#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
import warnings
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from ax.core.experiment import Experiment
from ax.core.objective import MultiObjective
from ax.core.optimization_config import (
MultiObjectiveOptimizationConfig,
OptimizationConfig,
)
from ax.core.outcome_constraint import ObjectiveThreshold
from ax.exceptions.core import UserInputError
from ax.plot.base import AxPlotConfig, AxPlotTypes, CI_OPACITY, DECIMALS
from ax.plot.color import COLORS, DISCRETE_COLOR_SCALE, rgba
from ax.plot.helper import _format_CI, _format_dict, extend_range
from ax.plot.pareto_utils import ParetoFrontierResults
from ax.service.utils.best_point_mixin import BestPointMixin
from ax.utils.common.typeutils import checked_cast, not_none
from plotly import express as px
from scipy.stats import norm
DEFAULT_CI_LEVEL: float = 0.9
VALID_CONSTRAINT_OP_NAMES = {"GEQ", "LEQ"}
def _make_label(
mean: float, sem: float, name: str, is_relative: bool, Z: Optional[float]
) -> str:
estimate = str(round(mean, DECIMALS))
perc = "%" if is_relative else ""
ci = (
""
if (Z is None or np.isnan(sem))
else _format_CI(estimate=mean, sd=sem, relative=is_relative, zval=Z)
)
return f"{name}: {estimate}{perc} {ci}<br>"
def _filter_outliers(Y: np.ndarray, m: float = 2.0) -> np.ndarray:
std_filter = abs(Y - np.median(Y, axis=0)) < m * np.std(Y, axis=0)
return Y[np.all(abs(std_filter), axis=1)]
[docs]def scatter_plot_with_hypervolume_trace_plotly(experiment: Experiment) -> go.Figure:
"""
Plots the hypervolume of the Pareto frontier after each iteration with the same
color scheme as the Pareto frontier plot. This is useful for understanding if the
frontier is expanding or if the optimization has stalled out.
Arguments:
experiment: MOO experiment to calculate the hypervolume trace from
"""
hypervolume_trace = BestPointMixin._get_trace(experiment=experiment)
df = pd.DataFrame(
{
"hypervolume": hypervolume_trace,
"trial_index": [*range(len(hypervolume_trace))],
}
)
return px.line(
data_frame=df,
x="trial_index",
y="hypervolume",
title="Pareto Frontier Hypervolume Trace",
markers=True,
)
[docs]def scatter_plot_with_pareto_frontier_plotly(
Y: np.ndarray,
Y_pareto: Optional[np.ndarray],
metric_x: Optional[str],
metric_y: Optional[str],
reference_point: Optional[Tuple[float, float]],
minimize: Optional[Union[bool, Tuple[bool, bool]]] = True,
hovertext: Optional[Iterable[str]] = None,
) -> go.Figure:
"""Plots a scatter of all points in ``Y`` for ``metric_x`` and ``metric_y``
with a reference point and Pareto frontier from ``Y_pareto``.
Points in the scatter are colored in a gradient representing their trial index,
with metric_x on x-axis and metric_y on y-axis. Reference point is represented
as a star and Pareto frontier –– as a line. The frontier connects to the reference
point via projection lines.
NOTE: Both metrics should have the same minimization setting, passed as `minimize`.
Args:
Y: Array of outcomes, of which the first two will be plotted.
Y_pareto: Array of Pareto-optimal points, first two outcomes in which will be
plotted.
metric_x: Name of first outcome in ``Y``.
metric_Y: Name of second outcome in ``Y``.
reference_point: Reference point for ``metric_x`` and ``metric_y``.
minimize: Whether the two metrics in the plot are being minimized or maximized.
