75 kwargs['stdout'] = kwargs['stderr'] = subprocess.PIPE
---> 76 proc = _run_input_lines(cmd, input_lines, kwargs=kwargs)
77 else:
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:96, in _run_input_lines(cmd, input_lines, kwargs)
95 def _run_input_lines(cmd, input_lines, *, kwargs):
---> 96 popen = subprocess.Popen(cmd, stdin=subprocess.PIPE, **kwargs)
98 stdin_write = popen.stdin.write
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/subprocess.py:1039, in Popen.__init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize, process_group)
1036 self.stderr = io.TextIOWrapper(self.stderr,
1037 encoding=encoding, errors=errors)
-> 1039 self._execute_child(args, executable, preexec_fn, close_fds,
1040 pass_fds, cwd, env,
1041 startupinfo, creationflags, shell,
1042 p2cread, p2cwrite,
1043 c2pread, c2pwrite,
1044 errread, errwrite,
1045 restore_signals,
1046 gid, gids, uid, umask,
1047 start_new_session, process_group)
1048 except:
1049 # Cleanup if the child failed starting.
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/subprocess.py:1991, in Popen._execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session, process_group)
1990 if err_filename is not None:
-> 1991 raise child_exception_type(errno_num, err_msg, err_filename)
1992 else:
FileNotFoundError: [Errno 2] No such file or directory: PosixPath('dot')
The above exception was the direct cause of the following exception:
ExecutableNotFound Traceback (most recent call last)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:195, in AnalysisCardBase._ipython_display_(self)
191 display(card._body_papermill())
193 return
--> 195 display(HTML(self._repr_html_()))
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:224, in AnalysisCardBase._repr_html_(self)
220 # require.js is not compatible with ES6 import used by plotly.js so we must
221 # null out `define` here. This does not affect rendering.
222 plotlyjs_script = f"<script>define = null;{get_plotlyjs()}</script>"
--> 224 return plotlyjs_script + self._to_html(depth=0)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:340, in AnalysisCardGroup._to_html(self, depth)
336 def _to_html(self, depth: int) -> str:
337 return html_group_card_template.format(
338 title_str=self.title,
339 subtitle_str=self.subtitle,
--> 340 body_html=self._body_html(depth=depth),
341 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:323, in AnalysisCardGroup._body_html(self, depth)
320 res.append(leaves_grid)
321 leaf_cards = []
--> 323 res.append(child._to_html(depth=depth + 1))
325 # Collect the accumulated leaves a final time to append to the result.
326 if len(leaf_cards) > 0:
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:340, in AnalysisCardGroup._to_html(self, depth)
336 def _to_html(self, depth: int) -> str:
337 return html_group_card_template.format(
338 title_str=self.title,
339 subtitle_str=self.subtitle,
--> 340 body_html=self._body_html(depth=depth),
341 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:329, in AnalysisCardGroup._body_html(self, depth)
325 # Collect the accumulated leaves a final time to append to the result.
326 if len(leaf_cards) > 0:
327 leaves_grid = html_grid_template.format(
328 card_divs="".join(
--> 329 [card._to_html(depth=depth + 1) for card in leaf_cards]
330 )
331 )
332 res.append(leaves_grid)
334 return "\n".join(res)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:230, in AnalysisCardBase._to_html(self, depth)
226 def _to_html(self, depth: int) -> str:
227 return html_card_template.format(
228 title_str=self.title,
229 subtitle_str=self.subtitle,
--> 230 body_html=self._body_html(depth=depth),
231 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/analysis/graphviz/graphviz_analysis.py:21, in GraphvizAnalysisCard._body_html(self, depth)
17 def _body_html(self, depth: int) -> str:
18 """
19 Return the a HTML div with the Graphviz figure as an SVG.
20 """
---> 21 svg = self.get_digraph().pipe(format="svg")
23 return f"<div>{svg}</div>"
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:104, in Pipe.pipe(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
55 def pipe(self,
56 format: typing.Optional[str] = None,
57 renderer: typing.Optional[str] = None,
(...) 61 engine: typing.Optional[str] = None,
62 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
63 """Return the source piped through the Graphviz layout command.
64
65 Args:
(...) 102 '<?xml version='
103 """
--> 104 return self._pipe_legacy(format,
105 renderer=renderer,
106 formatter=formatter,
107 neato_no_op=neato_no_op,
108 quiet=quiet,
109 engine=engine,
110 encoding=encoding)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/_tools.py:185, in deprecate_positional_args.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
177 wanted = ', '.join(f'{name}={value!r}'
178 for name, value in deprecated.items())
179 warnings.warn(f'The signature of {func_name} will be reduced'
180 f' to {supported_number} positional arg{s_}{qualification}'
181 f' {list(supported)}: pass {wanted} as keyword arg{s_}',
182 stacklevel=stacklevel,
183 category=category)
--> 185 return func(*args, **kwargs)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:121, in Pipe._pipe_legacy(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
112 @_tools.deprecate_positional_args(supported_number=1, ignore_arg='self')
113 def _pipe_legacy(self,
114 format: typing.Optional[str] = None,
(...) 119 engine: typing.Optional[str] = None,
120 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
--> 121 return self._pipe_future(format,
122 renderer=renderer,
123 formatter=formatter,
124 neato_no_op=neato_no_op,
125 quiet=quiet,
126 engine=engine,
127 encoding=encoding)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:161, in Pipe._pipe_future(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
159 else:
160 return raw.decode(encoding)
--> 161 return self._pipe_lines(*args, input_encoding=self.encoding, **kwargs)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/piping.py:161, in pipe_lines(engine, format, input_lines, input_encoding, renderer, formatter, neato_no_op, quiet)
155 cmd = dot_command.command(engine, format,
156 renderer=renderer,
157 formatter=formatter,
158 neato_no_op=neato_no_op)
159 kwargs = {'input_lines': (line.encode(input_encoding) for line in input_lines)}
--> 161 proc = execute.run_check(cmd, capture_output=True, quiet=quiet, **kwargs)
162 return proc.stdout
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:81, in run_check(cmd, input_lines, encoding, quiet, **kwargs)
79 except OSError as e:
80 if e.errno == errno.ENOENT:
---> 81 raise ExecutableNotFound(cmd) from e
82 raise
84 if not quiet and proc.stderr:
ExecutableNotFound: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:76, in run_check(cmd, input_lines, encoding, quiet, **kwargs)
75 kwargs['stdout'] = kwargs['stderr'] = subprocess.PIPE
---> 76 proc = _run_input_lines(cmd, input_lines, kwargs=kwargs)
77 else:
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:96, in _run_input_lines(cmd, input_lines, kwargs)
95 def _run_input_lines(cmd, input_lines, *, kwargs):
---> 96 popen = subprocess.Popen(cmd, stdin=subprocess.PIPE, **kwargs)
98 stdin_write = popen.stdin.write
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/subprocess.py:1039, in Popen.__init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize, process_group)
1036 self.stderr = io.TextIOWrapper(self.stderr,
1037 encoding=encoding, errors=errors)
-> 1039 self._execute_child(args, executable, preexec_fn, close_fds,
1040 pass_fds, cwd, env,
1041 startupinfo, creationflags, shell,
1042 p2cread, p2cwrite,
1043 c2pread, c2pwrite,
1044 errread, errwrite,
1045 restore_signals,
1046 gid, gids, uid, umask,
1047 start_new_session, process_group)
1048 except:
1049 # Cleanup if the child failed starting.
