更新决策树的内容，做ppt

src/python/03.DecisionTree/DTSklearn.py

@@ -51,7 +51,7 @@ def predict_train(x_train, y_train):
     return y_pre, clf
 
 
-def show_precision_recall(x, clf,  y_train, y_pre):
+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
@@ -110,7 +110,7 @@ if __name__ == '__main__':
     y_pre, clf = predict_train(x_train, y_train)
 
     # 展现 准确率与召回率
-    show_precision_recall(x, clf, y_train, y_pre)
+    show_precision_recall(x, y, clf, y_train, y_pre)
 
     # 可视化输出
     show_pdf(clf)

src/python/03.DecisionTree/DecisionTree.py

@@ -7,9 +7,9 @@ Update on 2017-02-27
 Decision Tree Source Code for Machine Learning in Action Ch. 3
 @author: Peter Harrington/jiangzhonglian
 '''
-from math import log
 import operator
-import DecisionTreePlot as dtPlot
+from math import log
+import decisionTreePlot as dtPlot
 
 
 def createDataSet():
@@ -130,7 +130,9 @@ def chooseBestFeatureToSplit(dataSet):
             prob = len(subDataSet)/float(len(dataSet))
             newEntropy += prob * calcShannonEnt(subDataSet)
         # gain[信息增益] 值越大，意味着该分类提供的信息量越大，该特征对分类的不确定程度越小
+        # 也就说： 列进行group分组后，对应的类别越多，信息量越大，那么香农熵越小，那么信息增益就越大，所以gain越大
         infoGain = baseEntropy - newEntropy
+        # print 'infoGain=', infoGain, 'bestFeature=', i
         if (infoGain > bestInfoGain):
             bestInfoGain = infoGain
             bestFeature = i

src/python/03.DecisionTree/DecisionTreePlot.py

@@ -128,5 +128,5 @@ def retrieveTree(i):
     return listOfTrees[i]
 
 
-myTree = retrieveTree(0)
+myTree = retrieveTree(1)
 createPlot(myTree)

src/python/09.RegTrees/RTSklearn.py

@@ -0,0 +1,50 @@
+#!/usr/bin/python
+# coding:utf8
+
+'''
+Created on 2017-03-10
+Update on 2017-03-10
+author: jiangzhonglian
+content: 回归树
+'''
+
+print(__doc__)
+
+
+# Import the necessary modules and libraries
+import numpy as np
+from sklearn.tree import DecisionTreeRegressor
+import matplotlib.pyplot as plt
+
+
+# Create a random dataset
+rng = np.random.RandomState(1)
+X = np.sort(5 * rng.rand(80, 1), axis=0)
+y = np.sin(X).ravel()
+print X, '\n\n\n-----------\n\n\n', y
+y[::5] += 3 * (0.5 - rng.rand(16))
+
+
+# Fit regression model
+regr_1 = DecisionTreeRegressor(max_depth=2, min_samples_leaf=5)
+regr_2 = DecisionTreeRegressor(max_depth=5, min_samples_leaf=5)
+regr_1.fit(X, y)
+regr_2.fit(X, y)
+
+
+# Predict
+X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
+y_1 = regr_1.predict(X_test)
+y_2 = regr_2.predict(X_test)
+
+
+# Plot the results
+plt.figure()
+plt.scatter(X, y, c="darkorange", label="data")
+plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
+plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
+plt.xlabel("data")
+plt.ylabel("target")
+plt.title("Decision Tree Regression")
+plt.legend()
+plt.show()

src/python/09.RegTrees/treeExplore.py

@@ -7,14 +7,14 @@ Update on 2017-03-08
 Tree-Based Regression Methods Source Code for Machine Learning in Action Ch. 9
 @author: jiangzhonglian
 '''
+import regTrees
 from Tkinter import *
 from numpy import *
-import regTrees
 
 import matplotlib
-matplotlib.use('TkAgg')
-from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
 from matplotlib.figure import Figure
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+matplotlib.use('TkAgg')
 
 
 def test_widget_text(root):