iris 数据分类

• 花匠小妙招
• 2024-11-10 20:25

  转载：

#coding=utf-8

"""

#演示目的：利用鸢尾花数据集画出P-R曲线,mooc

"""

print(__doc__)

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.model_selection import KFold

from sklearn.model_selection import train_test_split

#from sklearn.cross_validation import train_test_split

from sklearn.preprocessing import label_binarize

from sklearn.multiclass import OneVsRestClassifier

#以iris数据为例，画出P-R曲线

iris =datasets.load_iris()

x =iris.data

y =iris.target

#print(x)

print(x.shape,y.shape)

y=label_binarize(y,classes=[0,1,2])

n_classes=y.shape[1]

print(y.shape)

'''

#print(y)

print(x.shape,y.shape)

'''

random_state=np.random.RandomState(0)

#print(random_state)

n_samples,n_features=x.shape# 分别是行数和列数

print("n_samples:",n_samples,"n_features:",n_features)

x=np.c_[x,random_state.randn(n_samples,200*n_features)] # The different of randn and random

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.5,random_state=random_state)

#print("x_train",x_train,"x_test:",x_test)

classifier = OneVsRestClassifier(svm.SVC(kernel='linear',probability=True,random_state=random_state))

y_socre = classifier.fit(x_train,y_train).decision_function(x_test)

#print("classifier:",classifier)

#print("y_socre:",y_socre)

precision=dict()

recall=dict()

average_precision=dict()

for i in range(n_classes):

precision[i],recall[i],_= precision_recall_curve(y_test[:,i],y_socre[:,i])

average_precision[i]= average_precision_score(y_test[:,i],y_socre[:,i])

precision["micro"],recall["micro"],_=precision_recall_curve(y_test.ravel(),y_socre.ravel())

average_precision["micro"] =average_precision_score(y_test,y_socre,average="micro")

plt.clf()

plt.plot(recall["micro"],precision["micro"],label='micro-average Precision-recall'.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.legend(loc="lower right")#legend 是用于设置图例的函数

plt.show()

https://www.icourse163.org/learn/HIT-1206320802?tid=1206635203#/learn/content?type=detail&id=1212706083&sm=1

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