Parquet conversion and README yaml editing

UsenetArchiveIT.py

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+from datasets import DatasetBuilder, SplitGenerator, Split, Features, Value, ClassLabel, BuilderConfig, Version, DatasetInfo, DownloadManager, ArrowBasedBuilder
+import glob
+import json
+import multiprocessing as mp
+import os
+import pyarrow as pa
+import pyarrow.parquet as pq
+import pandas as pd
+import pyarrow as pa
+import pyarrow.json
+# jsonl 
+
+pattern="*.bz2"
+
+paths=glob.glob(pattern)
+
+# exclude txt files
+
+paths=[file for file in paths if not ".txt." in file]
+
+n_files=len(paths)
+
+# labels are file names without the extension .jsonl.bz2
+
+labels=[file.replace(".jsonl.bz2","") for file in paths]
+
+
+
+## handle parquet conversion
+
+# create parquet directory
+
+dl_manager = DownloadManager()
+
+parquet_dir="parquet"
+
+
+    
+
+def convert_jsonl_to_parquet(file_list, parquet_dir, chunk_size=100000):
+    """Converts JSONL files to Parquet with memory efficiency.
+
+    Args:
+        file_list (list): List of JSONL file paths.
+        parquet_dir (str): Path to store output Parquet files.
+        chunk_size (int): Number of records to write to each Parquet file.
+    """
+
+    os.makedirs(parquet_dir, exist_ok=True)  # Create output directory
+
+    parquet_file_index = 0
+    current_records = [] 
+    file_index = 0
+    for file in file_list:
+        # try:
+            reader = pa.json.read_json(file)  # PyArrow JSON reader
+            
+            for batch in reader:
+                pandas_df = batch.to_pandas()
+                print(pandas_df.shape)
+                current_records.extend(pandas_df.to_dict('list'))
+                if len(current_records) >= chunk_size:
+                    table = pa.Table.from_pandas(pd.DataFrame(current_records))
+                    parquet_filename = f"output_{parquet_file_index}.parquet"
+                    parquet_path = os.path.join(parquet_dir, parquet_filename)
+                    pq.write_table(table, parquet_path)
+
+                    current_records = [] 
+                    parquet_file_index += 1
+        # except Exception as e:
+        #     print(f"Error in file {file} with error {e}")
+            file_index += 1
+            print(f"Finished processing file {file_index} of {len(file_list)}")
+    print(f"Writing last chunk to parquet file {parquet_file_index}")
+    # Write any remaining data in the last chunk
+    if current_records:
+        table = pa.Table.from_pandas(pd.DataFrame(current_records))
+        parquet_filename = f"output_{parquet_file_index}.parquet"
+        parquet_path = os.path.join(parquet_dir, parquet_filename)
+        pq.write_table(table, parquet_path)
+    
+    print(f"Conversion complete, wrote {parquet_file_index + 1} Parquet files.")
+    
+
+
+
+
+class UsenetConfig(BuilderConfig):
+    def __init__(self, version, **kwargs):
+        ().__init__(version, **kwargs)
+        
+        
+        
+        
+        
+
+        
+
+
+class UsenetArchiveIt(ArrowBasedBuilder):
+    VERSION = "1.0.0"  # Example version
+    
+    BUILDER_CONFIG_CLASS = UsenetConfig
+    
+    BUILDER_CONFIGS = [
+        UsenetConfig(
+            name="usenet_archive_it",
+            version=Version("1.0.0"),
+            description="Usenet Archive-It dataset",
+        ),
+    ]
+    
+    def _info(self):
+        # Specify dataset features here
+        return DatasetInfo(
+            features=Features({
+                "title": Value("string"),
+                "author": Value("string"),
+                "id":  Value("int32"),
+                "timestamp": Value("string"),
+                "progressive_number": Value("int32"),
+                "original_url": Value("string"),
+                "newsgroup": Value("string"), # this could be a label but difficult to get all possible labels
+                "text": Value("string"),
+            }),)
+
+    def _split_generators(self, dl_manager):
+        n = mp.cpu_count()//10  # Number of paths to process at a time
+        print(f"Extracting {n} files at a time")
+        if not os.path.isdir('parquet'):
+            extracted_files = []
+            for i in range(0, len(paths), n):
+                
+                files = paths[i:i+n]
+                extracted_files.extend(dl_manager.extract(files, num_proc=len(files)))
+                print(f"Extracted {files}")
+        else:
+            extracted_files = glob.glob(parquet_dir + "/*.parquet")
+            
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"filepath": extracted_files},
+            ),
+            
+        ]
+
+    def _generate_tables(self, filepath):
+        
+        # print("Filepath: ", filepath)
+        
+        # if parquet files are not present, convert jsonl to parquet
+        if not os.path.exists(parquet_dir):
+            print("Generating parquet files from jsonl files...")
+            convert_jsonl_to_parquet(filepath, parquet_dir)
+            
+        # read parquet files
+        parquet_files=glob.glob(parquet_dir+"/*.parquet")
+        
+
+        for index, file in enumerate(parquet_files):
+                table = pq.read_table(file)
+                yield index, table
+        
+        
+        # for file in parquet_files:
+        #     table = pq.read_table(file)
+        #     df = table.to_pandas()
+        #     for index, row in df.iterrows():
+        #         yield index, row.to_dict()
+        
+        
+        # Yields (key, example) tuples from the dataset
+        # id=0
+        # for file in filepath:
+        #     # Open and yield examples from the compressed JSON files
+        #     with open(file, "r") as f:
+        #         for i, line in enumerate(f):
+        #             try:
+        #                 data = json.loads(line)
+        #                 yield id, data
+        #                 id+=1
+        #             except Exception as e:
+        #                 print(f"Error in file {file} at line {i} with error {e}")
+
+
+# Finally, set the name of the dataset to match the script name
+datasets = UsenetArchiveIt