决策树算法python实现

以下是一个简单的决策树算法的Python实现：

1. 导入必要的库

import pandas as pd
import numpy as np

2. 定义节点类

class Node:
    def __init__(self, feature=None, threshold=None, left=None, right=None, value=None):
        self.feature = feature 
        self.threshold = threshold
        self.left = left     
        self.right = right   
        self.value = value   

3. 定义决策树类

class DecisionTree:
    def __init__(self, max_depth=None):
        self.max_depth = max_depth
        
    def fit(self, X, y):
        self.n_features_ = X.shape[1]  
        self.tree_ = self._grow_tree(X, y) 
        
    def predict(self, X):
        return [self._predict(inputs) for inputs in X]
    
    def _best_split(self, X, y):
        m = y.size
        if m <= 1:
            return None, None
        num_parent = [np.sum(y == c) for c in range(self.n_classes_)]  
        best_gini = 1.0 - sum((n / m) ** 2 for n in num_parent) 
            
        best_idx, best_thr = None, None
        for idx in range(self.n_features_):
            thresholds, classes = zip(*sorted(zip(X[:, idx], y)))
            num_left = [0] * self.n_classes_
            num_right = num_parent.copy()
            for i in range(1, m):  
                c = classes[i - 1]
                num_left[c] += 1
                num_right[c] -= 1
                gini_left = 1.0 - sum((num_left[x] / i) ** 2 for x in range(self.n_classes_))
                gini_right = 1.0 - sum((num_right[x] / (m - i)) ** 2 for x in range(self.n_classes_))
                gini = (i * gini_left + (m - i) * gini_right) / m
                if thresholds[i] == thresholds[i - 1]:
                    continue  
                if gini < best_gini:
                    best_gini = gini
                    best_idx = idx
                    best_thr = (thresholds[i] + thresholds[i - 1]) / 2  
        
        return best_idx, best_thr
    
    def _grow_tree(self, X, y, depth=0):
        num_samples_per_class = [np.sum(y == i) for i in range(self.n_classes_)]
        predicted_class = np.argmax(num_samples_per_class)
        node = Node(value=predicted_class)
        
        if depth < self.max_depth:
            idx, thr = self._best_split(X, y)
            if idx is not None:
                indices_left = X[:, idx] < thr
                X_left, y_left = X[indices_left], y[indices_left]
                X_right, y_right = X[~indices_left], y[~indices_left]
                node.feature = idx
                node.threshold = thr
                node.left = self._grow_tree(X_left, y_left, depth + 1)
                node.right = self._grow_tree(X_right, y_right, depth + 1)
                
        return node
    
    def _predict(self, inputs):
        node = self.tree_
        while node.left:
            if inputs[node.feature] < node.threshold:
                node = node.left
            else:
                node = node.right
        return node.value

4. 测试算法

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

dt = DecisionTree(max_depth=4)
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))