From c82d04f31230a4f09f4bd342f8fa2fce913fc192 Mon Sep 17 00:00:00 2001 From: malteos Date: Wed, 27 May 2026 23:27:10 +0200 Subject: [PATCH 1/2] feat: Add notebook for per-segment classifier score drift MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds `notebooks/compare_classifier_scores_segment_drift.ipynb`, a sibling to the existing baseline-vs-focus comparison notebook. Loads the per-segment `classify-warc` output CSVs for `CC-SUPPLEMENTAL-2026-22`, parses the `YYYYMMDDHHMMSS` token from each filename, and plots how the classifier score evolves over the lifetime of the focus crawl: - mean ± SEM (sized so real mean shifts are distinguishable from sampling noise; raw stdev is uninformative for the bimodal score distribution), - median with IQR (p25-p75) and outer (p10-p90) percentile bands, - high-confidence rate (P(score >= 0.5) and P(score >= 0.9)), - mean and median on shared axes as a consolidated view. Auto-discovers segment files and drops under-sized segments (n < 1000) from the plots while keeping them in the per-segment table for auditability. --- ...pare_classifier_scores_segment_drift.ipynb | 941 ++++++++++++++++++ 1 file changed, 941 insertions(+) create mode 100644 notebooks/compare_classifier_scores_segment_drift.ipynb diff --git a/notebooks/compare_classifier_scores_segment_drift.ipynb b/notebooks/compare_classifier_scores_segment_drift.ipynb new file mode 100644 index 0000000..0b377fd --- /dev/null +++ b/notebooks/compare_classifier_scores_segment_drift.ipynb @@ -0,0 +1,941 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell-00-title", + "metadata": {}, + "source": [ + "# Classifier Score Drift Across Focus-Crawl Segments\n", + "\n", + "Sibling to `compare_classifier_scores.ipynb`. That notebook answers \"is the focus crawl scoring higher than the baseline overall?\". This one zooms into the **focus crawl only** and asks:\n", + "\n", + "> **Do classifier scores drift over the lifetime of the focus crawl?**\n", + "\n", + "Each segment of `CC-SUPPLEMENTAL-2026-22` was fetched at a different wall-clock time, encoded in the WARC path as a 14-digit `YYYYMMDDHHMMSS` token (e.g. `20260524073839`). `ccoa classify-warc` was run per segment and produced one CSV per segment in `data/gneissweb-science-top-10k/`, named `CC-SUPPLEMENTAL-2026-22_segment__scores.csv` (with a `.summary.csv` sidecar we ignore).\n", + "\n", + "We load each per-segment CSV, summarise `prediction_score`, and plot the result against the segment datetime, oldest on the left. Under-sized segments (`n < MIN_RECORDS`) are dropped from the plots — they give noisy point estimates that masquerade as drift — but they remain in the per-segment table so the exclusion is auditable.\n", + "\n", + "Plots produced:\n", + "\n", + "1. **Mean ± SEM** — segment mean with standard-error bars (`std / √n`), so \"is this mean shift real?\" is visible at a glance. We deliberately do *not* plot the raw within-segment stdev: classifier scores are bimodal so the stdev is saturated near its max regardless of stability, and would just fill the axis.\n", + "2. **Median + percentile bands** — median with IQR (p25–p75) and outer (p10–p90) shaded bands. Shows where the body and tails of each segment actually sit; the heavy shading is itself informative because it makes the bimodality visible.\n", + "3. **High-confidence rate** — fraction of records with `score ≥ 0.5` and `score ≥ 0.9` per segment. Collapses the bimodal distribution into the action-relevant numbers (\"what fraction will survive a downstream filter?\").\n", + "4. **Mean + median side by side** — both as plain lines on the same axes. A quick consolidated view; divergence between the two lines flags tail-weight changes that aren't visible from either summary alone.\n", + "\n", + "## Setup\n", + "\n", + "```bash\n", + "uv sync --extra notebooks\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cell-01-imports", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-27T21:25:58.451925Z", + "iopub.status.busy": "2026-05-27T21:25:58.451611Z", + "iopub.status.idle": "2026-05-27T21:25:58.890213Z", + "shell.execute_reply": "2026-05-27T21:25:58.889744Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SCORES_DIR: ../data/gneissweb-science-top-10k (exists=True)\n" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "\n", + "import re\n", + "from datetime import datetime\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.dates as mdates\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Resolve a data/ path that works whether Jupyter was launched from the repo\n", + "# root or from inside notebooks/ (same pattern as compare_classifier_scores.ipynb).