"""
title = "Observed metric values"
if isinstance(minimize, bool):
minimize = (minimize, minimize)
Xs = Y[:, 0]
Ys = Y[:, 1]
experimental_points_scatter = [
go.Scatter(
x=Xs,
y=Ys,
mode="markers",
marker={
"color": np.linspace(0, 100, int(len(Xs) * 1.05)),
"colorscale": "magma",
"colorbar": {
"tickvals": [0, 50, 100],
"ticktext": [
1,
"iteration",
len(Xs),
],
},
},
name="Experimental points",
hovertemplate="%{text}",
text=hovertext,
)
]
# No Pareto frontier is drawn if none is provided, or if the frontier consists of
# a single point and no reference points are provided.
if (
Y_pareto is None
or len(Y_pareto) == 0
or (len(Y_pareto) == 1 and reference_point is None)
):
# `Y_pareto` input was not specified
range_x = extend_range(lower=min(Y[:, 0]), upper=max(Y[:, 0]))
range_y = extend_range(lower=min(Y[:, 1]), upper=max(Y[:, 1]))
pareto_step = reference_point_lines = reference_point_star = []
else:
title += " with Pareto frontier"
if reference_point:
if minimize is None:
minimize = tuple(
reference_point[i] >= max(Y_pareto[:, i]) for i in range(2)
)
reference_point_star = [
go.Scatter(
x=[reference_point[0]],
y=[reference_point[1]],
mode="markers",
marker={
"color": rgba(COLORS.STEELBLUE.value),
"size": 25,
"symbol": "star",
},
)
]
extra_point_x = min(Y_pareto[:, 0]) if minimize[0] else max(Y_pareto[:, 0])
reference_point_line_1 = go.Scatter(
x=[extra_point_x, reference_point[0]],
y=[reference_point[1], reference_point[1]],
mode="lines",
marker={"color": rgba(COLORS.STEELBLUE.value)},
)
extra_point_y = min(Y_pareto[:, 1]) if minimize[1] else max(Y_pareto[:, 1])
reference_point_line_2 = go.Scatter(
x=[reference_point[0], reference_point[0]],
y=[extra_point_y, reference_point[1]],
mode="lines",
marker={"color": rgba(COLORS.STEELBLUE.value)},
)
reference_point_lines = [reference_point_line_1, reference_point_line_2]
Y_pareto_with_extra = np.concatenate(
(
[[extra_point_x, reference_point[1]]],
Y_pareto,
[[reference_point[0], extra_point_y]],
),
axis=0,
)
pareto_step = [
go.Scatter(
x=Y_pareto_with_extra[:, 0],
y=Y_pareto_with_extra[:, 1],
mode="lines",
line_shape="hv",
marker={"color": rgba(COLORS.STEELBLUE.value)},
)
]
range_x = (
extend_range(lower=min(Y_pareto[:, 0]), upper=reference_point[0])
if minimize[0]
else extend_range(lower=reference_point[0], upper=max(Y_pareto[:, 0]))
)
range_y = (
extend_range(lower=min(Y_pareto[:, 1]), upper=reference_point[1])
if minimize[1]
else extend_range(lower=reference_point[1], upper=max(Y_pareto[:, 1]))
)
else: # Reference point was not specified
pareto_step = [
go.Scatter(
x=Y_pareto[:, 0],
y=Y_pareto[:, 1],
mode="lines",
line_shape="hv",
marker={"color": rgba(COLORS.STEELBLUE.value)},
)
]
reference_point_lines = reference_point_star = []
range_x = extend_range(lower=min(Y_pareto[:, 0]), upper=max(Y_pareto[:, 0]))
range_y = extend_range(lower=min(Y_pareto[:, 1]), upper=max(Y_pareto[:, 1]))
layout = go.Layout(
title=title,
showlegend=False,
xaxis={"title": metric_x or "", "range": range_x},
yaxis={"title": metric_y or "", "range": range_y},
)
return go.Figure(
layout=layout,
data=pareto_step
+ reference_point_lines
+ experimental_points_scatter
+ reference_point_star,
)
[docs]def scatter_plot_with_pareto_frontier(
Y: np.ndarray,
Y_pareto: np.ndarray,
metric_x: str,
metric_y: str,
reference_point: Tuple[float, float],
minimize: bool = True,
) -> AxPlotConfig:
return AxPlotConfig(
data=scatter_plot_with_pareto_frontier_plotly(
Y=Y,
Y_pareto=Y_pareto,
metric_x=metric_x,
metric_y=metric_y,
reference_point=reference_point,
),
plot_type=AxPlotTypes.GENERIC,
)
def _get_single_pareto_trace(
frontier: ParetoFrontierResults,
CI_level: float,
legend_label: str = "mean",
trace_color: Tuple[int] = COLORS.STEELBLUE.value,
show_parameterization_on_hover: bool = True,
) -> go.Scatter:
