def show(self):
"""Plot the tree using matplotlib
"""
if self._tree is None:
raise RuntimeError("Estimator not fitted, call `fit` first")
import tree_plotter
tree_plotter.createPlot(self._tree)
import tree_plotter
import matplotlib.patches as mpatch
def retrieveTree(i):
listOfTrees = [{'不浮出水面是否可以生存': {'不能生存': '非鱼类', '可以生存': {'是否有脚蹼': {'没有': '非鱼类', '有': '鱼类'}}}},
{'不浮出水面是否可以生存': {'不能生存': '非鱼类', '可以生存': {'是否有脚蹼': {'没有': {'是否有头': {'没有': '非鱼类', '有': '鱼类'}}, '有': '非鱼类'}}}},
{'是否有脚蹼': {'没有': '非鱼类', '有': {'不浮出水面是否可以生存': {'可以生存': '鱼类', '不能生存': '非鱼类'}}}}]
return listOfTrees[i]
if __name__ == '__main__':
myTree = retrieveTree(1)
tree_plotter.createPlot(myTree)
# -*- coding: utf-8 -*-
'''
作者: 李高俊
版本: 1.0
日期: 2018/11/22/
项目名称： 预测隐形眼睛类型
'''
import numpy as np
import decisiontree_practice as tree_pra
import tree_plotter as tree_plt
"""
步骤：
1. 收集数据：已有数据集lense.txt
2. 准备数据：解析数据集，将其转换为能被程序理解的数据
3. 分析数据：快速检查数据，看看有无错误数据，使用createplot()绘制树形图
4. 训练模型：creattree函数
5. 测试模型：编写测试函数判断函数验证决策树的正误
6. 使用模型：储存树的结构，以便下次使用
"""
fr = open('lenses.txt')
lenses = [inst.strip().split('\t') for inst in fr.readlines()]
lensesLabels = ['age', 'prescript', 'astigmatic', 'tearRate']
lensesTree = tree_pra.creatTree(lenses, lensesLabels)
tree_plt.createPlot(lensesTree)
import decision_tree
import json
import tree_plotter
fr = open(r'/home/zhaoguanyi/PycharmProjects/Decision Tree/watermelon.txt')
listWm = [inst.strip().split('\t') for inst in fr.readlines()] # 读取数据集
print(listWm)
labels = ['色泽', '根蒂', '敲声', '纹理', '脐部', '触感'] # 标签
Trees = decision_tree.createTree(listWm, labels) # 构建决策树
print(json.dumps(Trees, ensure_ascii=False)) # 打印决策树
# 测试
labels = ['色泽', '根蒂', '敲声', '纹理', '脐部', '触感']
for i in range(17):
testData = listWm[i][:6]
print(testData)
testClass = decision_tree.classify(Trees, labels, testData) # 测试
print(json.dumps(testClass, ensure_ascii=False))
tree_plotter.createPlot(Trees) # 可视化决策树
import trees_classifier,tree_plotter
import pprint as pr
dataFile = open('lenses.txt')
l = dataFile.readlines()
dataSet1 = []
labels1=[]
for line in l:
line = line.strip()
line = line.split('\t')
dataSet1.append(line[0:-1])
labels1.append(line[-1])
colNames1 = ['age','preScript','astigmatic?','Tear-Rate']
root=trees_classifier.createTree(dataSet1,labels1,colNames1)
pr.pprint(root)
tree_plotter.createPlot(root)
import my_decision_tree
import tree_plotter
from sklearn import tree
if __name__ == '__main__':
fr = open('lenses.txt')
lenses = [inst.strip().split('\t') for inst in fr.readlines()]
lenses_labels = ['age', 'prescript', 'astigmatic', 'tear_rate']
lenses_tree = my_decision_tree.createTree(lenses, lenses_labels)
print(lenses_tree)
tree_plotter.createPlot(lenses_tree)
# d = {'tear_rate': {'reduced': 'no lenses', 'normal': {'astigmatic': {'no': {'age': {'pre': 'soft', 'young': 'soft',
# 'presbyopic': {'prescript': {'hyper': 'soft', 'myope': 'no lenses'}}}}, 'yes': {'prescript': {'hyper': {'age':
# {'pre': 'no lenses', 'young': 'hard', 'presbyopic': 'no lenses'}}, 'myope': 'hard'}}}}}}
# num = tree_plotter.getNumLeafs(d)
# print(num)
# clf = tree.DecisionTreeClassifier()
# lenses = clf.fit(lenses, lenses_labels)
header_, data_set_, = get_formatted_data(fname)
# create decision tree from the data
decision_tree = create_tree(data_set_, header_)
print('Print Generated Decision Tree in dictionary mode:\n{}\n'.format(
decision_tree))
# making prediction
pred_header = [
'Occupied', 'Price', 'Music', 'Location', 'VIP', 'Favorite Beer'
]
pred_values = ['Moderate', 'Cheap', 'Loud', ' City-Center', ' No', 'No']
result = predict(pred_header, pred_values[:], decision_tree)
print('Prediction mode\nFor params:{}\nResult :{}\n'.format(
pred_values, result))
# print tree in plain text
results = tree_plotter_linear(decision_tree)
print('\nDecision tree in print mode:')
for result in results:
if result: # not print empty lines
print(','.join(result))
# generate and show graph of the tree
print(
'\n\nGraph output: (note that matplotlib part of code was from third party)'
)
import tree_plotter
tree_plotter.createPlot(decision_tree)