266 lines
8.3 KiB
Python
266 lines
8.3 KiB
Python
# -*- coding: utf-8 -*-
|
||
"""
|
||
训练与对比实验模块
|
||
|
||
功能：
|
||
1. 单模型训练与评估
|
||
2. 模型对比实验（LR vs MLP）
|
||
3. 向量化对比实验（BoW vs TF-IDF）
|
||
4. 超参数对比实验
|
||
"""
|
||
|
||
import numpy as np
|
||
import time
|
||
from datetime import datetime
|
||
from model_numpy import create_model
|
||
from dataset import load_data
|
||
from config import *
|
||
|
||
|
||
def train_single_model(X_train, y_train, X_test, y_test,
|
||
model_type, vectorizer_type, epochs, batch_size, lr, hidden_size):
|
||
"""
|
||
训练单个模型并返回结果
|
||
"""
|
||
print(f"\n{'='*60}")
|
||
print(f"训练配置:")
|
||
print(f" 模型: {model_type.upper()}")
|
||
print(f" 向量: {vectorizer_type.upper()}")
|
||
print(f" 学习率: {lr}")
|
||
if model_type == 'mlp':
|
||
print(f" 隐藏层大小: {hidden_size}")
|
||
print(f" 训练轮数: {epochs}")
|
||
print(f"{'='*60}")
|
||
|
||
# 重新加载数据（确保向量化方式正确）
|
||
X_tr, y_tr, X_te, y_te, _ = load_data(
|
||
DATA_DIR, MAX_FEATURES, MAX_SEQ_LEN, vectorizer_type
|
||
)
|
||
|
||
# 计算类别权重（解决数据不平衡问题）
|
||
pos_count = sum(y_tr)
|
||
neg_count = len(y_tr) - pos_count
|
||
n_samples = len(y_tr)
|
||
n_classes = 2
|
||
|
||
if USE_CLASS_WEIGHT:
|
||
# 权重 = n_samples / (n_classes * n_class_i)
|
||
# 负面样本少，权重更大
|
||
weight_pos = n_samples / (n_classes * pos_count)
|
||
weight_neg = n_samples / (n_classes * neg_count)
|
||
class_weight = {0: weight_neg, 1: weight_pos}
|
||
print(f" 类别权重: 正面={weight_pos:.2f}, 负面={weight_neg:.2f}")
|
||
else:
|
||
class_weight = None
|
||
print(f" 类别权重: 不使用")
|
||
|
||
# 创建模型
|
||
model = create_model(
|
||
model_type=model_type,
|
||
input_size=X_tr.shape[1],
|
||
hidden_size=hidden_size,
|
||
num_classes=NUM_CLASSES,
|
||
learning_rate=lr,
|
||
keep_prob=KEEP_PROB,
|
||
class_weight=class_weight
|
||
)
|
||
|
||
# 训练
|
||
start_time = time.time()
|
||
model.fit(X_tr, y_tr, X_te, y_te, epochs=epochs, batch_size=batch_size, verbose=True)
|
||
train_time = time.time() - start_time
|
||
|
||
# 最终评估
|
||
train_acc = model.accuracy(X_tr, y_tr)
|
||
test_acc = model.accuracy(X_te, y_te)
|
||
|
||
print(f"\n最终结果:")
|
||
print(f" 训练准确率: {train_acc:.4f}")
|
||
print(f" 测试准确率: {test_acc:.4f}")
|
||
print(f" 训练时间: {train_time:.2f}秒")
|
||
|
||
# 保存模型权重
|
||
# 生成模型文件名（包含配置标识和时间戳）
|
||
weight_suffix = "weighted" if USE_CLASS_WEIGHT else "noweight"
|
||
timestamp = datetime.now().strftime("%m%d_%H%M%S")
|
||
model_path = f"model_{model_type}_{vectorizer_type}_{weight_suffix}_{timestamp}"
