利用鸢尾花数据集画出P-R曲线

#coding=utf-8
"""
#演示目的：利用鸢尾花数据集画出P-R曲线
"""
print(__doc__)
 
import matplotlib.pyplot as plt
import numpy as np
from sklearn import svm, datasets
from sklearn.metrics import precision_recall_curve#精确率召回率评价曲线模块
from sklearn.metrics import average_precision_score
from sklearn.preprocessing import label_binarize#标签二值化处理，问题是3个类分类问题，要转换为2个类的问题，1对其余
from sklearn.multiclass import OneVsRestClassifier#一对其余
 
#from sklearn.cross_validation import train_test_split  #适用于anaconda 3.6及以前版本
from sklearn.model_selection import train_test_split#适用于anaconda 3.7，训练集测试集拆分模块
 
#以iris数据为例，画出P-R曲线
iris = datasets.load_iris()
X = iris.data#数据150x4
y = iris.target#标签150x1
 
# 标签二值化,将三个类转为001, 010, 100的格式.因为这是个多类分类问题，后面将要采用
#OneVsRestClassifier策略转为二类分类问题
y = label_binarize(y, classes=[0, 1, 2])#y转化为150x3
n_classes = y.shape[1]#3
print (y)
 
# 增加了800维的噪声特征
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
 #                                 150                4
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]#增加200*4,列
 
# Split into training and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=random_state) #随机数，填0或不填，每次都会不一样
# test_size=.5训练集和测试集的拆分比例
# Run classifier probability : boolean, optional (default=False)Whether to enable probability estimates. This must be enabled prior to calling fit, and will slow down that method.
# 1对其余
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True, random_state=random_state))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
 #                        训练集                       测试集
# Compute Precision-Recall and plot curve  
#下面的下划线是返回的阈值。作为一个名称：此时“_”作为临时性的名称使用。
#表示分配了一个特定的名称，但是并不会在后面再次用到该名称。
precision = dict()
recall = dict()
average_precision = dict()
for i in range(n_classes):
    # 画线
    precision[i], recall[i], _ = precision_recall_curve(y_test[:, i],  y_score[:, i]) #The last precision and recall values are 1. and 0. respectively and do not have a corresponding threshold. This ensures that the graph starts on the x axis.
    average_precision[i] = average_precision_score(y_test[:, i], y_score[:, i])#切片，第i个类的分类结果性能
 
# Compute micro-average curve and area. ravel()将多维数组降为一维
precision["micro"], recall["micro"], _ = precision_recall_curve(y_test.ravel(),  y_score.ravel())
average_precision["micro"] = average_precision_score(y_test, y_score, average="micro") #This score corresponds to the area under the precision-recall curve.
 
# Plot Precision-Recall curve for each class
plt.clf()#clf 函数用于清除当前图像窗口
plt.plot(recall["micro"], precision["micro"],
         label='micro-average Precision-recall curve (area = {0:0.2f})'.format(average_precision["micro"]))
for i in range(n_classes):
    plt.plot(recall[i], precision[i],
             label='Precision-recall curve of class {0} (area = {1:0.2f})'.format(i, average_precision[i]))
 
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05]) #xlim、ylim：分别设置X、Y轴的显示范围。
plt.xlabel('Recall', fontsize=16)
plt.ylabel('Precision',fontsize=16)
plt.title('Extension of Precision-Recall curve to multi-class',fontsize=16)
plt.legend(loc="lower right")#legend 是用于设置图例的函数
plt.show()