97 lines
3.7 KiB
Python
97 lines
3.7 KiB
Python
"""
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============================================================================
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Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV
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============================================================================
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Multiple metric parameter search can be done by setting the ``scoring``
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parameter to a list of metric scorer names or a dict mapping the scorer names
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to the scorer callables.
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The scores of all the scorers are available in the ``cv_results_`` dict at keys
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ending in ``'_<scorer_name>'`` (``'mean_test_precision'``,
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``'rank_test_precision'``, etc...)
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The ``best_estimator_``, ``best_index_``, ``best_score_`` and ``best_params_``
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correspond to the scorer (key) that is set to the ``refit`` attribute.
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"""
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# Author: Raghav RV <rvraghav93@gmail.com>
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# License: BSD
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import numpy as np
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from matplotlib import pyplot as plt
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from sklearn.datasets import make_hastie_10_2
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from sklearn.model_selection import GridSearchCV
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from sklearn.metrics import make_scorer
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from sklearn.metrics import accuracy_score
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from sklearn.tree import DecisionTreeClassifier
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print(__doc__)
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###############################################################################
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# Running ``GridSearchCV`` using multiple evaluation metrics
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# ----------------------------------------------------------
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#
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X, y = make_hastie_10_2(n_samples=8000, random_state=42)
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# The scorers can be either be one of the predefined metric strings or a scorer
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# callable, like the one returned by make_scorer
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scoring = {'AUC': 'roc_auc', 'Accuracy': make_scorer(accuracy_score)}
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# Setting refit='AUC', refits an estimator on the whole dataset with the
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# parameter setting that has the best cross-validated AUC score.
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# That estimator is made available at ``gs.best_estimator_`` along with
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# parameters like ``gs.best_score_``, ``gs.best_params_`` and
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# ``gs.best_index_``
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gs = GridSearchCV(DecisionTreeClassifier(random_state=42),
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param_grid={'min_samples_split': range(2, 403, 10)},
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scoring=scoring, cv=5, refit='AUC', return_train_score=True)
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gs.fit(X, y)
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results = gs.cv_results_
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###############################################################################
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# Plotting the result
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# -------------------
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plt.figure(figsize=(13, 13))
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plt.title("GridSearchCV evaluating using multiple scorers simultaneously",
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fontsize=16)
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plt.xlabel("min_samples_split")
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plt.ylabel("Score")
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ax = plt.gca()
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ax.set_xlim(0, 402)
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ax.set_ylim(0.73, 1)
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# Get the regular numpy array from the MaskedArray
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X_axis = np.array(results['param_min_samples_split'].data, dtype=float)
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for scorer, color in zip(sorted(scoring), ['g', 'k']):
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for sample, style in (('train', '--'), ('test', '-')):
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sample_score_mean = results['mean_%s_%s' % (sample, scorer)]
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sample_score_std = results['std_%s_%s' % (sample, scorer)]
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ax.fill_between(X_axis, sample_score_mean - sample_score_std,
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sample_score_mean + sample_score_std,
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alpha=0.1 if sample == 'test' else 0, color=color)
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ax.plot(X_axis, sample_score_mean, style, color=color,
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alpha=1 if sample == 'test' else 0.7,
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label="%s (%s)" % (scorer, sample))
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best_index = np.nonzero(results['rank_test_%s' % scorer] == 1)[0][0]
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best_score = results['mean_test_%s' % scorer][best_index]
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# Plot a dotted vertical line at the best score for that scorer marked by x
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ax.plot([X_axis[best_index], ] * 2, [0, best_score],
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linestyle='-.', color=color, marker='x', markeredgewidth=3, ms=8)
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# Annotate the best score for that scorer
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ax.annotate("%0.2f" % best_score,
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(X_axis[best_index], best_score + 0.005))
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plt.legend(loc="best")
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plt.grid(False)
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plt.show()
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