sklearn同时运行多个模型并进行可视化

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
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.model_selection import train_test_split
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA

%matplotlib inline
 
h = .02  # step size in the mesh
#最近邻、线性支持向量机、RBF支持向量机、决策树、随机森林、AdaBoost、朴素贝叶斯、LDA、QDA 
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
         "Random Forest", "AdaBoost", "Naive Bayes", "LDA", "QDA"]
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(),
    LDA(),
    QDA()]
"""
make_classification用于生成数据样本
n_features：特征个数=n_informative（）+n_redundant+n_repeated
n_informative：多信息特征的个数
n_redundant：冗余信息，informative特征的随机线性组合
n_repeated：重复信息，随机提取n_informative和n_redundant特征
n_classes：分类类别
n_clusters_per_class：某一个类别是由几个cluster构成的
weights：列表类型，权重比
class_sep：乘以超立方体大小的因子。较大的值分散了簇/类，并使分类任务更容易。默认为1
random_state: 
如果是int，random_state是随机数发生器使用的种子; 
如果RandomState实例，random_state是随机数生成器; 
如果没有，则随机数生成器是np.random使用的RandomState实例。
返回值：
X：形状数组[n_samples，n_features]
生成的样本。
y：形状数组[n_samples]
每个样本的类成员的整数标签。
"""
X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                           random_state=1, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)

#生成三种形式的数据，月亮型、圆型、线性可分型 
datasets = [make_moons(noise=0.3, random_state=0),
            make_circles(noise=0.2, factor=0.5, random_state=1),
            linearly_separable
            ]
#创建一个新的图表，参数是尺寸，单位为英寸。            
figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
#遍历数据集
for ds in datasets:
    # preprocess dataset, split into training and test part
    #取得数据集和标签
    X, y = ds
    #StandardScaler：去均值和方差归一化。且是针对每一个特征维度来做的，而不是针对样本。
    X = StandardScaler().fit_transform(X)
    #划分训练集和测试集
    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
    #np.meshgrid：从坐标向量中返回坐标矩阵
    """
    直观理解：
    二维坐标系中,X轴可以取三个值1,2,3, Y轴可以取三个值7,8, 请问可以获得多少个点的坐标?
    显而易见是6个:
    (1,7)(2,7)(3,7)
    (1,8)(2,8)(3,8)
    """
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
 
    # just plot the dataset first
    ## 绘图库中的颜色查找表。比如A1是红色,A2是浅蓝色。 这样一种映射关系
    cm = plt.cm.RdBu
    cm_bright = ListedColormap(['#FF0000', '#0000FF'])
    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
    for name, clf in zip(names, classifiers):
        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
        clf.fit(X_train, y_train)
        score = clf.score(X_test, y_test)
 
        # 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()