# coding=utf-8import numpy as npimport random as rdfrom math import logimport matplotlib.pyplot as pltfrom tree import build_tree, predictimport pickledef random_forest_training(data_train, trees_num):''' 构建随机森林:param data_train:(list)训练数据:param trees_num:(int)分类树个数:return: trees_result(list)每棵树的最好划分trees_feature(list)每棵树中对原始特征的选择'''trees_result = []trees_feature = []n = np.shape(data_train)[1] # 样本的维度，即样本个数是mxn，此处n是列，包含数据和结果，因此其特征值个数是n也就是2if n > 2:k = int(log(n - 1, 2)) + 1 # 设置特征值个数else:k = 1# 开始构建每一课树,需要分类树的个数for i in range(trees_num):# 1. 随机选择m个样本，k个特征(打乱数据的数据编号，随机分配index，再将数据组合，返回data_samples)data_samples, feature = choose_samples(data_train, k)# 2. 构建每一棵分类树（建立tree的class）tree = build_tree(data_samples)# 3. 保存训练好的分类树trees_result.append(tree)# 4.保存好该分类树使用到的特征trees_feature.append(feature)return trees_result, trees_featuredef choose_samples(data, k):''' 从样本中随机选择样本及其特征，随机分配选择数据的index，返回重新组合的数据:param data: (list)原始数据集:param k: (int)选择特征的个数:return: data_samples(list)被选择出来的样本feature(list)被选择的特征索引index'''m, n = np.shape(data) # 样本的个数和样本特征的个数# 1.选择出k个特征的indexfeature = []for j in range(k):feature.append(rd.randint(0, n - 2)) # n-1列是标签# 2.选择出m个样本的indexindex = []for i in range(m):index.append(rd.randint(0, m - 1))# 3.从data中选择出m个样本的k个特征，组成数据集data_samplesdata_samples = []for i in range(m):data_tmp = []for fea in feature:data_tmp.append(data[index[i]][fea])data_tmp.append(data[index[i]][-1])data_samples.append(data_tmp)return data_samples, featuredef load_data(file_name):data_train = []f = open(file_name)for line in f.readlines():lines = line.strip().split('\t')data_tmp = []for x in lines:data_tmp.append(float(x))data_train.append(data_tmp)f.close()return data_traindef get_predict(trees_result, trees_feature, data_train):'''利用训练好的随机森林模型对样本进行预测:param trees_result::param trees_feature::param data_train::return:'''m_tree = len(trees_result)# 手动设置的50个树节点m = np.shape(data_train)[0]result = []for i in range(m_tree):clf = trees_result[i]feature = trees_feature[i]data = split_data(data_train, feature)result_i = []for j in range(m):# 查看每个样本与计算出来的树比较，判断数据是左、右子树result_i.append(list(predict(data[j][0:-1], clf).keys())[0])result.append(result_i)final_predict = np.sum(result, axis=0)return final_predictdef split_data(data_train, feature):m = np.shape(data_train)[0]data = []for i in range(m):data_x_tmp = []for x in feature:data_x_tmp.append(data_train[i][x])data_x_tmp.append(data_train[i][-1])data.append(data_x_tmp)return datadef cal_correct_rate(data_train, final_predict):''' 计算模型的预测准确性:param data_train::param result::return:'''m = len(final_predict)corr = 0.0for i in range(m):if data_train[i][-1] * final_predict[i] > 0:corr += 1return corr / mdef save_model(trees_result, trees_feature, result_file, feature_file):m = len(trees_feature)f_fea = open(feature_file, 'w')for i in range(m):fea_tmp = []for x in trees_feature[i]:fea_tmp.append(str(x))f_fea.writelines('\t'.join(fea_tmp))f_fea.write('\n')f_fea.close()with open(result_file, 'wb') as f:pickle.dump(trees_result, f)def draw(file_name):x0List = []y0List = []x1List = []y1List = []f = open(file_name, 'r')for line in f.readlines():lines = line.strip().split()if lines[2] == '1':x0List.append(float(lines[0]))y0List.append(float(lines[1]))else:x1List.append(float(lines[0]))y1List.append(float(lines[1]))fig = plt.figure()ax = fig.add_subplot(111)ax.scatter(x0List, y0List, s=10, c='red')ax.scatter(x1List, y1List, s=10, c='green')plt.show()if __name__ == '__main__':# 1.导入训练数据print('------1. load data-----')data_train = load_data('data.txt')# print(feature)# 2.训练随机森林模型print('-----2. random forest training-----')trees_result, trees_feature = random_forest_training(data_train, 50)# 3.保存最终的模型print('----3.get prediction correct rate----')result = get_predict(trees_result, trees_feature, data_train)corr_rate = cal_correct_rate(data_train, result)print('\t---correct rate:{}----'.format(corr_rate))# 4.保存模型print('-----4. save model------')save_model(trees_result, trees_feature, 'result_file', 'feature_file')draw('data.txt')
结果如图：
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