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Generate High Cardinality Data
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import pandas as pd | |
import numpy as np | |
import pyarrow.parquet as pq | |
from tqdm import tqdm | |
import os | |
import argparse | |
def parse_args(): | |
parser = argparse.ArgumentParser(description='Generate a large categorical dataset') | |
parser.add_argument('--col1_cardinality', type=int, default=50_000_000, help='Cardinality of column 1') | |
parser.add_argument('--col2_cardinality', type=int, default=10, help='Cardinality of column 2') | |
parser.add_argument('--col3_cardinality', type=int, default=4, help='Cardinality of column 3') | |
parser.add_argument('--total_rows', type=int, default=100_000_000, help='Total number of rows to generate') | |
parser.add_argument('--output_dir', type=str, default='./data/simulated', help='Output directory for dataset') | |
return parser.parse_args() | |
def generate_dataset_chunk(chunk_size, col1_start, col1_cardinality, col2_values, col3_values): | |
col1 = np.arange(col1_start, col1_start + chunk_size) % col1_cardinality | |
col2 = np.random.choice(col2_values, size=chunk_size) | |
col3 = np.random.choice(col3_values, size=chunk_size) | |
df = pd.DataFrame({ | |
'col1': col1, | |
'col2': col2, | |
'col3': col3 | |
}) | |
return df | |
def write_parquet_file(df, output_dir, file_index): | |
file_name = f'chunk_{file_index:05d}.parquet' | |
file_path = os.path.join(output_dir, file_name) | |
df.to_parquet(file_path, engine='pyarrow', index=False) | |
return file_path | |
def generate_large_categorical_dataset(output_dir, total_rows, col1_cardinality, col2_cardinality, col3_cardinality, chunk_size=1000000): | |
os.makedirs(output_dir, exist_ok=True) | |
col2_values = list(range(1, col2_cardinality + 1)) | |
col3_values = list(range(1, col3_cardinality + 1)) | |
num_chunks = (total_rows + chunk_size - 1) // chunk_size | |
file_paths = [] | |
for i in tqdm(range(num_chunks), desc="Generating chunks"): | |
chunk_start = i * chunk_size | |
chunk_end = min((i + 1) * chunk_size, total_rows) | |
actual_chunk_size = chunk_end - chunk_start | |
df_chunk = generate_dataset_chunk( | |
actual_chunk_size, | |
chunk_start, | |
col1_cardinality, | |
col2_values, | |
col3_values | |
) | |
file_path = write_parquet_file(df_chunk, output_dir, i) | |
file_paths.append(file_path) | |
# Ensure all values in col2 and col3 are present | |
df_final = pd.DataFrame({ | |
'col1': np.arange(col1_cardinality, col1_cardinality + len(col2_values) * len(col3_values)), | |
'col2': np.repeat(col2_values, len(col3_values)), | |
'col3': np.tile(col3_values, len(col2_values)) | |
}) | |
file_path = write_parquet_file(df_final, output_dir, num_chunks) | |
file_paths.append(file_path) | |
return file_paths | |
def read_dataset(file_paths): | |
return pq.ParquetDataset(file_paths).read() | |
if __name__ == "__main__": | |
args = parse_args() | |
output_dir = os.path.join(args.output_dir, 'source_dataset') | |
file_paths = generate_large_categorical_dataset( | |
output_dir, | |
args.total_rows, | |
args.col1_cardinality, | |
args.col2_cardinality, | |
args.col3_cardinality | |
) | |
# Verify the dataset | |
dataset = read_dataset(file_paths) | |
df_verify = dataset.to_pandas() | |
print(f"Total rows: {len(df_verify)}") | |
print(f"Col1 unique values: {df_verify['col1'].nunique()}") | |
print(f"Col2 unique values: {df_verify['col2'].nunique()}") | |
print(f"Col3 unique values: {df_verify['col3'].nunique()}") |
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