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"""
plot_terminator_improvement
===========================
.. autofunction:: optuna.visualization.matplotlib.plot_terminator_improvement
The following code snippet shows how to plot improvement potentials,
together with cross-validation errors.
"""
from lightgbm import LGBMClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
import optuna
from optuna.terminator import report_cross_validation_scores
from optuna.visualization.matplotlib import plot_terminator_improvement
def objective(trial):
X, y = load_wine(return_X_y=True)
clf = LGBMClassifier(
reg_alpha=trial.suggest_float("reg_alpha", 1e-8, 10.0, log=True),
reg_lambda=trial.suggest_float("reg_lambda", 1e-8, 10.0, log=True),
num_leaves=trial.suggest_int("num_leaves", 2, 256),
colsample_bytree=trial.suggest_float("colsample_bytree", 0.4, 1.0),
subsample=trial.suggest_float("subsample", 0.4, 1.0),
subsample_freq=trial.suggest_int("subsample_freq", 1, 7),
min_child_samples=trial.suggest_int("min_child_samples", 5, 100),
)
scores = cross_val_score(clf, X, y, cv=KFold(n_splits=5, shuffle=True))
report_cross_validation_scores(trial, scores)
return scores.mean()
study = optuna.create_study()
study.optimize(objective, n_trials=30)
plot_terminator_improvement(study, plot_error=True)
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