\n", + "DATA_DIR = Path(\"data\") if Path(\"data\").exists() else Path(\"..\") / \"data\"\n", + "SCORES_DIR = DATA_DIR / \"gneissweb-science-top-10k\"\n", + "\n", + "FOCUS_NAME = \"CC-SUPPLEMENTAL-2026-22\"\n", + "\n", + "print(f\"SCORES_DIR: {SCORES_DIR} (exists={SCORES_DIR.exists()})\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-02-discover-md", + "metadata": {}, + "source": [ + "## 1. Discover per-segment score files\n", + "\n", + "Files are named `CC-SUPPLEMENTAL-2026-22_segment__scores.csv`. We:\n", + "\n", + "- glob the directory,\n", + "- skip the `.summary.csv` sidecars,\n", + "- extract the 14-digit datetime token with a regex anchored at start/end so we don't accidentally pick up resume parts (`_1m_scores__resume-2.csv`) or other unrelated files,\n", + "- parse the token with `strptime(\"%Y%m%d%H%M%S\")`,\n", + "- sort chronologically.\n", + "\n", + "If zero files match, we raise — \"drift across no segments\" isn't a meaningful question." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cell-03-discover", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-27T21:25:58.891217Z", + "iopub.status.busy": "2026-05-27T21:25:58.891117Z", + "iopub.status.idle": "2026-05-27T21:25:58.894389Z", + "shell.execute_reply": "2026-05-27T21:25:58.893942Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Discovered 20 segment file(s) for CC-SUPPLEMENTAL-2026-22:\n", + " 2026-05-21 07:40:42 CC-SUPPLEMENTAL-2026-22_segment_20260521074042_scores.csv\n", + " 2026-05-22 07:20:19 CC-SUPPLEMENTAL-2026-22_segment_20260522072019_scores.csv\n", + " 2026-05-22 20:48:39 CC-SUPPLEMENTAL-2026-22_segment_20260522204839_scores.csv\n", + " 2026-05-23 07:11:17 CC-SUPPLEMENTAL-2026-22_segment_20260523071117_scores.csv\n", + " 2026-05-23 15:38:27 CC-SUPPLEMENTAL-2026-22_segment_20260523153827_scores.csv\n", + " 2026-05-23 23:53:50 CC-SUPPLEMENTAL-2026-22_segment_20260523235350_scores.csv\n", + " 2026-05-24 07:38:39 CC-SUPPLEMENTAL-2026-22_segment_20260524073839_scores.csv\n", + " 2026-05-24 15:05:44 CC-SUPPLEMENTAL-2026-22_segment_20260524150544_scores.csv\n", + " 2026-05-24 21:53:35 CC-SUPPLEMENTAL-2026-22_segment_20260524215335_scores.csv\n", + " 2026-05-25 04:13:49 CC-SUPPLEMENTAL-2026-22_segment_20260525041349_scores.csv\n", + " 2026-05-25 09:56:52 CC-SUPPLEMENTAL-2026-22_segment_20260525095652_scores.csv\n", + " 2026-05-25 15:32:27 CC-SUPPLEMENTAL-2026-22_segment_20260525153227_scores.csv\n", + " 2026-05-25 21:15:36 CC-SUPPLEMENTAL-2026-22_segment_20260525211536_scores.csv\n", + " 2026-05-26 02:47:21 CC-SUPPLEMENTAL-2026-22_segment_20260526024721_scores.csv\n", + " 2026-05-26 08:18:25 CC-SUPPLEMENTAL-2026-22_segment_20260526081825_scores.csv\n", + " 2026-05-26 13:42:33 CC-SUPPLEMENTAL-2026-22_segment_20260526134233_scores.csv\n", + " 2026-05-26 19:04:52 CC-SUPPLEMENTAL-2026-22_segment_20260526190452_scores.csv\n", + " 2026-05-27 00:17:43 CC-SUPPLEMENTAL-2026-22_segment_20260527001743_scores.csv\n", + " 2026-05-27 05:40:02 CC-SUPPLEMENTAL-2026-22_segment_20260527054002_scores.csv\n", + " 2026-05-27 11:00:13 CC-SUPPLEMENTAL-2026-22_segment_20260527110013_scores.csv\n" + ] + } + ], + "source": [ + "SEGMENT_RE = re.compile(rf\"^{re.escape(FOCUS_NAME)}_segment_(\\d{{14}})_scores\\.csv$\")\n", + "\n", + "segments: list[tuple[datetime, Path]] = []\n", + "for path in SCORES_DIR.glob(f\"{FOCUS_NAME}_segment_*_scores.csv\"):\n", + " if path.name.endswith(\".summary.csv\"):\n", + " continue\n", + " match = SEGMENT_RE.match(path.name)\n", + " if not match:\n", + " continue\n", + " dt = datetime.strptime(match.group(1), \"%Y%m%d%H%M%S\")\n", + " segments.append((dt, path))\n", + "\n", + "segments.sort(key=lambda item: item[0])\n", + "\n", + "if not segments:\n", + " raise RuntimeError(\n", + " f\"No segment score files found in {SCORES_DIR} matching \"\n", + " f\"{FOCUS_NAME}_segment__scores.csv\"\n", + " )\n", + "\n", + "print(f\"Discovered {len(segments)} segment file(s) for {FOCUS_NAME}:\")\n", + "for dt, path in segments:\n", + " print(f\" {dt:%Y-%m-%d %H:%M:%S} {path.name}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-04-summary-md", + "metadata": {}, + "source": [ + "## 2. Per-segment summary statistics\n", + "\n", + "Load only the `prediction_score` column from each CSV (matches the existing notebook's `load_scores`) and compute, per segment:\n", + "\n", + "- `n`, `mean`, `std` — record count and central tendency / spread of the raw scores.\n", + "- `sem` = `std / √n` — standard error of the segment mean. Tells you how confidently you know the mean of that segment; the right thing to compare across segments for \"is this drift real?