评估指标【交叉验证&ROC曲线】

 # -*- coding: utf-8 -*-
 """
 Created on Mon Sep 10 11:21:27 2018

 @author: zhen
 """
 from sklearn.datasets import fetch_mldata
 import numpy as np
 from sklearn.linear_model import SGDClassifier
 from sklearn.model_selection import cross_val_score
 from sklearn.model_selection import cross_val_predict
 from sklearn.metrics import precision_recall_curve
 import matplotlib
 import matplotlib.pyplot as plt
 from sklearn.metrics import roc_curve
 from sklearn.metrics import roc_auc_score
 from sklearn.ensemble import RandomForestClassifier

 mnist = fetch_mldata('MNIST original', data_home='D:/AnalyseData学习资源库/人工智能开发/分类评估/资料/test_data_home')

 x, y = mnist['data'], mnist['target']
 some_digit = x[36000]  #获取第36000行数据

 some_digit_image = some_digit.reshape(28, 28)

 plt.imshow(some_digit_image, cmap=matplotlib.cm.binary,
            interpolation='nearest', vmin=0, vmax=1)
 plt.axis('off')
 plt.show()

 x_train, x_test, y_train, y_test = x[:60000], x[60000:], y[:60000], y[60000:]
 shuffle_index = np.random.permutation(60000)

 x_train, y_train = x_train[shuffle_index], y_train[shuffle_index]

 y_train_5 = (y_train == 5)
 y_test_5 = (y_test == 5)

 sgd_clf = SGDClassifier(loss='log', random_state=42, max_iter=1000, tol=1e-4)
 sgd_clf.fit(x_train, y_train_5)  

 result = sgd_clf.predict([some_digit])

 print(cross_val_score(sgd_clf, x_train, y_train_5, cv=3, scoring='accuracy'))
 print(cross_val_score(sgd_clf, x_train, y_train_5, cv=3, scoring='precision'))
 print(cross_val_score(sgd_clf, x_train, y_train_5, cv=3, scoring='recall'))

 sgd_clf.fit(x_train, y_train_5)

 y_scores = sgd_clf.decision_function([some_digit])

 threshold = 0
 y_some_digit_pred = (y_scores > threshold)

 threshold = 200000
 y_some_digit_pred = (y_scores > threshold)

 # cv 数据集划分的个数
 y_scores = cross_val_predict(sgd_clf, x_train, y_train_5, cv=3, method='decision_function')

 precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)

 def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):
     plt.plot(thresholds, precisions[:-1], 'b--',label='Precision')
     plt.plot(thresholds, recalls[:-1], 'r--', label='Recall')
     plt.xlabel("Threshold")
     plt.legend(loc='upper left')
     plt.ylim([0, 1])
     plt.show()  

 def plot_roc_curve(fpr, tpr, label=None):
     plt.plot(fpr, tpr, linewidth=2, label='roc')
     plt.plot([0, 1], [0, 1], 'k--', label='mid')
     plt.legend(loc='lower right')
     # plt.axes([0, 1, 0, 1]) : 前两个参数表示坐标原点的位置，后两个表示x,y轴的长度
     plt.xlabel('fpr')
     plt.ylabel('tpr')
     plt.show()  

 plot_precision_recall_vs_threshold(precisions, recalls, thresholds)

 fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)
 plot_roc_curve(fpr, tpr)

 print(roc_auc_score(y_train_5, y_scores))

 forest_clf = RandomForestClassifier(random_state=42)
 y_probas_forest = cross_val_predict(forest_clf, x_train, y_train_5, cv=3, method='predict_proba')
 y_scores_forest = y_probas_forest[:, 1]
 fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)
 plt.plot(fpr, tpr, 'b:', label='SGD')
 plt.plot(fpr_forest, tpr_forest, label='Random Forest')
 plt.legend(loc='lower right')
 plt.show()

 print(roc_auc_score(y_train_5, y_scores_forest))

　　　　　　　　　　

总结：正向准确率和召回率在整体上成反比，可知在使用相同数据集，相同验证方式的情况下，随机森林要优于随机梯度下降！