增加模块；增加主调用命令

Feature_Selection_method/GA.py

@@ -0,0 +1,59 @@
+from deap import base, creator, tools, algorithms
+import numpy as np
+from sklearn.datasets import make_classification
+from sklearn.model_selection import cross_val_score
+from sklearn.ensemble import RandomForestClassifier
+
+
+def GA(X, y, n_generations=20, population_size=50, crossover_prob=0.7, mutation_prob=0.2):
+    """
+    使用遗传算法进行特征选择，返回选择的特征索引。
+
+    参数:
+    X (ndarray): 特征矩阵
+    y (ndarray): 标签
+    n_generations (int): 迭代次数
+    population_size (int): 种群大小
+    crossover_prob (float): 交叉概率
+    mutation_prob (float): 变异概率
+
+    返回:
+    list: 选择的特征索引
+    """
+    # 创建适应度和个体
+    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
+    creator.create("Individual", list, fitness=creator.FitnessMax)
+
+    toolbox = base.Toolbox()
+    toolbox.register("attr_bool", lambda: np.random.randint(0, 2))
+    toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=X.shape[1])
+    toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+
+    # 定义适应度函数
+    def evaluate(individual):
+        selected_features = [index for index, val in enumerate(individual) if val == 1]
+        if not selected_features:
+            return 0,  # 没有特征时适应度为 0
+        X_selected = X[:, selected_features]
+        clf = RandomForestClassifier(random_state=42)
+        score = cross_val_score(clf, X_selected, y, cv=5).mean()  # 5 折交叉验证
+        return score,
+
+    toolbox.register("evaluate", evaluate)
+    toolbox.register("mate", tools.cxTwoPoint)
+    toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
+    toolbox.register("select", tools.selTournament, tournsize=3)
+
+    # 初始化种群
+    population = toolbox.population(n=population_size)
+
+    # 运行遗传算法
+    result_population, _ = algorithms.eaSimple(population, toolbox, cxpb=crossover_prob,
+                                               mutpb=mutation_prob, ngen=n_generations,
+                                               verbose=False)
+
+    # 获取最优个体
+    best_individual = tools.selBest(result_population, k=1)[0]
+    selected_features = [index for index, val in enumerate(best_individual) if val == 1]
+
+    return selected_features