Upload ProDiff/preprocess_data_temporal.py with huggingface_hub

ProDiff/preprocess_data_temporal.py

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+import pandas as pd
+import h5py
+import numpy as np
+from tqdm import tqdm
+import os
+
+def create_h5_temporal_split(csv_path, train_h5_path, test_h5_path, train_ratio=0.8):
+    """
+    使用时间分割策略：每个用户的轨迹按时间分割，前80%用于训练，后20%用于测试
+    这样可以测试模型对同一用户未来轨迹的预测能力，同时保留所有用户的路径模式
+    """
+    print(f"Loading data from {csv_path}...")
+    try:
+        df = pd.read_csv(csv_path, parse_dates=['datetime'])
+    except Exception as e:
+        print(f"Error reading or parsing CSV: {e}")
+        return
+
+    print("Sorting data by user and time...")
+    df.sort_values(by=['userid', 'datetime'], inplace=True)
+
+    all_user_ids = df['userid'].unique()
+    print(f"Total users: {len(all_user_ids)}")
+    print(f"Using temporal split: {train_ratio*100:.0f}% for training, {(1-train_ratio)*100:.0f}% for testing")
+
+    # 为训练集和测试集创建HDF5文件
+    train_sample_count = 0
+    test_sample_count = 0
+    
+    with h5py.File(train_h5_path, 'w') as train_h5f, h5py.File(test_h5_path, 'w') as test_h5f:
+        
+        for user_id in tqdm(all_user_ids, desc="Processing users"):
+            user_df = df[df['userid'] == user_id].sort_values('datetime')
+            
+            # 按时间分割：前train_ratio用于训练，后面用于测试
+            split_point = int(len(user_df) * train_ratio)
+            
+            train_user_df = user_df.iloc[:split_point]
+            test_user_df = user_df.iloc[split_point:]
+            
+            # 处理训练数据（如果有足够的数据点）
+            if len(train_user_df) > 0:
+                timestamps = train_user_df['datetime'].apply(lambda x: x.timestamp()).values
+                latitudes = train_user_df['lat'].values
+                longitudes = train_user_df['lng'].values
+
+                train_user_group = train_h5f.create_group(f"{user_id}_train")
+                train_user_group.create_dataset('hours', data=timestamps, dtype='float64')
+                train_user_group.create_dataset('latitudes', data=latitudes, dtype='float64')
+                train_user_group.create_dataset('longitudes', data=longitudes, dtype='float64')
+                train_sample_count += len(timestamps)
+            
+            # 处理测试数据（如果有足够的数据点）
+            if len(test_user_df) > 0:
+                timestamps = test_user_df['datetime'].apply(lambda x: x.timestamp()).values
+                latitudes = test_user_df['lat'].values
+                longitudes = test_user_df['lng'].values
+
+                test_user_group = test_h5f.create_group(f"{user_id}_test")
+                test_user_group.create_dataset('hours', data=timestamps, dtype='float64')
+                test_user_group.create_dataset('latitudes', data=latitudes, dtype='float64')
+                test_user_group.create_dataset('longitudes', data=longitudes, dtype='float64')
+                test_sample_count += len(timestamps)
+    
+    print(f"\nData processing complete!")
+    print(f"Training samples: {train_sample_count}")
+    print(f"Testing samples: {test_sample_count}")
+    print(f"Train file saved to: {train_h5_path}")
+    print(f"Test file saved to: {test_h5_path}")
+
+def create_h5_mixed_split(csv_path, train_h5_path, test_h5_path, full_test_users=5, temporal_ratio=0.8):
+    """
+    混合分割策略：
+    - 少数用户完全作为测试集（测试跨用户泛化）
+    - 其余用户按时间分割（测试时间泛化）
+    """
+    print(f"Loading data from {csv_path}...")
+    try:
+        df = pd.read_csv(csv_path, parse_dates=['datetime'])
+    except Exception as e:
+        print(f"Error reading or parsing CSV: {e}")
+        return
+
+    print("Sorting data by user and time...")
