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classification_model/Classification/ClassicClsHY.py
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235
classification_model/Classification/ClassicClsHY.py
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import numpy as np
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import pandas as pd
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from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_score, recall_score, f1_score
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import sklearn.svm as svm
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from sklearn.cross_decomposition import PLSRegression
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split
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from skopt import BayesSearchCV
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from skopt.space import Real, Integer
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from xgboost import XGBClassifier
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import lightgbm as lgb
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import catboost as cb
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import StratifiedKFold
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from sklearn.neural_network import MLPClassifier
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# 固定随机种子
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def set_random_seed(seed=42):
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np.random.seed(seed)
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set_random_seed()
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# 交叉验证（多核心支持）
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def cross_validate_model(model, X, y, cv=5, n_jobs=-1):
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"""
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多核心交叉验证
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"""
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scores = cross_val_score(model, X, y, cv=cv, n_jobs=n_jobs)
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print(f"Cross-validation accuracy: {scores.mean():.4f} ± {scores.std():.4f}")
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return scores
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# 混淆矩阵与分类报告
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def evaluate_model(y_true, y_pred, dataset_name="Test"):
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"""
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性能评估，包含分类报告和混淆矩阵。
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"""
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print(f"{dataset_name} Classification Report:")
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print(classification_report(y_true, y_pred))
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# 计算混淆矩阵
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cm = confusion_matrix(y_true, y_pred)
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# 返回多个性能指标的字典，包括F1分数
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return {
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"accuracy": accuracy_score(y_true, y_pred),
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"precision": precision_score(y_true, y_pred, average='weighted'),
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"recall": recall_score(y_true, y_pred, average='weighted'),
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"f1_score": f1_score(y_true, y_pred, average='weighted'),
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"confusion_matrix": cm
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}
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# 逻辑回归模型 (Logistic Regression)
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from sklearn.svm import SVC
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.ensemble import RandomForestClassifier
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import xgboost as xgb
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import lightgbm as lgb
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import catboost as cb
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import train_test_split
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# 1. SVM 贝叶斯优化
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def optimize_SVM(X_train, y_train, X_test, y_test):
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param_space = {
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'C': (0.01, 10.0, 'uniform'),
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'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
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'gamma': (1e-4, 1e-1, 'log-uniform')
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}
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model = SVC()
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optimizer = BayesSearchCV(model, param_space, n_iter=50, cv=5, n_jobs=-1, verbose=0, scoring='f1_weighted') # 使用f1_weighted作为评分标准
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optimizer.fit(X_train, y_train)
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best_params = optimizer.best_params_
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# 使用最优超参数训练并评估模型
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best_model = optimizer.best_estimator_
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y_train_pred = best_model.predict(X_train)
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y_test_pred = best_model.predict(X_test)
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train_metrics = evaluate_model(y_train, y_train_pred, dataset_name="Train")
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test_metrics = evaluate_model(y_test, y_test_pred, dataset_name="Test")
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return best_params, train_metrics, test_metrics
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# 2. KNN 贝叶斯优化
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def optimize_KNN(X_train, y_train, X_test, y_test):
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param_space = {
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'n_neighbors': (1, 20),
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'weights': ['uniform', 'distance'],
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'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']
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}
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model = KNeighborsClassifier()
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optimizer = BayesSearchCV(model, param_space, n_iter=50, cv=5, n_jobs=-1, verbose=0, scoring='f1_weighted') # 使用f1_weighted作为评分标准
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optimizer.fit(X_train, y_train)
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best_params = optimizer.best_params_
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# 使用最优超参数训练并评估模型
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best_model = optimizer.best_estimator_
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y_train_pred = best_model.predict(X_train)
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y_test_pred = best_model.predict(X_test)
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train_metrics = evaluate_model(y_train, y_train_pred, dataset_name="Train")
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test_metrics = evaluate_model(y_test, y_test_pred, dataset_name="Test")
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return best_params, train_metrics, test_metrics
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# 3. XGBoost 贝叶斯优化
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def optimize_XGBoost(X_train, y_train, X_test, y_test):
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param_space = {
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'n_estimators': Integer(50, 500),
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'max_depth': Integer(3, 10),
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'learning_rate': Real(1e-4, 1.0, prior='log-uniform'),
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'subsample': Real(0.1, 1.0),
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'colsample_bytree': Real(0.1, 1.0)
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}
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model = XGBClassifier(tree_method='gpu_hist', gpu_id=0)