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/subprocess.py:1991, in Popen._execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session, process_group)
1990 if err_filename is not None:
-> 1991 raise child_exception_type(errno_num, err_msg, err_filename)
1992 else:
FileNotFoundError: [Errno 2] No such file or directory: PosixPath('dot')
The above exception was the direct cause of the following exception:
ExecutableNotFound Traceback (most recent call last)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:224, in AnalysisCardBase._repr_html_(self)
220 # require.js is not compatible with ES6 import used by plotly.js so we must
221 # null out `define` here. This does not affect rendering.
222 plotlyjs_script = f"<script>define = null;{get_plotlyjs()}</script>"
--> 224 return plotlyjs_script + self._to_html(depth=0)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:340, in AnalysisCardGroup._to_html(self, depth)
336 def _to_html(self, depth: int) -> str:
337 return html_group_card_template.format(
338 title_str=self.title,
339 subtitle_str=self.subtitle,
--> 340 body_html=self._body_html(depth=depth),
341 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:323, in AnalysisCardGroup._body_html(self, depth)
320 res.append(leaves_grid)
321 leaf_cards = []
--> 323 res.append(child._to_html(depth=depth + 1))
325 # Collect the accumulated leaves a final time to append to the result.
326 if len(leaf_cards) > 0:
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:340, in AnalysisCardGroup._to_html(self, depth)
336 def _to_html(self, depth: int) -> str:
337 return html_group_card_template.format(
338 title_str=self.title,
339 subtitle_str=self.subtitle,
--> 340 body_html=self._body_html(depth=depth),
341 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:329, in AnalysisCardGroup._body_html(self, depth)
325 # Collect the accumulated leaves a final time to append to the result.
326 if len(leaf_cards) > 0:
327 leaves_grid = html_grid_template.format(
328 card_divs="".join(
--> 329 [card._to_html(depth=depth + 1) for card in leaf_cards]
330 )
331 )
332 res.append(leaves_grid)
334 return "\n".join(res)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:230, in AnalysisCardBase._to_html(self, depth)
226 def _to_html(self, depth: int) -> str:
227 return html_card_template.format(
228 title_str=self.title,
229 subtitle_str=self.subtitle,
--> 230 body_html=self._body_html(depth=depth),
231 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/analysis/graphviz/graphviz_analysis.py:21, in GraphvizAnalysisCard._body_html(self, depth)
17 def _body_html(self, depth: int) -> str:
18 """
19 Return the a HTML div with the Graphviz figure as an SVG.
20 """
---> 21 svg = self.get_digraph().pipe(format="svg")
23 return f"<div>{svg}</div>"
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:104, in Pipe.pipe(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
55 def pipe(self,
56 format: typing.Optional[str] = None,
57 renderer: typing.Optional[str] = None,
(...) 61 engine: typing.Optional[str] = None,
62 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
63 """Return the source piped through the Graphviz layout command.
64
65 Args:
(...) 102 '<?xml version='
103 """
--> 104 return self._pipe_legacy(format,
105 renderer=renderer,
106 formatter=formatter,
107 neato_no_op=neato_no_op,
108 quiet=quiet,
109 engine=engine,
110 encoding=encoding)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/_tools.py:185, in deprecate_positional_args.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
177 wanted = ', '.join(f'{name}={value!r}'
178 for name, value in deprecated.items())
179 warnings.warn(f'The signature of {func_name} will be reduced'
180 f' to {supported_number} positional arg{s_}{qualification}'
181 f' {list(supported)}: pass {wanted} as keyword arg{s_}',
182 stacklevel=stacklevel,
183 category=category)
--> 185 return func(*args, **kwargs)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:121, in Pipe._pipe_legacy(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
112 @_tools.deprecate_positional_args(supported_number=1, ignore_arg='self')
113 def _pipe_legacy(self,
114 format: typing.Optional[str] = None,
(...) 119 engine: typing.Optional[str] = None,
120 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
--> 121 return self._pipe_future(format,
122 renderer=renderer,
123 formatter=formatter,
124 neato_no_op=neato_no_op,
125 quiet=quiet,
126 engine=engine,
127 encoding=encoding)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:161, in Pipe._pipe_future(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
159 else:
160 return raw.decode(encoding)
--> 161 return self._pipe_lines(*args, input_encoding=self.encoding, **kwargs)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/piping.py:161, in pipe_lines(engine, format, input_lines, input_encoding, renderer, formatter, neato_no_op, quiet)
155 cmd = dot_command.command(engine, format,
156 renderer=renderer,
157 formatter=formatter,
158 neato_no_op=neato_no_op)
159 kwargs = {'input_lines': (line.encode(input_encoding) for line in input_lines)}
--> 161 proc = execute.run_check(cmd, capture_output=True, quiet=quiet, **kwargs)
162 return proc.stdout
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:81, in run_check(cmd, input_lines, encoding, quiet, **kwargs)
79 except OSError as e:
80 if e.errno == errno.ENOENT:
---> 81 raise ExecutableNotFound(cmd) from e
82 raise
84 if not quiet and proc.stderr:
ExecutableNotFound: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH
<ax.core.analysis_card.AnalysisCardGroup at 0x7fd7502af250>
This runs all applicable analyses for the current experiment state, including health checks that flag potential issues like model fit problems or exploration gaps. Review these before continuing.
