"""
=========================
Caching nearest neighbors
=========================

This example demonstrates how to precompute the k nearest neighbors before
using them in KNeighborsClassifier. KNeighborsClassifier can compute the
nearest neighbors internally, but precomputing them can have several benefits,
such as finer parameter control, caching for multiple use, or custom
implementations.

Here we use the caching property of pipelines to cache the nearest neighbors
graph between multiple fits of KNeighborsClassifier. The first call is slow
since it computes the neighbors graph, while subsequent calls are faster as they
do not need to recompute the graph. Here the durations are small since the
dataset is small, but the gain can be more substantial when the dataset grows
larger, or when the grid of parameter to search is large.

"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: 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()

"""
=========================
Caching nearest neighbors
=========================

This examples demonstrates how to precompute the k nearest neighbors before
using them in KNeighborsClassifier. KNeighborsClassifier can compute the
nearest neighbors internally, but precomputing them can have several benefits,
such as finer parameter control, caching for multiple use, or custom
implementations.

Here we use the caching property of pipelines to cache the nearest neighbors
graph between multiple fits of KNeighborsClassifier. The first call is slow
since it computes the neighbors graph, while subsequent call are faster as they
do not need to recompute the graph. Here the durations are small since the
dataset is small, but the gain can be more substantial when the dataset grows
larger, or when the grid of parameter to search is large.

"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: 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()
