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yambda / benchmarks /scripts /make_multievent.py
ploshkin's picture
Add code for benchmarking
1be89f3 verified
import os
import click
import polars as pl
@click.command()
@click.option(
"--src_dir",
type=click.Path(exists=True, file_okay=False),
required=True,
help="Path to the directory containing source parquet files, e.g., './50m'.",
)
@click.option(
"--dst_dir",
type=click.Path(file_okay=False),
required=False,
help="Path to the directory where Parquet files will be saved. "
"If not specified, Parquet files are saved in 'src_dir'. e.g., './out'.",
)
@click.option(
"--file_name",
type=str,
default="multi_event",
help="Base name for the output Parquet file. Default is 'multi_event'.",
)
def cli(src_dir: str, dst_dir: str, file_name: str):
if dst_dir is None:
dst_dir = src_dir
print(f"{src_dir=}, {dst_dir=}, {file_name=}")
make_multievent_dataset(src_dir, dst_dir, file_name)
def make_multievent_dataset(src_dir: str, dst_dir: str, file_name: str):
os.makedirs(dst_dir, exist_ok=True)
dislikes = pl.scan_parquet(os.path.join(src_dir, "dislikes.parquet"))
likes = pl.scan_parquet(os.path.join(src_dir, "likes.parquet"))
listens = pl.scan_parquet(os.path.join(src_dir, "listens.parquet"))
undislikes = pl.scan_parquet(os.path.join(src_dir, "undislikes.parquet"))
unlikes = pl.scan_parquet(os.path.join(src_dir, "unlikes.parquet"))
events = pl.Enum(["listen", "dislike", "like", "undislike", "unlike"])
combined_df = pl.concat(
[
listens.with_columns(
pl.lit("listen").cast(events).alias("event_type"),
),
dislikes.with_columns(
pl.lit(None).alias("played_ratio_pct"),
pl.lit(None).alias("track_length_seconds"),
pl.lit("dislike").cast(events).alias("event_type"),
),
likes.with_columns(
pl.lit(None).alias("played_ratio_pct"),
pl.lit(None).alias("track_length_seconds"),
pl.lit("like").cast(events).alias("event_type"),
),
undislikes.with_columns(
pl.lit(None).alias("played_ratio_pct"),
pl.lit(None).alias("track_length_seconds"),
pl.lit("undislike").cast(events).alias("event_type"),
),
unlikes.with_columns(
pl.lit(None).alias("played_ratio_pct"),
pl.lit(None).alias("track_length_seconds"),
pl.lit("unlike").cast(events).alias("event_type"),
),
]
).sort(
by=[
"uid",
"timestamp",
],
maintain_order=True,
)
combined_df.with_columns(pl.col("event_type").cast(events)).sink_parquet(
os.path.join(dst_dir, file_name + ".parquet"),
compression="lz4",
statistics=True,
)
if __name__ == "__main__":
cli()