86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
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"""
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Successive Halving Iterations
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=============================
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This example illustrates how a successive halving search
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(:class:`~sklearn.model_selection.HalvingGridSearchCV` and
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:class:`~sklearn.model_selection.HalvingRandomSearchCV`)
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iteratively chooses the best parameter combination out of
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multiple candidates.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from scipy.stats import randint
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from sklearn import datasets
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.experimental import enable_halving_search_cv # noqa
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from sklearn.model_selection import HalvingRandomSearchCV
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# %%
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# We first define the parameter space and train a
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# :class:`~sklearn.model_selection.HalvingRandomSearchCV` instance.
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rng = np.random.RandomState(0)
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X, y = datasets.make_classification(n_samples=400, n_features=12, random_state=rng)
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clf = RandomForestClassifier(n_estimators=20, random_state=rng)
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param_dist = {
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"max_depth": [3, None],
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"max_features": randint(1, 6),
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"min_samples_split": randint(2, 11),
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"bootstrap": [True, False],
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"criterion": ["gini", "entropy"],
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}
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rsh = HalvingRandomSearchCV(
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estimator=clf, param_distributions=param_dist, factor=2, random_state=rng
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)
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rsh.fit(X, y)
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# %%
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# We can now use the `cv_results_` attribute of the search estimator to inspect
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# and plot the evolution of the search.
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results = pd.DataFrame(rsh.cv_results_)
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results["params_str"] = results.params.apply(str)
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results.drop_duplicates(subset=("params_str", "iter"), inplace=True)
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mean_scores = results.pivot(
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index="iter", columns="params_str", values="mean_test_score"
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)
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ax = mean_scores.plot(legend=False, alpha=0.6)
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labels = [
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f"iter={i}\nn_samples={rsh.n_resources_[i]}\nn_candidates={rsh.n_candidates_[i]}"
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for i in range(rsh.n_iterations_)
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]
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ax.set_xticks(range(rsh.n_iterations_))
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ax.set_xticklabels(labels, rotation=45, multialignment="left")
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ax.set_title("Scores of candidates over iterations")
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ax.set_ylabel("mean test score", fontsize=15)
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ax.set_xlabel("iterations", fontsize=15)
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plt.tight_layout()
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plt.show()
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# %%
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# Number of candidates and amount of resource at each iteration
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# -------------------------------------------------------------
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#
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# At the first iteration, a small amount of resources is used. The resource
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# here is the number of samples that the estimators are trained on. All
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# candidates are evaluated.
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#
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# At the second iteration, only the best half of the candidates is evaluated.
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# The number of allocated resources is doubled: candidates are evaluated on
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# twice as many samples.
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#
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# This process is repeated until the last iteration, where only 2 candidates
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# are left. The best candidate is the candidate that has the best score at the
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# last iteration.
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