+    df.sort_values(by=['userid', 'datetime'], inplace=True)
+
+    all_user_ids = df['userid'].unique()
+    
+    # 随机选择几个用户完全作为测试集
+    np.random.seed(42)  # 固定随机种子确保可重复
+    full_test_user_ids = set(np.random.choice(all_user_ids, size=full_test_users, replace=False))
+    temporal_split_user_ids = set(all_user_ids) - full_test_user_ids
+    
+    print(f"Total users: {len(all_user_ids)}")
+    print(f"Users for temporal split: {len(temporal_split_user_ids)}")
+    print(f"Users completely in test set: {len(full_test_user_ids)}")
+    print(f"Full test users: {sorted(full_test_user_ids)}")
+
+    train_sample_count = 0
+    test_sample_count = 0
+    
+    with h5py.File(train_h5_path, 'w') as train_h5f, h5py.File(test_h5_path, 'w') as test_h5f:
+        
+        # 处理时间分割的用户
+        for user_id in tqdm(temporal_split_user_ids, desc="Processing temporal split users"):
+            user_df = df[df['userid'] == user_id].sort_values('datetime')
+            
+            split_point = int(len(user_df) * temporal_ratio)
+            train_user_df = user_df.iloc[:split_point]
+            test_user_df = user_df.iloc[split_point:]
+            
+            # 训练数据
+            if len(train_user_df) > 0:
+                timestamps = train_user_df['datetime'].apply(lambda x: x.timestamp()).values
+                latitudes = train_user_df['lat'].values
+                longitudes = train_user_df['lng'].values
+
+                train_user_group = train_h5f.create_group(f"{user_id}_temporal")
+                train_user_group.create_dataset('hours', data=timestamps, dtype='float64')
+                train_user_group.create_dataset('latitudes', data=latitudes, dtype='float64')
+                train_user_group.create_dataset('longitudes', data=longitudes, dtype='float64')
+                train_sample_count += len(timestamps)
+            
+            # 测试数据
+            if len(test_user_df) > 0:
+                timestamps = test_user_df['datetime'].apply(lambda x: x.timestamp()).values
+                latitudes = test_user_df['lat'].values
+                longitudes = test_user_df['lng'].values
+
+                test_user_group = test_h5f.create_group(f"{user_id}_temporal")
+                test_user_group.create_dataset('hours', data=timestamps, dtype='float64')
+                test_user_group.create_dataset('latitudes', data=latitudes, dtype='float64')
+                test_user_group.create_dataset('longitudes', data=longitudes, dtype='float64')
+                test_sample_count += len(timestamps)
+        
+        # 处理完全作为测试集的用户
+        for user_id in tqdm(full_test_user_ids, desc="Processing full test users"):
+            user_df = df[df['userid'] == user_id].sort_values('datetime')
+            
+            timestamps = user_df['datetime'].apply(lambda x: x.timestamp()).values
+            latitudes = user_df['lat'].values
+            longitudes = user_df['lng'].values
+
+            test_user_group = test_h5f.create_group(f"{user_id}_full")
+            test_user_group.create_dataset('hours', data=timestamps, dtype='float64')
+            test_user_group.create_dataset('latitudes', data=latitudes, dtype='float64')
+            test_user_group.create_dataset('longitudes', data=longitudes, dtype='float64')
+            test_sample_count += len(timestamps)
+    
+    print(f"\nMixed split processing complete!")
+    print(f"Training samples: {train_sample_count}")
+    print(f"Testing samples: {test_sample_count}")
+    print(f"Train file saved to: {train_h5_path}")
+    print(f"Test file saved to: {test_h5_path}")
+
+if __name__ == '__main__':
+    # 配置
+    # 直接使用新的轨迹数据文件
+    CSV_DATA_PATH = 'data/matched_trajectory_data.csv'
+    output_dir = 'data'
+    
+    print(f"将使用输入文件: {CSV_DATA_PATH}")
+    print("将使用时间分割策略生成 train_temporal.h5 和 test_temporal.h5")
+    
+    # 定义输出路径
+    TRAIN_H5_PATH = os.path.join(output_dir, 'train_temporal.h5')
+    TEST_H5_PATH = os.path.join(output_dir, 'test_temporal.h5')
+    
+    # 运行转换
+    create_h5_temporal_split(CSV_DATA_PATH, TRAIN_H5_PATH, TEST_H5_PATH)
+    
+    # 验证生成的文件
+    print("\n验证生成的HDF5文件...")
+    try:
+        with h5py.File(TRAIN_H5_PATH, 'r') as h5f:
+            print(f"训练集包含 {len(h5f.keys())} 个用户组")
+            if h5f.keys():
+                sample_key = list(h5f.keys())[0]
+                sample_group = h5f[sample_key]
+                print(f"示例用户组 '{sample_key}':")
+                for dset_name in sample_group.keys():
+                    dset = sample_group[dset_name]
+                    print(f"  - {dset_name}: {dset.shape}")
+    except Exception as e:
+        print(f"验证文件时出错: {e}")