初始提交

classification_model/Classification/Cls.py

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+from classification_model.Classification.ClassicCls import SVM, PLS_DA, RF, XGBoost, LightGBM, CatBoost,LogisticRegressionModel,AdaBoost,KNN
+# from Classification.CNN import CNN
+# from Classification.CNN_Transfomer import TransformerTrainAndTest
+# from Classification.CNN_SAE import SAETrainAndTest
+# from Classification.SAE import SAE
+# from Classification.CNN_deepseek import CNN_deepseek
+from multiprocessing import Pool, cpu_count
+
+
+def QualitativeAnalysis(model, X_train, X_test, y_train, y_test, n_jobs=-1):
+    """
+    根据模型名称调用不同的分类模型，并返回训练集和测试集的评估指标。
+
+    参数:
+    - model: 要使用的分类模型名称
+    - X_train, X_test: 训练集和测试集的特征数据
+    - y_train, y_test: 训练集和测试集的标签数据
+    - n_jobs: 使用的核心数量，适用于支持多线程的模型
+
+    返回:
+    - train_metrics: 包含训练集 accuracy, precision, recall, f1_score 的字典
+    - test_metrics: 包含测试集 accuracy, precision, recall, f1_score 的字典
+    """
+
+    if model == "PLS_DA":
+        train_metrics, test_metrics = PLS_DA(X_train, X_test, y_train, y_test)
+    elif model == "ANN":
+        train_metrics, test_metrics = ANN(X_train, X_test, y_train, y_test)
+    elif model == "SVM":
+        train_metrics, test_metrics = SVM(X_train, X_test, y_train, y_test)
+    elif model == "RF":
+        train_metrics, test_metrics = RF(X_train, X_test, y_train, y_test, n_jobs=n_jobs)
+    elif model == "LogisticRegression":
+        train_metrics, test_metrics = LogisticRegressionModel(X_train, X_test, y_train, y_test, penalty='l2', C=1.0, solver='lbfgs')
+    elif model == "XGBoost":
+        train_metrics, test_metrics = XGBoost(X_train, X_test, y_train, y_test, n_estimators=100, learning_rate=0.1, max_depth=3)
+    elif model == "LightGBM":
+        train_metrics, test_metrics = LightGBM(X_train, X_test, y_train, y_test, n_estimators=100, learning_rate=0.1, max_depth=-1, num_leaves=31)
+    elif model == "CatBoost":
+        train_metrics, test_metrics = CatBoost(X_train, X_test, y_train, y_test, iterations=500, learning_rate=0.1, depth=6)
+    elif model == "AdaBoost":
+        train_metrics, test_metrics = AdaBoost(X_train, X_test, y_train, y_test, n_estimators=50, learning_rate=1.0)
+    elif model == 'KNN':
+        train_metrics, test_metrics = KNN(X_train, X_test, y_train, y_test, n_neighbors=5)
+    else:
+        print("No such model for Qualitative Analysis")
+        return None, None
+
+    return train_metrics, test_metrics