西瓜数据集决策树python不调库代码实现

以下是使用Python实现西瓜数据集决策树的代码，不需要调用任何库：

import math

# 定义节点类
class Node:
    def __init__(self, feature=None, value=None, results=None, tb=None, fb=None):
        self.feature = feature  # 用于划分数据集的特征
        self.value = value  # 特征的值
        self.results = results  # 存储叶子节点的分类结果
        self.tb = tb  # 左子树
        self.fb = fb  # 右子树

# 计算数据集的熵
def entropy(data):
    results = {}
    for row in data:
        r = row[-1]
        if r not in results:
            results[r] = 0
        results[r] += 1
    ent = 0.0
    for r in results:
        p = float(results[r]) / len(data)
        ent -= p * math.log(p, 2)
    return ent

# 根据特征和特征值划分数据集
def divide_data(data, feature, value):
    split_func = None
    if isinstance(value, int) or isinstance(value, float):
        split_func = lambda row: row[feature] >= value
    else:
        split_func = lambda row: row[feature] == value
    set1 = [row for row in data if split_func(row)]
    set2 = [row for row in data if not split_func(row)]
    return (set1, set2)

# 选择最好的特征和特征值来划分数据集
def find_best_feature(data):
    best_feature = -1
    best_value = None
    best_gain = 0.0
    base_entropy = entropy(data)
    for feature in range(len(data[0]) - 1):
        feature_values = set([row[feature] for row in data])
        for value in feature_values:
            set1, set2 = divide_data(data, feature, value)
            p = float(len(set1)) / len(data)
            gain = base_entropy - p * entropy(set1) - (1 - p) * entropy(set2)
            if gain > best_gain:
                best_feature = feature
                best_value = value
                best_gain = gain
    return (best_feature, best_value)

# 构建决策树
def build_tree(data):
    if len(data) == 0:
        return Node()
    results = [row[-1] for row in data]
    if results.count(results[0]) == len(results):
        return Node(results=results[0])
    best_feature, best_value = find_best_feature(data)
    set1, set2 = divide_data(data, best_feature, best_value)
    tb = build_tree(set1)
    fb = build_tree(set2)
    return Node(feature=best_feature, value=best_value, tb=tb, fb=fb)

# 打印决策树
def print_tree(tree, indent=''):
    if tree.results is not None:
        print(str(tree.results))
    else:
        print(str(tree.feature) + ':' + str(tree.value) + '? ')
        print(indent + 'T->', end='')
        print_tree(tree.tb, indent + '  ')
        print(indent + 'F->', end='')
        print_tree(tree.fb, indent + '  ')

# 对新数据进行分类
def classify(tree, data):
    if tree.results is not None:
        return tree.results
    else:
        v = data[tree.feature]
        branch = None
        if isinstance(v, int) or isinstance(v, float):
            if v >= tree.value:
                branch = tree.tb
            else:
                branch = tree.fb
        else:
            if v == tree.value:
                branch = tree.tb
            else:
                branch = tree.fb
        return classify(branch, data)

# 测试决策树
def test_tree(tree, test_data):
    correct = 0
    for row in test_data:
        if classify(tree, row[:-1]) == row[-1]:
            correct += 1
    accuracy = float(correct) / len(test_data)
    print('Accuracy: %.2f%%' % (accuracy * 100))

# 加载西瓜数据集
def load_watermelon():
    data = [
        [1, 1, 1, 1, 'yes'],
        [1, 1, 1, 0, 'yes'],
        [1, 0, 1, 0, 'no'],
        [0, 1, 0, 1, 'no'],
        [0, 1, 0, 0, 'no'],
        [0, 0, 1, 1, 'no'],
        [0, 1, 1, 0, 'no'],
        [1, 1, 0, 1, 'no'],
        [1, 0, 0, 0, 'no'],
        [0, 1, 0, 1, 'no']
    ]
    return data

# 加载西瓜数据集2
def load_watermelon2():
    data = [
        [0.697, 0.460, 1, 'yes'],
        [0.774, 0.376, 1, 'yes'],
        [0.634, 0.264, 1, 'yes'],
        [0.608, 0.318, 1, 'yes'],
        [0.556, 0.215, 1, 'yes'],
        [0.403, 0.237, 1, 'yes'],
        [0.481, 0.149, 1, 'yes'],
        [0.437, 0.211, 1, 'yes'],
        [0.666, 0.091, 0, 'no'],
        [0.243, 0.267, 0, 'no'],
        [0.245, 0.057, 0, 'no'],
        [0.343, 0.099, 0, 'no'],
        [0.639, 0.161, 0, 'no'],
        [0.657, 0.198, 0, 'no'],
        [0.360, 0.370, 0, 'no'],
        [0.593, 0.042, 0, 'no'],
        [0.719, 0.103, 0, 'no']
    ]
    return data

# 加载西瓜数据集3
def load_watermelon3():
    data = [
        ['青绿', '蜷缩', '浊响', '清晰', '凹陷', '硬滑', '是'],
        ['乌黑', '蜷缩', '沉闷', '清晰', '凹陷', '硬滑', '是'],
        ['乌黑', '蜷缩', '浊响', '清晰', '凹陷', '硬滑', '是'],
        ['青绿', '稍蜷', '浊响', '清晰', '稍凹', '软粘', '是'],
        ['浅白', '稍蜷', '浊响', '清晰', '稍凹', '软粘', '是'],
        ['青绿', '硬挺', '清脆', '清晰', '平坦', '软粘', '是'],
        ['乌黑', '稍蜷', '沉闷', '稍糊', '稍凹', '硬滑', '是'],
        ['乌黑', '稍蜷', '浊响', '清晰', '稍凹', '软粘', '是'],
        ['乌黑', '稍蜷', '沉闷', '稍糊', '稍凹', '硬滑', '否'],
        ['青绿', '硬挺', '清脆', '清晰', '平坦', '硬滑', '否'],
        ['浅白', '硬挺', '清脆', '模糊', '平坦', '软粘', '否'],
        ['浅白', '蜷缩', '浊响', '模糊', '平坦', '硬滑', '否'],
        ['青绿', '稍蜷', '浊响', '稍糊', '凹陷', '硬滑', '否'],
        ['浅白', '稍蜷', '沉闷', '稍糊', '凹陷', '硬滑', '否'],
        ['乌黑', '稍蜷', '浊响', '清晰', '稍凹', '软粘', '否'],
        ['浅白', '蜷缩', '浊响', '模糊', '平坦', '硬滑', '否'],
        ['青绿', '蜷缩', '沉闷', '稍糊', '稍凹', '硬滑', '否']
    ]
    return data

# 加载西瓜数据集4
def load_watermelon4():
    data = [
        ['青绿', '蜷缩', '浊响', '清晰', '凹陷', '硬滑', '是'],
        ['乌黑', '蜷缩', '沉闷', '清晰', '凹陷', '硬滑', '是'],
        ['乌黑', '蜷缩', '浊响', '清晰', '凹陷', '硬滑', '是'],
        ['青绿', '稍蜷', '浊响', '清晰', '稍凹', '软粘', '是'],
        ['浅白', '稍蜷', '浊响', '清晰',