primary_means = frontier.means[frontier.primary_metric]
primary_sems = frontier.sems[frontier.primary_metric]
secondary_means = frontier.means[frontier.secondary_metric]
secondary_sems = frontier.sems[frontier.secondary_metric]
absolute_metrics = frontier.absolute_metrics
all_metrics = frontier.means.keys()
if frontier.arm_names is None:
arm_names = [f"Parameterization {i}" for i in range(len(frontier.param_dicts))]
else:
arm_names = [f"Arm {name}" for name in frontier.arm_names]
if CI_level is not None:
Z = norm.ppf(1 - (1 - CI_level) / 2)
else:
Z = None
labels = []
for i, param_dict in enumerate(frontier.param_dicts):
label = f"<b>{arm_names[i]}</b><br>"
for metric in all_metrics:
metric_lab = _make_label(
mean=frontier.means[metric][i],
sem=frontier.sems[metric][i],
name=metric,
is_relative=metric not in absolute_metrics,
Z=Z,
)
label += metric_lab
parameterization = (
_format_dict(param_dict, "Parameterization")
if show_parameterization_on_hover
else ""
)
label += parameterization
labels.append(label)
return go.Scatter(
name=legend_label,
legendgroup=legend_label,
x=secondary_means,
y=primary_means,
error_x={
"type": "data",
"array": Z * np.array(secondary_sems),
"thickness": 2,
"color": rgba(trace_color, CI_OPACITY),
},
error_y={
"type": "data",
"array": Z * np.array(primary_sems),
"thickness": 2,
"color": rgba(trace_color, CI_OPACITY),
},
mode="markers",
text=labels,
hoverinfo="text",
marker={"color": rgba(trace_color)},
)
[docs]def plot_pareto_frontier(
frontier: ParetoFrontierResults,
CI_level: float = DEFAULT_CI_LEVEL,
show_parameterization_on_hover: bool = True,
) -> AxPlotConfig:
"""Plot a Pareto frontier from a ParetoFrontierResults object.
Args:
frontier (ParetoFrontierResults): The results of the Pareto frontier
computation.
CI_level (float, optional): The confidence level, i.e. 0.95 (95%)
show_parameterization_on_hover (bool, optional): If True, show the
parameterization of the points on the frontier on hover.
Returns:
AEPlotConfig: The resulting Plotly plot definition.
"""
trace = _get_single_pareto_trace(
frontier=frontier,
CI_level=CI_level,
show_parameterization_on_hover=show_parameterization_on_hover,
)
shapes = []
primary_threshold = None
secondary_threshold = None
if frontier.objective_thresholds is not None:
primary_threshold = frontier.objective_thresholds.get(
frontier.primary_metric, None
)
secondary_threshold = frontier.objective_thresholds.get(
frontier.secondary_metric, None
)
absolute_metrics = frontier.absolute_metrics
rel_x = frontier.secondary_metric not in absolute_metrics
rel_y = frontier.primary_metric not in absolute_metrics
if primary_threshold is not None:
shapes.append(
{
"type": "line",
"xref": "paper",
"x0": 0.0,
"x1": 1.0,
"yref": "y",
"y0": primary_threshold,
"y1": primary_threshold,
"line": {"color": rgba(COLORS.CORAL.value), "width": 3},
}
)
if secondary_threshold is not None:
shapes.append(
{
"type": "line",
"yref": "paper",
"y0": 0.0,
"y1": 1.0,
"xref": "x",
"x0": secondary_threshold,
"x1": secondary_threshold,
"line": {"color": rgba(COLORS.CORAL.value), "width": 3},
}
)
layout = go.Layout(
title="Pareto Frontier",
xaxis={
"title": frontier.secondary_metric,
"ticksuffix": "%" if rel_x else "",
"zeroline": True,
},
yaxis={
"title": frontier.primary_metric,
"ticksuffix": "%" if rel_y else "",
"zeroline": True,
},
hovermode="closest",
legend={"orientation": "h"},
width=750,
height=500,
margin=go.layout.Margin(pad=4, l=225, b=75, t=75), # noqa E741
shapes=shapes,
)
fig = go.Figure(data=[trace], layout=layout)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
[docs]def plot_multiple_pareto_frontiers(
frontiers: Dict[str, ParetoFrontierResults],
CI_level: float = DEFAULT_CI_LEVEL,
show_parameterization_on_hover: bool = True,
) -> AxPlotConfig:
"""Plot a Pareto frontier from a ParetoFrontierResults object.
