UsenetArchiveIT.py

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