ID3算法决策树测试代码
时间: 2023-10-12 12:12:09 浏览: 52
以下是使用Python实现ID3算法决策树的测试代码示例:
```python
import pandas as pd
import numpy as np
from math import log2
class DecisionTree():
def __init__(self):
self.tree = {}
def fit(self, X, y):
self.tree = self.build_tree(X, y)
def predict(self, X):
preds = []
for _, sample in X.iterrows():
preds.append(self.traverse_tree(sample, self.tree))
return preds
def traverse_tree(self, sample, node):
if isinstance(node, str):
return node
else:
feature, values_dict = node
value = sample[feature]
if value not in values_dict:
return None
else:
return self.traverse_tree(sample, values_dict[value])
def build_tree(self, X, y):
num_samples = len(y)
num_features = len(X.columns)
# If all samples have the same label, return that label
if len(set(y)) == 1:
return y[0]
# If no more features, return the most common label
if num_features == 0:
return max(set(y), key=y.count)
# Choose the best feature to split on
best_feature = None
best_gain = -1
for feature in X.columns:
gain = self.information_gain(X[feature], y)
if gain > best_gain:
best_feature = feature
best_gain = gain
# Create a new node and split the data
node = (best_feature, {})
feature_values = set(X[best_feature])
for value in feature_values:
subset_X, subset_y = self.split_data(X, y, best_feature, value)
if len(subset_y) == 0:
node[1][value] = max(set(y), key=y.count)
else:
node[1][value] = self.build_tree(subset_X, subset_y)
return node
def split_data(self, X, y, feature, value):
subset_X = X[X[feature] == value].drop(feature, axis=1)
subset_y = y[X[feature] == value]
return subset_X, subset_y
def entropy(self, y):
num_samples = len(y)
value_counts = pd.Series(y).value_counts(normalize=True)
return -(value_counts * np.log2(value_counts)).sum()
def information_gain(self, X_feature, y):
base_entropy = self.entropy(y)
feature_values = set(X_feature)
new_entropy = 0
for value in feature_values:
subset_y = y[X_feature == value]
new_entropy += len(subset_y) / len(y) * self.entropy(subset_y)
return base_entropy - new_entropy
```
这段代码实现了一个简单的决策树分类器,使用ID3算法进行决策树的构建。其中,`fit`方法用于训练模型,`predict`方法用于预测新样本的分类,`build_tree`方法用于递归构建决策树,`split_data`方法用于根据某个特征的某个取值将数据集分割成子集,`entropy`方法用于计算数据集的信息熵,`information_gain`方法用于计算某个特征的信息增益。
你可以使用该代码实现决策树的测试。
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