Args:
frontiers (Dict[str, ParetoFrontierResults]): The results of
the Pareto frontier computation.
CI_level (float, optional): The confidence level, i.e. 0.95 (95%)
show_parameterization_on_hover (bool, optional): If True, show the
parameterization of the points on the frontier on hover.
Returns:
AEPlotConfig: The resulting Plotly plot definition.
"""
first_frontier = list(frontiers.values())[0]
traces = []
for i, (method, frontier) in enumerate(frontiers.items()):
# Check the two metrics are the same as the first frontier
if (
frontier.primary_metric != first_frontier.primary_metric
or frontier.secondary_metric != first_frontier.secondary_metric
):
raise ValueError("All frontiers should have the same pairs of metrics.")
trace = _get_single_pareto_trace(
frontier=frontier,
legend_label=method,
trace_color=DISCRETE_COLOR_SCALE[i % len(DISCRETE_COLOR_SCALE)],
CI_level=CI_level,
show_parameterization_on_hover=show_parameterization_on_hover,
)
traces.append(trace)
shapes = []
primary_threshold = None
secondary_threshold = None
if frontier.objective_thresholds is not None:
primary_threshold = frontier.objective_thresholds.get(
frontier.primary_metric, None
)
secondary_threshold = frontier.objective_thresholds.get(
frontier.secondary_metric, None
)
absolute_metrics = frontier.absolute_metrics
rel_x = frontier.secondary_metric not in absolute_metrics
rel_y = frontier.primary_metric not in absolute_metrics
if primary_threshold is not None:
shapes.append(
{
"type": "line",
"xref": "paper",
"x0": 0.0,
"x1": 1.0,
"yref": "y",
"y0": primary_threshold,
"y1": primary_threshold,
"line": {"color": rgba(COLORS.CORAL.value), "width": 3},
}
)
if secondary_threshold is not None:
shapes.append(
{
"type": "line",
"yref": "paper",
"y0": 0.0,
"y1": 1.0,
"xref": "x",
"x0": secondary_threshold,
"x1": secondary_threshold,
"line": {"color": rgba(COLORS.CORAL.value), "width": 3},
}
)
layout = go.Layout(
title="Pareto Frontier",
xaxis={
"title": frontier.secondary_metric,
"ticksuffix": "%" if rel_x else "",
"zeroline": True,
},
yaxis={
"title": frontier.primary_metric,
"ticksuffix": "%" if rel_y else "",
"zeroline": True,
},
hovermode="closest",
legend={
"orientation": "h",
"yanchor": "top",
"y": -0.20,
"xanchor": "auto",
"x": 0.075,
},
width=750,
height=550,
margin=go.layout.Margin(pad=4, l=225, b=125, t=75), # noqa E741
shapes=shapes,
)
fig = go.Figure(data=traces, layout=layout)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
[docs]def interact_pareto_frontier(
frontier_list: List[ParetoFrontierResults],
CI_level: float = DEFAULT_CI_LEVEL,
show_parameterization_on_hover: bool = True,
label_dict: Optional[Dict[str, str]] = None,
) -> AxPlotConfig:
"""Plot a pareto frontier from a list of objects
Args:
frontier_list: List of ParetoFrontierResults objects to be plotted.
CI_level: CI level for error bars.
show_parameterization_on_hover: Show parameterization on hover.
label_dict: Map from metric name to shortened alias to use on plot.
