数学推导+纯Python实现机器学习算法20：随机森林

import numpy as np# 该模块为自定义模块，封装了构建决策树的基本方法from ClassificationTree import *from sklearn.datasets import make_classificationfrom 


    
sklearn.model_selection import train_test_split# 树的棵数n_estimators = 10# 列抽样最大特征数max_features = 15# 生成模拟二分类数据集X, y = make_classification(n_samples=1000, n_features=20, n_redundant=0, n_informative=2,                           random_state=1, n_clusters_per_class=1)rng = np.random.RandomState(2)X += 2 * rng.uniform(size=X.shape)# 划分数据集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

# 自助抽样选择训练数据子集def bootstrap_sampling(X, y):    X_y = np.concatenate([X, y.reshape(-1,1)], axis=1)    np.random.shuffle(X_y)    n_samples = X.shape[0]    sampling_subsets = []
    for _ in range(n_estimators):        # 第一个随机性，行抽样        idx1 = np.random.choice(n_samples, n_samples, replace=True)        bootstrap_Xy = X_y[idx1, :]        bootstrap_X = bootstrap_Xy[:, :-1]        bootstrap_y = bootstrap_Xy[:, -1]        sampling_subsets.append([bootstrap_X, bootstrap_y])    return sampling_subsets

trees = []# 基于决策树构建森林for _ in range(n_estimators):    tree = ClassificationTree(min_samples_split=2, min_impurity=0,                              max_depth=3)    trees.append(tree)

# 随机森林训练def fit(X, y):    # 对森林中每棵树训练一个双随机抽样子集    n_features = X.shape[1]    sub_sets = bootstrap_sampling(X, y)    for i in range(n_estimators):        sub_X, sub_y = sub_sets[i]        # 第二个随机性，列抽样        idx2 = np.random.choice(n_features, max_features, replace=True)        sub_X = sub_X[:, idx2]        trees[i].fit(sub_X, sub_y)        trees[i].feature_indices = idx2        print('The {}th tree is trained done...'.format(i+1))

class RandomForest():    def __init__(self, n_estimators=100, min_samples_split=2, min_gain=0,                 max_depth=float("inf"), max_features=None):        # 树的棵树        self.n_estimators = n_estimators        # 树最小分裂样本数


    
        self.min_samples_split = min_samples_split        # 最小增益        self.min_gain = min_gain        # 树最大深度        self.max_depth = max_depth        # 所使用最大特征数        self.max_features = max_features
        self.trees = []        # 基于决策树构建森林        for _ in range(self.n_estimators):            tree = ClassificationTree(min_samples_split=self.min_samples_split, min_impurity=self.min_gain,                                      max_depth=self.max_depth)            self.trees.append(tree)                # 自助抽样    def bootstrap_sampling(self, X, y):        X_y = np.concatenate([X, y.reshape(-1,1)], axis=1)        np.random.shuffle(X_y)        n_samples = X.shape[0]        sampling_subsets = []
        for _ in range(self.n_estimators):            # 第一个随机性，行抽样            idx1 = np.random.choice(n_samples, n_samples, replace=True)            bootstrap_Xy = X_y[idx1, :]            bootstrap_X = bootstrap_Xy[:, :-1]            bootstrap_y = bootstrap_Xy[:, -1]            sampling_subsets.append([bootstrap_X, bootstrap_y])        return sampling_subsets                # 随机森林训练    def fit(self, X, y):        # 对森林中每棵树训练一个双随机抽样子集        sub_sets = self.bootstrap_sampling(X, y)        n_features = X.shape[1]        # 设置max_feature        if self.max_features == None:            self.max_features = int(np.sqrt(n_features))                for i in range(self.n_estimators):            # 第二个随机性，列抽样            sub_X, sub_y = sub_sets[i]            idx2 = np.random.choice(n_features, self.max_features, replace=True)            sub_X = sub_X[:, idx2]            self.trees[i].fit(sub_X, sub_y)            # 保存每次列抽样的列索引，方便预测时每棵树调用            self.trees[i].feature_indices = idx2            print('The {}th tree is trained done...'.format(i+1))        # 随机森林预测    def predict(self, X):        y_preds = []        for i in range(self.n_estimators):            idx = self.trees[i].feature_indices            sub_X = X[:, idx]            y_pred = self.trees[i].predict(sub_X)            y_preds.append(y_pred)                    y_preds = np.array(y_preds).T        res = []        for j in y_preds:            res.append(np.bincount(j.astype('int')).argmax())        return res

rf = RandomForest(n_estimators=10, max_features=15)rf.fit(X_train, y_train)y_pred = rf.predict(X_test)print(accuracy_score(y_test, y_pred))

    
0.78

from sklearn.ensemble import RandomForestClassifierclf = RandomForestClassifier(max_depth=3, random_state=0)clf.fit(X_train, y_train)y_pred = clf.predict(X_test)print(accuracy_score(y_test, y_pred))

0.8