深度学习算法中的协同训练（Co-training）

pythonCopy codefrom sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# 生成示例数据集
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, random_state=42)
# 将数据集划分为两个视角
X1, X2, y_train, y_unlabeled = train_test_split(X, y, test_size=0.5, random_state=42)
# 初始化两个分类器
clf1 = SVC()
clf2 = SVC()
# 迭代训练
for _ in range(5):
    # 在视角1上训练分类器1
    clf1.fit(X1, y_train)
    # 在视角2上训练分类器2
    clf2.fit(X2, y_train)
    
    # 使用分类器1对视角2上的未标记数据进行预测，并选择置信度高的样本进行标记
    y_pred1 = clf1.predict(X2)
    confident_samples1 = X2[y_pred1 == y_unlabeled]
    confident_labels1 = y_pred1[y_pred1 == y_unlabeled]
    X1 = np.concatenate((X1, confident_samples1))
    y_train = np.concatenate((y_train, confident_labels1))
    
    # 使用分类器2对视角1上的未标记数据进行预测，并选择置信度高的样本进行标记
    y_pred2 = clf2.predict(X1)
    confident_samples2 = X1[y_pred2 == y_unlabeled]
    confident_labels2 = y_pred2[y_pred2 == y_unlabeled]
    X2 = np.concatenate((X2, confident_samples2))
    y_train = np.concatenate((y_train, confident_labels2))
# 在测试集上评估分类器的性能
X_test, y_test = make_classification(n_samples=200, n_features=10, n_informative=5, random_state=42)
y_pred1 = clf1.predict(X_test)
y_pred2 = clf2.predict(X_test)
ensemble_pred = np.array([y_pred1[i] if y_pred1[i] == y_pred2[i] else np.random.choice([y_pred1[i], y_pred2[i]]) for i in range(len(y_pred1))])
# 输出分类器的准确率
print("Classifier 1 accuracy:", accuracy_score(y_test, y_pred1))
print("Classifier 2 accuracy:", accuracy_score(y_test, y_pred2))
print("Ensemble accuracy:", accuracy_score(y_test, ensemble_pred))

pythonCopy codeimport operator
import numpy as np
import random
import math
from deap import algorithms, base, creator, tools
# 定义问题适应度函数
def multi_objective_fitness(individual):
    # 计算任务1的适应度
    fitness1 = individual[0] + individual[1]
    # 计算任务2的适应度
    fitness2 = abs(individual[0] - individual[1])
    return fitness1, fitness2
# 定义遗传编程问题
creator.create("Fitness", base.Fitness, weights=(-1.0, -1.0))
creator.create("Individual", list, fitness=creator.Fitness)
toolbox = base.Toolbox()
# 生成基因的方式
toolbox.register("gene", random.uniform, -10, 10)
# 生成个体的方式
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.gene, n=2)
# 生成种群的方式
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# 定义评估函数
toolbox.register("evaluate", multi_objective_fitness)
# 定义交叉操作
toolbox.register("mate", tools.cxOnePoint)
# 定义变异操作
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1)
# 定义选择操作
toolbox.register("select", tools.selNSGA2)
# 创建种群
population = toolbox.population(n=50)
# 迭代进化
NGEN = 50
for gen in range(NGEN):
    offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
    fits = toolbox.map(toolbox.evaluate, offspring)
    for fit, ind in zip(fits, offspring):
        ind.fitness.values = fit
    population = toolbox.select(offspring, k=len(population))
# 输出最优个体
best_individual = tools.selBest(population, k=1)[0]
print("Best individual:", best_individual)