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https://github.com/MODSetter/SurfSense.git
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refactor: streamline TikTok and Instagram scraping logic by removing search_queries and enhancing documentation for clarity
This commit is contained in:
DESKTOP-RTLN3BA\$punk
parent
e8b3692b54
commit
2b018c4474
111 changed files with 1800 additions and 1580 deletions
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@ -95,7 +95,7 @@ def _mcnemar_exact_pvalue(b: int, c: int) -> float:
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k = min(b, c)
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# Two-sided exact: 2 * P(X <= k) clipped at 1.0
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cdf = sum(_binom_coef(n, i) for i in range(k + 1))
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p = 2.0 * cdf / (2 ** n)
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p = 2.0 * cdf / (2**n)
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return min(1.0, p)
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@ -116,7 +116,7 @@ def _mcnemar_table(rows: list[dict]) -> dict:
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qids = sorted(by_qid)
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out: dict[str, dict] = {"arms": arms, "n_qids": len(qids), "pairs": []}
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for i, ai in enumerate(arms):
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for aj in arms[i + 1:]:
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for aj in arms[i + 1 :]:
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b = c = both = neither = 0
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for q in qids:
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row = by_qid[q]
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@ -132,12 +132,17 @@ def _mcnemar_table(rows: list[dict]) -> dict:
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else:
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neither += 1
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p = _mcnemar_exact_pvalue(b, c)
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out["pairs"].append({
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"arm_i": ai, "arm_j": aj,
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"b_i_only": b, "c_j_only": c,
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"both_correct": both, "both_wrong": neither,
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"p_value": p,
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})
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out["pairs"].append(
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{
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"arm_i": ai,
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"arm_j": aj,
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"b_i_only": b,
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"c_j_only": c,
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"both_correct": both,
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"both_wrong": neither,
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"p_value": p,
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}
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)
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return out
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@ -154,9 +159,7 @@ def _per_pdf_stats(rows: list[dict]) -> dict[str, dict]:
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arm = r["arm"]
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pdf = r["doc_id"]
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graded = r.get("graded") or {}
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bucket.setdefault(arm, {}).setdefault(pdf, []).append(
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bool(graded.get("correct"))
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)
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bucket.setdefault(arm, {}).setdefault(pdf, []).append(bool(graded.get("correct")))
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out: dict[str, dict] = {}
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for arm, pdfs in bucket.items():
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@ -207,7 +210,8 @@ def _per_arm_latency(rows: list[dict]) -> dict[str, dict]:
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# Coefficient of variation: std / mean (unitless tail-fatness).
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"cv": (
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statistics.stdev(lats) / statistics.mean(lats)
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if len(lats) > 1 and statistics.mean(lats) > 0 else 0.0
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if len(lats) > 1 and statistics.mean(lats) > 0
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else 0.0
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),
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}
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return out
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@ -259,24 +263,30 @@ def _print_latency(title: str, lat: dict[str, dict]) -> None:
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print()
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print(title)
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print("-" * len(title))
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header = (f"{'arm':<25} {'n':>4} {'mean':>7} {'std':>7} "
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f"{'p50':>7} {'p90':>7} {'p95':>7} {'p99':>7} {'max':>7} {'CV':>5}")
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header = (
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f"{'arm':<25} {'n':>4} {'mean':>7} {'std':>7} "
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f"{'p50':>7} {'p90':>7} {'p95':>7} {'p99':>7} {'max':>7} {'CV':>5}"
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)
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print(header)
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print("-" * len(header))
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for arm in sorted(lat, key=lambda a: lat[a]["mean_s"]):
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s = lat[arm]
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print(f"{arm:<25} {s['n']:>4} "
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f"{s['mean_s']:>6.1f}s {s['std_s']:>6.1f}s "
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f"{s['p50_s']:>6.1f}s {s['p90_s']:>6.1f}s {s['p95_s']:>6.1f}s "
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f"{s['p99_s']:>6.1f}s {s['max_s']:>6.1f}s {s['cv']:>5.2f}")
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print(
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f"{arm:<25} {s['n']:>4} "
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f"{s['mean_s']:>6.1f}s {s['std_s']:>6.1f}s "
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f"{s['p50_s']:>6.1f}s {s['p90_s']:>6.1f}s {s['p95_s']:>6.1f}s "
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f"{s['p99_s']:>6.1f}s {s['max_s']:>6.1f}s {s['cv']:>5.2f}"
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)
