def test_plot_slice_log_scale() -> None:
study = create_study()
study.add_trial(
create_trial(
value=0.0,
params={"x_linear": 1.0, "y_log": 1e-3},
distributions={
"x_linear": UniformDistribution(0.0, 3.0),
"y_log": LogUniformDistribution(1e-5, 1.0),
},
)
)
# Plot a parameter.
# TODO(ytknzw): Add more specific assertion with the test case.
figure = plot_slice(study, params=["y_log"])
assert figure.has_data()
figure = plot_slice(study, params=["x_linear"])
assert figure.has_data()
# Plot multiple parameters.
# TODO(ytknzw): Add more specific assertion with the test case.
figure = plot_slice(study)
assert len(figure) == 2
assert figure[0].has_data()
assert figure[1].has_data()
def test_plot_slice_log_scale() -> None:
study = create_study()
study.add_trial(
create_trial(
value=0.0,
params={"x_linear": 1.0, "y_log": 1e-3},
distributions={
"x_linear": UniformDistribution(0.0, 3.0),
"y_log": LogUniformDistribution(1e-5, 1.0),
},
)
)
# Plot a parameter.
figure = plot_slice(study, params=["y_log"])
assert len(figure.get_lines()) == 0
assert figure.xaxis.label.get_text() == "y_log"
figure = plot_slice(study, params=["x_linear"])
assert len(figure.get_lines()) == 0
assert figure.xaxis.label.get_text() == "x_linear"
# Plot multiple parameters.
figure = plot_slice(study)
assert len(figure) == 2
assert len(figure[0].get_lines()) == 0
assert len(figure[1].get_lines()) == 0
assert figure[0].xaxis.label.get_text() == "x_linear"
assert figure[1].xaxis.label.get_text() == "y_log"
def _log_plots(run,
study: optuna.Study,
visualization_backend='plotly',
log_plot_contour=True,
log_plot_edf=True,
log_plot_parallel_coordinate=True,
log_plot_param_importances=True,
log_plot_pareto_front=True,
log_plot_slice=True,
log_plot_intermediate_values=True,
log_plot_optimization_history=True,
):
if visualization_backend == 'matplotlib':
import optuna.visualization.matplotlib as vis
elif visualization_backend == 'plotly':
import optuna.visualization as vis
else:
raise NotImplementedError(f'{visualization_backend} visualisation backend is not implemented')
if vis.is_available:
params = list(p_name for t in study.trials for p_name in t.params.keys())
if log_plot_contour and any(params):
run['visualizations/plot_contour'] = neptune.types.File.as_html(vis.plot_contour(study))
if log_plot_edf:
run['visualizations/plot_edf'] = neptune.types.File.as_html(vis.plot_edf(study))
if log_plot_parallel_coordinate:
run['visualizations/plot_parallel_coordinate'] = \
neptune.types.File.as_html(vis.plot_parallel_coordinate(study))
if log_plot_param_importances and len(study.get_trials(states=(optuna.trial.TrialState.COMPLETE, optuna.trial.TrialState.PRUNED,))) > 1:
try:
run['visualizations/plot_param_importances'] = neptune.types.File.as_html(vis.plot_param_importances(study))
except (RuntimeError, ValueError, ZeroDivisionError):
# Unable to compute importances
pass
if log_plot_pareto_front and study._is_multi_objective() and visualization_backend == 'plotly':
run['visualizations/plot_pareto_front'] = neptune.types.File.as_html(vis.plot_pareto_front(study))
if log_plot_slice and any(params):
run['visualizations/plot_slice'] = neptune.types.File.as_html(vis.plot_slice(study))
if log_plot_intermediate_values and any(trial.intermediate_values for trial in study.trials):
# Intermediate values plot if available only if the above condition is met
run['visualizations/plot_intermediate_values'] = \
neptune.types.File.as_html(vis.plot_intermediate_values(study))
if log_plot_optimization_history:
run['visualizations/plot_optimization_history'] = \
neptune.types.File.as_html(vis.plot_optimization_history(study))
def test_plot_slice() -> None:
# Test with no trial.
study = prepare_study_with_trials(no_trials=True)
figure = plot_slice(study)
assert not figure.has_data()
study = prepare_study_with_trials(with_c_d=False)
# Test with a trial.
# TODO(ytknzw): Add more specific assertion with the test case.
figure = plot_slice(study)
assert len(figure) == 2
assert figure[0].has_data()
assert figure[1].has_data()
