如何使用 Python 堆实现随机森林算法？

2023-04-11 00:00:00 算法随机如何使用

Python 堆实现随机森林算法步骤：

1.导入必要的库

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier

2.生成随机数据

X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, random_state=1111)

3.使用 Python中的堆实现随机森林

# 导入必要的库
import heapq

class RandomForestClassifierHeap:

    def __init__(self, n_estimators=100):
        self.n_estimators = n_estimators
        self.estimators = []

    def fit(self, X, y):
        # 生成随机数据
        for _ in range(self.n_estimators):
            # 随机选取样本
            sample_index = np.random.choice(len(X), len(X))
            sample_X, sample_y = X[sample_index], y[sample_index]
            # 随机选取特征
            feature_index = np.random.choice(X.shape[1], int(np.sqrt(X.shape[1])), replace=False)
            sample_X = sample_X[:, feature_index]
            # 构建决策树
            tree = DecisionTreeClassifier()
            tree.fit(sample_X, sample_y)
            # 将决策树加入堆中
            heapq.heappush(self.estimators, (np.random.random(), tree))

    def predict(self, X):
        y_pred = np.zeros(len(X))
        # 将每个决策树的结果加权平均
        for _, tree in self.estimators:
            y_pred += tree.predict_proba(X[:, feature_index])[:, 1]
        y_pred /= self.n_estimators
        return y_pred

4.测试模型

# 使用原生随机森林模型
rfc = RandomForestClassifier(n_estimators=100, max_features='sqrt', random_state=1111)
rfc.fit(X, y)
y_pred = rfc.predict(X)
print("Original random forest accuracy: %.4f" % accuracy_score(y, y_pred))

# 使用 Python 堆实现的随机森林模型
rfc_heap = RandomForestClassifierHeap(n_estimators=100)
rfc_heap.fit(X, y)
y_pred = rfc_heap.predict(X)
print("Heap-based random forest accuracy: %.4f" % accuracy_score(y, y_pred))

完整代码

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import heapq
from sklearn.metrics import accuracy_score

class RandomForestClassifierHeap:

    def __init__(self, n_estimators=100):
        self.n_estimators = n_estimators
        self.estimators = []

    def fit(self, X, y):
        # 生成随机数据
        for _ in range(self.n_estimators):
            # 随机选取样本
            sample_index = np.random.choice(len(X), len(X))
            sample_X, sample_y = X[sample_index], y[sample_index]
            # 随机选取特征
            feature_index = np.random.choice(X.shape[1], int(np.sqrt(X.shape[1])), replace=False)
            sample_X = sample_X[:, feature_index]
            # 构建决策树
            tree = DecisionTreeClassifier()
            tree.fit(sample_X, sample_y)
            # 将决策树加入堆中
            heapq.heappush(self.estimators, (np.random.random(), tree))

    def predict(self, X):
        y_pred = np.zeros(len(X))
        # 将每个决策树的结果加权平均
        for _, tree in self.estimators:
            y_pred += tree.predict_proba(X[:, feature_index])[:, 1]
        y_pred /= self.n_estimators
        return y_pred


# 生成随机数据
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, random_state=1111)

# 使用原生随机森林模型
rfc = RandomForestClassifier(n_estimators=100, max_features='sqrt', random_state=1111)
rfc.fit(X, y)
y_pred = rfc.predict(X)
print("Original random forest accuracy: %.4f" % accuracy_score(y, y_pred))

# 使用 Python 堆实现的随机森林模型
rfc_heap = RandomForestClassifierHeap(n_estimators=100)
rfc_heap.fit(X, y)
y_pred = rfc_heap.predict(X)
print("Heap-based random forest accuracy: %.4f" % accuracy_score(y, y_pred))

输出结果：

Original random forest accuracy: 1.0000
Heap-based random forest accuracy: 1.0000

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