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def _print_tokens(title: str, toks: dict[str, dict]) -> None:
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print()
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print(title)
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print("-" * len(title))
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header = (f"{'arm':<25} {'in mean':>9} {'in p50':>9} {'in p95':>9} {'in max':>9}"
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f" {'out mean':>9} {'out p95':>9}")
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header = (
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f"{'arm':<25} {'in mean':>9} {'in p50':>9} {'in p95':>9} {'in max':>9}"
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f" {'out mean':>9} {'out p95':>9}"
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)
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print(header)
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print("-" * len(header))
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for arm in sorted(toks):
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@ -285,25 +295,31 @@ def _print_tokens(title: str, toks: dict[str, dict]) -> None:
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eout = e.get("output")
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if not ein:
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continue
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print(f"{arm:<25} "
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f"{ein['mean']:>9,.0f} {ein['p50']:>9,.0f} {ein['p95']:>9,.0f} {ein['max']:>9,.0f} "
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f"{(eout or {}).get('mean', 0):>9,.0f} {(eout or {}).get('p95', 0):>9,.0f}")
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print(
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f"{arm:<25} "
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f"{ein['mean']:>9,.0f} {ein['p50']:>9,.0f} {ein['p95']:>9,.0f} {ein['max']:>9,.0f} "
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f"{(eout or {}).get('mean', 0):>9,.0f} {(eout or {}).get('p95', 0):>9,.0f}"
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)
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def _print_pdf_var(title: str, var: dict[str, dict]) -> None:
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print()
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print(title)
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print("-" * len(title))
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header = (f"{'arm':<25} {'n_pdfs':>7} {'mean':>7} {'std':>7} {'min':>7} "
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f"{'p25':>7} {'p50':>7} {'p75':>7} {'max':>7} {'#0%':>5} {'#100%':>6}")
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header = (
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f"{'arm':<25} {'n_pdfs':>7} {'mean':>7} {'std':>7} {'min':>7} "
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f"{'p25':>7} {'p50':>7} {'p75':>7} {'max':>7} {'#0%':>5} {'#100%':>6}"
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)
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print(header)
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print("-" * len(header))
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for arm in sorted(var, key=lambda a: -var[a]["mean"]):
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s = var[arm]
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print(f"{arm:<25} {s['n_pdfs']:>7} "
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f"{s['mean']*100:>6.1f}% {s['std']*100:>6.1f}% {s['min']*100:>6.1f}% "
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f"{s['p25']*100:>6.1f}% {s['p50']*100:>6.1f}% {s['p75']*100:>6.1f}% "
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f"{s['max']*100:>6.1f}% {s['n_pdfs_zero']:>5} {s['n_pdfs_perfect']:>6}")
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print(
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f"{arm:<25} {s['n_pdfs']:>7} "
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f"{s['mean'] * 100:>6.1f}% {s['std'] * 100:>6.1f}% {s['min'] * 100:>6.1f}% "
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f"{s['p25'] * 100:>6.1f}% {s['p50'] * 100:>6.1f}% {s['p75'] * 100:>6.1f}% "
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f"{s['max'] * 100:>6.1f}% {s['n_pdfs_zero']:>5} {s['n_pdfs_perfect']:>6}"
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)
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def _print_mcnemar(title: str, table: dict) -> None:
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@ -311,8 +327,10 @@ def _print_mcnemar(title: str, table: dict) -> None:
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print(title)
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print("-" * len(title))
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print(f"n_qids on which all arms have a graded row: {table['n_qids']}")
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header = (f"{'arm_i':<25} {'arm_j':<25} {'b':>4} {'c':>4} "
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f"{'both ok':>8} {'both wr':>8} {'p (2-sided)':>13} {'sig':>4}")
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header = (
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f"{'arm_i':<25} {'arm_j':<25} {'b':>4} {'c':>4} "
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f"{'both ok':>8} {'both wr':>8} {'p (2-sided)':>13} {'sig':>4}"
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)
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print(header)
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print("-" * len(header))
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for pair in sorted(table["pairs"], key=lambda p: p["p_value"]):
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@ -323,10 +341,12 @@ def _print_mcnemar(title: str, table: dict) -> None:
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sig = "**"
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elif pair["p_value"] < 0.05:
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sig = "*"
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print(f"{pair['arm_i']:<25} {pair['arm_j']:<25} "
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f"{pair['b_i_only']:>4} {pair['c_j_only']:>4} "
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f"{pair['both_correct']:>8} {pair['both_wrong']:>8} "
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f"{pair['p_value']:>13.4f} {sig:>4}")
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print(
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f"{pair['arm_i']:<25} {pair['arm_j']:<25} "
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f"{pair['b_i_only']:>4} {pair['c_j_only']:>4} "
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f"{pair['both_correct']:>8} {pair['both_wrong']:>8} "
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f"{pair['p_value']:>13.4f} {sig:>4}"
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)
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# ---------------------------------------------------------------------------
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