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DTSklearn.py
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117 lines (97 loc) · 3.92 KB
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#!/usr/bin/python
# coding: utf8
# 原始链接： http://blog.csdn.net/lsldd/article/details/41223147
# 《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
import numpy as np
from sklearn import tree
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import classification_report
from sklearn.cross_validation import train_test_split
def createDataSet():
''' 数据读入 '''
data = []
labels = []
with open("input/3.DecisionTree/data.txt") as ifile:
for line in ifile:
# 特征： 身高 体重 label： 胖瘦
tokens = line.strip().split(' ')
data.append([float(tk) for tk in tokens[:-1]])
labels.append(tokens[-1])
# 特征数据
x = np.array(data)
# label分类的标签数据
labels = np.array(labels)
# 预估结果的标签数据
y = np.zeros(labels.shape)
''' 标签转换为0/1 '''
y[labels == 'fat'] = 1
print data, '-------', x, '-------', labels, '-------', y
return x, y
def predict_train(x_train, y_train):
'''
使用信息熵作为划分标准，对决策树进行训练
参考链接： http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier
'''
clf = tree.DecisionTreeClassifier(criterion='entropy')
# print(clf)
clf.fit(x_train, y_train)
''' 系数反映每个特征的影响力。越大表示该特征在分类中起到的作用越大 '''
print 'feature_importances_: %s' % clf.feature_importances_
'''测试结果的打印'''
y_pre = clf.predict(x_train)
# print(x_train)
print(y_pre)
print(y_train)
print(np.mean(y_pre == y_train))
return y_pre, clf
def show_precision_recall(x, y, clf, y_train, y_pre):
'''
准确率与召回率
参考链接： http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve
'''
precision, recall, thresholds = precision_recall_curve(y_train, y_pre)
# 计算全量的预估结果
answer = clf.predict_proba(x)[:, 1]
'''
展现 准确率与召回率
precision 准确率
recall 召回率
f1-score 准确率和召回率的一个综合得分
support 参与比较的数量
参考链接：http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report
'''
# target_names 以 y的label分类为准
target_names = ['thin', 'fat']
print(classification_report(y, answer, target_names=target_names))
print(answer)
print(y)
def show_pdf(clf):
'''
可视化输出
把决策树结构写入文件: http://sklearn.lzjqsdd.com/modules/tree.html
Mac报错：pydotplus.graphviz.InvocationException: GraphViz's executables not found
解决方案：sudo brew install graphviz
参考写入： http://www.jianshu.com/p/59b510bafb4d
'''
# with open("testResult/tree.dot", 'w') as f:
# from sklearn.externals.six import StringIO
# tree.export_graphviz(clf, out_file=f)
import pydotplus
from sklearn.externals.six import StringIO
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("output/3.DecisionTree/tree.pdf")
# from IPython.display import Image
# Image(graph.create_png())
if __name__ == '__main__':
x, y = createDataSet()
''' 拆分训练数据与测试数据， 80%做训练 20%做测试 '''
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
print '拆分数据：', x_train, x_test, y_train, y_test
# 得到训练的预测结果集
y_pre, clf = predict_train(x_train, y_train)
# 展现 准确率与召回率
show_precision_recall(x, y, clf, y_train, y_pre)
# 可视化输出
show_pdf(clf)