使用类特定属性加权朴素贝叶斯 (CSAW_NB) 算法实现乳腺癌数据集分类

以下是使用类特定属性加权朴素贝叶斯算法 (Class Specific Attribute Weighting Naive Bayes，CSAW_NB) 来实现breast-cancer数据集的Python代码：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

class CSAW_NB:
    def __init__(self):
        self.class_prior = None
        self.means = None
        self.variances = None
        self.class_specific_weights = None
    
    def fit(self, X, y):
        n_samples, n_features = X.shape
        classes = np.unique(y)
        n_classes = len(classes)
        
        # Calculate class prior probabilities
        self.class_prior = np.zeros(n_classes)
        for i, c in enumerate(classes):
            self.class_prior[i] = np.sum(y == c) / n_samples
        
        # Calculate mean and variance for each feature and class
        self.means = np.zeros((n_classes, n_features))
        self.variances = np.zeros((n_classes, n_features))
        for i, c in enumerate(classes):
            X_c = X[y == c]
            self.means[i] = X_c.mean(axis=0)
            self.variances[i] = X_c.var(axis=0)
        
        # Calculate class-specific attribute weights
        self.class_specific_weights = np.zeros((n_classes, n_features))
        for i, c in enumerate(classes):
            for j in range(n_features):
                numerator = 0
                denominator = 0
                for k in range(n_classes):
                    if k != i:
                        numerator += (self.means[i, j] - self.means[k, j]) ** 2
                        denominator += self.variances[k, j]
                self.class_specific_weights[i, j] = numerator / denominator
    
    def predict(self, X):
        n_samples, n_features = X.shape
        n_classes = len(self.class_prior)
        y_pred = np.zeros(n_samples)
        for i in range(n_samples):
            posteriors = np.zeros(n_classes)
            for j in range(n_classes):
                likelihood = 1
                for k in range(n_features):
                    likelihood *= np.exp(-0.5 * self.class_specific_weights[j, k] * (X[i, k] - self.means[j, k]) ** 2)
                    likelihood /= np.sqrt(2 * np.pi * self.variances[j, k])
                posteriors[j] = likelihood * self.class_prior[j]
            y_pred[i] = np.argmax(posteriors)
        return y_pred

# Load breast-cancer dataset
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data'
df = pd.read_csv(url, header=None)
X = df.iloc[:, 2:].values
y = df.iloc[:, 1].values
y[y == 'M'] = 1
y[y == 'B'] = 0
y = y.astype(int)

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Fit CSAW_NB model and make predictions
model = CSAW_NB()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# Calculate accuracy score
acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)

在这个代码中，我们首先导入必要的库，然后定义了一个名为CSAW_NB的类，它包含了用于训练和预测的fit()和predict()方法。在fit()方法中，我们首先计算每个类的类先验概率，并计算每个特征和类的均值和方差。然后，我们计算每个类特定属性的权重。在predict()方法中，我们使用贝叶斯公式计算每个类的后验概率，并选择具有最大后验概率的类别作为预测类别。最后，我们加载breast-cancer数据集，将其分为训练和测试集，拟合CSAW_NB模型并进行预测，最后计算准确率得分并输出。