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optimizer = BayesSearchCV(model, param_space, n_iter=50, cv=5, n_jobs=-1, verbose=0, scoring='f1_weighted') # 使用f1_weighted作为评分标准
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optimizer.fit(X_train, y_train)
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best_params = optimizer.best_params_
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best_model = optimizer.best_estimator_
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y_train_pred = best_model.predict(X_train)
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y_test_pred = best_model.predict(X_test)
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train_metrics = evaluate_model(y_train, y_train_pred, dataset_name="Train")
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test_metrics = evaluate_model(y_test, y_test_pred, dataset_name="Test")
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return best_params, train_metrics, test_metrics
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# 4. Random Forest 贝叶斯优化
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def optimize_RF(X_train, y_train, X_test, y_test):
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param_space = {
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'n_estimators': (50, 500),
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'max_depth': (3, 15),
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'min_samples_split': (2, 20),
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'min_samples_leaf': (1, 20),
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'max_features': ['auto', 'sqrt', 'log2']
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}
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model = RandomForestClassifier(random_state=42)
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optimizer = BayesSearchCV(model, param_space, n_iter=50, cv=5, n_jobs=-1, verbose=0, scoring='f1_weighted') # 使用f1_weighted作为评分标准
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optimizer.fit(X_train, y_train)
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best_params = optimizer.best_params_
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# 使用最优超参数训练并评估模型
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best_model = optimizer.best_estimator_
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y_train_pred = best_model.predict(X_train)
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y_test_pred = best_model.predict(X_test)
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train_metrics = evaluate_model(y_train, y_train_pred, dataset_name="Train")
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test_metrics = evaluate_model(y_test, y_test_pred, dataset_name="Test")
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return best_params, train_metrics, test_metrics
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# 5. CatBoost 贝叶斯优化
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def optimize_CatBoost(X_train, y_train, X_test, y_test):
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param_space = {
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'iterations': (50, 500),
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'learning_rate': (0.01, 0.3, 'uniform'),
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'depth': (3, 10),
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'l2_leaf_reg': (1, 10, 'uniform'),
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'bagging_temperature': (0, 1, 'uniform')
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}
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model = cb.CatBoostClassifier(task_type='GPU', random_seed=42, verbose=0)
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optimizer = BayesSearchCV(model, param_space, n_iter=50, cv=5, n_jobs=-1, verbose=0, scoring='f1_weighted') # 使用f1_weighted作为评分标准
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optimizer.fit(X_train, y_train)
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best_params = optimizer.best_params_
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# 使用最优超参数训练并评估模型
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best_model = optimizer.best_estimator_
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y_train_pred = best_model.predict(X_train)
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y_test_pred = best_model.predict(X_test)
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train_metrics = evaluate_model(y_train, y_train_pred, dataset_name="Train")
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test_metrics = evaluate_model(y_test, y_test_pred, dataset_name="Test")
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return best_params, train_metrics, test_metrics
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# 6. Logistic Regression 贝叶斯优化
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def optimize_LogisticRegression(X_train, y_train, X_test, y_test):
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param_space = {
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'C': (1e-5, 1e5, 'log-uniform'),
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'penalty': ['l1', 'l2'],
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'solver': ['lbfgs', 'liblinear', 'saga']
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}
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model = LogisticRegression(multi_class='multinomial', random_state=42)
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optimizer = BayesSearchCV(model, param_space, n_iter=50, cv=5, n_jobs=-1, verbose=0, scoring='f1_weighted') # 使用f1_weighted作为评分标准
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optimizer.fit(X_train, y_train)
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best_params = optimizer.best_params_
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# 使用最优超参数训练并评估模型
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best_model = optimizer.best_estimator_
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y_train_pred = best_model.predict(X_train)
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y_test_pred = best_model.predict(X_test)
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train_metrics = evaluate_model(y_train, y_train_pred, dataset_name="Train")
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test_metrics = evaluate_model(y_test, y_test_pred, dataset_name="Test")
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return best_params, train_metrics, test_metrics
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# 7. Neural Network (ANN) 贝叶斯优化
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def optimize_ANN(X_train, y_train, X_test, y_test):
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param_space = {
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'hidden_layer_sizes': [(10,), (50,), (100,), (10, 10), (50, 50)],
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'activation': ['relu', 'tanh', 'logistic'],
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'solver': ['adam', 'sgd'],
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'alpha': (1e-5, 1e-1, 'log-uniform'),
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'learning_rate': ['constant', 'invscaling', 'adaptive']
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}
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model = MLPClassifier(max_iter=500, random_state=42)
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optimizer = BayesSearchCV(model, param_space, n_iter=50, cv=5, n_jobs=-1, verbose=0, scoring='f1_weighted') # 使用f1_weighted作为评分标准
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optimizer.fit(X_train, y_train)
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best_params = optimizer.best_params_
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# 使用最优超参数训练并评估模型
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best_model = optimizer.best_estimator_
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y_train_pred = best_model.predict(X_train)
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y_test_pred = best_model.predict(X_test)
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train_metrics = evaluate_model(y_train, y_train_pred, dataset_name="Train")
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test_metrics = evaluate_model(y_test, y_test_pred, dataset_name="Test")
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return best_params, train_metrics, test_metrics
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