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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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基于交叉验证代码的训练脚本
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"""
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import pickle
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import numpy as np
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from pathlib import Path
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from sklearn.svm import SVC
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from sklearn.feature_selection import SelectKBest, mutual_info_classif
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from sklearn.model_selection import RandomizedSearchCV, StratifiedKFold
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import StandardScaler
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from scipy.stats import randint, loguniform
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import joblib
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def load_dataset(pkl_path):
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"""加载数据集"""
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with open(pkl_path, 'rb') as f:
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data = pickle.load(f)
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return data['matrix'], data['label']
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def train_cross_validated_model(dataset_path, pipeline_save_path, scaler_save_path):
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# 加载数据
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X_train, y_train = load_dataset(dataset_path)
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print(f"加载数据集：{X_train.shape[0]}个样本，{X_train.shape[1]}维特征")
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# 将标签转换为-1/1格式（与交叉验证代码一致）
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y_train_signed = np.where(y_train == 0, -1, 1)
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# 创建流水线（包含特征选择和SVM）
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pipe = Pipeline([
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('sel', SelectKBest(mutual_info_classif)),
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('svm', SVC(kernel='rbf', class_weight='balanced', probability=True))
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])
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# 参数分布（与交叉验证代码一致）
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n_features = X_train.shape[1]
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param_dist = {
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'sel__k': randint(1, n_features + 1),
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'svm__C': loguniform(1e-3, 1e3),
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'svm__gamma': loguniform(1e-6, 1e1)
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}
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# 随机搜索
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cv_inner = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
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search = RandomizedSearchCV(
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pipe,
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param_distributions=param_dist,
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n_iter=30, # 减少迭代次数以加快训练
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scoring='roc_auc',
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cv=cv_inner,
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n_jobs=-1,
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random_state=42,
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verbose=1
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)
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print("开始随机搜索优化...")
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search.fit(X_train, y_train_signed)
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best_params = search.best_params_
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print(f"\n最佳参数: {best_params}")
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print(f"最佳交叉验证AUC: {search.best_score_:.4f}")
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# 训练最终模型
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final_model = search.best_estimator_
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final_model.fit(X_train, y_train_signed)
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# 单独保存标准化器（用于GUI中的特征标准化）
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scaler = StandardScaler().fit(X_train)
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# 保存模型
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joblib.dump(final_model, pipeline_save_path)
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joblib.dump(scaler, scaler_save_path)
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print(f"流水线模型已保存至: {pipeline_save_path}")
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print(f"标准化器已保存至: {scaler_save_path}")
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return final_model, scaler
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if __name__ == "__main__":
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# 使用你的训练集路径
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DATASET_PATH = r"D:\Python\空心检测\pythonProject\feature_dataset.pkl"
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PIPELINE_PATH = "pipeline_model.pkl" # 与GUI中设置的路径一致
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SCALER_PATH = "scaler.pkl"
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train_cross_validated_model(DATASET_PATH, PIPELINE_PATH, SCALER_PATH) |
