缓存最近邻#

此示例演示如何在使用 KNeighborsClassifier 之前预先计算 k 个最近邻。KNeighborsClassifier 可以内部计算最近邻，但预先计算它们可以带来一些好处，例如更精细的参数控制、用于多次使用的缓存或自定义实现。

这里我们使用管道的缓存属性在 KNeighborsClassifier 的多次拟合之间缓存最近邻图。第一次调用很慢，因为它计算了邻居图，而后续调用更快，因为它们不需要重新计算图。这里持续时间很短，因为数据集很小，但当数据集变大或要搜索的参数网格很大时，收益会更加显著。

Classification accuracy, Fit time (with caching)

# Author: Tom Dupre la Tour
#
# License: BSD 3 clause
from tempfile import TemporaryDirectory

import matplotlib.pyplot as plt

from sklearn.datasets import load_digits
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier, KNeighborsTransformer
from sklearn.pipeline import Pipeline

X, y = load_digits(return_X_y=True)
n_neighbors_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]

# The transformer computes the nearest neighbors graph using the maximum number
# of neighbors necessary in the grid search. The classifier model filters the
# nearest neighbors graph as required by its own n_neighbors parameter.
graph_model = KNeighborsTransformer(n_neighbors=max(n_neighbors_list), mode="distance")
classifier_model = KNeighborsClassifier(metric="precomputed")

# Note that we give `memory` a directory to cache the graph computation
# that will be used several times when tuning the hyperparameters of the
# classifier.
with TemporaryDirectory(prefix="sklearn_graph_cache_") as tmpdir:
    full_model = Pipeline(
        steps=[("graph", graph_model), ("classifier", classifier_model)], memory=tmpdir
    )

    param_grid = {"classifier__n_neighbors": n_neighbors_list}
    grid_model = GridSearchCV(full_model, param_grid)
    grid_model.fit(X, y)

# Plot the results of the grid search.
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
axes[0].errorbar(
    x=n_neighbors_list,
    y=grid_model.cv_results_["mean_test_score"],
    yerr=grid_model.cv_results_["std_test_score"],
)
axes[0].set(xlabel="n_neighbors", title="Classification accuracy")
axes[1].errorbar(
    x=n_neighbors_list,
    y=grid_model.cv_results_["mean_fit_time"],
    yerr=grid_model.cv_results_["std_fit_time"],
    color="r",
)
axes[1].set(xlabel="n_neighbors", title="Fit time (with caching)")
fig.tight_layout()
plt.show()

脚本的总运行时间： (0 分钟 1.341 秒)