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import json
from pathlib import Path
import logging
from typing import Any
import sys
import numpy as np
import joblib # type: ignore
from tqdm import tqdm # type: ignore
import pandas as pd
from .. import util
from .metric import metric_registry
logging.basicConfig(level=logging.INFO)
def load_data(path: Path) -> list[np.ndarray]:
data = []
with path.open() as fo:
for line in fo:
row = json.loads(line)
if len(row) == 0:
continue
data.append(np.array(row))
return data
def analyze_data(input_path: Path) -> dict[str, Any] | None:
# TODO Account for EOS
name = path_to_name(input_path)
logging.debug(f"Analyzing: {name}")
metrics = None
try:
data = load_data(input_path)
metrics = dict(kv for f in metric_registry for kv in f(data).items())
metadata_path = input_path.parent / "metadata.json"
with util.update_json(metadata_path) as md:
md["metrics"] = md.get("metrics", {})
md["metrics"]["analysis"] = metrics
except Exception as e:
logging.warning(f"{name} failed due to {e}")
raise e # TODO remove
finally:
logging.debug(f"Finished: {name}")
return metrics
def path_to_name(path: Path) -> str:
comps = list(path.parents[-3:-2]) + list(path.parents[-5::-1])
return "/".join(x.name for x in comps)
def main() -> None:
paths = list(Path("./systems").glob("*/data/**/corpus.jsonl"))
funcs = [joblib.delayed(analyze_data)(path) for path in paths]
parallel = joblib.Parallel(n_jobs=-1, return_as="generator")(funcs)
results = list(tqdm(parallel, total=len(funcs)))
results = [x for x in results if x is not None]
for p, r in zip(paths, results):
r["name"] = path_to_name(p)
df = pd.DataFrame(results)
df.set_index("name", inplace=True)
df.to_csv("table.csv")
def generate_plots(df: pd.DataFrame) -> None:
summary = df.describe()
summary.drop(["count", "mean", "std"], inplace=True)
summary = summary.T
to_int_rows = ["Unique Tokens", "Unique Lines", "Token Count", "Line Count"]
col_rename = {
"25%": "$25\\%$",
"50%": "$50\\%$",
"75%": "$75\\%$",
}
_summary = pd.DataFrame(columns=summary.columns)
for k in summary.index:
if k in to_int_rows:
fmtr = lambda x: f"${int(x)}$"
else:
fmtr = lambda x: f"${x:.2f}$"
_summary.loc[k] = summary.loc[k].apply(fmtr)
summary = _summary
summary.rename(columns=col_rename, inplace=True)
# print(summary)
summary.to_latex("summary.tex", column_format="lrrrrr")
# print(df.corr())
df = df.sort_values(by="name")
df.rename(index=lambda x: x.replace("_", r"\_"), inplace=True)
col_groups = [
["Token Count", "Line Count", "Tokens per Line", "Tokens per Line SD"],
["Unique Tokens", "Unique Lines", "EoS Token Present", "EoS Padding"],
["1-gram Entropy", "1-gram Normalized Entropy", "Entropy per Line"],
["2-gram Entropy", "2-gram Conditional Entropy"],
]
for i, cols in enumerate(col_groups):
df.to_latex(f"big-table-{i+1}.tex", columns=cols)
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