#!/usr/bin/env python3 """Generate pattern-frequency report as percentage of source token counts. Outputs: - summary for whole corpus (counts + share of total tokens) - per-source counts + share within source - optional markdown snippet and optional bar chart """ from __future__ import annotations import argparse import json from pathlib import Path import matplotlib.pyplot as plt import pyarrow.compute as pc import pyarrow.parquet as pq PATTERNS = [ ("w roku", "w roku"), ("klasyfikacji", "klasyfikacji"), ("ustawa", "ustawa"), ("artykuł", "artykuł"), ("parlament", "parlament"), ("rozporządzenie", "rozporządzenie"), ("w pobliżu", "w pobliżu"), ("mieszkańców", "mieszkańców"), ("Dz.U.", "dz\\.u\\."), ] def load_tokens_by_source(root: Path) -> dict[str, int]: by_source = {} for stats_file in sorted((root / "data").glob("*/*.stats.json")): src = stats_file.parent.name payload = json.loads(stats_file.read_text(encoding="utf-8")) by_source[src] = int(payload["tokens"]) return by_source def count_patterns_for_source(parquet_path: Path) -> dict[str, int]: counts = {name: 0 for name, _ in PATTERNS} pf = pq.ParquetFile(parquet_path) for rg in range(pf.num_row_groups): table = pf.read_row_group(rg, columns=["text"]) text = table["text"] text = pc.utf8_lower(text) text = pc.replace_substring_regex(text, pattern="\\r?\\n", replacement=" ") for label, pattern in PATTERNS: if label == "Dz.U.": cnt = pc.count_substring_regex(text, pattern) else: cnt = pc.count_substring(text, pattern) counts[label] += int(pc.sum(cnt).as_py()) return counts def compute_counts(data_root: Path) -> tuple[dict[str, int], dict[str, dict[str, int]]]: tokens = load_tokens_by_source(data_root) source_counts = {} total_counts = {label: 0 for label, _ in PATTERNS} for parquet_path in sorted((data_root / "data").glob("*/*.parquet")): source = parquet_path.parent.name counts = count_patterns_for_source(parquet_path) source_counts[source] = counts for label, cnt in counts.items(): total_counts[label] += cnt return total_counts, source_counts, tokens def write_markdown(total_counts, source_counts, tokens, out_md: Path) -> None: total_tokens = sum(tokens.values()) lines = [] lines.append("## Pattern frequency on corpus\n") lines.append(f"- total tokens (tiktoken proxy): `{total_tokens:,}`\n") lines.append("| pattern | count | share of all tokens |") lines.append("|---|---:|---:|") for label, _ in PATTERNS: c = total_counts[label] lines.append(f"| `{label}` | {c:,} | {c/total_tokens*100:.4f}% |") lines.append("") lines.append("| source | pattern | count | per-token share |") lines.append("|---|---|---:|---:|") for source in sorted(source_counts): src_tokens = tokens[source] for label, _ in PATTERNS: c = source_counts[source][label] lines.append(f"| {source} | `{label}` | {c:,} | {c/src_tokens*100:.5f}% |") out_md.write_text("\n".join(lines) + "\n", encoding="utf-8") def write_hf_snippet(total_counts, source_counts, tokens, total_tokens: int, out_md: Path) -> None: patterns = [label for label, _ in PATTERNS] lines = [] lines.append("## Phrase frequency in corpus (token-normalized)") lines.append("") lines.append(f"- Total token count (tiktoken proxy): **{total_tokens:,}**") lines.append("") lines.append("| Pattern | Count | Share of all tokens |") lines.append("|---|---:|---:|") for label in patterns: c = total_counts[label] lines.append(f"| `{label}` | {c:,} | {c / total_tokens * 100:.4f}% |") lines.append("") lines.append("### Per-source shares") lines.append("") lines.append("| source | pattern | count | share of source tokens |") lines.append("|---|---|---:|---:|") ordered_sources = sorted(source_counts) for source in ordered_sources: src_tok = tokens[source] for label in patterns: c = source_counts[source][label] lines.append(f"| `{source}` | `{label}` | {c:,} | {c / src_tok * 100:.5f}% |") lines.append("") lines.append("![Overall pattern counts](artifacts/pattern_frequency_overall.png)") lines.append("") lines.append("![w roku by source](artifacts/pattern_frequency_w_roku.png)") lines.append("![klasyfikacji by source](artifacts/pattern_frequency_klasyfikacji.png)") lines.append("![ustawa by source](artifacts/pattern_frequency_ustawa.png)") lines.append("![artykuł by source](artifacts/pattern_frequency_artykul.png)") lines.append("![parlament by source](artifacts/pattern_frequency_parlament.png)") lines.append("![rozporządzenie by source](artifacts/pattern_frequency_rozporządzenie.png)") lines.append("![w pobliżu by source](artifacts/pattern_frequency_w_pobliżu.png)") lines.append("![mieszkańców by source](artifacts/pattern_frequency_mieszkańców.png)") lines.append("![Dz.U. by source](artifacts/pattern_frequency_dzu.png)") out_md.write_text("\n".join(lines) + "\n", encoding="utf-8") def plot(total_counts, source_counts, tokens, out_png: Path) -> None: out_png.parent.mkdir(parents=True, exist_ok=True) patterns = [label for label, _ in PATTERNS] totals = [total_counts[p] for p in patterns] # overall share chart plt.figure(figsize=(10, 4)) plt.bar(patterns, totals, color="#2b8cbe") plt.title("Pattern count in full corpus") plt.ylabel("count") plt.xlabel("pattern") plt.xticks(rotation=25, ha="right") plt.tight_layout() total_png = out_png.with_name(out_png.stem + "_overall" + out_png.suffix) plt.savefig(total_png, dpi=140) plt.close() # per-source percentage heatmap-like bars ordered_sources = sorted(source_counts) for pattern in patterns: vals = [source_counts[src][pattern] / tokens[src] * 100 for src in ordered_sources] plt.figure(figsize=(10, 4)) plt.bar(ordered_sources, vals) plt.title(f"{pattern} share per source (% of source tokens)") plt.ylabel("% of tokens") plt.xticks(rotation=30, ha="right") plt.tight_layout() safe = pattern.replace(" ", "_").replace("ł", "l").replace(".", "").lower() plt.savefig(out_png.parent / f"{out_png.stem}_{safe}.png", dpi=140) plt.close() def parse_args(): ap = argparse.ArgumentParser() ap.add_argument("--data-root", type=Path, default=Path("."), help="repo root") ap.add_argument("--out-md", type=Path, default=Path("pattern_frequency_report.md")) ap.add_argument("--out-png", type=Path, default=Path("artifacts/pattern_frequency.png")) ap.add_argument( "--out-hf", type=Path, default=Path("artifacts/pattern_frequency_hf_snippet.md"), help="HF model card snippet to paste into README.md on Hugging Face", ) return ap.parse_args() def main(): args = parse_args() total_counts, source_counts, tokens = compute_counts(args.data_root) total_tokens = sum(tokens.values()) args.out_md.parent.mkdir(parents=True, exist_ok=True) write_markdown(total_counts, source_counts, tokens, args.out_md) write_hf_snippet(total_counts, source_counts, tokens, total_tokens, args.out_hf) plot(total_counts, source_counts, tokens, args.out_png) print(f"wrote: {args.out_md}") print(f"wrote: {args.out_hf}") print(f"wrote: {args.out_png.with_name(args.out_png.stem + '_overall' + args.out_png.suffix)}") for label, _ in PATTERNS: safe = label.replace(' ', '_').replace('ł', 'l').replace('.', '').lower() print(f"wrote: {args.out_png.parent / f'{args.out_png.stem}_{safe}.png'}") if __name__ == "__main__": main()