\".\n", + "- `p10`, `p25`, `median`, `p75`, `p90` — robust location plus inner (IQR) and outer (p10–p90) percentile bands. With a bimodal score distribution these say where the body and the tails of each segment actually sit, which a single number can't.\n", + "- `rate_ge_0p5`, `rate_ge_0p9` — fraction of records crossing two confidence thresholds. These collapse the bimodal score distribution into one interpretable number per threshold (\"share of records the classifier is confident / very-confident are science\").\n", + "\n", + "One row per segment, indexed by segment datetime, sorted oldest-first." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cell-05-summary", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-27T21:25:58.895371Z", + "iopub.status.busy": "2026-05-27T21:25:58.895311Z", + "iopub.status.idle": "2026-05-27T21:25:59.924284Z", + "shell.execute_reply": "2026-05-27T21:25:59.923963Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nmeanstdsemp10p25medianp75p90rate_ge_0p5rate_ge_0p9
datetime
2026-05-21 07:40:427740.6454420.4244060.0152550.0015250.1254910.9615381.0000081.0000100.6343670.547804
2026-05-22 07:20:191000000.5031060.4460970.0014110.0000120.0053120.4942160.9965041.0000080.4987900.393040
2026-05-22 20:48:391000000.5475540.4435620.0014030.0000180.0092920.7154770.9981421.0000090.5516600.428440
2026-05-23 07:11:171000000.5724310.4228420.0013370.0014690.0586750.7342650.9975741.0000060.5755400.419660
2026-05-23 15:38:271000000.5516770.4266480.0013490.0005840.0436490.6756660.9963291.0000050.5516300.402800
2026-05-23 23:53:501000000.5273710.4292500.0013570.0004510.0239670.6045780.9935020.9999900.5316600.376320
2026-05-24 07:38:391000000.5219900.4284780.0013550.0002530.0207520.5894610.9917320.9999810.5281600.365830
2026-05-24 15:05:441000000.5270640.4283960.0013550.0005050.0261200.6084800.9935310.9999830.5323000.366630
2026-05-24 21:53:351000000.5240740.4312010.0013640.0002610.0167110.6114860.9921600.9999750.5318700.370950
2026-05-25 04:13:491000000.5000880.4327390.0013680.0002310.0118640.5119720.9872280.9999100.5034200.350300
2026-05-25 09:56:521000000.5143280.4303290.0013610.0002560.0167930.5663780.9865620.9999360.5174500.360930
2026-05-25 15:32:271000000.5104020.4258170.0013470.0004920.0208590.5533540.9815950.9998760.5154200.346200
2026-05-25 21:15:361000000.5019660.4235450.0013390.0005590.0201940.5152590.9790700.9998610.5045000.335550
2026-05-26 02:47:211000000.5206400.4250890.0013440.0007210.0227750.5861740.9850400.9998760.5255700.355060
2026-05-26 08:18:251000000.5226660.4278260.0013530.0006500.0251100.6005380.9886980.9999350.5286600.363900
2026-05-26 13:42:331000000.5101520.4234950.0013390.0003970.0206170.5517780.9812720.9999170.5162200.338440
2026-05-26 19:04:521000000.5205540.4245180.0013420.0007100.0254600.5821000.9843430.9999100.5269400.352970
2026-05-27 00:17:431000000.5059810.4198010.0013280.0005840.0230500.5369800.9749770.9998800.5124700.325260
2026-05-27 05:40:021000000.5032480.4272930.0013510.0006340.0187470.5405590.9824620.9999020.5115100.338890
2026-05-27 11:00:131000000.4770820.4324730.0013680.0003590.0147490.4423710.9771440.9998880.4870900.322470
\n", + "
" + ], + "text/plain": [ + " n mean std sem p10 p25 \\\n", + "datetime \n", + "2026-05-21 07:40:42 774 0.645442 0.424406 0.015255 0.001525 0.125491 \n", + "2026-05-22 07:20:19 100000 0.503106 0.446097 0.001411 0.000012 0.005312 \n", + "2026-05-22 20:48:39 100000 0.547554 0.443562 0.001403 0.000018 0.009292 \n", + "2026-05-23 07:11:17 100000 0.572431 0.422842 0.001337 0.001469 0.058675 \n", + "2026-05-23 15:38:27 100000 0.551677 0.426648 0.001349 0.000584 0.043649 \n", + "2026-05-23 23:53:50 100000 0.527371 0.429250 0.001357 0.000451 0.023967 \n", + "2026-05-24 07:38:39 100000 0.521990 0.428478 0.001355 0.000253 0.020752 \n", + "2026-05-24 15:05:44 100000 0.527064 0.428396 0.001355 0.000505 0.026120 \n", + "2026-05-24 21:53:35 100000 0.524074 0.431201 0.001364 0.000261 0.016711 \n", + "2026-05-25 04:13:49 100000 0.500088 0.432739 0.001368 0.000231 0.011864 \n", + "2026-05-25 09:56:52 100000 0.514328 0.430329 0.001361 0.000256 0.016793 \n", + "2026-05-25 15:32:27 100000 0.510402 0.425817 0.001347 0.000492 0.020859 \n", + "2026-05-25 21:15:36 100000 0.501966 0.423545 0.001339 0.000559 0.020194 \n", + "2026-05-26 02:47:21 100000 0.520640 0.425089 0.001344 0.000721 0.022775 \n", + "2026-05-26 08:18:25 100000 0.522666 0.427826 0.001353 0.000650 0.025110 \n", + "2026-05-26 13:42:33 100000 0.510152 0.423495 0.001339 0.000397 0.020617 \n", + "2026-05-26 19:04:52 100000 0.520554 0.424518 0.001342 0.000710 0.025460 \n", + "2026-05-27 00:17:43 100000 0.505981 0.419801 0.001328 0.000584 0.023050 \n", + "2026-05-27 05:40:02 100000 0.503248 0.427293 0.001351 0.000634 0.018747 \n", + "2026-05-27 11:00:13 100000 0.477082 0.432473 0.001368 0.000359 0.014749 \n", + "\n", + " median p75 p90 rate_ge_0p5 rate_ge_0p9 \n", + "datetime \n", + "2026-05-21 07:40:42 0.961538 1.000008 1.000010 0.634367 0.547804 \n", + "2026-05-22 07:20:19 0.494216 0.996504 1.000008 0.498790 0.393040 \n", + "2026-05-22 20:48:39 0.715477 0.998142 1.000009 0.551660 0.428440 \n", + "2026-05-23 07:11:17 0.734265 0.997574 1.000006 0.575540 0.419660 \n", + "2026-05-23 15:38:27 0.675666 0.996329 1.000005 0.551630 0.402800 \n", + "2026-05-23 23:53:50 0.604578 0.993502 0.999990 0.531660 0.376320 \n", + "2026-05-24 07:38:39 0.589461 0.991732 0.999981 0.528160 0.365830 \n", + "2026-05-24 15:05:44 0.608480 0.993531 0.999983 0.532300 0.366630 \n", + "2026-05-24 21:53:35 0.611486 0.992160 0.999975 0.531870 0.370950 \n", + "2026-05-25 04:13:49 0.511972 0.987228 0.999910 0.503420 0.350300 \n", + "2026-05-25 09:56:52 0.566378 0.986562 0.999936 0.517450 0.360930 \n", + "2026-05-25 15:32:27 0.553354 0.981595 0.999876 0.515420 0.346200 \n", + "2026-05-25 21:15:36 0.515259 0.979070 0.999861 0.504500 0.335550 \n", + "2026-05-26 02:47:21 0.586174 0.985040 0.999876 0.525570 0.355060 \n", + "2026-05-26 08:18:25 0.600538 0.988698 0.999935 0.528660 0.363900 \n", + "2026-05-26 13:42:33 0.551778 0.981272 0.999917 0.516220 0.338440 \n", + "2026-05-26 19:04:52 0.582100 0.984343 0.999910 0.526940 0.352970 \n", + "2026-05-27 00:17:43 0.536980 0.974977 0.999880 0.512470 0.325260 \n", + "2026-05-27 05:40:02 0.540559 0.982462 0.999902 0.511510 0.338890 \n", + "2026-05-27 11:00:13 0.442371 0.977144 0.999888 0.487090 0.322470 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def summarise_segment(csv_path: Path) -> dict[str, float]:\n", + " \"\"\"Return per-segment summary stats for the `prediction_score` column.\"\"\"\n", + " scores = pd.read_csv(csv_path, usecols=[\"prediction_score\"])[\"prediction_score\"].astype(float)\n", + " n = len(scores)\n", + " std = scores.std()\n", + " return {\n", + " \"n\": n,\n", + " \"mean\": scores.mean(),\n", + " \"std\": std,\n", + " \"sem\": std / np.sqrt(n) if n > 0 else float(\"nan\"),\n", + " \"p10\": scores.quantile(0.10),\n", + " \"p25\": scores.quantile(0.25),\n", + " \"median\": scores.median(),\n", + " \"p75\": scores.quantile(0.75),\n", + " \"p90\": scores.quantile(0.90),\n", + " \"rate_ge_0p5\": float((scores >= 0.5).mean()),\n", + " \"rate_ge_0p9\": float((scores >= 0.9).mean()),\n", + " }\n", + "\n", + "\n", + "rows = []\n", + "for dt, path in segments:\n", + " stats = summarise_segment(path)\n", + " stats[\"datetime\"] = dt\n", + " rows.append(stats)\n", + "\n", + "segment_df = (\n", + " pd.DataFrame(rows)\n", + " .set_index(\"datetime\")\n", + " .sort_index()\n", + " [[\"n\", \"mean\", \"std\", \"sem\", \"p10\", \"p25\", \"median\", \"p75\", \"p90\", \"rate_ge_0p5\", \"rate_ge_0p9\"]]\n", + ")\n", + "segment_df.round(6)" + ] + }, + { + "cell_type": "markdown", + "id": "a28f76bd", + "metadata": {}, + "source": [ + "### Drop under-sized segments\n", + "\n", + "The table above shows one segment with `n` well below the per-segment target (~100 000). With ~1 000 records or fewer, the mean/median estimates have wide sampling uncertainty and will visually dominate the drift plots as a fake \"outlier\". We filter to segments with `n >= 1000` before plotting; the full table above is left intact so the dropped segment(s) are still visible in the record." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bc2825bd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-27T21:25:59.925349Z", + "iopub.status.busy": "2026-05-27T21:25:59.925295Z", + "iopub.status.idle": "2026-05-27T21:25:59.928120Z", + "shell.execute_reply": "2026-05-27T21:25:59.927839Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropping 1 segment(s) with n < 1000:\n", + " 2026-05-21 07:40:42 n=774\n", + "Plotting 19 segment(s).\n" + ] + } + ], + "source": [ + "MIN_RECORDS = 1000\n", + "\n", + "dropped = segment_df[segment_df[\"n\"] < MIN_RECORDS]\n", + "filtered_df = segment_df[segment_df[\"n\"] >= MIN_RECORDS]\n", + "\n", + "if len(dropped):\n", + " print(f\"Dropping {len(dropped)} segment(s) with n < {MIN_RECORDS}:\")\n", + " for ts, row in dropped.iterrows():\n", + " print(f\" {ts:%Y-%m-%d %H:%M:%S} n={int(row['n'])}\")\n", + "else:\n", + " print(f\"No segments below n={MIN_RECORDS}; nothing dropped.\")\n", + "\n", + "print(f\"Plotting {len(filtered_df)} segment(s).