Continuing the Optimization#
The optimization state is preserved, so we can simply run more iterations:
# Run remaining 20 iterations
RE(agent.optimize(20))
╭───────────────────────────────────────────────── Optimization ──────────────────────────────────────────────────╮ │ Optimizer AxOptimizer │ │ Actuators kbv-radius, kbh-radius │ │ Sensors det │ │ Iterations 20 more (10 completed, 30 total) │ │ Run UID 78930a79-127d-4e22-9275-76c17274483a │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:29] ax.api.client: Generated new trial 10 with parameters {'kbv-radius': 37928.750018, 'kbh-radius': 19461.746838} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:29] ax.api.client: Trial 10 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:33] ax.api.client: Generated new trial 11 with parameters {'kbv-radius': 37982.653316, 'kbh-radius': 18122.120993} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:34] ax.api.client: Trial 11 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:38] ax.api.client: Generated new trial 12 with parameters {'kbv-radius': 37907.582695, 'kbh-radius': 18930.191017} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:38] ax.api.client: Trial 12 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:42] ax.api.client: Generated new trial 13 with parameters {'kbv-radius': 37998.949688, 'kbh-radius': 19947.026184} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:42] ax.api.client: Trial 13 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:48] ax.api.client: Generated new trial 14 with parameters {'kbv-radius': 38034.41096, 'kbh-radius': 15000.0} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:49] ax.api.client: Trial 14 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:11:54] ax.api.client: Generated new trial 15 with parameters {'kbv-radius': 37997.690204, 'kbh-radius': 16954.290265} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:11:54] ax.api.client: Trial 15 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:12:00] ax.api.client: Generated new trial 16 with parameters {'kbv-radius': 38002.983364, 'kbh-radius': 17622.956655} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:12:00] ax.api.client: Trial 16 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:12:07] ax.api.client: Generated new trial 17 with parameters {'kbv-radius': 38040.269936, 'kbh-radius': 16285.980029} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:12:08] ax.api.client: Trial 17 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:12:15] ax.api.client: Generated new trial 18 with parameters {'kbv-radius': 38166.783964, 'kbh-radius': 19134.983112} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:12:15] ax.api.client: Trial 18 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:12:22] ax.api.client: Generated new trial 19 with parameters {'kbv-radius': 37508.397328, 'kbh-radius': 18997.290584} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:12:22] ax.api.client: Trial 19 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:12:29] ax.api.client: Generated new trial 20 with parameters {'kbv-radius': 38253.218102, 'kbh-radius': 17238.104501} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:12:29] ax.api.client: Trial 20 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:12:37] ax.api.client: Generated new trial 21 with parameters {'kbv-radius': 38120.454698, 'kbh-radius': 19664.231765} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:12:37] ax.api.client: Trial 21 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/linear_operator/utils/cholesky.py:41: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal
warnings.warn(
[INFO 04-19 07:12:46] ax.api.client: Generated new trial 22 with parameters {'kbv-radius': 38138.470862, 'kbh-radius': 18344.384498} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:12:46] ax.api.client: Trial 22 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:12:55] ax.api.client: Generated new trial 23 with parameters {'kbv-radius': 38041.17327, 'kbh-radius': 20242.172021} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:12:55] ax.api.client: Trial 23 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/linear_operator/utils/cholesky.py:41: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal
warnings.warn(
[INFO 04-19 07:13:04] ax.api.client: Generated new trial 24 with parameters {'kbv-radius': 38110.599072, 'kbh-radius': 16653.433345} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:13:05] ax.api.client: Trial 24 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:13:13] ax.api.client: Generated new trial 25 with parameters {'kbv-radius': 37782.674075, 'kbh-radius': 22253.682151} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:13:13] ax.api.client: Trial 25 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:13:22] ax.api.client: Generated new trial 26 with parameters {'kbv-radius': 32836.751829, 'kbh-radius': 21683.414255} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:13:23] ax.api.client: Trial 26 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/linear_operator/utils/cholesky.py:41: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal
warnings.warn(
[INFO 04-19 07:13:33] ax.api.client: Generated new trial 27 with parameters {'kbv-radius': 38104.124521, 'kbh-radius': 15796.744414} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:13:33] ax.api.client: Trial 27 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/linear_operator/utils/cholesky.py:41: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal
warnings.warn(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/linear_operator/utils/cholesky.py:41: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal
warnings.warn(
[INFO 04-19 07:13:45] ax.api.client: Generated new trial 28 with parameters {'kbv-radius': 38096.834564, 'kbh-radius': 17896.330907} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:13:45] ax.api.client: Trial 28 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:13:55] ax.api.client: Generated new trial 29 with parameters {'kbv-radius': 37374.798066, 'kbh-radius': 21247.555059} using GenerationNode MBM.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
[INFO 04-19 07:13:55] ax.api.client: Trial 29 marked COMPLETED.
