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Author: wzy6642

DecisionTree_Project2/DecisionTree.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jul 17 21:52:06 2018
+数据的Labels依次是age、prescript、astigmatic、tearRate、class
+年龄、症状、是否散光、眼泪数量、分类标签
+@author: wzy
+"""
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+import pandas as pd
+import numpy as np
+import pydotplus
+from sklearn.externals.six import StringIO
+from sklearn import tree
+
+if __name__ == '__main__':
+    # 加载文件
+    with open('lenses.txt') as fr:
+        # 处理文件，去掉每行两头的空白符，以\t分隔每个数据
+        lenses = [inst.strip().split('\t') for inst in fr.readlines()]
+    # 提取每组数据的类别，保存在列表里
+    lenses_targt = []
+    for each in lenses:
+        # 存储Label到lenses_targt中
+        lenses_targt.append([each[-1]])
+    # 特征标签
+    lensesLabels = ['age', 'prescript', 'astigmatic', 'tearRate']
+    # 保存lenses数据的临时列表
+    lenses_list = []
+    # 保存lenses数据的字典，用于生成pandas
+    lenses_dict = {}
+    # 提取信息，生成字典
+    for each_label in lensesLabels:
+        for each in lenses:
+            # index方法用于从列表中找出某个值第一个匹配项的索引位置
+            lenses_list.append(each[lensesLabels.index(each_label)])
+        lenses_dict[each_label] = lenses_list
+        lenses_list = []
+    # 打印字典信息
+    # print(lenses_dict)
+    # 生成pandas.DataFrame用于对象的创建
+    lenses_pd = pd.DataFrame(lenses_dict)
+    # 打印数据
+    # print(lenses_pd)
+    # 创建LabelEncoder对象
+    le = LabelEncoder()
+    # 为每一列序列化
+    for col in lenses_pd.columns:
+        # fit_transform()干了两件事：fit找到数据转换规则，并将数据标准化
+        # transform()直接把转换规则拿来用,需要先进行fit
+        # transform函数是一定可以替换为fit_transform函数的，fit_transform函数不能替换为transform函数
+        lenses_pd[col] = le.fit_transform(lenses_pd[col])
+    # 打印归一化的结果
+    # print(lenses_pd)
+    # 创建DecisionTreeClassifier()类
+    clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=4)
+    # 使用数据构造决策树
+    # fit(X,y):Build a decision tree classifier from the training set(X,y)
+    # 所有的sklearn的API必须先fit
+    clf = clf.fit(lenses_pd.values.tolist(), lenses_targt)
+    dot_data = StringIO()
+    # 绘制决策树
+    tree.export_graphviz(clf, out_file=dot_data, feature_names=lenses_pd.keys(),
+                         class_names=clf.classes_, filled=True, rounded=True, 
+                         special_characters=True)
+    graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
+    # 保存绘制好的决策树，以PDF的形式存储。
+    graph.write_pdf("tree.pdf")
+    #预测
+    print(clf.predict([[1,1,1,0]]))      
+              

DecisionTree_Project2/lenses.txt

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+young	myope	no	reduced	no lenses
+young	myope	no	normal	soft
+young	myope	yes	reduced	no lenses
+young	myope	yes	normal	hard
+young	hyper	no	reduced	no lenses
+young	hyper	no	normal	soft
+young	hyper	yes	reduced	no lenses
+young	hyper	yes	normal	hard
+pre	myope	no	reduced	no lenses
+pre	myope	no	normal	soft
+pre	myope	yes	reduced	no lenses
+pre	myope	yes	normal	hard
+pre	hyper	no	reduced	no lenses
+pre	hyper	no	normal	soft
+pre	hyper	yes	reduced	no lenses
+pre	hyper	yes	normal	no lenses
+presbyopic	myope	no	reduced	no lenses
+presbyopic	myope	no	normal	no lenses
+presbyopic	myope	yes	reduced	no lenses
+presbyopic	myope	yes	normal	hard
+presbyopic	hyper	no	reduced	no lenses
+presbyopic	hyper	no	normal	soft
+presbyopic	hyper	yes	reduced	no lenses
+presbyopic	hyper	yes	normal	no lenses