|
||
model.save(model_path)
|
||
print(f" 权重文件: {model_path}_*.npy")
|
||
|
||
return {
|
||
'model_type': model_type,
|
||
'vectorizer_type': vectorizer_type,
|
||
'train_acc': train_acc,
|
||
'test_acc': test_acc,
|
||
'train_time': train_time,
|
||
'lr': lr,
|
||
'hidden_size': hidden_size
|
||
}
|
||
|
||
|
||
def run_comparison_experiments():
|
||
"""
|
||
运行对比实验
|
||
|
||
实验设计：
|
||
1. 不同向量化方法的效果对比 (BoW vs TF-IDF)
|
||
2. 不同模型的效果对比 (LR vs MLP)
|
||
3. 超参数对比 (学习率, 隐藏层大小)
|
||
"""
|
||
print("\n" + "=" * 70)
|
||
print("文本分类对比实验")
|
||
print("=" * 70)
|
||
|
||
results = []
|
||
|
||
# ===== 实验1: 向量化方法对比 (固定模型=LR) =====
|
||
print("\n\n" + "▶ " * 20)
|
||
print("实验1: 向量化方法对比 (模型=LR, 学习率=0.05)")
|
||
print("▶ " * 20)
|
||
|
||
for vec_type in COMPARE_VECTORS:
|
||
result = train_single_model(
|
||
None, None, None, None,
|
||
model_type='lr',
|
||
vectorizer_type=vec_type,
|
||
epochs=50,
|
||
batch_size=BATCH_SIZE,
|
||
lr=0.05,
|
||
hidden_size=HIDDEN_SIZE
|
||
)
|
||
results.append(result)
|
||
|
||
# ===== 实验2: 模型复杂度对比 (固定向量=TF-IDF) =====
|
||
print("\n\n" + "▶ " * 20)
|
||
print("实验2: 模型复杂度对比 (向量=TF-IDF)")
|
||
print("▶ " * 20)
|
||
|
||
for model_type in COMPARE_MODELS:
|
||
result = train_single_model(
|
||
None, None, None, None,
|
||
model_type=model_type,
|
||
vectorizer_type='tfidf',
|
||
epochs=50,
|
||
batch_size=BATCH_SIZE,
|
||
lr=LEARNING_RATE,
|
||
hidden_size=HIDDEN_SIZE
|
||
)
|
||
results.append(result)
|
||
|
||
# ===== 实验3: 学习率对比 (MLP) =====
|
||
print("\n\n" + "▶ " * 20)
|
||
print("实验3: 学习率对比 (模型=MLP, 向量=TF-IDF)")
|
||
print("▶ " * 20)
|
||
|
||
for lr in [0.01, 0.1]:
|
||
result = train_single_model(
|
||
None, None, None, None,
|
||
model_type='mlp',
|
||
vectorizer_type='tfidf',
|
||
epochs=50,
|
||
batch_size=BATCH_SIZE,
|
||
lr=lr,
|
||
hidden_size=64
|
||
)
|
||
results.append(result)
|
||
|
||
# ===== 实验4: 隐藏层大小对比 (MLP) =====
|
||
print("\n\n" + "▶ " * 20)
|
||
print("实验4: 隐藏层大小对比 (模型=MLP, 向量=TF-IDF)")
|
||
print("▶ " * 20)
|
||
|
||
for hidden_size in [32, 128]:
|
||
result = train_single_model(
|
||
None, None, None, None,
|
||
model_type='mlp',
|
||
vectorizer_type='tfidf',
|
||
epochs=50,
|
||
batch_size=BATCH_SIZE,
|
||
lr=LEARNING_RATE,
|
||
hidden_size=hidden_size
|
||
)
|
||
results.append(result)
|
||
|
||
# ===== 汇总报告 =====
|
||
print("\n\n" + "=" * 70)
|
||
print("实验结果汇总")
|
||
print("=" * 70)
|
||
|
||
print(f"\n{'配置':<40} {'训练准确率':<12} {'测试准确率':<12} {'训练时间(秒)':<10}")
