sklearn同时运行多个模型并进行可视化

参考：https://blog.csdn.net/qq_34106574/article/details/82016442

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()

结果：

将其中的准确率用表格表示：

数据	最近邻	线性SVM	RBF-SVM	决策树	随机森林	Adaboost	朴素贝叶斯	LDA	QDA
月型	0.93	0.78	0.90	0.82	0.88	0.93	0.80	0.80	0.80
圆型	0.93	0.47	0.97	0.78	0.80	0.85	0.90	0.38	0.90
线性型	0.93	0.93	0.95	0.93	0.95	0.93	0.97	0.97	0.93

当然，这仅仅是利用简单的模型试了试，并没有将每一个模型的参数都调至最优，但还是能说明一些问题的：

1、在数据是线性可分的情况下，各种方法的分类准确率差别都不是很大。

2、线性分类器对非线性可分的数据无能为力。

3、最近邻和RBF-SVM在三种数据上表现得都比较不错。