Source code for optuna.visualization.matplotlib._intermediate_values
from optuna._experimental import experimental_func
from optuna.logging import get_logger
from optuna.study import Study
from optuna.visualization._intermediate_values import _get_intermediate_plot_info
from optuna.visualization._intermediate_values import _IntermediatePlotInfo
from optuna.visualization.matplotlib._matplotlib_imports import _imports
if _imports.is_successful():
from optuna.visualization.matplotlib._matplotlib_imports import Axes
from optuna.visualization.matplotlib._matplotlib_imports import plt
_logger = get_logger(__name__)
[docs]
@experimental_func("2.2.0")
def plot_intermediate_values(study: Study) -> "Axes":
"""Plot intermediate values of all trials in a study with Matplotlib.
.. note::
Please refer to `matplotlib.pyplot.legend
<https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html>`_
to adjust the style of the generated legend.
Example:
The following code snippet shows how to plot intermediate values.
.. plot::
import optuna
def f(x):
return (x - 2) ** 2
def df(x):
return 2 * x - 4
def objective(trial):
lr = trial.suggest_float("lr", 1e-5, 1e-1, log=True)
x = 3
for step in range(128):
y = f(x)
trial.report(y, step=step)
if trial.should_prune():
raise optuna.TrialPruned()
gy = df(x)
x -= gy * lr
return y
sampler = optuna.samplers.TPESampler(seed=10)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=16)
optuna.visualization.matplotlib.plot_intermediate_values(study)
.. seealso::
Please refer to :func:`optuna.visualization.plot_intermediate_values` for an example.
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their intermediate
values.
Returns:
A :class:`matplotlib.axes.Axes` object.
"""
_imports.check()
return _get_intermediate_plot(_get_intermediate_plot_info(study))
def _get_intermediate_plot(info: _IntermediatePlotInfo) -> "Axes":
# Set up the graph style.
plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots(tight_layout=True)
ax.set_title("Intermediate Values Plot")
ax.set_xlabel("Step")
ax.set_ylabel("Intermediate Value")
cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots.
trial_infos = info.trial_infos
for i, tinfo in enumerate(trial_infos):
ax.plot(
tuple((x for x, _ in tinfo.sorted_intermediate_values)),
tuple((y for _, y in tinfo.sorted_intermediate_values)),
color=cmap(i) if tinfo.feasible else "#CCCCCC",
marker=".",
alpha=0.7,
label="Trial{}".format(tinfo.trial_number),
)
if len(trial_infos) >= 2:
ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0.0)
return ax