first virsion

WenDesi · WenDesi · commit 75d31f732ece · 2016-10-18T12:52:49.000+08:00
diff --git a/decision_tree/decision_tree.py b/decision_tree/decision_tree.py
@@ -0,0 +1,242 @@
+#encoding=utf-8
+
+import cv2
+import time
+import math
+import numpy as np
+import pandas as pd
+
+
+from sklearn.cross_validation import train_test_split
+from sklearn.metrics import accuracy_score
+
+total_class = 10
+
+# 二值化
+def binaryzation(img):
+    cv_img = img.astype(np.uint8)
+    cv2.threshold(cv_img,50,1,cv2.cv.CV_THRESH_BINARY_INV,cv_img)
+    return cv_img
+
+def binaryzation_features(trainset):
+    features = []
+
+    for img in trainset:
+        img = np.reshape(img,(28,28))
+        cv_img = img.astype(np.uint8)
+
+        img_b = binaryzation(cv_img)
+        # hog_feature = np.transpose(hog_feature)
+        features.append(img_b)
+
+    features = np.array(features)
+    features = np.reshape(features,(-1,784))
+
+    return features
+
+
+class Tree(object):
+    def __init__(self,node_type,Class = None, feature = None):
+        self.node_type = node_type
+        self.dict = {}
+        self.Class = Class
+        self.feature = feature
+
+    def add_tree(self,val,tree):
+        self.dict[val] = tree
+
+    def predict(self,features):
+        if self.node_type == 'leaf':
+            return self.Class
+
+        print 'in'
+
+        tree = self.dict[features[self.feature]]
+        return tree.predict(features)
+
+def calc_ent(x):
+    """
+        calculate shanno ent of x
+    """
+
+    x_value_list = set([x[i] for i in range(x.shape[0])])
+    ent = 0.0
+    for x_value in x_value_list:
+        p = float(x[x == x_value].shape[0]) / x.shape[0]
+        logp = np.log2(p)
+        ent -= p * logp
+
+    return ent
+
+def calc_condition_ent(x, y):
+    """
+        calculate ent H(y|x)
+    """
+
+    # calc ent(y|x)
+    x_value_list = set([x[i] for i in range(x.shape[0])])
+    ent = 0.0
+    for x_value in x_value_list:
+        sub_y = y[x == x_value]
+        temp_ent = calc_ent(sub_y)
+        ent += (float(sub_y.shape[0]) / y.shape[0]) * temp_ent
+
+    return ent
+
+def calc_ent_grap(x,y):
+    """
+        calculate ent grap
+    """
+
+    base_ent = calc_ent(y)
+    condition_ent = calc_condition_ent(x, y)
+    ent_grap = base_ent - condition_ent
+
+    return ent_grap
+
+def train(train_set,train_label,features,epsilon):
+    global total_class
+
+    LEAF = 'leaf'
+    INTERNAL = 'internal'
+
+
+    # 步骤1——如果train_set中的所有实例都属于同一类Ck
+    label_dict = [0 for i in xrange(total_class)]
+    for label in train_label:
+        label_dict[label] += 1
+
+    for label, label_count in enumerate(label_dict):
+        if label_count == len(train_label):
+            tree = Tree(LEAF,Class = label)
+            return tree
+
+    # 步骤2——如果features为空
+    max_len,max_class = 0,0
+    for i in xrange(total_class):
+        class_i = filter(lambda x:x==i,train_label)
+        if len(class_i) > max_len:
+            max_class = i
+            max_len = len(class_i)
+
+    if len(features) == 0:
+        tree = Tree(LEAF,Class = max_class)
+        return tree
+
+    # 步骤3——计算信息增益
+    max_feature = 0
+    max_gda = 0
+
+    D = train_label
+    HD = calc_ent(D)
+    for feature in features:
+        A = np.array(train_set[:,feature].flat)
+        gda = HD - calc_condition_ent(A,D)
+
+        if gda > max_gda:
+            max_gda,max_feature = gda,feature
+
+    # 步骤4——小于阈值
+    if max_gda < epsilon:
+        tree = Tree(LEAF,Class = max_class)
+        return tree
+
+    # 步骤5——构建非空子集
+    sub_features = filter(lambda x:x!=max_feature,features)
+    tree = Tree(INTERNAL,feature=max_feature)
+
+    feature_col = np.array(train_set[:,max_feature].flat)
+    feature_value_list = set([feature_col[i] for i in range(feature_col.shape[0])])
+    for feature_value in feature_value_list:
+
+        index = []
+        for i in xrange(len(train_label)):
+            if train_set[i][max_feature] == feature_value:
+                index.append(i)
+
+        sub_train_set = train_set[index]
+        sub_train_label = train_label[index]
+
+        sub_tree = train(sub_train_set,sub_train_label,sub_features,epsilon)
+        tree.add_tree(feature_value,sub_tree)
+
+    return tree
+
+def predict(test_set,tree):
+
+    result = []
+    for features in test_set:
+        tmp_predict = tree.predict(features)
+        result.append(tmp_predict)
+    return np.array(result)
+
+
+
+if __name__ == '__main__':
+    # classes = [0,0,1,1,0,0,0,1,1,1,1,1,1,1,0]
+    #
+    # age = [0,0,0,0,0,1,1,1,1,1,2,2,2,2,2]
+    # occupation = [0,0,1,1,0,0,0,1,0,0,0,0,1,1,0]
+    # house = [0,0,0,1,0,0,0,1,1,1,1,1,0,0,0]
+    # loan = [0,1,1,0,0,0,1,1,2,2,2,1,1,2,0]
+    #
+    # features = []
+    #
+    # for i in range(15):
+    #     feature = [age[i],occupation[i],house[i],loan[i]]
+    #     features.append(feature)
+    #
+    # trainset = np.array(features)
+    #
+    # tree = train(trainset,np.array(classes),[0,1,2,3],0.1)
+    #
+    # print type(tree)
+    # features = [0,0,0,1]
+    # print tree.predict(np.array(features))
+
+
+    print 'Start read data'
+
+    time_1 = time.time()
+
+    raw_data = pd.read_csv('../data/train.csv',header=0)
+    data = raw_data.values
+
+    imgs = data[0::,1::]
+    labels = data[::,0]
+
+    features = binaryzation_features(imgs)
+
+    # 选取 2/3 数据作为训练集， 1/3 数据作为测试集
+    train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33, random_state=23323)
+    # print train_features.shape
+    # print train_features.shape
+
+    time_2 = time.time()
+    print 'read data cost ',time_2 - time_1,' second','\n'
+
+    print 'Start training'
+    tree = train(train_features,train_labels,[i for i in range(784)],0.2)
+    print type(tree)
+    print 'knn do not need to train'
+    time_3 = time.time()
+    print 'training cost ',time_3 - time_2,' second','\n'
+
+    print 'Start predicting'
+    test_predict = predict(test_features,tree)
+    time_4 = time.time()
+    print 'predicting cost ',time_4 - time_3,' second','\n'
+
+    score = accuracy_score(test_labels,test_predict)
+    print "The accruacy socre is ", score
+
+
+
+
+
+
+
+
+
+
+
