From 4d9dea4960e0b0aeb0eae8a3f14a216a6809d96b Mon Sep 17 00:00:00 2001
From: wh <2627521256@qq.com>
Date: Mon, 10 Nov 2025 21:38:46 +0800
Subject: [PATCH] 1

---
 75%25%.py | 58 -------------------------------------------------------
 1 file changed, 58 deletions(-)
 delete mode 100644 75%25%.py

diff --git a/75%25%.py b/75%25%.py
deleted file mode 100644
index cd68ac0..0000000
--- a/75%25%.py
+++ /dev/null
@@ -1,58 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-使用 75% 训练 / 25% 测试 的方式评估 SVM（输出 ACC & AUC）
-"""
-
-import pickle
-import numpy as np
-from pathlib import Path
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.svm import SVC
-from sklearn.metrics import accuracy_score, roc_auc_score
-
-
-# ---------- 1. 数据路径 ----------
-PKL_PATH = Path(r"D:\Python\空心检测\pythonProject\feature_dataset.pkl")
-
-# ---------- 2. 读取特征 ----------
-def load_pkl_matrix(path: Path):
-    with open(path, "rb") as f:
-        data = pickle.load(f)
-    return data["matrix"], data["label"]
-
-X, y = load_pkl_matrix(PKL_PATH)
-y = y.ravel()                           # shape (N,)
-
-# ---------- 3. 75% / 25% 拆分 ----------
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, test_size=0.25, random_state=42, stratify=y, shuffle=True
-)
-
-# ---------- 4. 标准化 + SVM ----------
-scaler = StandardScaler().fit(X_train)
-X_train_std = scaler.transform(X_train)
-X_test_std  = scaler.transform(X_test)
-
-svm = SVC(
-    kernel="rbf",
-    C=10,
-    gamma="scale",
-    probability=True,
-    class_weight="balanced",
-    random_state=42,
-)
-svm.fit(X_train_std, y_train)
-
-# ---------- 5. 评估 ----------
-y_pred        = svm.predict(X_test_std)
-y_proba_pos   = svm.predict_proba(X_test_std)[:, list(svm.classes_).index(1)]
-
-acc = accuracy_score(y_test, y_pred)
-auc = roc_auc_score(y_test, y_proba_pos)
-
-print("\n========== 评估结果 ==========")
-print(f"样本总数: {len(y)}  |  训练: {len(y_train)}  测试: {len(y_test)}")
-print(f"ACC  = {acc:.4f}")
-print(f"AUC  = {auc:.4f}")