update forks

.gitignore

@@ -1,9 +1,11 @@
 ### OSX ###
 # General
 .DS_Store
+.gitignore
 .AppleDouble
 .LSOverride
-
+*.xml
+*.iml
 # Icon must end with two \r
 Icon
 

numpy_ml/trees/cart_tree_imp.py

@@ -0,0 +1,96 @@
+from numpy import *
+
+
+# 载入数据
+def loadDataSet(fileName):
+ dataMat = []
+ fr = open(fileName)
+ for line in fr.readlines():
+ curLine = line.strip().split('\t')
+ # python3不适用：fltLine = map(float,curLine) 修改为：
+ fltLine = list(map(float, curLine)) # 将每行映射成浮点数，python3返回值改变，所以需要
+ dataMat.append(fltLine)
+ return dataMat
+
+
+# 切分数据集为两个子集
+def binSplitDataSet(dataSet, feature, value): # 数据集 待切分特征 特征值
+ mat0 = dataSet[nonzero(dataSet[:, feature] > value)[0], :]
+ mat1 = dataSet[nonzero(dataSet[:, feature] <= value)[0], :]
+ # 下面原书代码报错 index 0 is out of bounds,使用上面两行代码
+ # mat0 = dataSet[nonzero(dataSet[:, feature] > value)[0], :][0]
+ # mat1 = dataSet[nonzero(dataSet[:, feature] <= value)[0], :][0]
+ return mat0, mat1
+
+
+# Tree结点类型：回归树
+def regLeaf(dataSet): # 生成叶结点，在回归树中是目标变量特征的均值
+ return mean(dataSet[:, -1])
+
+
+# 误差计算函数：回归误差
+def regErr(dataSet): # 计算目标的平方误差（均方误差*总样本数）
+ return var(dataSet[:, -1]) * shape(dataSet)[0]
+
+
+# 二元切分
+def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(0, 1)):
+ # 切分特征的参数阈值，用户初始设置好
+ tolS = ops[0] # 允许的误差下降值
+ tolN = ops[1] # 切分的最小样本数
+ # 若所有特征值都相同，停止切分
+ featureNum = len(set(dataSet[:, -1].T.tolist()[0]))
+ if len(set(dataSet[:, -1].T.tolist()[0])) == 1: # 倒数第一列转化成list 不重复
+ return None, leafType(dataSet) # 如果剩余特征数为1，停止切分1。
+ # 找不到好的切分特征，调用regLeaf直接生成叶结点
+ m, n = shape(dataSet)
+ S = errType(dataSet) # 最好的特征通过计算平均误差
+ bestS = inf
+ bestIndex = 0
+ bestValue = 0
+ for featIndex in range(n - 1): # 遍历数据的每个属性特征
+ # for splitVal in set(dataSet[:,featIndex]): python3报错修改为下面
+ for splitVal in set((dataSet[:, featIndex].T.A.tolist())[0]): # 遍历每个特征里不同的特征值
+ mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal) # 对每个特征进行二元分类
+ if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): continue
+ newS = errType(mat0) + errType(mat1)
+ if newS < bestS: # 更新为误差最小的特征
+ bestIndex = featIndex
+ bestValue = splitVal
+ bestS = newS
+ # 如果切分后误差效果下降不大，则取消切分，直接创建叶结点
+ if (S - bestS) < tolS:
+ return None, leafType(dataSet) # 停止切分2
+ mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue)
+ # 判断切分后子集大小，小于最小允许样本数停止切分3
+ if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN):
+ return None, leafType(dataSet)
+ return bestIndex, bestValue # 返回特征编号和用于切分的特征值
+
+
+# 构建tree
+def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(0, 1)):
+ # 数据集默认NumPy Mat 其他可选参数【结点类型：回归树，误差计算函数，ops包含树构建所需的其他元组】
+ feat, val = chooseBestSplit(dataSet, leafType, errType, ops)
+ if feat == None: return val # 满足停止条件时返回叶结点值
+ # 切分后赋值
+ retTree = {}
+ retTree['spInd'] = feat
+ retTree['spVal'] = val
+ # 切分后的左右子树
+ lSet, rSet = binSplitDataSet(dataSet, feat, val)
+ retTree['left'] = createTree(lSet, leafType, errType, ops)
+ retTree['right'] = createTree(rSet, leafType, errType, ops)
+ return retTree
+
+
+if __name__ == "__main__":
+ myDat = mat(loadDataSet('train_data'))
+ print(createTree(myDat))
+
+ # 绘制数据点图
+ import matplotlib.pyplot as plt
+
+ plt.plot(myDat[:, 0], myDat[:, 1], 'ro')
+ plt.show()
+

numpy_ml/trees/scikit_learn_tree.py

@@ -0,0 +1,40 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.tree import DecisionTreeRegressor
+from sklearn import linear_model
+
+# Data set，skl官方代码给出样例
+x = np.array(list(range(1, 11))).reshape(-1, 1)
+y = np.array([5.56, 5.70, 5.91, 6.40, 6.80, 7.05, 8.90, 8.70, 9.00, 9.05]).ravel()
+
+# Fit regression model 和手工计算的一样，是个三段函数
+# x≤3.5 5.72
+# 3.5⩽x≤6.5 6.75
+# 6.5 < x 8.91
+
+model1 = DecisionTreeRegressor(max_depth=1)
+model2 = DecisionTreeRegressor(max_depth=3, max_leaf_nodes=4, min_samples_leaf=3)
+model3 = linear_model.LinearRegression()
+model1.fit(x, y)
+model2.fit(x, y)
+model3.fit(x, y)
+
+# Predict
+X_test = np.arange(0.0, 10.0, 0.01)[:, np.newaxis]
+y_1 = model1.predict(X_test)
+y_2 = model2.predict(X_test)
+y_3 = model3.predict(X_test)
+
+# Plot the results
+plt.figure()
+plt.scatter(x, y, s=20, edgecolor="black",
+ c="darkorange", label="data")
+plt.plot(X_test, y_1, color="cornflowerblue",
+ label="max_depth=1", linewidth=2)
+plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=3", linewidth=2)
+plt.plot(X_test, y_3, color='red', label='liner regression', linewidth=2)
+plt.xlabel("data")
+plt.ylabel("target")
+plt.title("Decision Tree Regression")
+plt.legend()
+plt.show()

numpy_ml/trees/train_data

@@ -0,0 +1,10 @@
+1 4.50
+2 4.75
+3 4.91
+4 5.34
+5 5.80
+6 7.05
+7 7.90
+8 8.23
+9 8.70
+10 9.00