──────────────────────────────────────────────── Iteration 11 / 30 ────────────────────────────────────────────────
Acquire UID 77a32995-0a4b-43ea-8541-d08a9ac1ada9
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 10 │ 19461.7 │ 37928.8 │ 16.9587 │ 18654.2 │ 51.9701 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 15.2716 max: 157.145 mean: 52.1228 intensity min: 8454.65 max: 18735.6 mean: 16011 width min: 12.4174 max: 208.19 mean: 101.013 (11 pts sampled)
──────────────────────────────────────────────── Iteration 12 / 30 ────────────────────────────────────────────────
Acquire UID f1b567d0-36a8-4a62-adfe-37d084c791f6
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 11 │ 18122.1 │ 37982.7 │ 15.5554 │ 18483.5 │ 123.678 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 15.2716 max: 157.145 mean: 49.0755 intensity min: 8454.65 max: 18735.6 mean: 16217.1 width min: 12.4174 max: 208.19 mean: 102.902 (12 pts sampled)
──────────────────────────────────────────────── Iteration 13 / 30 ────────────────────────────────────────────────
Acquire UID ab1bec04-3a7c-4c89-930b-e0d312533743
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 12 │ 18930.2 │ 37907.6 │ 16.8822 │ 19236.4 │ 81.9475 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 15.2716 max: 157.145 mean: 46.5991 intensity min: 8454.65 max: 19236.4 mean: 16449.3 width min: 12.4174 max: 208.19 mean: 101.29 (13 pts sampled)
──────────────────────────────────────────────── Iteration 14 / 30 ────────────────────────────────────────────────
Acquire UID 5aa6f696-45ab-4314-a1a9-cda38234b946
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 13 │ 19947 │ 37998.9 │ 16.2446 │ 17710.8 │ 27.5282 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 15.2716 max: 157.145 mean: 44.4309 intensity min: 8454.65 max: 19236.4 mean: 16539.4 width min: 12.4174 max: 208.19 mean: 96.0212 (14 pts sampled)
──────────────────────────────────────────────── Iteration 15 / 30 ────────────────────────────────────────────────
Acquire UID dc7b63bc-1807-4891-b4ab-14e0c2d86deb
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 14 │ 15000 │ 38034.4 │ 11.8107 │ 13118.7 │ 217.094 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 42.2562 intensity min: 8454.65 max: 19236.4 mean: 16311.4 width min: 12.4174 max: 217.094 mean: 104.093 (15 pts sampled)
──────────────────────────────────────────────── Iteration 16 / 30 ────────────────────────────────────────────────
Acquire UID 4b59b73d-172e-4ca7-873b-7ee051b68eae
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 15 │ 16954.3 │ 37997.7 │ 14.7244 │ 18221.2 │ 187.086 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 40.5355 intensity min: 8454.65 max: 19236.4 mean: 16430.7 width min: 12.4174 max: 217.094 mean: 109.28 (16 pts sampled)
──────────────────────────────────────────────── Iteration 17 / 30 ────────────────────────────────────────────────
Acquire UID 6fa0d390-35ae-4ff1-9669-a88948b6cf95
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩ │ 0 │ 16 │ 17623 │ 38003 │ 15.6697 │ 18514.6 │ 153.63 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴────────┘
height min: 11.8107 max: 157.145 mean: 39.0728 intensity min: 8454.65 max: 19236.4 mean: 16553.3 width min: 12.4174 max: 217.094 mean: 111.889 (17 pts sampled)
──────────────────────────────────────────────── Iteration 18 / 30 ────────────────────────────────────────────────
Acquire UID 17c72a60-89eb-4659-96f3-aef74642159f
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 17 │ 16286 │ 38040.3 │ 13.078 │ 16294.2 │ 200.373 │ └───────┴───────────────┴────────────┴────────────┴────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 37.6286 intensity min: 8454.65 max: 19236.4 mean: 16538.9 width min: 12.4174 max: 217.094 mean: 116.804 (18 pts sampled)
──────────────────────────────────────────────── Iteration 19 / 30 ────────────────────────────────────────────────
Acquire UID 811ebdf0-d87c-4211-99ee-a64c94b2e40e
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 18 │ 19135 │ 38166.8 │ 17.2345 │ 18996.1 │ 69.7656 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 36.5553 intensity min: 8454.65 max: 19236.4 mean: 16668.3 width min: 12.4174 max: 217.094 mean: 114.329 (19 pts sampled)
──────────────────────────────────────────────── Iteration 20 / 30 ────────────────────────────────────────────────
Acquire UID a714211c-4201-4c2a-be7d-74159e4dfc7c
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 19 │ 18997.3 │ 37508.4 │ 18.5451 │ 19225.9 │ 80.0315 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 35.6547 intensity min: 8454.65 max: 19236.4 mean: 16796.1 width min: 12.4174 max: 217.094 mean: 112.614 (20 pts sampled)
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Acquire UID 3d65e3ff-5ad6-4218-ac6a-e77c44d1908a
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 20 │ 17238.1 │ 38253.2 │ 14.9986 │ 17926.3 │ 170.405 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 34.6711 intensity min: 8454.65 max: 19236.4 mean: 16850 width min: 12.4174 max: 217.094 mean: 115.366 (21 pts sampled)
──────────────────────────────────────────────── Iteration 22 / 30 ────────────────────────────────────────────────
Acquire UID 6e653115-21ce-44f0-b835-31d7135ce071
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 21 │ 19664.2 │ 38120.5 │ 17.5814 │ 18861.4 │ 43.2586 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 33.8943 intensity min: 8454.65 max: 19236.4 mean: 16941.4 width min: 12.4174 max: 217.094 mean: 112.088 (22 pts sampled)
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Acquire UID a1ac4761-647d-428e-8f4a-1d78c7526e28
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 22 │ 18344.4 │ 38138.5 │ 16.5994 │ 19242.5 │ 115.118 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 33.1424 intensity min: 8454.65 max: 19242.5 mean: 17041.4 width min: 12.4174 max: 217.094 mean: 112.22 (23 pts sampled)
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Acquire UID a369d3c8-de9f-4985-b52c-2f48538145cf
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 23 │ 20242.2 │ 38041.2 │ 15.6463 │ 17120.7 │ 17.4396 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 32.4134 intensity min: 8454.65 max: 19242.5 mean: 17044.7 width min: 12.4174 max: 217.094 mean: 108.271 (24 pts sampled)
──────────────────────────────────────────────── Iteration 25 / 30 ────────────────────────────────────────────────
Acquire UID 53a7d6bb-636d-437a-87c5-fe008c33bac0
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 24 │ 16653.4 │ 38110.6 │ 14.1767 │ 17394.6 │ 194.491 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 31.6839 intensity min: 8454.65 max: 19242.5 mean: 17058.7 width min: 12.4174 max: 217.094 mean: 111.72 (25 pts sampled)
