比較有和沒有鄰域成分分析的最近鄰#

一個比較使用和不使用鄰域成分分析的最近鄰分類的範例。

它將繪製當在原始特徵上使用歐氏距離時，由最近鄰分類器給出的類別決策邊界，相對於在使用鄰域成分分析學習的轉換之後使用歐氏距離。後者旨在找到一個線性轉換，以最大化訓練集上的（隨機）最近鄰分類準確度。

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier, NeighborhoodComponentsAnalysis
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

n_neighbors = 1

dataset = datasets.load_iris()
X, y = dataset.data, dataset.target

# we only take two features. We could avoid this ugly
# slicing by using a two-dim dataset
X = X[:, [0, 2]]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, stratify=y, test_size=0.7, random_state=42
)

h = 0.05  # step size in the mesh

# Create color maps
cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])
cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])

names = ["KNN", "NCA, KNN"]

classifiers = [
    Pipeline(
        [
            ("scaler", StandardScaler()),
            ("knn", KNeighborsClassifier(n_neighbors=n_neighbors)),
        ]
    ),
    Pipeline(
        [
            ("scaler", StandardScaler()),
            ("nca", NeighborhoodComponentsAnalysis()),
            ("knn", KNeighborsClassifier(n_neighbors=n_neighbors)),
        ]
    ),
]

for name, clf in zip(names, classifiers):
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)

    _, ax = plt.subplots()
    DecisionBoundaryDisplay.from_estimator(
        clf,
        X,
        cmap=cmap_light,
        alpha=0.8,
        ax=ax,
        response_method="predict",
        plot_method="pcolormesh",
        shading="auto",
    )

    # Plot also the training and testing points
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor="k", s=20)
    plt.title("{} (k = {})".format(name, n_neighbors))
    plt.text(
        0.9,
        0.1,
        "{:.2f}".format(score),
        size=15,
        ha="center",
        va="center",
        transform=plt.gca().transAxes,
    )

plt.show()

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