\")\n", + "\n", + "datetimes = (\n", + " filtered_df.index.to_pydatetime()\n", + " if hasattr(filtered_df.index, \"to_pydatetime\")\n", + " else list(filtered_df.index)\n", + ")\n", + "means = filtered_df[\"mean\"].to_numpy()\n", + "sems = filtered_df[\"sem\"].to_numpy()\n", + "medians = filtered_df[\"median\"].to_numpy()\n", + "p10 = filtered_df[\"p10\"].to_numpy()\n", + "p25 = filtered_df[\"p25\"].to_numpy()\n", + "p75 = filtered_df[\"p75\"].to_numpy()\n", + "p90 = filtered_df[\"p90\"].to_numpy()\n", + "rate_05 = filtered_df[\"rate_ge_0p5\"].to_numpy()\n", + "rate_09 = filtered_df[\"rate_ge_0p9\"].to_numpy()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-06-mean-md", + "metadata": {}, + "source": [ + "## 3. Mean ± SEM over time\n", + "\n", + "Error bars are the **standard error of the mean** (`std / √n`), *not* the within-segment stdev. Why: classifier scores are bimodal (most pages near 0, a real-science tail near 1), so the raw stdev is essentially saturated near its theoretical max regardless of how stable the segment is — it would fill the entire `[0, 1]` axis and tell us nothing. SEM, in contrast, answers the question we actually care about: \"is the apparent zig-zag between segment means real, or just sampling noise?\". With ~100 k records per segment the SEM is small, so non-overlapping bars between two segments mean their mean difference is well outside what re-sampling alone would produce.\n", + "\n", + "Within-segment spread (the large stdev) is still recorded in the `std` column of the table above for reference; we just don't plot it because it doesn't carry drift information." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cell-07-mean-plot", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-27T21:25:59.929032Z", + "iopub.status.busy": "2026-05-27T21:25:59.928987Z", + "iopub.status.idle": "2026-05-27T21:25:59.978775Z", + "shell.execute_reply": "2026-05-27T21:25:59.978409Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 5))\n", + "ax.errorbar(\n", + " datetimes,\n", + " means,\n", + " yerr=sems,\n", + " fmt=\"o-\",\n", + " capsize=4,\n", + " color=\"C0\",\n", + " ecolor=\"C0\",\n", + " alpha=0.85,\n", + " label=\"mean ± SEM\",\n", + ")\n", + "ax.set_xlabel(\"segment datetime (UTC, oldest left)\")\n", + "ax.set_ylabel(\"prediction_score\")\n", + "ax.set_title(f\"{FOCUS_NAME}: per-segment mean ± SEM of classifier score\")\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y-%m-%d %H:%M\"))\n", + "ax.xaxis.set_major_locator(mdates.AutoDateLocator())\n", + "fig.autofmt_xdate(rotation=30)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend(loc=\"best\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-08-median-md", + "metadata": {}, + "source": [ + "## 4. Median with percentile bands over time\n", + "\n", + "Median (p50) is the cleanest single-number tracker of \"where the bulk of the segment sits\". We add two shaded bands so the distribution *shape* is visible, not just its centre:\n", + "\n", + "- **Inner band — IQR (p25–p75)**: the middle 50 % of the segment. Where the body actually lives.\n", + "- **Outer band — p10–p90**: where 80 % of records lie. Captures the tails without being thrown around by individual outliers.\n", + "\n", + "Reading the fan:\n", + "- If both bands are **wide and roughly symmetric around the median**, the segment is a genuine mixture.\n", + "- If the bands are **clamped to 0 (bottom) or 1 (top)**, the corresponding mode is the dominant one — characteristic of this bimodal classifier.\n", + "- A **shrinking IQR over time** with a stable median means the segment is becoming more cleanly bimodal (records pile up at the extremes); a **growing IQR** means more mid-confidence content is appearing." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cell-09-median-plot", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-27T21:25:59.979783Z", + "iopub.status.busy": "2026-05-27T21:25:59.979719Z", + "iopub.status.idle": "2026-05-27T21:26:00.019213Z", + "shell.execute_reply": "2026-05-27T21:26:00.018804Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 5))\n", + "ax.fill_between(datetimes, p10, p90, alpha=0.15, color=\"C1\", label=\"p10–p90 (inner 80%)\")\n", + "ax.fill_between(datetimes, p25, p75, alpha=0.30, color=\"C1\", label=\"IQR (p25–p75)\")\n", + "ax.plot(datetimes, medians, marker=\"o\", linestyle=\"-\", color=\"C1\", label=\"median (p50)\")\n", + "ax.set_ylim(0.0, 1.0)\n", + "ax.set_xlabel(\"segment datetime (UTC, oldest left)\")\n", + "ax.set_ylabel(\"prediction_score\")\n", + "ax.set_title(f\"{FOCUS_NAME}: per-segment median + percentile bands\")\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y-%m-%d %H:%M\"))\n", + "ax.xaxis.set_major_locator(mdates.AutoDateLocator())\n", + "fig.autofmt_xdate(rotation=30)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend(loc=\"best\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0c393033", + "metadata": {}, + "source": [ + "## 5. High-confidence rate over time\n", + "\n", + "Mean and median are scalar summaries of a bimodal distribution — useful, but they don't directly answer the downstream question: *how much of each segment will survive a confidence filter?