71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
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"""
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=========================
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Caching nearest neighbors
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=========================
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This examples demonstrates how to precompute the k nearest neighbors before
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using them in KNeighborsClassifier. KNeighborsClassifier can compute the
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nearest neighbors internally, but precomputing them can have several benefits,
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such as finer parameter control, caching for multiple use, or custom
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implementations.
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Here we use the caching property of pipelines to cache the nearest neighbors
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graph between multiple fits of KNeighborsClassifier. The first call is slow
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since it computes the neighbors graph, while subsequent call are faster as they
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do not need to recompute the graph. Here the durations are small since the
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dataset is small, but the gain can be more substantial when the dataset grows
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larger, or when the grid of parameter to search is large.
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"""
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# Author: Tom Dupre la Tour
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#
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# License: BSD 3 clause
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from tempfile import TemporaryDirectory
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import matplotlib.pyplot as plt
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from sklearn.datasets import load_digits
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from sklearn.model_selection import GridSearchCV
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from sklearn.neighbors import KNeighborsClassifier, KNeighborsTransformer
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from sklearn.pipeline import Pipeline
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X, y = load_digits(return_X_y=True)
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n_neighbors_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]
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# The transformer computes the nearest neighbors graph using the maximum number
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# of neighbors necessary in the grid search. The classifier model filters the
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# nearest neighbors graph as required by its own n_neighbors parameter.
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graph_model = KNeighborsTransformer(n_neighbors=max(n_neighbors_list), mode="distance")
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classifier_model = KNeighborsClassifier(metric="precomputed")
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# Note that we give `memory` a directory to cache the graph computation
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# that will be used several times when tuning the hyperparameters of the
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# classifier.
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with TemporaryDirectory(prefix="sklearn_graph_cache_") as tmpdir:
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full_model = Pipeline(
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steps=[("graph", graph_model), ("classifier", classifier_model)], memory=tmpdir
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)
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param_grid = {"classifier__n_neighbors": n_neighbors_list}
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grid_model = GridSearchCV(full_model, param_grid)
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grid_model.fit(X, y)
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# Plot the results of the grid search.
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fig, axes = plt.subplots(1, 2, figsize=(8, 4))
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axes[0].errorbar(
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x=n_neighbors_list,
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y=grid_model.cv_results_["mean_test_score"],
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yerr=grid_model.cv_results_["std_test_score"],
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)
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axes[0].set(xlabel="n_neighbors", title="Classification accuracy")
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axes[1].errorbar(
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x=n_neighbors_list,
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y=grid_model.cv_results_["mean_fit_time"],
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yerr=grid_model.cv_results_["std_fit_time"],
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color="r",
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)
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axes[1].set(xlabel="n_neighbors", title="Fit time (with caching)")
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fig.tight_layout()
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plt.show()
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