──────────────────────────────────────────────── Iteration 26 / 30 ────────────────────────────────────────────────
Acquire UID 0f217284-21bf-4d5c-a36c-d6ac3aa0c398
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 25 │ 22253.7 │ 37782.7 │ 18.4885 │ 18820.2 │ 61.6321 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 31.1764 intensity min: 8454.65 max: 19242.5 mean: 17126.5 width min: 12.4174 max: 217.094 mean: 109.793 (26 pts sampled)
──────────────────────────────────────────────── Iteration 27 / 30 ────────────────────────────────────────────────
Acquire UID e2d76e57-5236-445e-89be-d89d19aba4d3
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 26 │ 21683.4 │ 32836.8 │ 90.7809 │ 16274.7 │ 33.3051 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 33.384 intensity min: 8454.65 max: 19242.5 mean: 17094.9 width min: 12.4174 max: 217.094 mean: 106.96 (27 pts sampled)
──────────────────────────────────────────────── Iteration 28 / 30 ────────────────────────────────────────────────
Acquire UID 8d342f26-252b-419d-9f53-73c348f75300
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 27 │ 15796.7 │ 38104.1 │ 12.9125 │ 15169.3 │ 210.733 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 32.6528 intensity min: 8454.65 max: 19242.5 mean: 17026.2 width min: 12.4174 max: 217.094 mean: 110.666 (28 pts sampled)
──────────────────────────────────────────────── Iteration 29 / 30 ────────────────────────────────────────────────
Acquire UID fcf83d13-2997-45e7-bf18-3aeb120c8aef
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 28 │ 17896.3 │ 38096.8 │ 15.5149 │ 18686.2 │ 140.013 │ └───────┴───────────────┴────────────┴────────────┴─────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 32.0619 intensity min: 8454.65 max: 19242.5 mean: 17083.4 width min: 12.4174 max: 217.094 mean: 111.678 (29 pts sampled)
──────────────────────────────────────────────── Iteration 30 / 30 ────────────────────────────────────────────────
Acquire UID c5e6fc54-b308-432a-aad3-e0ce99c857e0
┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┓ ┃ Event ┃ Suggestion ID ┃ kbh-radius ┃ kbv-radius ┃ height ┃ intensity ┃ width ┃ ┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━┩ │ 0 │ 29 │ 21247.6 │ 37374.8 │ 18.394 │ 17161.5 │ 19.9828 │ └───────┴───────────────┴────────────┴────────────┴────────┴───────────┴─────────┘
height min: 11.8107 max: 157.145 mean: 31.6063 intensity min: 8454.65 max: 19242.5 mean: 17086 width min: 12.4174 max: 217.094 mean: 108.622 (30 pts sampled)
Summary Statistics ┏━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┓ ┃ Name ┃ Type ┃ Min ┃ Max ┃ Mean ┃ Std ┃ Count ┃ ┡━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━┩ │ kbh-radius │ param │ 15000 │ 31937.6 │ 19911.7 │ 3660.54 │ 30 │ │ kbv-radius │ param │ 27542.8 │ 44114.5 │ 37364.8 │ 2795.94 │ 30 │ │ height │ outcome │ 11.8107 │ 157.145 │ 31.6063 │ 34.8521 │ 30 │ │ intensity │ outcome │ 8454.65 │ 19242.5 │ 17086 │ 2461.22 │ 30 │ │ width │ outcome │ 12.4174 │ 217.094 │ 108.622 │ 69.5046 │ 30 │ └────────────┴─────────┴─────────┴─────────┴─────────┴─────────┴───────┘
────────────────────────────────────────────── Optimization Complete ──────────────────────────────────────────────
('78930a79-127d-4e22-9275-76c17274483a',
'77a32995-0a4b-43ea-8541-d08a9ac1ada9',
'f1b567d0-36a8-4a62-adfe-37d084c791f6',
'ab1bec04-3a7c-4c89-930b-e0d312533743',
'5aa6f696-45ab-4314-a1a9-cda38234b946',
'dc7b63bc-1807-4891-b4ab-14e0c2d86deb',
'4b59b73d-172e-4ca7-873b-7ee051b68eae',
'6fa0d390-35ae-4ff1-9669-a88948b6cf95',
'17c72a60-89eb-4659-96f3-aef74642159f',
'811ebdf0-d87c-4211-99ee-a64c94b2e40e',
'a714211c-4201-4c2a-be7d-74159e4dfc7c',
'3d65e3ff-5ad6-4218-ac6a-e77c44d1908a',
'6e653115-21ce-44f0-b835-31d7135ce071',
'a1ac4761-647d-428e-8f4a-1d78c7526e28',
'a369d3c8-de9f-4985-b52c-2f48538145cf',
'53a7d6bb-636d-437a-87c5-fe008c33bac0',
'0f217284-21bf-4d5c-a36c-d6ac3aa0c398',
'e2d76e57-5236-445e-89be-d89d19aba4d3',
'8d342f26-252b-419d-9f53-73c348f75300',
'fcf83d13-2997-45e7-bf18-3aeb120c8aef',
'c5e6fc54-b308-432a-aad3-e0ce99c857e0')
Understanding the Results#
After optimization, we can examine what the agent learned. Let’s run the full suite of analyses again to see how things have improved:
_ = agent.ax_client.compute_analyses()
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:13:58] ax.service.utils.best_point: Using inferred objective thresholds: [ObjectiveThreshold(intensity >= 12592.094650202525), ObjectiveThreshold(width <= 237.51604559649311), ObjectiveThreshold(height <= 20.831940017100695)], as objective thresholds were not specified as part of the optimization configuration on the experiment.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/data.py:365: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
self.full_df.groupby(self.DEDUPLICATE_BY_COLUMNS).apply(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:14:02] ax.service.utils.best_point: Using inferred objective thresholds: [ObjectiveThreshold(intensity >= 12592.094650202525), ObjectiveThreshold(width <= 237.51604559649311), ObjectiveThreshold(height <= 20.831940017100695)], as objective thresholds were not specified as part of the optimization configuration on the experiment.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:14:03] ax.service.utils.best_point: Using inferred objective thresholds: [ObjectiveThreshold(intensity >= 12592.094650202525), ObjectiveThreshold(width <= 237.51604559649311), ObjectiveThreshold(height <= 20.831940017100695)], as objective thresholds were not specified as part of the optimization configuration on the experiment.
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:76, in run_check(cmd, input_lines, encoding, quiet, **kwargs)
75 kwargs['stdout'] = kwargs['stderr'] = subprocess.PIPE
---> 76 proc = _run_input_lines(cmd, input_lines, kwargs=kwargs)
77 else:
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:96, in _run_input_lines(cmd, input_lines, kwargs)
95 def _run_input_lines(cmd, input_lines, *, kwargs):
---> 96 popen = subprocess.Popen(cmd, stdin=subprocess.PIPE, **kwargs)
98 stdin_write = popen.stdin.write
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/subprocess.py:1039, in Popen.__init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize, process_group)
1036 self.stderr = io.TextIOWrapper(self.stderr,
1037 encoding=encoding, errors=errors)
-> 1039 self._execute_child(args, executable, preexec_fn, close_fds,
1040 pass_fds, cwd, env,
1041 startupinfo, creationflags, shell,
1042 p2cread, p2cwrite,
1043 c2pread, c2pwrite,
1044 errread, errwrite,
1045 restore_signals,
1046 gid, gids, uid, umask,
1047 start_new_session, process_group)
1048 except:
1049 # Cleanup if the child failed starting.
