Upload ProDiff/preprocess_data.py with huggingface_hub

ProDiff/preprocess_data.py

@@ -0,0 +1,92 @@
+import pandas as pd
+import h5py
+import numpy as np
+from tqdm import tqdm
+import os
+
+def create_h5_from_csv(csv_path, train_h5_path, test_h5_path, test_split_ratio=0.1):
+    """
+    Reads trajectory data from a CSV file, processes it, and saves it into
+    HDF5 files structured for the TrajectoryDataset.
+
+    The HDF5 file will have a group for each user, containing datasets for
+    'hours' (as Unix timestamps), 'latitudes', and 'longitudes'.
+    """
+    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()
+    test_user_count = int(len(all_user_ids) * test_split_ratio)
+    test_user_ids = set(np.random.choice(all_user_ids, size=test_user_count, replace=False))
+    
+    print(f"Total users: {len(all_user_ids)}")
+    print(f"Training users: {len(all_user_ids) - test_user_count}")
+    print(f"Test users: {test_user_count}")
+
+    # Process for both train and test sets
+    for h5_path, user_ids, set_name in [(train_h5_path, all_user_ids - test_user_ids, "train"), 
+                                        (test_h5_path, test_user_ids, "test")]:
+        
+        if not user_ids:
+            print(f"No users for {set_name} set, skipping.")
+            continue
+
+        print(f"\nCreating {set_name} HDF5 file at {h5_path}...")
+        with h5py.File(h5_path, 'w') as h5f:
+            # Group by userid
+            grouped = df[df['userid'].isin(user_ids)].groupby('userid')
+            
+            for user_id, user_df in tqdm(grouped, desc=f"Processing {set_name} users"):
+                # Ensure data is sorted by time for each user
+                user_df = user_df.sort_values('datetime')
+                
+                # Convert datetime to unix timestamp for 'hours'
+                # The original code used 'hours', but absolute time is more robust.
+                timestamps = user_df['datetime'].apply(lambda x: x.timestamp()).values
+                latitudes = user_df['lat'].values
+                longitudes = user_df['lng'].values
+
+                # Create a group for the user
+                user_group = h5f.create_group(str(user_id))
+                
+                # Store data in the user's group
+                user_group.create_dataset('hours', data=timestamps, dtype='float64')
+                user_group.create_dataset('latitudes', data=latitudes, dtype='float64')
+                user_group.create_dataset('longitudes', data=longitudes, dtype='float64')
+        
+        print(f"{set_name.capitalize()} data processing complete. File saved to {h5_path}")
+
+
+if __name__ == '__main__':
+    # Configuration
+    CSV_DATA_PATH = 'data/May_trajectory_data.csv'
+    
+    # Define output paths inside the 'data' directory
+    output_dir = 'data'
+    os.makedirs(output_dir, exist_ok=True)
+    TRAIN_H5_PATH = os.path.join(output_dir, 'train.h5')
+    TEST_H5_PATH = os.path.join(output_dir, 'test.h5')
+
+    # Run the conversion
+    create_h5_from_csv(CSV_DATA_PATH, TRAIN_H5_PATH, TEST_H5_PATH)
+    
+    # Optional: Verify the created file structure for one user
+    print("\nVerifying HDF5 file structure...")
+    try:
+        with h5py.File(TRAIN_H5_PATH, 'r') as h5f:
+            if list(h5f.keys()):
+                sample_user_id = list(h5f.keys())[0]
+                print(f"Sample user '{sample_user_id}' in {TRAIN_H5_PATH}:")
+                for dset in h5f[sample_user_id].keys():
+                    print(f"  - Dataset: {dset}, Shape: {h5f[sample_user_id][dset].shape}")
+            else:
+                print("Train HDF5 file is empty.")
+    except Exception as e:
+        print(f"Could not verify HDF5 file: {e}")