用pytorch写一个机器学习中的决策树算法
时间: 2024-05-15 22:13:22 浏览: 16
好的,下面是用 PyTorch 实现的决策树算法:
```python
import torch
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
class DecisionTree(torch.nn.Module):
def __init__(self, max_depth=2):
super(DecisionTree, self).__init__()
self.max_depth = max_depth
def forward(self, X):
return self.predict(X)
def predict(self, X):
if self.max_depth == 0 or len(set(X[:, -1].numpy())) == 1:
return torch.tensor([max(set(X[:, -1].numpy()), key=list(X[:, -1].numpy()).count)])
else:
best_feature, best_threshold = self.find_best_split(X)
if best_feature is None:
return torch.tensor([max(set(X[:, -1].numpy()), key=list(X[:, -1].numpy()).count)])
left_indices = X[:, best_feature] < best_threshold
right_indices = X[:, best_feature] >= best_threshold
left_tree = DecisionTree(max_depth=self.max_depth-1)
right_tree = DecisionTree(max_depth=self.max_depth-1)
left_tree.fit(X[left_indices])
right_tree.fit(X[right_indices])
return torch.cat([left_tree.predict(X[left_indices]), right_tree.predict(X[right_indices])])
def find_best_split(self, X):
best_gain = 0
best_feature = None
best_threshold = None
for feature in range(X.shape[1]-1):
unique_values = set(X[:, feature].numpy())
for threshold in unique_values:
left_indices = X[:, feature] < threshold
right_indices = X[:, feature] >= threshold
if len(X[left_indices]) > 0 and len(X[right_indices]) > 0:
gain = self.calculate_information_gain(X[left_indices], X[right_indices])
if gain > best_gain:
best_gain = gain
best_feature = feature
best_threshold = threshold
return best_feature, best_threshold
def calculate_entropy(self, y):
classes = set(y.numpy())
entropy = 0
for c in classes:
p_c = len(y[y==c]) / len(y)
entropy -= p_c * torch.log2(p_c)
return entropy
def calculate_information_gain(self, left, right):
p = float(len(left)) / (len(left) + len(right))
return self.calculate_entropy(left[:, -1]) * p + self.calculate_entropy(right[:, -1]) * (1 - p)
def fit(self, X):
self.tree = self.predict(X)
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
data = torch.tensor(X_train).float()
data[:, -1] = torch.tensor(y_train).float()
model = DecisionTree(max_depth=2)
model.fit(data)
predictions = model(torch.tensor(X_test).float())
print("Accuracy:", accuracy_score(y_test, predictions))
```
这里使用了 PyTorch 来实现决策树算法,使用了 Iris 数据集进行训练和测试。模型的 `forward` 方法会调用 `predict` 方法,`predict` 方法通过递归调用来构建决策树,如果遇到无法再分割的情况(即达到最大深度或所有样本都属于同一个类别),则返回该类别。决策树的分割依据是信息增益,通过计算每个特征的信息增益来选择最佳的分割特征和阈值。最后使用训练好的模型进行预测并计算准确率。
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)