python决策树算法代码不用sklearn

Python中除了使用scikit-learn库来创建决策树外，还可以使用其他第三方库如`DecisionTreeClassifier`模块来自`pydotplus`和`graphviz`。以下是一个简单的示例，展示如何手动实现ID3算法创建决策树：

import numpy as np
from collections import Counter

class DecisionNode:
    def __init__(self, feature=None, value=None, left=None, right=None, decision_type=None):
        self.feature = feature
        self.value = value
        self.left = left
        self.right = right
        self.decision_type = decision_type

class ID3DecissionTree:
    def __init__(self, features, target):
        self.root = self._grow_tree(features, target)

    def _entropy(self, counts):
        N = sum(counts.values())
        return -sum(count * np.log2(count / N) for count in counts.values())

    def _information_gain(self, parent_counts, child_counts):
        parent_entropy = self._entropy(parent_counts)
        weighted_child_entropy = sum(child_count / total * self._entropy(child_counts[child_key])
                                      for child_key, child_count in child_counts.items() for total in parent_counts.values())
        return parent_entropy - weighted_child_entropy

    def _grow_tree(self, remaining_features, labels):
        # 基本情况: 如果所有标签相同或只剩下一个特征，则返回叶节点
        if len(np.unique(labels)) == 1 or len(remaining_features) == 0:
            return DecisionNode(decision_type='leaf', value=np.argmax(Counter(labels).values()))

        best_feature = None
        max_information_gain = float('-inf')

        # 遍历剩余特征
        for feature_name, unique_values in remaining_features.items():
            for value in unique_values:
                # 划分数据集
                true_set, false_set = self._split_data(labels, feature_name, value)
                
                # 计算信息增益
                ig = self._information_gain(Counter(labels), {True: len(true_set), False: len(false_set)})
                if ig > max_information_gain:
                    max_information_gain = ig
                    best_feature = (feature_name, value)

        if best_feature is not None:
            feature_name, value = best_feature
            remaining_features.pop(feature_name)

            # 构建决策节点并递归生长左右子树
            decision_node = DecisionNode(feature=feature_name, value=value, 
                                        left=self._grow_tree(remaining_features, true_set),
                                        right=self._grow_tree(remaining_features, false_set))
            
            return decision_node
        else:
            # 如果找不到好特征，返回叶节点（假设随机猜测）
            return DecisionNode(decision_type='leaf', value=np.random.choice(list(set(labels))))

    @staticmethod
    def _split_data(labels, feature_name, value):
        true_labels = [label for label in labels if getattr(data[feature_name], 'lower')() == value.lower()]
        false_labels = [label for label in labels if getattr(data[feature_name], 'lower')() != value.lower()]
        return true_labels, false_labels

# 使用示例
features = [{'color': 'red'}, {'color': 'green'}, {'color': 'blue'}, {'color': 'red'}]
labels = ['apple', 'banana', 'orange', 'apple']
tree = ID3DecissionTree(features, labels)

在这个例子中，我们手动实现了ID3算法的基本流程，包括计算熵、信息增益以及数据划分。你可以根据需要调整这个基础框架来适应更复杂的需求。