* The **rate** of records crossing a threshold collapses the bimodal scores into one interpretable number per threshold:\n", + "\n", + "- `P(score ≥ 0.5)` — fraction of records the classifier puts on the \"science\" side of the natural decision boundary.\n", + "- `P(score ≥ 0.9)` — fraction high enough that you'd typically trust it without manual review.\n", + "\n", + "Track both: if the `0.9` rate drifts but the `0.5` rate is steady, the segment is staying half-science but losing high-confidence content (or vice versa). The gap between the two lines reflects how much of the segment sits in the mid-confidence band." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e57696d9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-27T21:26:00.020408Z", + "iopub.status.busy": "2026-05-27T21:26:00.020338Z", + "iopub.status.idle": "2026-05-27T21:26:00.060578Z", + "shell.execute_reply": "2026-05-27T21:26:00.060134Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 5))\n", + "ax.plot(datetimes, rate_05, marker=\"o\", linestyle=\"-\", color=\"C2\", label=\"P(score ≥ 0.5)\")\n", + "ax.plot(datetimes, rate_09, marker=\"s\", linestyle=\"-\", color=\"C3\", label=\"P(score ≥ 0.9)\")\n", + "ax.set_ylim(0.0, 1.0)\n", + "ax.set_xlabel(\"segment datetime (UTC, oldest left)\")\n", + "ax.set_ylabel(\"fraction of records\")\n", + "ax.set_title(f\"{FOCUS_NAME}: per-segment high-confidence rate\")\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y-%m-%d %H:%M\"))\n", + "ax.xaxis.set_major_locator(mdates.AutoDateLocator())\n", + "fig.autofmt_xdate(rotation=30)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend(loc=\"best\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c5609ff4", + "metadata": {}, + "source": [ + "## 6. Mean and median side by side\n", + "\n", + "A consolidated view: mean and median plotted as two lines on the same axes, no error bars or bands. Useful as a quick overall read — if the two lines move together, the bulk of the distribution is shifting with the centre of mass (the typical drift case). If they diverge, the tails are changing weight independently of the bulk: mean drifting above median means the high-confidence tail is gaining mass; mean drifting below median means the low-confidence tail is." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cfd17f78", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-27T21:26:00.061984Z", + "iopub.status.busy": "2026-05-27T21:26:00.061911Z", + "iopub.status.idle": "2026-05-27T21:26:00.101760Z", + "shell.execute_reply": "2026-05-27T21:26:00.101339Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 5))\n", + "ax.plot(datetimes, means, marker=\"o\", linestyle=\"-\", color=\"C0\", label=\"mean\")\n", + "ax.plot(datetimes, medians, marker=\"s\", linestyle=\"-\", color=\"C1\", label=\"median (p50)\")\n", + "ax.set_ylim(0.0, 1.0)\n", + "ax.set_xlabel(\"segment datetime (UTC, oldest left)\")\n", + "ax.set_ylabel(\"prediction_score\")\n", + "ax.set_title(f\"{FOCUS_NAME}: per-segment mean and median\")\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y-%m-%d %H:%M\"))\n", + "ax.xaxis.set_major_locator(mdates.AutoDateLocator())\n", + "fig.autofmt_xdate(rotation=30)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend(loc=\"best\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-10-conclusion-md", + "metadata": {}, + "source": [ + "## 7. How to read the plots\n", + "\n", + "- **Monotonic trend** (mean, median, or rate moving steadily up or down with time) — systematic drift; worth investigating whether the URL frontier shifted, the seed set changed, or the relative weight of high-/low-quality domains evolved over the crawl.\n", + "- **Non-overlapping SEM bars** on the mean plot — the mean difference between those segments is real, not just sampling noise. Overlapping bars say \"indistinguishable at this sample size\".\n", + "- **Mean and median moving together** (Section 6) — the whole distribution is shifting. **Mean and median diverging** — the tails are gaining or losing weight independently of the bulk.