#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from dataclasses import dataclass
from typing import Dict, List, Optional
import numpy as np
import pandas as pd
from ax.benchmark.benchmark_problem import BenchmarkProblem, SimpleBenchmarkProblem
from ax.core.base_trial import TrialStatus
from ax.core.batch_trial import BatchTrial
from ax.core.experiment import Experiment
from ax.core.optimization_config import MultiObjectiveOptimizationConfig
from ax.core.trial import Trial
from ax.core.utils import best_feasible_objective, feasible_hypervolume, get_model_times
from ax.plot.base import AxPlotConfig
from ax.plot.pareto_frontier import plot_multiple_pareto_frontiers
from ax.plot.pareto_utils import (
get_observed_pareto_frontiers,
ParetoFrontierResults,
)
from ax.plot.render import plot_config_to_html
from ax.plot.trace import (
optimization_times,
optimization_trace_all_methods,
optimization_trace_single_method,
)
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast, not_none
from ax.utils.report.render import h2_html, h3_html, p_html, render_report_elements
logger: logging.Logger = get_logger(__name__)
[docs]@dataclass
class BenchmarkResult:
# {method_name -> [[best objective per trial] per benchmark run]}
true_performance: Dict[str, np.ndarray]
# {method_name -> [total fit time per run]}
fit_times: Dict[str, List[float]]
# {method_name -> [total gen time per run]}
gen_times: Dict[str, List[float]]
# {method_name -> trials where generation strategy changed}
optimum: Optional[float] = None
model_transitions: Optional[Dict[str, Optional[List[int]]]] = None
is_multi_objective: bool = False
pareto_frontiers: Optional[Dict[str, ParetoFrontierResults]] = None
[docs]def aggregate_problem_results(
runs: Dict[str, List[Experiment]],
problem: BenchmarkProblem,
# Model transitions, can be obtained as `generation_strategy.model_transitions`
model_transitions: Optional[Dict[str, List[int]]] = None,
is_asynchronous: bool = False,
**kwargs,
) -> BenchmarkResult:
# Results will be put in {method -> results} dictionaries.
true_performances: Dict[str, List[np.ndarray]] = {}
fit_times: Dict[str, List[float]] = {}
gen_times: Dict[str, List[float]] = {}
exp = list(runs.values())[0][0]
is_moo = isinstance(exp.optimization_config, MultiObjectiveOptimizationConfig)
plot_pfs = is_moo and len(not_none(exp.optimization_config).objective.metrics) == 2
pareto_frontiers = {} if plot_pfs else None
for method, experiments in runs.items():
true_performances[method] = []
fit_times[method] = []
gen_times[method] = []
for experiment in experiments:
assert (
problem.name in experiment.name
), "Problem and experiment name do not match."
fit_time, gen_time = get_model_times(experiment=experiment)
true_performance = extract_optimization_trace(
experiment=experiment,
problem=problem,
is_asynchronous=is_asynchronous,
**kwargs,
)
# Compute the things we care about
# 1. True best objective value.
true_performances[method].append(true_performance)
# 2. Time
fit_times[method].append(fit_time)
gen_times[method].append(gen_time)
# TODO: If `evaluate_suggested` is True on the problem
# 3. True obj. value of model-predicted best
# 4. True feasiblity of model-predicted best
# 5. Model prediction MSE for each gen run
# only include pareto frontier for one experiment per method
if plot_pfs:
# pyre-ignore [16]
pareto_frontiers[method] = get_observed_pareto_frontiers(
experiment=experiment,
# pyre-ignore [6]
data=experiment.fetch_data(),
)[0]
# TODO: remove rows from <values>[method] of length different
# from the length of other rows, log warning when removing
return BenchmarkResult(
true_performance={m: np.array(v) for m, v in true_performances.items()},
# pyre-fixme[6]: [6]: Expected `Optional[Dict[str, Optional[List[int]]]]`
# but got `Optional[Dict[str, List[int]]]`
model_transitions=model_transitions,
optimum=problem.optimal_value,
fit_times=fit_times,
gen_times=gen_times,
is_multi_objective=is_moo,
pareto_frontiers=pareto_frontiers,
)
[docs]def make_plots(
benchmark_result: BenchmarkResult, problem_name: str, include_individual: bool
) -> List[AxPlotConfig]:
plots: List[AxPlotConfig] = []
# Plot objective at true best
ylabel = (
"Feasible Hypervolume"
if benchmark_result.is_multi_objective
else "Objective at best-feasible point observed so far"
)
plots.append(
optimization_trace_all_methods(
y_dict=benchmark_result.true_performance,
optimum=benchmark_result.optimum,
title=f"{problem_name}: Optimization Performance",
ylabel=ylabel,
)
)
if include_individual:
# Plot individual plots of a single method on a single problem.
for m, y in benchmark_result.true_performance.items():
plots.append(
optimization_trace_single_method(
y=y,
optimum=benchmark_result.optimum,
# model_transitions=benchmark_result.model_transitions[m],
title=f"{problem_name}, {m}: cumulative best objective",
ylabel=ylabel,
)
)
# Plot time
plots.append(
optimization_times(
fit_times=benchmark_result.fit_times,
gen_times=benchmark_result.gen_times,
title=f"{problem_name}: cumulative optimization times",
)
)
if benchmark_result.pareto_frontiers is not None:
plots.append(
plot_multiple_pareto_frontiers(
frontiers=not_none(benchmark_result.pareto_frontiers),
CI_level=0.0,
)
)
return plots
[docs]def generate_report(
benchmark_results: Dict[str, BenchmarkResult],
errors_encountered: Optional[List[str]] = None,
include_individual_method_plots: bool = False,
notebook_env: bool = False,
) -> str:
html_elements = [h2_html("Bayesian Optimization benchmarking suite report")]
for p, benchmark_result in benchmark_results.items():
html_elements.append(h3_html(f"{p}:"))
plots = make_plots(
benchmark_result,
problem_name=p,
include_individual=include_individual_method_plots,
)
html_elements.extend(plot_config_to_html(plt) for plt in plots)
if errors_encountered:
html_elements.append(h3_html("Errors encountered:"))
html_elements.extend(p_html(err) for err in errors_encountered)
else:
html_elements.append(h3_html("No errors encountered!"))
# Experiment name is used in header, which is disabled in this case.
return render_report_elements(
experiment_name="",
html_elements=html_elements,
header=False,
notebook_env=notebook_env,
)
def _extract_optimization_trace_from_metrics(experiment: Experiment) -> np.ndarray:
names = []
for trial in experiment.trials.values():
for i, arm in enumerate(trial.arms):
reps = int(trial.weights[i]) if isinstance(trial, BatchTrial) else 1
names.extend([arm.name] * reps)
iters_df = pd.DataFrame({"arm_name": names})
data_df = experiment.fetch_data(noisy=False).df
metrics = data_df["metric_name"].unique()
true_values = {}
for metric in metrics:
df_m = data_df[data_df["metric_name"] == metric]
# Get one row per arm
df_m = df_m.groupby("arm_name").first().reset_index()
df_b = pd.merge(iters_df, df_m, how="left", on="arm_name")
true_values[metric] = df_b["mean"].values
if isinstance(experiment.optimization_config, MultiObjectiveOptimizationConfig):
return feasible_hypervolume(
# pyre-fixme[6]: Expected `OptimizationConfig` for 1st param but got
# `Optional[ax.core.optimization_config.OptimizationConfig]`.
optimization_config=experiment.optimization_config,
values=true_values,
)
return best_feasible_objective(
# pyre-fixme[6]: Expected `OptimizationConfig` for 1st param but got
# `Optional[ax.core.optimization_config.OptimizationConfig]`.
optimization_config=experiment.optimization_config,
values=true_values,
)
def _extract_optimization_trace_from_synthetic_function(
experiment: Experiment, problem: SimpleBenchmarkProblem
) -> np.ndarray:
if any(isinstance(trial, BatchTrial) for trial in experiment.trials.values()):
raise NotImplementedError("Batched trials are not yet supported.")