"""
if not frontier_list:
raise ValueError("Must receive a non-empty list of pareto frontiers to plot.")
label_dict_use = {k: k for k in frontier_list[0].means}
if label_dict is not None:
label_dict_use.update(label_dict)
traces = []
shapes = []
for frontier in frontier_list:
config = plot_pareto_frontier(
frontier=frontier,
CI_level=CI_level,
show_parameterization_on_hover=show_parameterization_on_hover,
)
traces.append(config.data["data"][0])
shapes.append(config.data["layout"].get("shapes", []))
for i, trace in enumerate(traces):
if i == 0: # Only the first trace is initially set to visible
trace["visible"] = True
else: # All other plot traces are not visible initially
trace["visible"] = False
# TODO (jej): replace dropdown with two dropdowns, one for x one for y.
dropdown = []
for i, frontier in enumerate(frontier_list):
trace_cnt = 1
# Only one plot trace is visible at a given time.
visible = [False] * (len(frontier_list) * trace_cnt)
for j in range(i * trace_cnt, (i + 1) * trace_cnt):
visible[j] = True
rel_y = frontier.primary_metric not in frontier.absolute_metrics
rel_x = frontier.secondary_metric not in frontier.absolute_metrics
primary_metric = label_dict_use[frontier.primary_metric]
secondary_metric = label_dict_use[frontier.secondary_metric]
dropdown.append(
{
"method": "update",
"args": [
{"visible": visible, "method": "restyle"},
{
"yaxis.title": primary_metric,
"xaxis.title": secondary_metric,
"yaxis.ticksuffix": "%" if rel_y else "",
"xaxis.ticksuffix": "%" if rel_x else "",
"shapes": shapes[i],
},
],
"label": f"{primary_metric}<br>vs {secondary_metric}",
}
)
# Set initial layout arguments.
initial_frontier = frontier_list[0]
rel_x = initial_frontier.secondary_metric not in initial_frontier.absolute_metrics
rel_y = initial_frontier.primary_metric not in initial_frontier.absolute_metrics
secondary_metric = label_dict_use[initial_frontier.secondary_metric]
primary_metric = label_dict_use[initial_frontier.primary_metric]
layout = go.Layout(
title="Pareto Frontier",
xaxis={
"title": secondary_metric,
"ticksuffix": "%" if rel_x else "",
"zeroline": True,
},
yaxis={
"title": primary_metric,
"ticksuffix": "%" if rel_y else "",
"zeroline": True,
},
updatemenus=[
{
"buttons": dropdown,
"x": 0.075,
"xanchor": "left",
"y": 1.1,
"yanchor": "middle",
}
],
hovermode="closest",
legend={"orientation": "h"},
width=750,
height=500,
margin=go.layout.Margin(pad=4, l=225, b=75, t=75), # noqa E741
shapes=shapes[0],
)
fig = go.Figure(data=traces, layout=layout)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
[docs]def interact_multiple_pareto_frontier(
frontier_lists: Dict[str, List[ParetoFrontierResults]],
CI_level: float = DEFAULT_CI_LEVEL,
show_parameterization_on_hover: bool = True,
) -> AxPlotConfig:
"""Plot a Pareto frontiers from a list of lists of NamedParetoFrontierResults
objects that we want to compare.
Args:
frontier_lists (Dict[List[ParetoFrontierResults]]): A dictionary of multiple
lists of Pareto frontier computation results to plot for comparison.
Each list of ParetoFrontierResults contains a list of the results of
the same pareto frontier but under different pairs of metrics.
Different List[ParetoFrontierResults] must contain the the same pairs
of metrics for this function to work.
CI_level (float, optional): The confidence level, i.e. 0.95 (95%)
show_parameterization_on_hover (bool, optional): If True, show the
parameterization of the points on the frontier on hover.
Returns:
AEPlotConfig: The resulting Plotly plot definition.
"""
if not frontier_lists:
raise ValueError("Must receive a non-empty list of pareto frontiers to plot.")
# Check all the lists have the same length
vals = frontier_lists.values()
length = len(frontier_lists[next(iter(frontier_lists))])
if not all(len(item) == length for item in vals):
raise ValueError("Not all lists in frontier_lists have the same length.")