\n", + "- **Narrowing IQR band** (p25–p75) on the median plot — the segment is becoming more cleanly bimodal (fewer mid-confidence records); a widening band means more mid-confidence content.\n", + "- **Widening gap between the `≥ 0.5` and `≥ 0.9` rate lines** — more of the segment is mid-confidence; a narrowing gap means scores are concentrating at the high end.\n", + "- **One score-outlier segment** (among the plotted, full-sized segments) — look up the 14-digit token in the WARC fetch logs (`data/gneissweb-science-top-10k/logs/`) for that segment; it may have hit an atypical slice of URLs. Note that *size* outliers (segments with `n < MIN_RECORDS`) are already auto-dropped — see the per-segment table above for which segments were excluded." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 3c8fc0c754df54dcaf5ed044db578689f664af40 Mon Sep 17 00:00:00 2001 From: malteos Date: Thu, 28 May 2026 15:17:50 +0200 Subject: [PATCH 2/2] feat: Add notebook for per-segment MIME-type drift Adds notebooks/compare_mime_type_drift.ipynb, a sibling to the classifier score drift notebook. Parses CDX index files (*.cdx.gz) for the focus crawl segments, caches (warc_filename, offset, mime_detected) per CDX file under data/cache/ (mirroring the source layout, gitignored), and visualises composition drift with four bar plots: absolute counts, absolute counts excluding text/html, total records per segment, and normalized share. MIME values come from CDX mime-detected (content sniffed), with the long tail bucketed into "other" via a global top-N ranking so the legend stays stable across segments. --- notebooks/compare_mime_type_drift.ipynb | 1118 +++++++++++++++++++++++ 1 file changed, 1118 insertions(+) create mode 100644 notebooks/compare_mime_type_drift.ipynb diff --git a/notebooks/compare_mime_type_drift.ipynb b/notebooks/compare_mime_type_drift.ipynb new file mode 100644 index 0000000..36eb4f1 --- /dev/null +++ b/notebooks/compare_mime_type_drift.ipynb @@ -0,0 +1,1118 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell-00-title", + "metadata": {}, + "source": [ + "# MIME-Type Drift Across Focus-Crawl Segments\n", + "\n", + "**Research question:** Does the MIME-type composition of fetched records drift across the lifetime of the focus crawl `CC-SUPPLEMENTAL-2026-22`?\n", + "\n", + "This notebook is a sibling of [`compare_classifier_scores_segment_drift.ipynb`](compare_classifier_scores_segment_drift.ipynb), which tracks the classifier *score* per segment. Here we track the *MIME mix* (a fair sample of what kinds of resources were actually fetched).\n", + "\n", + "Source: CDX index files (`*.cdx.gz`) under `cc-focus-tools/data/CC-SUPPLEMENTAL-2026-22/segments/*/cdx/warc/`. We **never open the WARC payloads** — the MIME columns we need live in the CDX JSON blob already.\n", + "\n", + "Outputs:\n", + "\n", + "1. **Plot 1** — stacked bar of **absolute record counts** per segment (composition + segment size).\n", + "2. **Plot 2** — stacked bar of **normalized shares** per segment (composition only — the headline drift plot).\n", + "\n", + "To keep re-runs fast we cache `(warc_filename, offset, mime_detected)` triples per CDX file under `data/cache/CC-SUPPLEMENTAL-2026-22/segments/…` (mirrors the source layout). The cache is rebuilt only when the target file is missing or `FORCE_REBUILD = True`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cell-01-imports", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:00.760851Z", + "iopub.status.busy": "2026-05-28T13:12:00.760720Z", + "iopub.status.idle": "2026-05-28T13:12:01.109648Z", + "shell.execute_reply": "2026-05-28T13:12:01.109242Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FOCUS_NAME : CC-SUPPLEMENTAL-2026-22\n", + "CDX_ROOT : /Users/moc-malteos/repos/commoncrawl/cc-focus/cc-focus-tools/data/CC-SUPPLEMENTAL-2026-22/segments (exists=True)\n", + "CACHE_ROOT : ../data/cache/CC-SUPPLEMENTAL-2026-22/segments (exists=True)\n" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "\n", + "import gzip\n", + "import json\n", + "import re\n", + "import time\n", + "from datetime import datetime\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.dates as mdates # noqa: F401 (kept for parity with sibling notebook; not used directly)\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "FOCUS_NAME = \"CC-SUPPLEMENTAL-2026-22\"\n", + "\n", + "# Repo-root-aware data path (works both from repo root and from notebooks/).\n", + "DATA_DIR = Path(\"data\") if Path(\"data\").exists() else Path(\"..\") / \"data\"\n", + "\n", + "# CDX files live outside this repo — override here if your checkout differs.