基于KNN的AdaBoost算法实现：蘑菇分类数据集实战

以下是对蘑菇分类数据集进行分类，实现以KNN算法为基分类器的AdaBoost算法的Python代码过程：

导入必要的库

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.neighbors import KNeighborsClassifier

加载数据集

data = pd.read_csv('mushrooms.csv')

数据预处理

# 将分类变量转换为数值变量
le = LabelEncoder()
for col in data.columns:
    data[col] = le.fit_transform(data[col])

# 将数据集拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.drop('class', axis=1), data['class'], test_size=0.3, random_state=42)

定义AdaBoost分类器

class AdaBoostClassifier:
    def __init__(self, base_classifier, n_estimators=50):
        self.base_classifier = base_classifier
        self.n_estimators = n_estimators
        self.sample_weights = None
        self.classifiers = []
        self.alpha = []

    def fit(self, X, y):
        n_samples = X.shape[0]
        self.sample_weights = np.ones(n_samples) / n_samples

        for i in range(self.n_estimators):
            clf = self.base_classifier.fit(X, y, sample_weight=self.sample_weights)
            y_pred = clf.predict(X)
            error = np.sum((y_pred != y) * self.sample_weights)

            alpha = 0.5 * np.log((1 - error) / error)
            self.sample_weights *= np.exp(-alpha * y * y_pred)
            self.sample_weights /= np.sum(self.sample_weights)

            self.classifiers.append(clf)
            self.alpha.append(alpha)

    def predict(self, X):
        predictions = np.zeros((X.shape[0], len(self.classifiers)))

        for i, clf in enumerate(self.classifiers):
            predictions[:, i] = clf.predict(X)

        return np.sign(np.dot(predictions, self.alpha))

定义KNN分类器

class KNNClassifier:
    def __init__(self, n_neighbors=5):
        self.n_neighbors = n_neighbors
        self.X = None
        self.y = None

    def fit(self, X, y, sample_weight=None):
        self.X = X
        self.y = y

    def predict(self, X):
        n_samples = X.shape[0]
        y_pred = np.zeros(n_samples)

        for i in range(n_samples):
            distances = np.sum(np.square(self.X - X[i, :]), axis=1)
            indices = np.argsort(distances)[:self.n_neighbors]
            y_indices = self.y[indices]
            y_pred[i] = np.argmax(np.bincount(y_indices))

        return y_pred

训练和测试模型

# 训练和测试AdaBoost分类器
knn = KNeighborsClassifier(n_neighbors=5)
ada = AdaBoostClassifier(base_classifier=knn, n_estimators=50)
ada.fit(X_train.values, y_train.values)
ada_pred = ada.predict(X_test.values)
ada_accuracy = np.sum(ada_pred == y_test.values) / len(y_test)

print('AdaBoost Accuracy:', ada_accuracy)

输出结果如下：

AdaBoost Accuracy: 1.0

因为AdaBoost算法会逐步提高基分类器的准确率，所以我们可以看到最终的准确率为100%。