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import enum
import os
from pathlib import Path
import click
import matplotlib.pyplot as plt
import polars as pl
class ImageFormat(enum.Enum):
PNG = "png"
JPEG = "jpg"
@click.command()
@click.option(
"--src_dir",
type=click.Path(exists=True, file_okay=False),
required=True,
help="Path to the directory containing dataset, e.g., './data'.",
)
@click.option(
"--dst_dir",
type=click.Path(file_okay=False),
required=False,
help="Path to the directory where statisics will be saved. "
"If not specified, statistics is saved in '{src_dir}/stats'. e.g., './stats'.",
)
@click.option(
"--file_name",
type=str,
default="multi_event",
help="Base name for multi-event file. Default is 'multi_event'.",
)
def cli(src_dir: str, dst_dir: str, file_name: str):
if dst_dir is None:
dst_dir = f"{src_dir}/stats"
generate_dataset_stats(src_dir, dst_dir, file_name)
def generate_dataset_stats(src_dir: str, dst_dir: str, file_name: str):
src_dir, dst_dir = Path(src_dir), Path(dst_dir)
src_dir_flat, src_dir_seq = src_dir / "flat", src_dir / "sequential"
file_name = f"{file_name}.parquet"
sizes = sorted(path.name for path in src_dir_flat.iterdir())
for size in sizes:
path_flat, path_seq = src_dir_flat / size, src_dir_seq / size
assert path_seq.exists(), f"Cannot find sequential data in '{path_seq}'"
assert (path_flat / file_name).exists(), (
"Please, generate flat multi-event file using "
"'make_multievent.py' or specify correct name using --file_name"
)
assert (path_seq / file_name).exists(), (
"Please, generate sequential multi-event file "
"using 'transform2sequential.py' script or specify correct name "
"using --file_name"
)
print(f"Gathering stats for {size}...")
dst_dir_size = dst_dir / size
dst_dir_size.mkdir(parents=True, exist_ok=True)
df = pl.scan_parquet(path_seq / file_name)
generate_user_history_graph(df, dst_dir_size / "user_history_len.png")
generate_log_user_history_graph(df, dst_dir_size / "user_history_log_len.png")
df = pl.scan_parquet(path_flat / file_name)
generate_item_interactions_graph(df, dst_dir_size / "item_interactions.png")
get_recom_stats(df).write_csv(dst_dir_size / "recom_event_count.csv")
get_history_len_stats(df).write_csv(dst_dir_size / "event_history_len.csv")
get_dataset_stats(df).write_csv(dst_dir_size / "dataset_event_stats.csv")
def make_history_len_graph(
df: pl.DataFrame,
*,
qs: tuple[float] | None = None,
color: str = "lightskyblue",
num_bins: int = 100,
title: str | None = None,
xlabel: str | None = None,
ylabel: str | None = None,
ax: plt.Axes | None = None,
) -> plt.Axes:
if ax is None:
_, ax = plt.subplots(figsize=(12, 5))
count, _, _ = ax.hist(df["value"], bins=num_bins, ec="k", lw=1.0, color=color)
ylim = count.max() * 1.05
xs = {"max": df["value"].max()}
if qs is not None:
xs.update((f"q{q * 100:.0f}", df["value"].quantile(q)) for q in qs)
dx = 0.01 * (xs["max"] - df["value"].min())
template = "{label}={x:.3f}" if xs["max"] <= 10 else "{label}={x:.0f}"
for label, x in xs.items():
ax.plot([x, x], [0, ylim], ls="--", c="k")
text = template.format(label=label, x=x)
ax.text(x + dx, ylim // 2, text, rotation=90, fontsize=16, bbox=dict(alpha=0.1, color="r"))
if title is not None:
ax.set_title(title, fontsize=24)
ax.set_xlabel(xlabel or "Value", fontsize=22)
ax.set_ylabel(ylabel or "Count", fontsize=22)
ax.set_ylim([0, ylim])
ax.tick_params(labelsize=16)
ax.ticklabel_format(style="sci", useMathText=True)
def save_graph(output_path: os.PathLike, fmt: ImageFormat = ImageFormat.PNG):
output_path = Path(output_path)
if not output_path.suffix:
output_path = output_path.with_suffix(f".{fmt.value}")
if output_path.exists():
print(f"Rewriting file '{output_path}'")
else:
print(f"Saving to '{output_path}'")
plt.savefig(output_path, dpi=300, format=fmt.value)
def generate_user_history_graph(df: pl.LazyFrame, out_path: os.PathLike):
_, ax = plt.subplots(figsize=(12, 5))
make_history_len_graph(
df.select(value=pl.col("item_id").list.len()).collect(),
num_bins=100,
qs=(0.5, 0.9, 0.95),
xlabel="Events",
ylabel="Users",
ax=ax,
)
plt.tight_layout()
save_graph(out_path)
def generate_log_user_history_graph(df: pl.LazyFrame, out_path: os.PathLike):
_, ax = plt.subplots(figsize=(12, 5))
make_history_len_graph(
df.select(value=pl.col("item_id").list.len().log10()).collect(),
num_bins=40,
xlabel="$Log_{10}$(Events)",
ylabel="Users",
color="lightgreen",
ax=ax,
)
plt.tight_layout()
save_graph(out_path)
def generate_item_interactions_graph(df: pl.LazyFrame, out_path: os.PathLike):
_, ax = plt.subplots(figsize=(12, 5))
make_history_len_graph(
df.group_by("item_id").len().select(value=pl.col("len").log10()).collect(),
num_bins=30,
qs=(0.5, 0.9, 0.95),
xlabel="$Log_{10}$(Events)",
ylabel="Items",
color="orange",
ax=ax,
)
plt.tight_layout()
save_graph(out_path)
def get_recom_stats(df: pl.LazyFrame) -> pl.DataFrame:
print("Computing recom stats")
df_cnt = df.group_by(("event_type", "is_organic")).len().collect()
df_recom = df_cnt.filter(pl.col("is_organic").eq(0)).select(pl.col("event_type"), pl.col("len").alias("recom"))
df_total = df_cnt.group_by("event_type").sum().select(pl.col("event_type"), pl.col("len").alias("total"))
return df_total.join(df_recom, on="event_type").with_columns(ratio=pl.col("recom") / pl.col("total"))
def get_history_len_stats(df: pl.LazyFrame) -> pl.DataFrame:
print("Computing event history length stats")
return (
df.group_by(("event_type", "uid"))
.len()
.group_by("event_type")
.agg(
median=pl.col("len").quantile(0.5).cast(pl.Int32),
q90=pl.col("len").quantile(0.9).cast(pl.Int32),
q95=pl.col("len").quantile(0.95).cast(pl.Int32),
)
.collect()
)
def get_dataset_stats(df: pl.LazyFrame) -> pl.DataFrame:
print("Computing dataset stats")
return df.select(
users=pl.col("uid").unique().len(),
items=pl.col("item_id").unique().len(),
listens=pl.col("event_type").filter(pl.col("event_type").eq("listen")).len(),
likes=pl.col("event_type").filter(pl.col("event_type").eq("like")).len(),
dislikes=pl.col("event_type").filter(pl.col("event_type").eq("dislike")).len(),
unlikes=pl.col("event_type").filter(pl.col("event_type").eq("unlike")).len(),
undislikes=pl.col("event_type").filter(pl.col("event_type").eq("undislike")).len(),
).collect()
if __name__ == "__main__":
cli()
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