# Test with a trial to select parameter.
# TODO(ytknzw): Add more specific assertion with the test case.
figure = plot_slice(study, params=["param_a"])
assert figure.has_data()
# Test with wrong parameters.
with pytest.raises(ValueError):
plot_slice(study, params=["optuna"])
# Ignore failed trials.
def fail_objective(_: Trial) -> float:
raise ValueError
study = create_study()
study.optimize(fail_objective, n_trials=1, catch=(ValueError, ))
figure = plot_slice(study)
assert not figure.has_data()
def test_plot_slice_log_scale() -> None:
study = create_study()
study.add_trial(
create_trial(
value=0.0,
params={
"x_linear": 1.0,
"y_log": 1e-3
},
distributions={
"x_linear": FloatDistribution(0.0, 3.0),
"y_log": FloatDistribution(1e-5, 1.0, log=True),
},
))
# Plot a parameter.
figure = plot_slice(study, params=["y_log"])
assert len(figure.findobj(PathCollection)) == 1
assert figure.xaxis.label.get_text() == "y_log"
assert figure.xaxis.get_scale() == "log"
plt.savefig(BytesIO())
figure = plot_slice(study, params=["x_linear"])
assert len(figure.findobj(PathCollection)) == 1
assert figure.xaxis.label.get_text() == "x_linear"
assert figure.xaxis.get_scale() == "linear"
plt.savefig(BytesIO())
# Plot multiple parameters.
figure = plot_slice(study)
assert len(figure) == 2
assert len(figure[0].findobj(PathCollection)) == 1
assert len(figure[1].findobj(PathCollection)) == 1
assert figure[0].xaxis.label.get_text() == "x_linear"
assert figure[1].xaxis.label.get_text() == "y_log"
assert figure[0].xaxis.get_scale() == "linear"
assert figure[1].xaxis.get_scale() == "log"
plt.savefig(BytesIO())
def test_plot_slice() -> None:
# Test with no trial.
study = prepare_study_with_trials(no_trials=True)
figure = plot_slice(study)
assert len(figure.get_lines()) == 0
study = prepare_study_with_trials(with_c_d=False)
# Test with a trial.
figure = plot_slice(study)
assert len(figure) == 2
assert len(figure[0].get_lines()) == 0
assert len(figure[1].get_lines()) == 0
assert figure[0].yaxis.label.get_text() == "Objective Value"
# Test with a trial to select parameter.
figure = plot_slice(study, params=["param_a"])
assert len(figure.get_lines()) == 0
assert figure.yaxis.label.get_text() == "Objective Value"
# Test with a customized target value.
with pytest.warns(UserWarning):
figure = plot_slice(study, params=["param_a"], target=lambda t: t.params["param_b"])
assert len(figure.get_lines()) == 0
assert figure.yaxis.label.get_text() == "Objective Value"
# Test with a customized target name.
figure = plot_slice(study, target_name="Target Name")
assert len(figure) == 2
assert len(figure[0].get_lines()) == 0
assert len(figure[1].get_lines()) == 0
assert figure[0].yaxis.label.get_text() == "Target Name"
# Test with wrong parameters.
with pytest.raises(ValueError):
plot_slice(study, params=["optuna"])
# Ignore failed trials.
def fail_objective(_: Trial) -> float:
raise ValueError
study = create_study()
study.optimize(fail_objective, n_trials=1, catch=(ValueError,))
figure = plot_slice(study)
assert len(figure.get_lines()) == 0
def test_target_is_none_and_study_is_multi_obj() -> None:
study = create_study(directions=["minimize", "minimize"])
with pytest.raises(ValueError):
plot_slice(study)
def test_plot_slice() -> None:
# Test with no trial.
study = prepare_study_with_trials(no_trials=True)
figure = plot_slice(study)
assert len(figure.findobj(PathCollection)) == 0
plt.savefig(BytesIO())
study = prepare_study_with_trials(with_c_d=False)
# Test with a trial.
figure = plot_slice(study)
assert len(figure) == 2
assert len(figure[0].findobj(PathCollection)) == 1
assert len(figure[1].findobj(PathCollection)) == 1
assert figure[0].yaxis.label.get_text() == "Objective Value"
plt.savefig(BytesIO())
# Scatter plot data is available as PathCollection.
data0 = figure[0].findobj(PathCollection)[0].get_offsets().data
data1 = figure[1].findobj(PathCollection)[0].get_offsets().data
assert np.allclose(data0, [[1.0, 0.0], [2.5, 1.0]])
assert np.allclose(data1, [[2.0, 0.0], [0.0, 2.0], [1.0, 1.0]])
