pyhon实现决策树（ID3）算法进行数据的分类预测

本文参考了这篇博客和这篇博客  还有这篇 十分感谢

说明： 由于平时接触到的数据既有离散值也有连续值 所以该算法直接针对两种类型的数据都做了处理，另外划分属性采用的是熵最大原则，决策树实现时很容易过拟合 所以需要进行剪枝处理（此处未进行剪枝）

关于ID3算法进行分类预测的原理可以参考 这篇博客，不再赘述

主要写一下具体实现

我用的是老师给的数据全都是连续型数据，.xlsx文件直接改成扩展名.csv貌似程序会运行出错，具体可以参考 这篇，也就是另存为就可以

数据集    https://pan.baidu.com/s/1slul1Bv 密码: n8vf

以下由python实现 环境python3.0 (假装你已经安装了必要的包) 建议使用Anaconda安装，比较简单 代码是在上面几篇博客的基础上做了一点修改（主要是出错啊要不然谁改啊）

# -*- coding: utf-8 -* ''''' create on 2017/3/24, the day after our national football team beat south korea @author: PY131 ''''' #http://blog.csdn.net/snoopy_yuan/article/details/68959025 ''' definition of decision node class attr: attribution as parent for a new branching attr_down: dict: {key, value} key: categoric: categoric attr_value continuous: '<= div_value' for small part '> div_value' for big part value: children (Node class) label： class label (the majority of current sample labels) ''' class Node(object): def __init__(self, attr_init=None, label_init=None, attr_down_init={} ): self.attr = attr_init self.label = label_init self.attr_down = attr_down_init ''' Branching for decision tree using recursion @param df: the pandas dataframe of the data_set @return root: Node, the root node of decision tree ''' def TreeGenerate(df): # generating a new root node new_node = Node(None, None, {}) label_arr = df[df.columns[-1]] label_count = NodeLabel(label_arr) if label_count: # assert the label_count isn's empty new_node.label= max(label_count, key=label_count.get) # end if there is only 1 class in current node data # end if attribution array is empty if len(label_count) == 1 or len(label_arr) == 0: return new_node # get the optimal attribution for a new branching new_node.attr, div_value = OptAttr(df) # recursion if div_value == 0: # categoric variable value_count = ValueCount(df[new_node.attr]) for value in value_count: df_v = df[ df[new_node.attr].isin([value]) ] # get sub set # delete current attribution df_v = df_v.drop(new_node.attr, 1) new_node.attr_down[value] = TreeGenerate(df_v) else: # continuous variable # left and right child value_l = "<=%.3f" % div_value value_r = ">%.3f" % div_value df_v_l = df[ df[new_node.attr] <= div_value ] # get sub set df_v_r = df[ df[new_node.attr] > div_value ] new_node.attr_down[value_l] = TreeGenerate(df_v_l) new_node.attr_down[value_r] = TreeGenerate(df_v_r) return new_node ''' make a predict based on root @param root: Node, root Node of the decision tree @param df_sample: dataframe, a sample line ''' def Predict(root, df_sample): try : import re # using Regular Expression to get the number in string except ImportError : print("module re not found") while root.attr != None : # continuous variable #if df_sample[root.attr].dtype == (float, int): if df_sample[root.attr].dtype == 'float' or df_sample[root.attr].dtype =='int': # get the div_value from root.attr_down for key in list(root.attr_down): num = re.findall(r"\d+\.?\d*",key) div_value = float(num[0]) break if df_sample[root.attr].values[0] <= div_value: key = "<=%.3f" % div_value root = root.attr_down[key] else: key = ">%.3f" % div_value root = root.attr_down[key] # categoric variable else: key = df_sample[root.attr].values[0] # check whether the attr_value in the child branch if key in root.attr_down: root = root.attr_down[key] else: break return root.label ''' calculating the appeared label and it's counts @param label_arr: data array for class labels @return label_count: dict, the appeared label and it's counts ''' def NodeLabel(label_arr): label_count = {} # store count of label for label in label_arr: if label in label_count: label_count[label] += 1 else: label_count[label] = 1 return label_count ''' calculating the appeared value for categoric attribute and it's counts @param data_arr: data array for an attribute @return value_count: dict, the appeared value and it's counts ''' def ValueCount(data_arr): value_count = {} # store count of value for label in data_arr: if label in value_count: value_count[label] += 1 else: value_count[label] = 1 return value_count ''' find the optimal attributes of current data_set @param df: the pandas dataframe of the data_set @return opt_attr: the optimal attribution for branch @return div_value: for discrete variable value = 0 for continuous variable value = t for bisection divide value ''' def OptAttr(df): info_gain = 0 for attr_id in df.columns[1:-1]: info_gian_tmp, div_value_tmp = InfoGain(df, attr_id) if info_gian_tmp > info_gain : info_gain = info_gian_tmp opt_attr = attr_id div_value = div_value_tmp return opt_attr, div_value ''' calculating the information gain of an attribution @param df: dataframe, the pandas dataframe of the data_set @param attr_id: the target attribution in df @return info_gain: the information gain of current attribution @return div_value: for discrete variable, value = 0 for continuous variable, value = t (the division value) ''' def InfoGain(df, index): info_gain = InfoEnt(df.values[:,-1]) # info_gain for the whole label div_value = 0 # div_value for continuous attribute n = len(df[index]) # the number of sample # 1.for continuous variable using method of bisection #if df[index].dtype == (float, int) if df[index].dtype == 'float' or df[index].dtype == 'int': sub_info_ent = {} # store the div_value (div) and it's subset entropy #df = df.sort([index], ascending=1) # sorting via column df = df.sort_values([index], ascending=1) # sorting via column df = df.reset_index(drop=True) data_arr = df[index] label_arr = df[df.columns[-1]] for i in range(n-1): div = (data_arr[i] + data_arr[i+1]) / 2 sub_info_ent[div] = ( (i+1) * InfoEnt(label_arr[0:i+1]) / n ) + ( (n-i-1) * InfoEnt(label_arr[i+1:-1]) / n ) # our goal is to get the min subset entropy sum and it's divide value div_value, sub_info_ent_max = min(sub_info_ent.items(), key=lambda x: x[1]) info_gain -= sub_info_ent_max # 2.for discrete variable (categoric variable) else: data_arr = df[index] label_arr = df[df.columns[-1]] value_count = ValueCount(data_arr) for key in value_count: key_label_arr = label_arr[data_arr == key] info_gain -= value_count[key] * InfoEnt(key_label_arr) / n return info_gain, div_value ''' calculating the information entropy of an attribution @param label_arr: ndarray, class label array of data_arr @return ent: the information entropy of current attribution ''' def InfoEnt(label_arr): try : from math import log2 except ImportError : print("module math.log2 not found") ent = 0 n = len(label_arr) label_count = NodeLabel(label_arr) for key in label_count: ent -= ( label_count[key] / n ) * log2( label_count[key] / n ) return ent def DrawPNG(root, out_file): ''' visualization of decision tree from root. @param root: Node, the root node for tree. @param out_file: str, name and path of output file ''' try: from pydotplus import graphviz except ImportError: print("module pydotplus.graphviz not found") g = graphviz.Dot() # generation of new dot TreeToGraph(0, g, root) g2 = graphviz.graph_from_dot_data( g.to_string() ) g2.write_png(out_file) def TreeToGraph(i, g, root): ''' build a graph from root on @param i: node number in this tree @param g: pydotplus.graphviz.Dot() object @param root: the root node @return i: node number after modified # @return g: pydotplus.graphviz.Dot() object after modified @return g_node: the current root node in graphviz ''' try: from pydotplus import graphviz except ImportError: print("module pydotplus.graphviz not found") if root.attr == None: g_node_label = "Node:%d\n好瓜:%s" % (i, root.label) else: g_node_label = "Node:%d\n好瓜:%s\n属性:%s" % (i, root.label, root.attr) g_node = i g.add_node( graphviz.Node( g_node, label = g_node_label ) ) for value in list(root.attr_down): i, g_child = TreeToGraph(i+1, g, root.attr_down[value]) g.add_edge( graphviz.Edge(g_node, g_child, label = value) ) return i, g_node '''~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~''' '''~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~''' '''~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~''' '''this is in console by xuanxuan''' ''' import data and pre-analysis through data visualization ''' # using pandas dataframe for .csv read which contains chinese char. import pandas as pd #这一句看数据集的情况再定 中文的 '''data_file_encode = "gb18030" # the watermelon_3.csv is file codec type ''' #with open("../data/watermelon_3.csv", mode = 'r', encoding = data_file_encode) as data_file: #这一句也是根据数据集的情况再定 中文的 '''with open("watermelon_3.csv", mode = 'r', encoding = data_file_encode) as data_file: ''' #这一句是根据数据集合是英文的 with open("watermelon3_1_En.csv", mode = 'r') as data_file: df = pd.read_csv(data_file) # using seaborn for data visualization. # # load chinese font # import matplotlib as mpl # import matplotlib.pyplot as plt # import seaborn as sns # # sns.set(style="whitegrid", color_codes=True) # mpl.rcParams['font.sans-serif'] = ['Droid Sans Fallback'] # for chinese chararter visualization # mpl.rcParams['axes.unicode_minus'] = False # sns.set_context("poster") # # f1 = plt.figure(1) # sns.FacetGrid(df, hue="好瓜", size=5).map(plt.scatter, "密度", "含糖率").add_legend() # sns.plt.show() # # f2 = plt.figure(2) # sns.plt.subplot(221) # sns.swarmplot(x = "纹理", y = '密度', hue = "好瓜", data = df) # sns.plt.subplot(222) # sns.swarmplot(x = "敲声", y = '密度', hue = "好瓜", data = df) # sns.plt.subplot(223) # sns.swarmplot(x = "色泽", y = '含糖率', hue = "好瓜", data = df) # sns.plt.subplot(224) # sns.swarmplot(x = "敲声", y = '含糖率', hue = "好瓜", data = df) # sns.plt.show() ''' implementation of ID3 rely on decision_tree.py ''' import decision_tree root = decision_tree.TreeGenerate(df) # df = df.drop(['密度','含糖率'], 1) # df = df.drop(['色泽','根蒂','敲声','纹理','脐部','触感'], 1) accuracy_scores = [] ''' from random import sample for i in range(10): train = sample(range(len(df.index)), int(1*len(df.index)/2)) df_train = df.iloc[train] df_test = df.drop(train) # generate the tree root = decision_tree.TreeGenerate(df_train) # test the accuracy pred_true = 0 for i in df_test.index: label = decision_tree.Predict(root, df[df.index == i]) if label == df_test[df_test.columns[-1]][i]: pred_true += 1 accuracy = pred_true / len(df_test.index) accuracy_scores.append(accuracy) ''' # k-folds cross prediction n = len(df.index) k = 40 for i in range(k): m = int(n/k) test = [] for j in range(i*m, i*m+m): test.append(j) df_train = df.drop(test) df_test = df.iloc[test] root = decision_tree.TreeGenerate(df_train) # generate the tree # test the accuracy pred_true = 0 for i in df_test.index: label = decision_tree.Predict(root, df[df.index == i]) if label == df_test[df_test.columns[-1]][i]: pred_true += 1 accuracy = pred_true / len(df_test.index) accuracy_scores.append(accuracy) # print the prediction accuracy result accuracy_sum = 0 print("accuracy: ", end = "") for i in range(k): print("%.3f " % accuracy_scores[i], end = "") accuracy_sum += accuracy_scores[i] print("\naverage accuracy: %.3f" % (accuracy_sum/k)) # dicision tree visualization using pydotplus.graphviz

由于没做剪枝 所以该模型测试之后效果不是很好 总共75个数据 （哦对了 数据其实可以是中文的 对此做了处理 只要改一下编码 ’gb18030‘就可以)拿55个训练建立模型 20个进行测试 准确率是56.7% 其实没什么用，，，哎 不对 我用的数据集分类结果有三种，相比于不训练 33.3333%训练出的这个模型其实还是很好的？？？对于数据集只有两类（好瓜or 坏瓜 50%作为对照） 当拿35个进行训练 45个进行预测 准确率是62.5%   (其实我用的数据集不是特别规范 因为太有序了 这样无偏性就不是特别好，所以应该打乱顺序这样训练出来的模型才更有代表性

图传不上去，，，，，