DOC Fix FutureWarning in ensemble/plot_gradient_boosting_regularization.html #24960
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glemaitre
reviewed
Nov 16, 2022
| test_deviance = np.zeros((params["n_estimators"],), dtype=np.float64) | ||
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| for i, y_pred in enumerate(clf.staged_decision_function(X_test)): | ||
| # clf.loss_ assumes that y_test[i] in {0, 1} |
| for i, y_pred in enumerate(clf.staged_decision_function(X_test)): | ||
| # clf.loss_ assumes that y_test[i] in {0, 1} | ||
| test_deviance[i] = clf.loss_(y_test, y_pred) | ||
| test_deviance[i] = binomial_deviance(y_test, y_pred.ravel()) |
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Actually we can use staged_predict_proba directly:
for i, y_proba in enumerate(clf.staged_predict_proba(X_test)):
test_deviance[i] = 2 * log_loss(y_test, y_proba[:, 1])
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| def binomial_deviance(y, raw_predictions): | ||
| return -2 * np.mean((y * raw_predictions) - np.logaddexp(0, raw_predictions)) | ||
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You can remove this function and we will use the log_loss.
| "random_state": 2, | ||
| "min_samples_split": 5, | ||
| } | ||
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Please import the log loss after line 34:
from sklearn.metrics import log_loss| plt.ylabel("Test Set Deviance") | ||
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| plt.show() | ||
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Not really sure that we need this additional line.
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betatim
approved these changes
Nov 21, 2022
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betatim
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This PR feels like dejavu. LGTM though
Member
Indeed. |
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Thanks @Reddragonemperor58 |
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Reference Issues/PRs
Related to #24876
What does this implement/fix? Explain your changes.
Fix FutureWarning in ensemble/plot_gradient_boosting_regularization.html
Any other comments?