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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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使用 75% 训练 / 25% 测试 的方式评估 SVM（输出 ACC & AUC）
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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.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.svm import SVC
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from sklearn.metrics import accuracy_score, roc_auc_score
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# ---------- 1. 数据路径 ----------
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PKL_PATH = Path(r"D:\Python\空心检测\pythonProject\feature_dataset.pkl")
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# ---------- 2. 读取特征 ----------
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def load_pkl_matrix(path: Path):
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with open(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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X, y = load_pkl_matrix(PKL_PATH)
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y = y.ravel() # shape (N,)
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# ---------- 3. 75% / 25% 拆分 ----------
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.25, random_state=42, stratify=y, shuffle=True
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)
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# ---------- 4. 标准化 + SVM ----------
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scaler = StandardScaler().fit(X_train)
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X_train_std = scaler.transform(X_train)
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X_test_std = scaler.transform(X_test)
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svm = SVC(
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kernel="rbf",
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C=10,
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gamma="scale",
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probability=True,
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class_weight="balanced",
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random_state=42,
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)
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svm.fit(X_train_std, y_train)
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# ---------- 5. 评估 ----------
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y_pred = svm.predict(X_test_std)
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y_proba_pos = svm.predict_proba(X_test_std)[:, list(svm.classes_).index(1)]
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acc = accuracy_score(y_test, y_pred)
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auc = roc_auc_score(y_test, y_proba_pos)
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print("\n========== 评估结果 ==========")
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print(f"样本总数: {len(y)} | 训练: {len(y_train)} 测试: {len(y_test)}")
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print(f"ACC = {acc:.4f}")
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print(f"AUC = {auc:.4f}")
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