上传文件至 /

main.py

@@ -1,34 +1,60 @@
-# -*- coding: utf-8 -*-
-"""
-主程序入口
-
-使用方式：
-
-1. 运行单个模型（默认）：
-   python main.py
-   
-   修改 config.py 中的 MODEL_TYPE 和 VECTORIZER_TYPE 来切换配置
-
-2. 运行对比实验：
-   修改 config.py 中 RUN_COMPARISON = True
-   
-   这会依次运行：
-   - 实验1: BoW vs TF-IDF (固定LR模型)
-   - 实验2: LR vs MLP (固定TF-IDF)
-   - 实验3: 不同学习率对比
-   - 实验4: 不同隐藏层大小对比
-   
-   最后输出汇总报告
-"""
-
-from train import main
-
-if __name__ == '__main__':
-    print("\n" + "=" * 70)
-    print("文本分类实验 - 纯NumPy实现")
-    print("数据集: ChnSentiCorp (中文酒店评论)")
-    print("模型: Logistic Regression / MLP")
-    print("向量化: BoW / TF-IDF")
-    print("=" * 70 + "\n")
-    
-    main()
+import numpy as np
+from dataset import load_data, BoWVectorizer, TFIDFVectorizer
+from train import train
+import config as cfg
+import pickle
+import time
+
+# <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+texts, labels = load_data()
+labels = np.array(labels)
+
+# <20><><EFBFBD><EFBFBD>ѵ<EFBFBD><D1B5><EFBFBD><EFBFBD>/<2F><><EFBFBD>Լ<EFBFBD>
+np.random.seed(42)
+indices = np.random.permutation(len(texts))
+split = int(0.8 * len(texts))
+train_idx, test_idx = indices[:split], indices[split:]
+train_texts = [texts[i] for i in train_idx]
+test_texts = [texts[i] for i in test_idx]
+y_train, y_test = labels[train_idx], labels[test_idx]
+
+# <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+if cfg.VECTORIZER_TYPE == "bow":
+    vec = BoWVectorizer(cfg.MAX_FEATURES)
+else:
+    vec = TFIDFVectorizer(cfg.MAX_FEATURES)
+
+vec.fit(train_texts)
+X_train = np.array([vec.transform(t) for t in train_texts])
+X_test = np.array([vec.transform(t) for t in test_texts])
+
+# ѵ<><D1B5>
+print("="*50)
+print(f"ѵ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>:\n  ģ<><C4A3>: {cfg.MODEL_TYPE}\n  <20><><EFBFBD><EFBFBD>: {cfg.VECTORIZER_TYPE}\n  ѧϰ<D1A7><CFB0>: {cfg.LEARNING_RATE}")
+print("="*50)
+
+model, t = train(
+    X_train, y_train, X_test, y_test,
+    model_type=cfg.MODEL_TYPE,
+    lr=cfg.LEARNING_RATE,
+    epochs=cfg.NUM_EPOCHS,
+    use_weight=cfg.USE_CLASS_WEIGHT
+)
+
+# <20><><EFBFBD><EFBFBD>
+ts = time.strftime("%m%d_%H%M%S")
+name = f"model_{cfg.MODEL_TYPE}_{cfg.VECTORIZER_TYPE}_{'weighted' if cfg.USE_CLASS_WEIGHT else 'raw'}_{ts}"
+
+if cfg.MODEL_TYPE == "lr":
+    np.save(f"{name}_W.npy", model.W)
+    np.save(f"{name}_b.npy", model.b)
+else:
+    np.save(f"{name}_W1.npy", model.W1)
+    np.save(f"{name}_b1.npy", model.b1)
+    np.save(f"{name}_W2.npy", model.W2)
+    np.save(f"{name}_b2.npy", model.b2)
+
+with open(f"{name}_vec.pkl", "wb") as f:
+    pickle.dump(vec, f)
+
+print(f"\nģ<EFBFBD><EFBFBD><EFBFBD>ѱ<EFBFBD><EFBFBD><EFBFBD>: {name}_*.npy/*.pkl")