Datasets:
File size: 8,601 Bytes
03859b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | #!/usr/bin/env python3
"""Compute CodeMixCheck statistics directly from the released Parquet files.
python scripts/dataset_statistics.py # print markdown
python scripts/dataset_statistics.py --json stats.json
python scripts/dataset_statistics.py --data-dir path/to/other/parquet/root
Every number in README.md's statistics block is produced by `render_markdown()`
in this file; scripts/validate_dataset.py re-runs it and checks that the README
matches byte for byte.
Dependencies: pandas, pyarrow. Optional: wordfreq (heuristic English-lexicon share).
"""
from __future__ import annotations
import argparse
import json
import re
import unicodedata
from pathlib import Path
import pandas as pd
ROOT = Path(__file__).resolve().parents[1]
CONFIGS = ["fact_verification", "sentiment", "abuse_detection"]
SPLIT_ORDER = ["train", "validation", "test_synthetic", "test_curated", "test_human", "silver_unverified"]
TAMIL = "-"
TOKEN_RE = re.compile(f"[A-Za-z{TAMIL}]+")
TAMIL_RE = re.compile(f"[{TAMIL}]")
LATIN_RE = re.compile(r"[A-Za-z]")
EMOJI_RE = re.compile("[\U0001F000-\U0001FAFF☀-➿⬀-⯿️←-⇿⌀-⏿]")
def normalize(text: str) -> str:
t = unicodedata.normalize("NFC", text).casefold()
t = EMOJI_RE.sub(" ", t)
t = re.sub(f"[^a-z0-9{TAMIL}]+", " ", t)
return re.sub(r"\s+", " ", t).strip()
def load(data_dir: Path) -> dict[str, pd.DataFrame]:
out = {}
for cfg in CONFIGS:
files = sorted((data_dir / cfg).glob("*.parquet"))
if files:
out[cfg] = pd.concat([pd.read_parquet(f).assign(_file=f.stem) for f in files], ignore_index=True)
return out
def _tbl(df: pd.DataFrame) -> str:
cols = [str(c) for c in df.columns]
lines = ["| " + " | ".join(cols) + " |", "|" + "|".join(["---"] * len(cols)) + "|"]
for row in df.itertuples(index=False):
lines.append("| " + " | ".join(str(v) for v in row) + " |")
return "\n".join(lines)
def _xtab(df, row, col="_file"):
t = pd.crosstab(df[row].fillna("(null)"), df[col], margins=True, margins_name="total")
order = [c for c in SPLIT_ORDER if c in t.columns] + ["total"]
t = t[order].reset_index().rename(columns={row: row})
return t
def compute(data: dict[str, pd.DataFrame], with_lexicon: bool = True) -> dict:
zipf = None
if with_lexicon:
try:
from wordfreq import zipf_frequency as zipf # noqa: F401
except ImportError:
zipf = None
stats = {}
for cfg, df in data.items():
s = {}
s["rows"] = int(len(df))
s["split_counts"] = {k: int(v) for k, v in df._file.value_counts().items()}
s["label_by_split"] = _xtab(df, "label").to_dict("records")
s["provenance_by_split"] = _xtab(df, "provenance_class").to_dict("records")
s["label_origin_by_split"] = _xtab(df, "label_origin").to_dict("records")
s["human_validated_by_split"] = _xtab(df, "human_validated").to_dict("records")
s["label_status_by_split"] = _xtab(df, "label_status").to_dict("records")
s["script_by_split"] = _xtab(df, "script").to_dict("records")
r = df.tamil_script_token_ratio.astype(float)
ntok = df.text.map(lambda t: len(TOKEN_RE.findall(t)))
s["code_mixing"] = {
"mean_tokens_per_text": round(float(ntok.mean()), 2),
"median_tokens_per_text": float(ntok.median()),
"tamil_script_token_ratio_mean": round(float(r.mean()), 4),
"tamil_script_token_ratio_mean_over_texts_with_tamil": round(float(r[r > 0].mean()), 4) if (r > 0).any() else "n/a (no Tamil-script tokens)",
"pct_texts_with_any_tamil_script": round(100 * float((r > 0).mean()), 2),
"pct_texts_roman_only": round(100 * float((r == 0).mean()), 2),
"pct_texts_tamil_script_only": round(100 * float((r == 1).mean()), 2),
"pct_texts_with_emoji": round(100 * float(df.text.str.contains(EMOJI_RE).mean()), 2),
"pct_texts_with_hashtag": round(100 * float(df.text.str.contains(r"#\w").mean()), 2),
}
if zipf is not None:
