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