# Transform the frontier_lists to lists of frontiers where each list
# corresponds to one pair of metrics with multiple frontiers
list_of_frontiers = [
dict(zip(frontier_lists.keys(), t)) for t in zip(*frontier_lists.values())
]
# Get the traces and shapes for plotting
traces = []
shapes = []
for frontiers in list_of_frontiers:
config = plot_multiple_pareto_frontiers(
frontiers=frontiers,
CI_level=CI_level,
show_parameterization_on_hover=show_parameterization_on_hover,
)
for i in range(len(config.data["data"])):
traces.append(config.data["data"][i])
shapes.append(config.data["layout"].get("shapes", []))
num_frontiers = len(frontier_lists)
num_metric_pairs = len(list_of_frontiers)
for i, trace in enumerate(traces):
if (
i < num_frontiers
): # Only the traces for metric 1 v.s. metric 2 are initially set to visible
trace["visible"] = True
else: # All other plot traces are not visible initially
trace["visible"] = False
dropdown = []
for i, frontiers in enumerate(list_of_frontiers):
# Only plot traces for the current pair of metrics are visible at a given time.
visible = [False] * (num_metric_pairs * num_frontiers)
for j in range(i * num_frontiers, (i + 1) * num_frontiers):
visible[j] = True
# Get the first frontier for reference of metric names
first_frontier = list(frontiers.values())[0]
rel_y = first_frontier.primary_metric not in first_frontier.absolute_metrics
rel_x = first_frontier.secondary_metric not in first_frontier.absolute_metrics
primary_metric = first_frontier.primary_metric
secondary_metric = first_frontier.secondary_metric
dropdown.append(
{
"method": "update",
"args": [
{"visible": visible, "method": "restyle"},
{
"yaxis.title": primary_metric,
"xaxis.title": secondary_metric,
"yaxis.ticksuffix": "%" if rel_y else "",
"xaxis.ticksuffix": "%" if rel_x else "",
"shapes": shapes[i],
},
],
"label": f"{primary_metric} vs {secondary_metric}",
}
)
# Set initial layout arguments.
initial_first_frontier = list(list_of_frontiers[0].values())[0]
rel_x = (
initial_first_frontier.secondary_metric
not in initial_first_frontier.absolute_metrics
)
rel_y = (
initial_first_frontier.primary_metric
not in initial_first_frontier.absolute_metrics
)
secondary_metric = initial_first_frontier.secondary_metric
primary_metric = initial_first_frontier.primary_metric
layout = go.Layout(
title="Pareto Frontier",
xaxis={
"title": secondary_metric,
"ticksuffix": "%" if rel_x else "",
"zeroline": True,
},
yaxis={
"title": primary_metric,
"ticksuffix": "%" if rel_y else "",
"zeroline": True,
},
updatemenus=[
{
"buttons": dropdown,
"x": 0.075,
"xanchor": "left",
"y": 1.1,
"yanchor": "middle",
}
],
hovermode="closest",
legend={
"orientation": "h",
"yanchor": "top",
"y": -0.20,
"xanchor": "auto",
"x": 0.075,
},
showlegend=True,
width=750,
height=550,
margin=go.layout.Margin(pad=4, l=225, b=125, t=75), # noqa E741
shapes=shapes[0],
)
fig = go.Figure(data=traces, layout=layout)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def _pareto_frontier_plot_input_processing(
experiment: Experiment,
metric_names: Optional[Tuple[str, str]] = None,
reference_point: Optional[Tuple[float, float]] = None,
minimize: Optional[Union[bool, Tuple[bool, bool]]] = None,
) -> Tuple[Tuple[str, str], Optional[Tuple[float, float]], Optional[Tuple[bool, bool]]]:
"""Processes inputs for Pareto frontier + scatterplot.
Args:
experiment: An Ax experiment.
metric_names: The names of two metrics to be plotted. Defaults to the metrics
in the optimization_config.
reference_point: The 2-dimensional reference point to use when plotting the
Pareto frontier. Defaults to the value of the objective thresholds of each
variable.
minimize: Whether each metric is being minimized. Defaults to the direction
specified for each variable in the optimization config.
Returns:
metric_names: The names of two metrics to be plotted.
reference_point: The 2-dimensional reference point to use when plotting the
Pareto frontier.
minimize: Whether each metric is being minimized.
