Classifiersの比較ぱらぱらめくるscikit-learnのUser guide

http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
# Code source: Ga&#235;l Varoquaux
#              Andreas M&#252;ller
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
# 色々importする
# 基本的にはscikit_learnが持つデータ生成ツールと
# scikit_learnが持つclassifiers
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

h = .02  # step size in the mesh
# 名前文字列の１次元アレイ
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
         "Random Forest", "AdaBoost", "Naive Bayes", "Linear Discriminant Analysis","Quadratic Discriminant Analysis"]
# Classifiers (データを取るオブジェクト)も１次元アレイにできる
# それぞれの手法について、実行パラメタを与えてリストの要素にしている
classifiers = [
    KNeighborsClassifier(3),
    SVC(kernel="linear", C=0.025),
    SVC(gamma=2, C=1),
    DecisionTreeClassifier(max_depth=5),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
    AdaBoostClassifier(),
    GaussianNB(),
    LinearDiscriminantAnalysis(),
    QuadraticDiscriminantAnalysis()]

# make_classification()という関数を使って、説明変数・被説明変数をそれぞれ作っている
X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                           random_state=1, n_clusters_per_class=1)
# 乱雑項を入れる
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
# Xとyとを組にして格納
linearly_separable = (X, y)

# make_moons()なる三日月が入り組むようなデータセットの生成関数
# make_circles()なる環状データセットの生成関数による(X,y)の組を作りつつ
# 先に作ったlinearly_separableという(X,y)とともに、(X,y)を要素とする１次元アレイを作る
datasets = [make_moons(noise=0.3, random_state=0),
            make_circles(noise=0.2, factor=0.5, random_state=1),
            linearly_separable
            ]

figure = plt.figure(figsize=(27, 9))
i = 1　# データセット(３種)ｘclassifierの種類だけカウンタを回すための整数オブジェクト
# iterate over datasets データセットのループ
for ds in datasets:
    # preprocess dataset, split into training and test part
    X, y = ds # タプルds から２つのオブジェクトをX,yにアサイン
    X = StandardScaler().fit_transform(X) # 平均・分散で標準化するらしい
    # トレーニング用と検証用とにXとyとを分ける
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)

    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))

    # just plot the dataset first
    cm = plt.cm.RdBu
    cm_bright = ListedColormap(['#FF0000', '#0000FF'])
    # 描図パネルは縦にデータセット数、横にclassifiersの数と素の打点図
    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
    # Plot the training points
    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
    # and testing points
    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xticks(())
    ax.set_yticks(())
    i += 1

    # iterate over classifiers
    # Classifiersに関するループ。ただし、その名前の１次元ベクトルとともにループして取り出し
    for name, clf in zip(names, classifiers):
        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
        clf.fit(X_train, y_train) # すべてのClassifiersで等しくfit()関数を使える！
        score = clf.score(X_test, y_test) # そして結果の取り出しも同じ！

        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, m_max]x[y_min, y_max].
        if hasattr(clf, "decision_function"):
            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
        else:
            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

        # Plot also the training points
        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
        # and testing points
        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
                   alpha=0.6)

        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())
        ax.set_title(name)
        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
                size=15, horizontalalignment='right')
        i += 1

figure.subplots_adjust(left=.02, right=.98)
plt.show()