python-机器学习-随机森林算法
本文是用python学习机器学习系列的第五篇
随机森林算法是在决策树算法的基础上的改进，本文使用的基础决策树算法是引用第二篇文章中实现的决策数算法。
链接：python-机器学习-决策树算法
代码如下：
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport matplotlib as mplfrom sklearn import preprocessingimport refrom collections import defaultdictfrom sklearn.model_selection import train_test_splitimport DecisionTree as de# 随机森林class RandomForest:# 初始化def __init__(self, criterion='gini', max_depth=10, max_tree=20, random_sample=0.5):self.max_depth = max_depth # 最大树深self.criterion = criterion # 生成模式 ID3 或 ID4.5 或 giniself.max_tree = max_tree # 最大生成树数self.random_sample = random_sample # 随机样本比例self.forest = [] # 森林# 拟合函数def fit(self, x, y):data = np.hstack((x, y))for i in range(self.max_tree):ranData = self.randomSample(data)x2 = ranData[:, :-1]y2 = ranData[:, -1]model = de.DecisionTree(criterion=self.criterion, max_depth=self.max_depth)model.fit(x2, y2.reshape(len(y2), 1))self.forest.append(model)return self# 预测多个样本def predict(self, x):return np.array([self.hat(i) for i in x])# 预测单个样本def hat(self, x):result = 0account = 0ls = np.array([i.hat(x,i.tree) for i in self.forest])d = self.calculate_N(ls)for key, value in d.items():if value > account:account = valueresult = keyreturn result# 随机样本选择器def randomSample(self, data):l = len(data)indexs = np.random.choice(l, int(l*self.random_sample))return np.array([data[i, :] for i in indexs])# 计算列表并分类def calculate_N(self, x):s = set(x.reshape(1, len(x)).tolist()[0])d = {}for i in s:d[i] = list(x).count(i)return diris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'if __name__ == "__main__":mpl.rcParams['font.sans-serif'] = [u'SimHei']mpl.rcParams['axes.unicode_minus'] = Falsepath = u'8.iris.data' # 数据文件路径df = pd.read_csv(path, header=0)x = df.values[:, :-1]y = df.values[:, -1]print('x = \n', x)print('y = \n', y)le = preprocessing.LabelEncoder()le.fit(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])y = le.transform(y)# 为了可视化，仅使用前两列特征x = x[:, :2]x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=1)# 决策树参数估计model = RandomForest(criterion='gini', max_depth=5)model = model.fit(x_train, y_train.reshape(len(y_train), 1))y_test_hat = model.predict(x_test)# 测试数据# 保存# dot -Tpng -o 1.png 1.dot#f = open('.\\iris_tree.dot', 'w')#tree.export_graphviz(model.get_params('DTC')['DTC'], out_file=f)# 画图N, M = 100, 100 # 横纵各采样多少个值x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围t1 = np.linspace(x1_min, x1_max, N)t2 = np.linspace(x2_min, x2_max, M)x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点x_show = np.stack((x1.flat, x2.flat), axis=1) # 测试点# # 无意义，只是为了凑另外两个维度# # 打开该注释前，确保注释掉x = x[:, :2]# x3 = np.ones(x1.size) * np.average(x[:, 2])# x4 = np.ones(x1.size) * np.average(x[:, 3])# x_test = np.stack((x1.flat, x2.flat, x3, x4), axis=1) # 测试点cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])y_show_hat = model.predict(x_show) # 预测值print("xshow=" + str(x_show))print("yshow=" + str(y_show_hat))y_show_hat = y_show_hat.reshape(x1.shape) # 使之与输入的形状相同plt.figure(facecolor='w')plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light) # 预测值的显示plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test.ravel(),edgecolors='k', s=100, cmap=cm_dark, marker='o') # 测试数据plt.scatter(x[:, 0], x[:, 1], c=y.ravel(),edgecolors='k', s=40, cmap=cm_dark) # 全部数据plt.xlabel(iris_feature[0], fontsize=15)plt.ylabel(iris_feature[1], fontsize=15)plt.xlim(x1_min, x1_max)plt.ylim(x2_min, x2_max)plt.grid(True)plt.title(u'鸢尾花数据的随机森林分类', fontsize=17)plt.show()# 训练集上的预测结果y_test = y_test.reshape(-1)print(str(y_test_hat))print(str(y_test))result = (y_test_hat == y_test) # True则预测正确，False则预测错误acc = np.mean(result)print('准确度: %.2f%%' % (100 * acc))# 过拟合：错误率depth = np.arange(1, 15)err_list = []for d in depth:clf = RandomForest(criterion='gini', max_depth=d)clf = clf.fit(x_train, y_train.reshape(len(y_train), 1))y_test_hat = clf.predict(x_test) # 测试数据result = (y_test_hat == y_test) # True则预测正确，False则预测错误err = 1 - np.mean(result)err_list.append(err)print(d, ' 错误率: %.2f%%' % (100 * err))plt.figure(facecolor='w')plt.plot(depth, err_list, 'ro-', lw=2)plt.xlabel(u'决策树深度', fontsize=15)plt.ylabel(u'错误率', fontsize=15)plt.title(u'随机森林深度与过拟合', fontsize=17)plt.grid(True)plt.show()
运行结果：
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