"""
optimization_config = _validate_experiment_and_get_optimization_config(
experiment=experiment,
metric_names=metric_names,
reference_point=reference_point,
)
metric_names = _validate_and_maybe_get_default_metric_names(
metric_names=metric_names, optimization_config=optimization_config
)
objective_thresholds = _validate_experiment_and_maybe_get_objective_thresholds(
optimization_config=optimization_config,
metric_names=metric_names,
reference_point=reference_point,
)
reference_point = _validate_and_maybe_get_default_reference_point(
reference_point=reference_point,
objective_thresholds=objective_thresholds,
metric_names=metric_names,
)
minimize_output = _validate_and_maybe_get_default_minimize(
minimize=minimize,
objective_thresholds=objective_thresholds,
metric_names=metric_names,
optimization_config=optimization_config,
)
return metric_names, reference_point, minimize_output
def _validate_experiment_and_get_optimization_config(
experiment: Experiment,
metric_names: Optional[Tuple[str, str]] = None,
reference_point: Optional[Tuple[float, float]] = None,
minimize: Optional[Union[bool, Tuple[bool, bool]]] = None,
) -> Optional[OptimizationConfig]:
# If `optimization_config` is unspecified, check what inputs are missing and
# error/warn accordingly
if experiment.optimization_config is None:
if metric_names is None:
raise UserInputError(
"Inference of defaults failed. Please either specify `metric_names` "
"(and optionally `minimize` and `reference_point`) or provide an "
"experiment with an `optimization_config`."
)
if reference_point is None or minimize is None:
warnings.warn(
"Inference of defaults failed. Please specify `minimize` and "
"`reference_point` if available, or provide an experiment with an "
"`optimization_config` that contains an `objective` and "
"`objective_threshold` corresponding to each of `metric_names`: "
f"{metric_names}."
)
return None
return not_none(experiment.optimization_config)
def _validate_and_maybe_get_default_metric_names(
metric_names: Optional[Tuple[str, str]],
optimization_config: Optional[OptimizationConfig],
) -> Tuple[str, str]:
# Default metric_names is all metrics, producing an error if more than 2
if metric_names is None:
if not_none(optimization_config).is_moo_problem:
multi_objective = checked_cast(
MultiObjective, not_none(optimization_config).objective
)
metric_names = tuple(obj.metric.name for obj in multi_objective.objectives)
else:
raise UserInputError(
"Inference of `metric_names` failed. Expected `MultiObjective` but "
f"got {not_none(optimization_config).objective}. Please specify "
"`metric_names` of length 2 or provide an experiment whose "
"`optimization_config` has 2 objective metrics."
)
if metric_names is not None and len(metric_names) == 2:
return metric_names
raise UserInputError(
f"Expected 2 metrics but got {len(metric_names or [])}: {metric_names}. "
"Please specify `metric_names` of length 2 or provide an experiment whose "
"`optimization_config` has 2 objective metrics."
)
def _validate_experiment_and_maybe_get_objective_thresholds(
optimization_config: Optional[OptimizationConfig],
metric_names: Tuple[str, str],
reference_point: Optional[Tuple[float, float]],
) -> List[ObjectiveThreshold]:
objective_thresholds = []
# Validate `objective_thresholds` if `reference_point` is unspecified.
if reference_point is None:
objective_thresholds = checked_cast(
MultiObjectiveOptimizationConfig, optimization_config
).objective_thresholds
if any(
ot.relative for ot in objective_thresholds if ot.metric.name in metric_names
):
raise NotImplementedError(
"Pareto plotting not supported for experiments with relative objective "
"thresholds."
)
constraint_metric_names = {
objective_threshold.metric.name
for objective_threshold in objective_thresholds
}
missing_metric_names = set(metric_names) - set(constraint_metric_names)
if missing_metric_names:
warnings.warn(
"For automatic inference of reference point, expected one "
"`objective_threshold` for each metric in `metric_names`: "
f"{metric_names}. Missing {missing_metric_names}. Got "
f"{len(objective_thresholds)}: {objective_thresholds}. "
"Please specify `reference_point` or provide "
"an experiment whose `optimization_config` contains one "
"objective threshold for each metric. Returning an empty list."
