利用鸢尾花数据集绘制P-R曲线

#利用鸢尾花数据集绘制P-R曲线
print(__doc__)      #打印注释
 
import matplotlib.pyplot as plt
import numpy as np
from sklearn import svm, datasets
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier  #一对其余（每次将一个类作为正类，剩下的类作为负类）
 
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
 
#以iris数据为例，画出P-R曲线
iris = datasets.load_iris()
X = iris.data    #150*4
y = iris.target  #150*1
 
# 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])    #将150*1转化成150*3
n_classes = y.shape[1]                      #列的个数，等于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
 
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]   #行不变，只增加了列，150*804
 
# 训练集和测试集拆分，比例为0.5
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
 
# 一对其余，转换成两类，构建新的分类器
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True, random_state=random_state))
#训练集送给fit函数进行拟合训练，训练完后将测试集的样本特征注入，得到测试集中每个样本预测的分数
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
 
# 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):
    #对于每一类，计算精确率和召回率的序列（:表示所有行，i表示第i列）
    precision[i], recall[i], _ = precision_recall_curve(y_test[:, i],  y_score[:, i]) 
    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()

运行结果如下：