import pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
import joblib


# 加载数据集（使用你之前合并的feature_dataset00.pkl）
def load_dataset(pkl_path):
    with open(pkl_path, 'rb') as f:
        data = pickle.load(f)
    return data['matrix'], data['label']


# 训练模型
def train_and_save_model(dataset_path, model_save_path, scaler_save_path):
    # 加载数据
    X, y = load_dataset(dataset_path)
    print(f"加载数据集：{X.shape[0]}个样本，{X.shape[1]}维特征")

    # 划分训练集
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42, stratify=y
    )

    # 标准化
    scaler = StandardScaler()
    X_train_std = scaler.fit_transform(X_train)
    X_test_std = scaler.transform(X_test)

    # 训练SVM
    svm = SVC(kernel='rbf', class_weight='balanced', probability=True, random_state=42)
    svm.fit(X_train_std, y_train)

    # 评估
    y_pred = svm.predict(X_test_std)
    print(f"模型准确率：{accuracy_score(y_test, y_pred):.4f}")

    # 保存模型和标准化器
    joblib.dump(svm, model_save_path)
    joblib.dump(scaler, scaler_save_path)
    print(f"模型已保存至：{model_save_path}")
    print(f"标准化器已保存至：{scaler_save_path}")


if __name__ == "__main__":
    # 替换为你的数据集路径
    DATASET_PATH = r"D:\SummerSchool\mat_cv\mat_cv\feature_dataset.pkl"
    # 模型保存路径（与GUI代码中设置的路径一致）
    MODEL_PATH = "svm_model.pkl"
    SCALER_PATH = "scaler.pkl"

    train_and_save_model(DATASET_PATH, MODEL_PATH, SCALER_PATH)