|
||
print("-" * 76)
|
||
|
||
for r in results:
|
||
config = f"{r['model_type'].upper()} + {r['vectorizer_type'].upper()}"
|
||
if r['lr'] != LEARNING_RATE:
|
||
config += f", lr={r['lr']}"
|
||
if r['hidden_size'] != HIDDEN_SIZE:
|
||
config += f", h={r['hidden_size']}"
|
||
print(f"{config:<40} {r['train_acc']:<12.4f} {r['test_acc']:<12.4f} {r['train_time']:<10.2f}")
|
||
|
||
# 分析
|
||
print("\n" + "=" * 70)
|
||
print("结果分析")
|
||
print("=" * 70)
|
||
|
||
# 找出最佳配置
|
||
best = max(results, key=lambda x: x['test_acc'])
|
||
print(f"\n最佳测试准确率: {best['test_acc']:.4f}")
|
||
print(f"最佳配置: {best['model_type'].upper()} + {best['vectorizer_type'].upper()}")
|
||
|
||
# BoW vs TF-IDF 分析
|
||
bow_results = [r for r in results if r['vectorizer_type'] == 'bow' and r['model_type'] == 'lr']
|
||
tfidf_results = [r for r in results if r['vectorizer_type'] == 'tfidf' and r['model_type'] == 'lr']
|
||
|
||
if bow_results and tfidf_results:
|
||
bow_acc = bow_results[0]['test_acc']
|
||
tfidf_acc = tfidf_results[0]['test_acc']
|
||
print(f"\n向量化方法影响:")
|
||
print(f" BoW测试准确率: {bow_acc:.4f}")
|
||
print(f" TF-IDF测试准确率: {tfidf_acc:.4f}")
|
||
print(f" 差异: {abs(tfidf_acc - bow_acc):.4f} ({'TF-IDF更优' if tfidf_acc > bow_acc else 'BoW更优'})")
|
||
|
||
# LR vs MLP 分析
|
||
lr_results = [r for r in results if r['model_type'] == 'lr' and r['vectorizer_type'] == 'tfidf']
|
||
mlp_results = [r for r in results if r['model_type'] == 'mlp' and r['vectorizer_type'] == 'tfidf']
|
||
|
||
if lr_results and mlp_results:
|
||
lr_acc = lr_results[0]['test_acc']
|
||
mlp_acc = mlp_results[0]['test_acc']
|
||
print(f"\n模型复杂度影响:")
|
||
print(f" LR测试准确率: {lr_acc:.4f}")
|
||
print(f" MLP测试准确率: {mlp_acc:.4f}")
|
||
print(f" 差异: {abs(mlp_acc - lr_acc):.4f} ({'MLP更优' if mlp_acc > lr_acc else 'LR更优'})")
|
||
|
||
return results
|
||
|
||
|
||
def main():
|
||
"""主函数"""
|
||
print("=" * 70)
|
||
print("文本分类实验 - 纯NumPy实现")
|
||
print("数据集: ChnSentiCorp (中文酒店评论)")
|
||
print("=" * 70)
|
||
|
||
if RUN_COMPARISON:
|
||
# 运行对比实验
|
||
run_comparison_experiments()
|
||
else:
|
||
# 运行单个模型
|
||
train_single_model(
|
||
None, None, None, None,
|
||
model_type=MODEL_TYPE,
|
||
vectorizer_type=VECTORIZER_TYPE,
|
||
epochs=NUM_EPOCHS,
|
||
batch_size=BATCH_SIZE,
|
||
lr=LEARNING_RATE,
|
||
hidden_size=HIDDEN_SIZE
|
||
)
|
||
|
||
print("\n" + "=" * 70)
|
||
print("实验完成!")
|
||
print("=" * 70)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|