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/subprocess.py:1991, in Popen._execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session, process_group)
1990 if err_filename is not None:
-> 1991 raise child_exception_type(errno_num, err_msg, err_filename)
1992 else:
FileNotFoundError: [Errno 2] No such file or directory: PosixPath('dot')
The above exception was the direct cause of the following exception:
ExecutableNotFound Traceback (most recent call last)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:195, in AnalysisCardBase._ipython_display_(self)
191 display(card._body_papermill())
193 return
--> 195 display(HTML(self._repr_html_()))
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:224, in AnalysisCardBase._repr_html_(self)
220 # require.js is not compatible with ES6 import used by plotly.js so we must
221 # null out `define` here. This does not affect rendering.
222 plotlyjs_script = f"<script>define = null;{get_plotlyjs()}</script>"
--> 224 return plotlyjs_script + self._to_html(depth=0)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:340, in AnalysisCardGroup._to_html(self, depth)
336 def _to_html(self, depth: int) -> str:
337 return html_group_card_template.format(
338 title_str=self.title,
339 subtitle_str=self.subtitle,
--> 340 body_html=self._body_html(depth=depth),
341 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:323, in AnalysisCardGroup._body_html(self, depth)
320 res.append(leaves_grid)
321 leaf_cards = []
--> 323 res.append(child._to_html(depth=depth + 1))
325 # Collect the accumulated leaves a final time to append to the result.
326 if len(leaf_cards) > 0:
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:340, in AnalysisCardGroup._to_html(self, depth)
336 def _to_html(self, depth: int) -> str:
337 return html_group_card_template.format(
338 title_str=self.title,
339 subtitle_str=self.subtitle,
--> 340 body_html=self._body_html(depth=depth),
341 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:329, in AnalysisCardGroup._body_html(self, depth)
325 # Collect the accumulated leaves a final time to append to the result.
326 if len(leaf_cards) > 0:
327 leaves_grid = html_grid_template.format(
328 card_divs="".join(
--> 329 [card._to_html(depth=depth + 1) for card in leaf_cards]
330 )
331 )
332 res.append(leaves_grid)
334 return "\n".join(res)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:230, in AnalysisCardBase._to_html(self, depth)
226 def _to_html(self, depth: int) -> str:
227 return html_card_template.format(
228 title_str=self.title,
229 subtitle_str=self.subtitle,
--> 230 body_html=self._body_html(depth=depth),
231 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/analysis/graphviz/graphviz_analysis.py:21, in GraphvizAnalysisCard._body_html(self, depth)
17 def _body_html(self, depth: int) -> str:
18 """
19 Return the a HTML div with the Graphviz figure as an SVG.
20 """
---> 21 svg = self.get_digraph().pipe(format="svg")
23 return f"<div>{svg}</div>"
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:104, in Pipe.pipe(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
55 def pipe(self,
56 format: typing.Optional[str] = None,
57 renderer: typing.Optional[str] = None,
(...) 61 engine: typing.Optional[str] = None,
62 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
63 """Return the source piped through the Graphviz layout command.
64
65 Args:
(...) 102 '<?xml version='
103 """
--> 104 return self._pipe_legacy(format,
105 renderer=renderer,
106 formatter=formatter,
107 neato_no_op=neato_no_op,
108 quiet=quiet,
109 engine=engine,
110 encoding=encoding)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/_tools.py:185, in deprecate_positional_args.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
177 wanted = ', '.join(f'{name}={value!r}'
178 for name, value in deprecated.items())
179 warnings.warn(f'The signature of {func_name} will be reduced'
180 f' to {supported_number} positional arg{s_}{qualification}'
181 f' {list(supported)}: pass {wanted} as keyword arg{s_}',
182 stacklevel=stacklevel,
183 category=category)
--> 185 return func(*args, **kwargs)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:121, in Pipe._pipe_legacy(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
112 @_tools.deprecate_positional_args(supported_number=1, ignore_arg='self')
113 def _pipe_legacy(self,
114 format: typing.Optional[str] = None,
(...) 119 engine: typing.Optional[str] = None,
120 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
--> 121 return self._pipe_future(format,
122 renderer=renderer,
123 formatter=formatter,
124 neato_no_op=neato_no_op,
125 quiet=quiet,
126 engine=engine,
127 encoding=encoding)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:161, in Pipe._pipe_future(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
159 else:
160 return raw.decode(encoding)
--> 161 return self._pipe_lines(*args, input_encoding=self.encoding, **kwargs)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/piping.py:161, in pipe_lines(engine, format, input_lines, input_encoding, renderer, formatter, neato_no_op, quiet)
155 cmd = dot_command.command(engine, format,
156 renderer=renderer,
157 formatter=formatter,
158 neato_no_op=neato_no_op)
159 kwargs = {'input_lines': (line.encode(input_encoding) for line in input_lines)}
--> 161 proc = execute.run_check(cmd, capture_output=True, quiet=quiet, **kwargs)
162 return proc.stdout
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:81, in run_check(cmd, input_lines, encoding, quiet, **kwargs)
79 except OSError as e:
80 if e.errno == errno.ENOENT:
---> 81 raise ExecutableNotFound(cmd) from e
82 raise
84 if not quiet and proc.stderr:
ExecutableNotFound: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:76, in run_check(cmd, input_lines, encoding, quiet, **kwargs)
75 kwargs['stdout'] = kwargs['stderr'] = subprocess.PIPE
---> 76 proc = _run_input_lines(cmd, input_lines, kwargs=kwargs)
77 else:
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:96, in _run_input_lines(cmd, input_lines, kwargs)
95 def _run_input_lines(cmd, input_lines, *, kwargs):
---> 96 popen = subprocess.Popen(cmd, stdin=subprocess.PIPE, **kwargs)
98 stdin_write = popen.stdin.write
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/subprocess.py:1039, in Popen.__init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize, process_group)
1036 self.stderr = io.TextIOWrapper(self.stderr,
1037 encoding=encoding, errors=errors)
-> 1039 self._execute_child(args, executable, preexec_fn, close_fds,
1040 pass_fds, cwd, env,
1041 startupinfo, creationflags, shell,
1042 p2cread, p2cwrite,
1043 c2pread, c2pwrite,
1044 errread, errwrite,
1045 restore_signals,
1046 gid, gids, uid, umask,
1047 start_new_session, process_group)
1048 except:
1049 # Cleanup if the child failed starting.
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/subprocess.py:1991, in Popen._execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session, process_group)
1990 if err_filename is not None:
-> 1991 raise child_exception_type(errno_num, err_msg, err_filename)
1992 else:
FileNotFoundError: [Errno 2] No such file or directory: PosixPath('dot')
The above exception was the direct cause of the following exception:
ExecutableNotFound Traceback (most recent call last)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:224, in AnalysisCardBase._repr_html_(self)
220 # require.js is not compatible with ES6 import used by plotly.js so we must
221 # null out `define` here. This does not affect rendering.