# Test with a trial to select parameter.
figure = plot_slice(study, params=["param_a"])
assert len(figure.findobj(PathCollection)) == 1
assert figure.yaxis.label.get_text() == "Objective Value"
data0 = figure.findobj(PathCollection)[0].get_offsets().data
assert np.allclose(data0, [[1.0, 0.0], [2.5, 1.0]])
plt.savefig(BytesIO())
# Test with a customized target value.
with pytest.warns(UserWarning):
figure = plot_slice(study, params=["param_a"], target=lambda t: t.params["param_b"])
assert len(figure.findobj(PathCollection)) == 1
assert figure.yaxis.label.get_text() == "Objective Value"
data0 = figure.findobj(PathCollection)[0].get_offsets().data
assert np.allclose(data0, [[1.0, 2.0], [2.5, 1.0]])
plt.savefig(BytesIO())
# Test with a customized target name.
figure = plot_slice(study, target_name="Target Name")
assert len(figure) == 2
assert len(figure[0].findobj(PathCollection)) == 1
assert len(figure[1].findobj(PathCollection)) == 1
assert figure[0].yaxis.label.get_text() == "Target Name"
plt.savefig(BytesIO())
# Test with wrong parameters.
with pytest.raises(ValueError):
plot_slice(study, params=["optuna"])
# Ignore failed trials.
def fail_objective(_: Trial) -> float:
raise ValueError
study = create_study()
study.optimize(fail_objective, n_trials=1, catch=(ValueError,))
figure = plot_slice(study)
assert len(figure.get_lines()) == 0
assert len(figure.findobj(PathCollection)) == 0
plt.savefig(BytesIO())
def test_plot_slice() -> None:
# Test with no trial.
study = create_study(direction="minimize")
figure = plot_slice(study)
assert len(figure.findobj(PathCollection)) == 0
plt.savefig(BytesIO())
study = create_study(direction="minimize")
study.add_trial(
create_trial(
value=0.0,
params={
"param_a": 1.0,
"param_b": 2.0
},
distributions={
"param_a": FloatDistribution(0.0, 3.0),
"param_b": FloatDistribution(0.0, 3.0),
},
))
study.add_trial(
create_trial(
value=2.0,
params={"param_b": 0.0},
distributions={"param_b": FloatDistribution(0.0, 3.0)},
))
study.add_trial(
create_trial(
value=1.0,
params={
"param_a": 2.5,
"param_b": 1.0
},
distributions={
"param_a": FloatDistribution(0.0, 3.0),
"param_b": FloatDistribution(0.0, 3.0),
},
))
# Test with a trial.
figure = plot_slice(study)
assert len(figure) == 2
assert len(figure[0].findobj(PathCollection)) == 1
assert len(figure[1].findobj(PathCollection)) == 1
assert figure[0].yaxis.label.get_text() == "Objective Value"
plt.savefig(BytesIO())
# Scatter plot data is available as PathCollection.
data0 = figure[0].findobj(PathCollection)[0].get_offsets().data
data1 = figure[1].findobj(PathCollection)[0].get_offsets().data
assert np.allclose(data0, [[1.0, 0.0], [2.5, 1.0]])
assert np.allclose(data1, [[2.0, 0.0], [0.0, 2.0], [1.0, 1.0]])
# Test with a trial to select parameter.
figure = plot_slice(study, params=["param_a"])
assert len(figure.findobj(PathCollection)) == 1
assert figure.yaxis.label.get_text() == "Objective Value"
data0 = figure.findobj(PathCollection)[0].get_offsets().data
assert np.allclose(data0, [[1.0, 0.0], [2.5, 1.0]])
plt.savefig(BytesIO())
# Test with a customized target value.
with pytest.warns(UserWarning):
figure = plot_slice(study,
params=["param_a"],
target=lambda t: t.params["param_b"])
assert len(figure.findobj(PathCollection)) == 1
assert figure.yaxis.label.get_text() == "Objective Value"
data0 = figure.findobj(PathCollection)[0].get_offsets().data
assert np.allclose(data0, [[1.0, 2.0], [2.5, 1.0]])
plt.savefig(BytesIO())
# Test with a customized target name.
figure = plot_slice(study, target_name="Target Name")
assert len(figure) == 2
assert len(figure[0].findobj(PathCollection)) == 1
assert len(figure[1].findobj(PathCollection)) == 1
assert figure[0].yaxis.label.get_text() == "Target Name"
plt.savefig(BytesIO())
# Test with wrong parameters.
with pytest.raises(ValueError):
plot_slice(study, params=["optuna"])
# Ignore failed trials.
study = create_study()
study.optimize(fail_objective, n_trials=1, catch=(ValueError, ))
figure = plot_slice(study)
assert len(figure.get_lines()) == 0
assert len(figure.findobj(PathCollection)) == 0
plt.savefig(BytesIO())