true_values = []
for trial in experiment.trials.values():
parameters = not_none(checked_cast(Trial, trial).arm).parameters
# Expecting numerical parameters only.
value = problem.f(*[float(x) for x in parameters.values()]) # pyre-ignore[6]
true_values.append(value)
return best_feasible_objective(
# pyre-fixme[6]: Expected `OptimizationConfig` for 1st param but got
# `Optional[ax.core.optimization_config.OptimizationConfig]`.
optimization_config=experiment.optimization_config,
# pyre-fixme[6]: Expected `Dict[str, np.ndarray]` for 2nd param but got
# `Dict[str, List[typing.Any]]`.
values={problem.name: true_values},
)
def _extract_asynchronous_optimization_trace(
experiment: Experiment,
start_time: float,
end_time: float,
delta_t: float,
completed_time_key: str,
include_only_completed_trials: bool,
) -> np.ndarray:
"""Extract optimization trace for an asynchronous benchmark run. This involves
getting the `completed_time` from the trial `run_metadata`, as described by
the `completed_time_key`. From the `start_time`, `end_time`, and `delta_t`
arguments, a sequence of times is constructed. The returned optimization trace
is the best achieved value so far for each time, amongst completed (or early
stopped) trials.
Args:
experiment: The experiment from which to generate results.
start_time: The starting time.
end_time: The ending time.
delta_t: The increment between successive time points.
completed_time_key: The key from which we look up completed run times
from trial `run_metadata`.
include_only_completed_trials: Include results only from completed trials.
This will ignore trials that were early stopped.
Returns:
An array representing the optimization trace as a function of time.
"""
if any(isinstance(trial, BatchTrial) for trial in experiment.trials.values()):
raise NotImplementedError("Batched trials are not yet supported.")
def get_completed_time(row):
time = experiment.trials[row.trial_index].run_metadata[completed_time_key]
return pd.Series({"completed_time": time})
if include_only_completed_trials:
completed_trials = experiment.trial_indices_by_status[TrialStatus.COMPLETED]
data_df = experiment.fetch_trials_data(
trial_indices=completed_trials, noisy=False
).df
else:
data_df = experiment.fetch_data(noisy=False).df
minimize = experiment.optimization_config.objective.minimize # pyre-ignore[16]
num_periods_running = int((end_time - start_time) // delta_t + 1)
# TODO: Currently, the timestamps generated below must exactly match the
# `completed_time` column
iters_df = pd.DataFrame(
{"completed_time": np.arange(num_periods_running) * delta_t + start_time}
)
true_values = {}
for metric, df_m in data_df.groupby("metric_name"):
df_m = data_df[data_df["metric_name"] == metric]
# only keep the last data point for each arm
df_m = (
df_m.sort_values(["timestamp"], ascending=True)
.groupby("arm_name")
.tail(n=1)
)
# get completed times from run metadata
df_m["completed_time"] = df_m.apply(get_completed_time, axis=1)
# for trials that completed at the same time, keep only the best
df_m_g = df_m.groupby("completed_time")
df_m = (df_m_g.min() if minimize else df_m_g.max()).reset_index()
# take cumulative best wrt the completed time
df_m = df_m.sort_index()
df_m["mean"] = df_m["mean"].cummin() if minimize else df_m["mean"].cummax()
df_b = pd.merge(iters_df, df_m, how="left", on="completed_time")
# replace nans with Infs, which can be handled by `best_feasible_objective`
true_values[metric] = df_b["mean"].fillna(np.Inf if minimize else -np.Inf)
return best_feasible_objective(
# pyre-fixme[6]: Expected `OptimizationConfig` for 1st param but got
# `Optional[ax.core.optimization_config.OptimizationConfig]`.
optimization_config=experiment.optimization_config,
values=true_values,
)