)
return objective_thresholds
def _validate_and_maybe_get_default_reference_point(
reference_point: Optional[Tuple[float, float]],
objective_thresholds: List[ObjectiveThreshold],
metric_names: Tuple[str, str],
) -> Optional[Tuple[float, float]]:
if reference_point is None:
reference_point = {
objective_threshold.metric.name: objective_threshold.bound
for objective_threshold in objective_thresholds
}
missing_metric_names = set(metric_names) - set(reference_point)
if missing_metric_names:
warnings.warn(
"Automated determination of `reference_point` failed: missing metrics "
f"{missing_metric_names}. Please specify `reference_point` or provide "
"an experiment whose `optimization_config` has one "
"`objective_threshold` for each of two metrics. Returning `None`."
)
return None
reference_point = tuple(
reference_point[metric_name] for metric_name in metric_names
)
if len(reference_point) != 2:
warnings.warn(
f"Expected 2-dimensional `reference_point` but got {len(reference_point)} "
f"dimensions: {reference_point}. Please specify `reference_point` of "
"length 2 or provide an experiment whose optimization config has one "
"`objective_threshold` for each of two metrics. Returning `None`."
)
return None
return reference_point
def _validate_and_maybe_get_default_minimize(
minimize: Optional[Union[bool, Tuple[bool, bool]]],
objective_thresholds: List[ObjectiveThreshold],
metric_names: Tuple[str, str],
optimization_config: Optional[OptimizationConfig] = None,
) -> Optional[Tuple[bool, bool]]:
if minimize is None:
# Determine `minimize` defaults
minimize = tuple(
_maybe_get_default_minimize_single_metric(
metric_name=metric_name,
optimization_config=optimization_config,
objective_thresholds=objective_thresholds,
)
for metric_name in metric_names
)
# If either value of minimize is missing, return `None`
if any(i_min is None for i_min in minimize):
warnings.warn(
"Extraction of default `minimize` failed. Please specify `minimize` "
"of length 2 or provide an experiment whose `optimization_config` "
"includes 2 objectives. Returning None."
)
return None
minimize = tuple(not_none(i_min) for i_min in minimize)
# If only one bool provided, use for both dimensions
elif isinstance(minimize, bool):
minimize = (minimize, minimize)
if len(minimize) != 2:
warnings.warn(
f"Expected 2-dimensional `minimize` but got {len(minimize)} dimensions: "
f"{minimize}. Please specify `minimize` of length 2 or provide an "
"experiment whose `optimization_config` includes 2 objectives. Returning "
"None."
)
return None
return minimize
def _maybe_get_default_minimize_single_metric(
metric_name: str,
objective_thresholds: List[ObjectiveThreshold],
optimization_config: Optional[OptimizationConfig] = None,
) -> Optional[bool]:
minimize = None
# First try to get metric_name from optimization_config
if (
optimization_config is not None
and metric_name in optimization_config.objective.metric_names
):
if optimization_config.is_moo_problem:
multi_objective = checked_cast(
MultiObjective, optimization_config.objective
)
for objective in multi_objective.objectives:
if objective.metric.name == metric_name:
return objective.minimize
else:
return optimization_config.objective.minimize
# Next try to get minimize from objective_thresholds
if objective_thresholds is not None:
constraint_op_names = {
objective_threshold.op.name for objective_threshold in objective_thresholds
}
invalid_constraint_op_names = constraint_op_names - VALID_CONSTRAINT_OP_NAMES
if invalid_constraint_op_names:
raise ValueError(
"Operators of all constraints must be in "
f"{VALID_CONSTRAINT_OP_NAMES}. Got {invalid_constraint_op_names}.)"
)
minimize = {
objective_threshold.metric.name: objective_threshold.op.name == "LEQ"
for objective_threshold in objective_thresholds
}
minimize = minimize.get(metric_name)
if minimize is None:
warnings.warn(
f"Extraction of default `minimize` failed for metric {metric_name}. "
f"Ensure {metric_name} is an objective of the provided experiment. "
"Setting `minimize` to `None`."
)
return minimize