from functools import lru_cache
z = lru_cache(maxsize=None)(zipf)
stop = {"la", "nu", "da", "ah", "um", "ku", "na", "vera", "illa", "dha", "dhaan", "than", "ra",
"raa", "ma", "mo", "po", "pa", "ya", "eh", "oh", "ne", "nee", "naa", "naan", "un", "en",
"ena", "enna", "idhu", "adhu", "aana", "ana", "seri", "sema", "semma", "dei", "poda",
"machi", "macha", "bro", "broo", "pola", "kooda", "mattum", "thaan", "va", "vaa", "ok",
"okay", "apo", "ipo", "ippo", "appo", "inga", "anga", "oru", "rendu", "moonu", "mela",
"kela", "pathi", "paathi", "iru", "irukku", "pannu", "pannunga", "sollu", "sollunga",
"tha", "the", "of", "a"}
def share(t):
rom = [x.lower() for x in TOKEN_RE.findall(t) if LATIN_RE.search(x)]
if not rom:
return None
return sum(len(x) >= 3 and x not in stop and z(x, "en") >= 3.5 for x in rom) / len(rom)
sh = df.text.map(share).dropna()
s["code_mixing"]["heuristic_english_lexicon_share_of_roman_tokens_mean"] = round(float(sh.mean()), 4)
# missing values (% null) over all columns
miss = (df.drop(columns=["_file"]).isna().mean() * 100).round(2)
s["missing_pct"] = {k: float(v) for k, v in miss.items() if v > 0}
# duplicates
norm = df.text.map(normalize)
s["duplicates"] = {
"duplicate_ids": int(df.id.duplicated().sum()),
"exact_duplicate_texts": int(df.text.duplicated().sum()),
"normalised_duplicate_texts": int(norm.duplicated().sum()),
"normalised_text_shared_across_splits": int((pd.DataFrame({"n": norm, "f": df._file})
.groupby("n").f.nunique() > 1).sum()),
"rows_flagged_near_duplicate_C04": int(df.audit_flags.map(lambda x: "C04" in x).sum()),
"rows_flagged_cross_split_near_duplicate_D02": int(df.audit_flags.map(lambda x: "D02" in x).sum()),
"rows_flagged_cross_split_proposition_D01": int(df.audit_flags.map(lambda x: "D01" in x).sum()),
"is_content_duplicate_true": int(df.is_content_duplicate.sum()),
}
if "eval_subset" in df and df.eval_subset.notna().any():
s["eval_subset_by_label"] = pd.crosstab(df.eval_subset.fillna("(none)"), df.label).reset_index().to_dict("records")
stats[cfg] = s
return stats
def render_markdown(data: dict[str, pd.DataFrame], with_lexicon: bool = True) -> str:
st = compute(data, with_lexicon)
out = []
tot = sum(v["rows"] for v in st.values())
out.append(f"Total records in these files: **{tot}**.\n")
for cfg, s in st.items():
out.append(f"#### `{cfg}` ({s['rows']} records)\n")
df = data[cfg]
out.append("Label distribution by split:\n")
out.append(_tbl(_xtab(df, "label")) + "\n")
out.append("Provenance class by split:\n")
out.append(_tbl(_xtab(df, "provenance_class")) + "\n")
out.append("Label origin / human-validation status by split:\n")
out.append(_tbl(_xtab(df, "label_origin")) + "\n")
out.append(_tbl(_xtab(df, "human_validated")) + "\n")
out.append("Script by split:\n")
out.append(_tbl(_xtab(df, "script")) + "\n")
cm = s["code_mixing"]
out.append("Code-mixing / script statistics:\n")
out.append(_tbl(pd.DataFrame([{"statistic": k, "value": v} for k, v in cm.items()])) + "\n")
out.append("Duplicate statistics:\n")
out.append(_tbl(pd.DataFrame([{"check": k, "count": v} for k, v in s["duplicates"].items()])) + "\n")
out.append("Columns with missing values (% null):\n")
mp = s["missing_pct"]
out.append((_tbl(pd.DataFrame([{"column": k, "pct_null": v} for k, v in sorted(mp.items())]))
if mp else "none") + "\n")
return "\n".join(out).rstrip() + "\n"
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--data-dir", default=str(ROOT / "data"))
ap.add_argument("--json")
ap.add_argument("--no-lexicon", action="store_true")
a = ap.parse_args()
data = load(Path(a.data_dir))
if a.json:
Path(a.json).write_text(json.dumps(compute(data, not a.no_lexicon), indent=1, default=str))
print(render_markdown(data, not a.no_lexicon))
if __name__ == "__main__":
main()
|