Python 随机森林分类器实现 - 使用 sklearn.tree.DecisionTreeClassifier

import numpy as np from sklearn.tree import DecisionTreeClassifier

class rfc: '随机森林分类器'

def __init__(self, n_estimators = 100, random_state = 0):
    # 随机森林的大小
    self.n_estimators = n_estimators
    # 随机森林的随机种子
    self.random_state = random_state
    
def fit(self, X, y):
    '随机森林分类器拟合'
    self.y_classes = np.unique(y)
    # 决策树数组
    dts = []
    n = X.shape[0]
    rs = np.random.RandomState(self.random_state)
    for i in range(self.n_estimators):
        # 创建决策树分类器
        dt = DecisionTreeClassifier(random_state=rs.randint(np.iinfo(np.int32).max), max_features=9)
        # 根据随机生成的权重，拟合数据集
        dt.fit(X, y, sample_weight=np.bincount(rs.randint(0, n, n), minlength = n))
        dts.append(dt)
    self.trees = dts
        
def predict(self, X):
    '随机森林分类器预测'
    # 预测结果数组
    probas = np.zeros((X.shape[0], len(self.y_classes)))
    for i in range(self.n_estimators):
        # 决策树分类器
        dt = self.trees[i]
        # 依次预测结果可能性
        probas += dt.predict_proba(X)
    # 预测结果可能性取平均
    probas /= self.n_estimators
    # 返回预测结果
    return self.y_classes.take(np.argmax(probas, axis = 1), axis = 0)