# -*- coding: utf-8 -*- """ RandomForestClassifier skleran的9个模型在3份数据上的使用。 1. 知识点： sklearn自生成分类样本集、标准化、 画等高线图、拆分训练和测试集 2. 结果: 对于2维的线性和非线性的3个分类问题， 都证明了 随机森林可以取得较好效果。 """ 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 # make_classification 随机生成连续自变量和分类因变量 # make_moons 生成二维自变量 和分类自变量，生成半环形图、月亮型 # make_circles 生成二维自变量 和分类自变量，生成半环形图、月亮型 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 as LDA # 线性判别分析（LDA） from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA # 二次判别分析（QDA） h = .02 # step size in the mesh 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()] # 样本集包含2个自变量， n_classes=2表示因变量类别中包含2类 X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1,n_classes=2) #生成样本集 plt.scatter(X[:, 0], X[:, 1], marker='o', c=y) rng = np.random.RandomState(2) #每次实例生成后，第n个实例的第i次随机，永远与第m个实例的第i次随机相同。 X += 2 * rng.uniform(size=X.shape) linearly_separable = (X, y) 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 # iterate over datasets for ds in datasets: # preprocess dataset, split into training and test part X, y = ds X = StandardScaler().fit_transform(X) # 标准化转换 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4) #划分训练和测试集 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)) # 输出的xx，yy，就是坐标矩阵 # just plot the dataset first cm = plt.cm.RdBu cm_bright = ListedColormap(['#FF0000', '#0000FF']) ax = plt.subplot(len(datasets), len(classifiers) + 1, i) # 画布 # 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(()) #清空了坐标轴数字 ax.set_yticks(()) #清空了坐标轴数字 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()]) #np.c_按照行连接两个矩阵[[1,2],[1,2],[1,2]] ,对mesh矩阵中每个样本点输入 经过f(x,y)输出一个预测。 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) #画 预测函数在坐标系中的登高线图 # Plot also the training points ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) # 画训练数据散点图 # and testing points ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) # 测试数据的散点图 ax.set_xlim(xx.min(), xx.max()) #设置坐标轴范围 ax.set_ylim(yy.min(), yy.max()) ax.set_xticks(()) #清空坐标轴刻度 ax.set_yticks(()) ax.set_title(name) #设置正上方的标题 ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right') #在坐标系中指定位置，添加文本 i += 1 figure.subplots_adjust(left=.02, right=.98) #调整 图像间的空白区域 plt.show()
