Update

test-dataset-debug.py

@@ -151,7 +151,7 @@ class Patents(datasets.GeneratorBasedBuilder):
         # Download metadata
         # NOTE: data_files is a path to a pickled pandas DataFrame
         if self.config.data_files is None:
-            print(f'Loading / downloading metadata file: {_METADATA_URL}')
+            print(f'Loading or downloading metadata file: {_METADATA_URL}')
             metadata_file = dl_manager.download_and_extract(_METADATA_URL)
         else:
             print(f'Using metadata file: {self.config.data_files}')
@@ -161,7 +161,7 @@ class Patents(datasets.GeneratorBasedBuilder):
         # NOTE: data_dir is a path to a directory of json files, with one
         # json file per patent application
         if self.config.data_dir is None:
-            print('Loading / downloading data. This is a big file (360GB)!')
+            print('Loading or downloading data. If downloading, watch out! This is a huge file (360GB)!')
             json_dir = Path(dl_manager.download_and_extract(_DATA_URL))
             # NOTE: The extracted path contains a subfolder
             json_dir = json_dir / _DATA_SUBFOLDER_NAME
@@ -185,6 +185,8 @@ class Patents(datasets.GeneratorBasedBuilder):
         if self.config.query_string:
             df = df.query(self.config.query_string)
 
+        
+
         # Return only one dataset
         if self.config.train_only:
             if self.config.train_filing_start_date:
@@ -220,20 +222,25 @@ class Patents(datasets.GeneratorBasedBuilder):
 
         else:
 
+            # Check
+            if not all(self.config.train_filing_start_date, self.config.train_filing_end_date, 
+                       self.config.val_filing_start_date, self.config.train_filing_end_date):
+                raise ValueError("Please either use uniform_split or specify your exact \
+                    training and validation split dates.")
+
             # Does not assume that training_start_data < val_end_date
-            if self.config.train_filing_start_date:
-                print(f'Filtering by train filing start date: {self.config.train_filing_start_date}')
-                tdf = df[df['filing_date'] >= self.config.train_filing_start_date]
-                if self.config.train_filing_end_date:
-                    print(f'Filtering by train filing end date: {self.config.train_filing_end_date}')
-                    train_df = tdf[tdf['filing_date'] <= self.config.train_filing_end_date]
-
-            if self.config.val_filing_start_date:
-                print(f'Filtering by val filing start date: {self.config.val_filing_start_date}')
-                vdf = df[df['filing_date'] >= self.config.val_filing_start_date]
-                if self.config.val_filing_end_date:
-                    print(f'Filtering by val filing end date: {self.config.val_filing_end_date}')
-                    val_df = vdf[vdf['filing_date'] <= self.config.val_filing_end_date]
+            print(f'Filtering train dataset by filing start date: {self.config.train_filing_start_date}')
+            print(f'Filtering train dataset by filing end date: {self.config.train_filing_end_date}')
+            print(f'Filtering val dataset by filing start date: {self.config.val_filing_start_date}')
+            print(f'Filtering val dataset by filing end date: {self.config.val_filing_end_date}')
+            train_df = df[
+                (df['filing_date'] >= self.config.train_filing_start_date) & 
+                (df['filing_date'] < self.config.train_filing_end_date)
+            ]
+            val_df = df[
+                (df['filing_date'] >= self.config.val_filing_start_date) & 
+                (df['filing_date'] < self.config.val_filing_end_date)
+            ]
 
         # TODO: Can make this step faster
         if self.config.val_set_balancer: