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from __future__ import annotations
import pandas as pd
import pytest
from optuna import create_study
from optuna import create_trial
from optuna import Trial
from optuna.testing.storages import STORAGE_MODES
from optuna.testing.storages import StorageSupplier
from optuna.trial import TrialState
def test_study_trials_dataframe_with_no_trials() -> None:
study_with_no_trials = create_study()
trials_df = study_with_no_trials.trials_dataframe()
assert trials_df.empty
@pytest.mark.parametrize("storage_mode", STORAGE_MODES)
@pytest.mark.parametrize(
"attrs",
[
(
"number",
"value",
"datetime_start",
"datetime_complete",
"params",
"user_attrs",
"system_attrs",
"state",
),
(
"number",
"value",
"datetime_start",
"datetime_complete",
"duration",
"params",
"user_attrs",
"system_attrs",
"state",
"intermediate_values",
"_trial_id",
"distributions",
),
],
)
@pytest.mark.parametrize("multi_index", [True, False])
def test_trials_dataframe(storage_mode: str, attrs: tuple[str, ...], multi_index: bool) -> None:
def f(trial: Trial) -> float:
x = trial.suggest_int("x", 1, 1)
y = trial.suggest_categorical("y", (2.5,))
trial.set_user_attr("train_loss", 3)
trial.storage.set_trial_system_attr(trial._trial_id, "foo", "bar")
value = x + y # 3.5
# Test reported intermediate values, although it in practice is not "intermediate".
trial.report(value, step=0)
return value
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
study.optimize(f, n_trials=3)
df = study.trials_dataframe(attrs=attrs, multi_index=multi_index)
# Change index to access rows via trial number.
if multi_index:
df.set_index(("number", ""), inplace=True, drop=False)
else:
df.set_index("number", inplace=True, drop=False)
assert len(df) == 3
# Number columns are as follows (total of 13):
# non-nested: 6 (number, value, state, datetime_start, datetime_complete, duration)
# params: 2
# distributions: 2
# user_attrs: 1
# system_attrs: 1
# intermediate_values: 1
expected_n_columns = len(attrs)
if "params" in attrs:
expected_n_columns += 1
if "distributions" in attrs:
expected_n_columns += 1
assert len(df.columns) == expected_n_columns
for i in range(3):
assert df.number[i] == i
assert df.state[i] == "COMPLETE"
assert df.value[i] == 3.5
assert isinstance(df.datetime_start[i], pd.Timestamp)
assert isinstance(df.datetime_complete[i], pd.Timestamp)
if multi_index:
if "distributions" in attrs:
assert ("distributions", "x") in df.columns
assert ("distributions", "y") in df.columns
if "_trial_id" in attrs:
assert ("trial_id", "") in df.columns # trial_id depends on other tests.
if "duration" in attrs:
assert ("duration", "") in df.columns
assert df.params.x[i] == 1
assert df.params.y[i] == 2.5
assert df.user_attrs.train_loss[i] == 3
assert df.system_attrs.foo[i] == "bar"
else:
if "distributions" in attrs:
assert "distributions_x" in df.columns
assert "distributions_y" in df.columns
if "_trial_id" in attrs:
assert "trial_id" in df.columns # trial_id depends on other tests.
if "duration" in attrs:
assert "duration" in df.columns
assert df.params_x[i] == 1
assert df.params_y[i] == 2.5
assert df.user_attrs_train_loss[i] == 3
assert df.system_attrs_foo[i] == "bar"
@pytest.mark.parametrize("storage_mode", STORAGE_MODES)
def test_trials_dataframe_with_failure(storage_mode: str) -> None:
def f(trial: Trial) -> float:
x = trial.suggest_int("x", 1, 1)
y = trial.suggest_categorical("y", (2.5,))
trial.set_user_attr("train_loss", 3)
raise ValueError()
return x + y # 3.5
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
study.optimize(f, n_trials=3, catch=(ValueError,))
df = study.trials_dataframe()
# Change index to access rows via trial number.
df.set_index("number", inplace=True, drop=False)
assert len(df) == 3
# non-nested: 6, params: 2, user_attrs: 1 system_attrs: 0
assert len(df.columns) == 9
for i in range(3):
assert df.number[i] == i
assert df.state[i] == "FAIL"
assert df.value[i] is None
assert isinstance(df.datetime_start[i], pd.Timestamp)
assert isinstance(df.datetime_complete[i], pd.Timestamp)
assert isinstance(df.duration[i], pd.Timedelta)
assert df.params_x[i] == 1
assert df.params_y[i] == 2.5
assert df.user_attrs_train_loss[i] == 3
@pytest.mark.parametrize("attrs", [("value",), ("values",)])
@pytest.mark.parametrize("multi_index", [True, False])
def test_trials_dataframe_with_multi_objective_optimization(
attrs: tuple[str, ...], multi_index: bool
) -> None:
def f(trial: Trial) -> tuple[float, float]:
x = trial.suggest_float("x", 1, 1)
y = trial.suggest_float("y", 2, 2)
return x + y, x**2 + y**2 # 3, 5
# without set_metric_names()
study = create_study(directions=["minimize", "maximize"])
study.optimize(f, n_trials=1)
df = study.trials_dataframe(attrs=attrs, multi_index=multi_index)
if multi_index:
assert df.get("values")[0][0] == 3
assert df.get("values")[1][0] == 5
else:
assert df.values_0[0] == 3
assert df.values_1[0] == 5
# with set_metric_names()
study.set_metric_names(["v0", "v1"])
df = study.trials_dataframe(attrs=attrs, multi_index=multi_index)
if multi_index:
assert df.get("values")["v0"][0] == 3
assert df.get("values")["v1"][0] == 5
else:
assert df.get("values_v0")[0] == 3
assert df.get("values_v1")[0] == 5
@pytest.mark.parametrize("attrs", [("value",), ("values",)])
@pytest.mark.parametrize("multi_index", [True, False])
def test_trials_dataframe_with_multi_objective_optimization_with_fail_and_pruned(
attrs: tuple[str, ...], multi_index: bool
) -> None:
study = create_study(directions=["minimize", "maximize"])
study.add_trial(create_trial(state=TrialState.FAIL))
study.add_trial(create_trial(state=TrialState.PRUNED))
df = study.trials_dataframe(attrs=attrs, multi_index=multi_index)
# without set_metric_names()
if multi_index:
for i in range(2):
assert df.get("values")[0][i] is None
assert df.get("values")[1][i] is None
else:
for i in range(2):
assert df.values_0[i] is None
assert df.values_1[i] is None
# with set_metric_names()
study.set_metric_names(["v0", "v1"])
df = study.trials_dataframe(attrs=attrs, multi_index=multi_index)
if multi_index:
assert df.get("values")["v0"][0] is None
assert df.get("values")["v1"][0] is None
else:
assert df.get("values_v0")[0] is None
assert df.get("values_v1")[0] is None
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