222 plotlyjs_script = f"<script>define = null;{get_plotlyjs()}</script>"
--> 224 return plotlyjs_script + self._to_html(depth=0)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:340, in AnalysisCardGroup._to_html(self, depth)
336 def _to_html(self, depth: int) -> str:
337 return html_group_card_template.format(
338 title_str=self.title,
339 subtitle_str=self.subtitle,
--> 340 body_html=self._body_html(depth=depth),
341 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:323, in AnalysisCardGroup._body_html(self, depth)
320 res.append(leaves_grid)
321 leaf_cards = []
--> 323 res.append(child._to_html(depth=depth + 1))
325 # Collect the accumulated leaves a final time to append to the result.
326 if len(leaf_cards) > 0:
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:340, in AnalysisCardGroup._to_html(self, depth)
336 def _to_html(self, depth: int) -> str:
337 return html_group_card_template.format(
338 title_str=self.title,
339 subtitle_str=self.subtitle,
--> 340 body_html=self._body_html(depth=depth),
341 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:329, in AnalysisCardGroup._body_html(self, depth)
325 # Collect the accumulated leaves a final time to append to the result.
326 if len(leaf_cards) > 0:
327 leaves_grid = html_grid_template.format(
328 card_divs="".join(
--> 329 [card._to_html(depth=depth + 1) for card in leaf_cards]
330 )
331 )
332 res.append(leaves_grid)
334 return "\n".join(res)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/core/analysis_card.py:230, in AnalysisCardBase._to_html(self, depth)
226 def _to_html(self, depth: int) -> str:
227 return html_card_template.format(
228 title_str=self.title,
229 subtitle_str=self.subtitle,
--> 230 body_html=self._body_html(depth=depth),
231 )
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/analysis/graphviz/graphviz_analysis.py:21, in GraphvizAnalysisCard._body_html(self, depth)
17 def _body_html(self, depth: int) -> str:
18 """
19 Return the a HTML div with the Graphviz figure as an SVG.
20 """
---> 21 svg = self.get_digraph().pipe(format="svg")
23 return f"<div>{svg}</div>"
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:104, in Pipe.pipe(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
55 def pipe(self,
56 format: typing.Optional[str] = None,
57 renderer: typing.Optional[str] = None,
(...) 61 engine: typing.Optional[str] = None,
62 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
63 """Return the source piped through the Graphviz layout command.
64
65 Args:
(...) 102 '<?xml version='
103 """
--> 104 return self._pipe_legacy(format,
105 renderer=renderer,
106 formatter=formatter,
107 neato_no_op=neato_no_op,
108 quiet=quiet,
109 engine=engine,
110 encoding=encoding)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/_tools.py:185, in deprecate_positional_args.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
177 wanted = ', '.join(f'{name}={value!r}'
178 for name, value in deprecated.items())
179 warnings.warn(f'The signature of {func_name} will be reduced'
180 f' to {supported_number} positional arg{s_}{qualification}'
181 f' {list(supported)}: pass {wanted} as keyword arg{s_}',
182 stacklevel=stacklevel,
183 category=category)
--> 185 return func(*args, **kwargs)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:121, in Pipe._pipe_legacy(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
112 @_tools.deprecate_positional_args(supported_number=1, ignore_arg='self')
113 def _pipe_legacy(self,
114 format: typing.Optional[str] = None,
(...) 119 engine: typing.Optional[str] = None,
120 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
--> 121 return self._pipe_future(format,
122 renderer=renderer,
123 formatter=formatter,
124 neato_no_op=neato_no_op,
125 quiet=quiet,
126 engine=engine,
127 encoding=encoding)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/piping.py:161, in Pipe._pipe_future(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
159 else:
160 return raw.decode(encoding)
--> 161 return self._pipe_lines(*args, input_encoding=self.encoding, **kwargs)
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/piping.py:161, in pipe_lines(engine, format, input_lines, input_encoding, renderer, formatter, neato_no_op, quiet)
155 cmd = dot_command.command(engine, format,
156 renderer=renderer,
157 formatter=formatter,
158 neato_no_op=neato_no_op)
159 kwargs = {'input_lines': (line.encode(input_encoding) for line in input_lines)}
--> 161 proc = execute.run_check(cmd, capture_output=True, quiet=quiet, **kwargs)
162 return proc.stdout
File ~/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/graphviz/backend/execute.py:81, in run_check(cmd, input_lines, encoding, quiet, **kwargs)
79 except OSError as e:
80 if e.errno == errno.ENOENT:
---> 81 raise ExecutableNotFound(cmd) from e
82 raise
84 if not quiet and proc.stderr:
ExecutableNotFound: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH
<ax.core.analysis_card.AnalysisCardGroup at 0x7fd73045ae40>
We can also get a tabular summary of the trials:
agent.ax_client.summarize()
| trial_index | arm_name | trial_status | generation_node | intensity | width | height | kbv-radius | kbh-radius | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0_0 | COMPLETED | CenterOfSearchSpace | 16411.046875 | 149.343536 | 51.730312 | 35000.000000 | 25000.000000 |
| 1 | 1 | 1_0 | COMPLETED | Sobol | 13512.050781 | 201.430756 | 42.179260 | 35528.516769 | 29308.345318 |
| 2 | 2 | 2_0 | COMPLETED | Sobol | 12027.515625 | 20.912548 | 157.145172 | 27542.769965 | 20028.450154 |
| 3 | 3 | 3_0 | COMPLETED | Sobol | 8454.648438 | 208.190277 | 116.764359 | 31476.355288 | 31937.575676 |
| 4 | 4 | 4_0 | COMPLETED | Sobol | 16573.820312 | 87.833015 | 81.044258 | 44114.464223 | 18727.302644 |
| 5 | 5 | 5_0 | COMPLETED | MBM | 18190.746094 | 169.666656 | 15.560753 | 37684.518752 | 17302.231826 |
| 6 | 6 | 6_0 | COMPLETED | MBM | 17933.320312 | 75.805260 | 40.234680 | 40522.428456 | 22657.060851 |
