python3.4之決策樹

#!/usr/bin/env python
# coding=utf-8

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
import pydot
from sklearn.externals.six import StringIO

def loadDataSet():
    data = []
    label = []
    with open('D:python/fat.txt') as file:
        for line in file:
            tokens = line.strip().split(' ')
            data.append([float(tk) for tk in tokens[:-1]])
            label.append(tokens[-1])
    x = np.array(data)
    print('x:')
    print(x)
    label = np.array(label)
    y = np.zeros(label.shape)
    y[label == 'fat'] = 1
    print('y:')
    print(y)
    return x, y

def decisionTreeClf():
    x, y = loadDataSet()

    # 拆分資料集和訓練集
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
    print('x_train:');
    print(x_train)
    print('x_test:');
    print(x_test)
    print('y_train:');
    print(y_train)
    print('y_test:');
    print(y_test)
    # 使用資訊熵作為劃分標準
    clf = tree.DecisionTreeClassifier(criterion='entropy')
    print(clf)
    clf.fit(x_train, y_train)
    dot_data = StringIO() 
    with open("iris.dot", 'w') as f: 
        f=tree.export_graphviz(clf, out_file=f)
        tree.export_graphviz(clf, out_file=dot_data)
        graph = pydot.graph_from_dot_data(dot_data.getvalue())  
        graph[0].write_pdf("ex.pdf")  
#         Image(graph.create_png())
    # 列印特徵在分類起到的作用性
    print(clf.feature_importances_)

    # 列印測試結果
    answer = clf.predict(x_train)
    print('x_train:')
    print(x_train)
    print('answer:')
    print(answer)
    print('y_train:')
    print(y_train)
    print('計算正確率：')
    print(np.mean(answer == y_train))

    # 準確率與召回率
    precision, recall, thresholds = precision_recall_curve(y_train, clf.predict(x_train)
)
    answer = clf.predict_proba(x)[:, 1]
    print(classification_report(y, answer, target_names=['thin', 'fat']))

decisionTreeClf()
# print('ll')

資料集fat.txt檔案內容如下：

1.5 50 thin
1.5 60 fat
1.6 40 thin
1.6 60 fat
1.7 60 thin
1.7 80 fat
1.8 60 thin
1.8 90 fat
1.9 70 thin
1.9 80 fat

所需要的python包有：
pygraphviz (1.3.1)
pyparsing (2.1.10)
scikit-learn (0.18.1)
pygraphviz (1.3.1)包是視覺化包。
下載視覺化工具：
graphviz-2.38.msi
百度搜尋安裝視覺化工具。