python不使用库实现随机森林并应用于实例
时间: 2023-09-05 21:15:13 浏览: 58
好的,我来回答你的问题。
随机森林是一种基于决策树的集成算法,可以用于分类和回归问题。在Python中,可以使用scikit-learn库来实现随机森林。
但是,如果不使用库,可以通过以下步骤实现随机森林:
1. 从数据集中随机选择n个样本,用于构建一个决策树。
2. 从所有特征中随机选择k个特征,用于构建决策树。
3. 对于每个节点,在k个特征中选择最佳分割点。
4. 重复步骤1-3,构建多个决策树。
5. 对于分类问题,使用投票法来确定最终的分类结果。对于回归问题,使用平均值来计算预测值。
下面是一个示例代码,用于实现随机森林并应用于实例:
```python
import random
from collections import Counter
class DecisionTree:
def __init__(self, max_depth=5, min_samples_split=2):
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.feature_idx = None
self.threshold = None
self.left = None
self.right = None
self.label = None
def fit(self, X, y):
if len(y) < self.min_samples_split or self.max_depth == 0:
self.label = Counter(y).most_common(1)[0][0]
return
m, n = X.shape
self.feature_idx = random.sample(range(n), int(n**0.5))
best_gain = -float('inf')
for i in self.feature_idx:
thresholds = set(X[:, i])
for t in thresholds:
y_left = y[X[:, i] <= t]
y_right = y[X[:, i] > t]
if len(y_left) == 0 or len(y_right) == 0:
continue
gain = self._information_gain(y, y_left, y_right)
if gain > best_gain:
best_gain = gain
self.feature_idx = i
self.threshold = t
X_left, y_left, X_right, y_right = self._split(X, y)
self.left = DecisionTree(self.max_depth-1, self.min_samples_split)
self.right = DecisionTree(self.max_depth-1, self.min_samples_split)
self.left.fit(X_left, y_left)
self.right.fit(X_right, y_right)
def _split(self, X, y):
X_left = X[X[:, self.feature_idx] <= self.threshold]
y_left = y[X[:, self.feature_idx] <= self.threshold]
X_right = X[X[:, self.feature_idx] > self.threshold]
y_right = y[X[:, self.feature_idx] > self.threshold]
return X_left, y_left, X_right, y_right
def _information_gain(self, y, y_left, y_right):
p = len(y_left) / len(y)
return self._entropy(y) - p*self._entropy(y_left) - (1-p)*self._entropy(y_right)
def _entropy(self, y):
count = Counter(y)
proportions = [v/len(y) for v in count.values()]
return -sum(p * np.log2(p) for p in proportions)
def predict(self, X):
if self.label is not None:
return self.label
if X[self.feature_idx] <= self.threshold:
return self.left.predict(X)
else:
return self.right.predict(X)
class RandomForest:
def __init__(self, n_trees=10, max_depth=5, min_samples_split=2):
self.n_trees = n_trees
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.trees = []
def fit(self, X, y):
for i in range(self.n_trees):
tree = DecisionTree(self.max_depth, self.min_samples_split)
idx = random.sample(range(len(X)), len(X))
tree.fit(X[idx], y[idx])
self.trees.append(tree)
def predict(self, X):
predictions = []
for tree in self.trees:
predictions.append(tree.predict(X))
return Counter(predictions).most_common(1)[0][0]
```
在上面的代码中,首先定义了一个决策树类`DecisionTree`,它包括`fit`和`predict`方法,用于构建和预测决策树。然后定义了一个随机森林类`RandomForest`,它包括`fit`和`predict`方法,用于构建和预测随机森林。
使用随机森林对一个数据集进行分类的例子如下:
```python
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 生成一个二分类的数据集
X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_state=42)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 构建随机森林
rf = RandomForest(n_trees=10, max_depth=5, min_samples_split=2)
rf.fit(X_train, y_train)
# 预测测试集
y_pred = [rf.predict(x) for x in X_test]
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")
```
以上就是一个简单的随机森林实现,希望能够对你有所帮助。