| 7 | 7 | 7_0 | COMPLETED | MBM | 18318.800781 | 36.463322 | 20.089252 | 38615.920550 | 21656.046132 |
| 8 | 8 | 8_0 | COMPLETED | MBM | 17309.546875 | 12.417444 | 15.271626 | 37873.970853 | 20617.303811 |
| 9 | 9 | 9_0 | COMPLETED | MBM | 18735.648438 | 97.109985 | 16.371954 | 38158.347400 | 18640.476644 |
| 10 | 10 | 10_0 | COMPLETED | MBM | 18654.164062 | 51.970116 | 16.958710 | 37928.750018 | 19461.746838 |
| 11 | 11 | 11_0 | COMPLETED | MBM | 18483.511719 | 123.677605 | 15.555395 | 37982.653316 | 18122.120993 |
| 12 | 12 | 12_0 | COMPLETED | MBM | 19236.410156 | 81.947495 | 16.882217 | 37907.582695 | 18930.191017 |
| 13 | 13 | 13_0 | COMPLETED | MBM | 17710.757812 | 27.528166 | 16.244621 | 37998.949688 | 19947.026184 |
| 14 | 14 | 14_0 | COMPLETED | MBM | 13118.660156 | 217.094055 | 11.810706 | 38034.410960 | 15000.000000 |
| 15 | 15 | 15_0 | COMPLETED | MBM | 18221.218750 | 187.085846 | 14.724368 | 37997.690204 | 16954.290265 |
| 16 | 16 | 16_0 | COMPLETED | MBM | 18514.605469 | 153.629761 | 15.669703 | 38002.983364 | 17622.956655 |
| 17 | 17 | 17_0 | COMPLETED | MBM | 16294.238281 | 200.373016 | 13.078042 | 38040.269936 | 16285.980029 |
| 18 | 18 | 18_0 | COMPLETED | MBM | 18996.144531 | 69.765579 | 17.234526 | 38166.783964 | 19134.983112 |
| 19 | 19 | 19_0 | COMPLETED | MBM | 19225.886719 | 80.031471 | 18.545055 | 37508.397328 | 18997.290584 |
| 20 | 20 | 20_0 | COMPLETED | MBM | 17926.335938 | 170.404953 | 14.998608 | 38253.218102 | 17238.104501 |
| 21 | 21 | 21_0 | COMPLETED | MBM | 18861.402344 | 43.258617 | 17.581388 | 38120.454698 | 19664.231765 |
| 22 | 22 | 22_0 | COMPLETED | MBM | 19242.503906 | 115.118309 | 16.599400 | 38138.470862 | 18344.384498 |
| 23 | 23 | 23_0 | COMPLETED | MBM | 17120.722656 | 17.439602 | 15.646281 | 38041.173270 | 20242.172021 |
| 24 | 24 | 24_0 | COMPLETED | MBM | 17394.582031 | 194.491028 | 14.176724 | 38110.599072 | 16653.433345 |
| 25 | 25 | 25_0 | COMPLETED | MBM | 18820.230469 | 61.632099 | 18.488487 | 37782.674075 | 22253.682151 |
| 26 | 26 | 26_0 | COMPLETED | MBM | 16274.718750 | 33.305138 | 90.780884 | 32836.751829 | 21683.414255 |
| 27 | 27 | 27_0 | COMPLETED | MBM | 15169.312500 | 210.733322 | 12.912532 | 38104.124521 | 15796.744414 |
| 28 | 28 | 28_0 | COMPLETED | MBM | 18686.187500 | 140.013412 | 15.514874 | 38096.834564 | 17896.330907 |
| 29 | 29 | 29_0 | COMPLETED | MBM | 17161.488281 | 19.982779 | 18.393990 | 37374.798066 | 21247.555059 |
Visualizing the Surrogate Model#
The plot_objective method shows how an objective varies across the DOF space, based on the surrogate model the agent built:
_ = agent.plot_objective(x_dof_name="kbh-radius", y_dof_name="kbv-radius", objective_name="intensity")
intensity (Mean) vs. kbh-radius, kbv-radius
The contour plot visualizes the predicted outcomes for intensity across a two-dimensional parameter space, with other parameters held fixed at the center of the search space. This plot helps in identifying regions of optimal performance and understanding how changes in the selected parameters influence the predicted outcomes. Contour lines represent levels of constant predicted values, providing insights into the gradient and potential optima within the parameter space.
This plot reveals the landscape the optimizer explored. Peaks (for maximization) or valleys (for minimization) show where good configurations lie.
Applying the Optimal Configuration#
The Pareto frontier contains all optimal trade-off solutions. Let’s retrieve one and apply it to see the resulting beam:
optimal_parameters = next(iter(agent.ax_client.get_pareto_frontier()))[0]
optimal_parameters
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/adapter/transforms/winsorize.py:126: AxOptimizationWarning: Encountered a `MultiObjective` without objective thresholds. We will winsorize each objective separately. We strongly recommend specifying the objective thresholds when using multi-objective optimization.
metric_signature: _get_cutoffs(
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/generators/torch/botorch_moo_utils.py:268: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
obj_mask = torch.tensor(obj_indices, device=objective_weights.device)
[INFO 04-19 07:14:04] ax.service.utils.best_point: Using inferred objective thresholds: [ObjectiveThreshold(intensity >= 12592.094650202525), ObjectiveThreshold(width <= 237.51604559649311), ObjectiveThreshold(height <= 20.831940017100695)], as objective thresholds were not specified as part of the optimization configuration on the experiment.
{'kbv-radius': 37998.9496881064, 'kbh-radius': 19947.02618403819}
Now move the mirrors to these optimal positions and acquire an image:
from bluesky.plans import list_scan
uid = RE(list_scan(
[det],
kbv.radius, [optimal_parameters[kbv.radius.name]],
kbh.radius, [optimal_parameters[kbh.radius.name]],
))
image = tiled_client[uid[0]]["primary/det_image"].read().squeeze()
plt.imshow(image)
plt.colorbar()
plt.show()
What You’ve Learned#
In this tutorial, you worked through a complete Bayesian optimization workflow:
DOFs define the search space—the parameters you can control and their allowed ranges
Objectives specify your goals and whether to minimize or maximize each one
Evaluation functions extract meaningful metrics from experimental data
The Agent coordinates everything, building a model of your system and intelligently exploring the parameter space
Health checks let you diagnose optimization progress and catch issues early
These same components apply to any optimization problem: swap the simulated devices for real hardware, adjust the DOFs and objectives for your system, and write an evaluation function that extracts your metrics.
Next Steps#
Learn about custom acquisition plans for more complex measurement sequences
Explore DOF constraints to encode physical limitations
See outcome constraints to enforce requirements on your results
See Also#
blop_simpackage for XRT simulated beamline control