\n", + "CDX_ROOT = (\n", + " Path(\"/Users/moc-malteos/repos/commoncrawl/cc-focus/cc-focus-tools/data\")\n", + " / FOCUS_NAME\n", + " / \"segments\"\n", + ")\n", + "CACHE_ROOT = DATA_DIR / \"cache\" / FOCUS_NAME / \"segments\"\n", + "\n", + "FORCE_REBUILD = False # flip to True to rebuild the per-CDX cache\n", + "MIN_RECORDS = 100_000 # drop tiny / probe / aborted segments (real segments hold 1–3 M records)\n", + "TOP_N = 8 # number of MIME types kept individually; rest → \"other\"\n", + "\n", + "print(f\"FOCUS_NAME : {FOCUS_NAME}\")\n", + "print(f\"CDX_ROOT : {CDX_ROOT} (exists={CDX_ROOT.exists()})\")\n", + "print(f\"CACHE_ROOT : {CACHE_ROOT} (exists={CACHE_ROOT.exists()})\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-02-discover-md", + "metadata": {}, + "source": [ + "## 1. Discover crawl segments\n", + "\n", + "Each segment directory under `CDX_ROOT` is named `` — the fetch start time. We pick those up by regex, parse them to `datetime`, and sort oldest-first.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cell-03-discover", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:01.110606Z", + "iopub.status.busy": "2026-05-28T13:12:01.110524Z", + "iopub.status.idle": "2026-05-28T13:12:01.117305Z", + "shell.execute_reply": "2026-05-28T13:12:01.116995Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 23 segments:\n", + " 2026-05-21T07:40:42 20260521074042 (1 .cdx.gz)\n", + " 2026-05-22T07:20:19 20260522072019 (233 .cdx.gz)\n", + " 2026-05-22T20:48:39 20260522204839 (258 .cdx.gz)\n", + " 2026-05-23T07:11:17 20260523071117 (238 .cdx.gz)\n", + " 2026-05-23T15:38:27 20260523153827 (247 .cdx.gz)\n", + " 2026-05-23T23:53:50 20260523235350 (235 .cdx.gz)\n", + " 2026-05-24T07:38:39 20260524073839 (222 .cdx.gz)\n", + " 2026-05-24T15:05:44 20260524150544 (211 .cdx.gz)\n", + " 2026-05-24T21:53:35 20260524215335 (203 .cdx.gz)\n", + " 2026-05-25T04:13:49 20260525041349 (191 .cdx.gz)\n", + " 2026-05-25T09:56:52 20260525095652 (185 .cdx.gz)\n", + " 2026-05-25T15:32:27 20260525153227 (179 .cdx.gz)\n", + " 2026-05-25T21:15:36 20260525211536 (175 .cdx.gz)\n", + " 2026-05-26T02:47:21 20260526024721 (173 .cdx.gz)\n", + " 2026-05-26T08:18:25 20260526081825 (167 .cdx.gz)\n", + " 2026-05-26T13:42:33 20260526134233 (162 .cdx.gz)\n", + " 2026-05-26T19:04:52 20260526190452 (159 .cdx.gz)\n", + " 2026-05-27T00:17:43 20260527001743 (156 .cdx.gz)\n", + " 2026-05-27T05:40:02 20260527054002 (156 .cdx.gz)\n", + " 2026-05-27T11:00:13 20260527110013 (152 .cdx.gz)\n", + " 2026-05-27T16:22:06 20260527162206 (150 .cdx.gz)\n", + " 2026-05-27T21:50:03 20260527215003 (147 .cdx.gz)\n", + " 2026-05-28T03:12:42 20260528031242 (145 .cdx.gz)\n" + ] + } + ], + "source": [ + "SEGMENT_DIR_RE = re.compile(r\"^\\d{14}$\")\n", + "\n", + "segments: list[tuple[datetime, Path]] = []\n", + "for d in sorted(CDX_ROOT.iterdir()):\n", + " if d.is_dir() and SEGMENT_DIR_RE.match(d.name):\n", + " dt = datetime.strptime(d.name, \"%Y%m%d%H%M%S\")\n", + " segments.append((dt, d))\n", + "\n", + "if not segments:\n", + " raise RuntimeError(f\"No segment directories matching ^\\\\d{{14}}$ under {CDX_ROOT}\")\n", + "\n", + "print(f\"Found {len(segments)} segments:\")\n", + "for dt, d in segments:\n", + " n_cdx = sum(1 for _ in (d / \"cdx\" / \"warc\").glob(\"*.cdx.gz\"))\n", + " print(f\" {dt.isoformat()} {d.name} ({n_cdx} .cdx.gz)\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-04-cache-md", + "metadata": {}, + "source": [ + "## 2. Build the per-CDX MIME cache\n", + "\n", + "For each `*.cdx.gz` source file we materialise a sibling `*.csv.gz` under `data/cache/` containing three columns: `warc_filename, offset, mime_detected`. The MIME value is taken from the CDX `mime-detected` field (content-sniffed, more comparable across origins than the raw `Content-Type` header), lower-cased and stripped of any `; charset=…` parameters; missing / empty values become `\"unknown\"`.\n", + "\n", + "The cache mirrors the source layout 1:1, so the cache for `CDX_ROOT/
/cdx/warc/foo.cdx.gz` lives at `CACHE_ROOT/
/cdx/warc/foo.csv.gz`. Build is skipped when the target already exists — set `FORCE_REBUILD = True` above to invalidate.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cell-05-cache", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:01.118234Z", + "iopub.status.busy": "2026-05-28T13:12:01.118187Z", + "iopub.status.idle": "2026-05-28T13:12:01.235198Z", + "shell.execute_reply": "2026-05-28T13:12:01.234824Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4145 CDX files to consider.\n", + "Built 0 new cache files (0 rows) in 0.0s; 4145 were already cached.\n" + ] + } + ], + "source": [ + "def build_cdx_cache(cdx_path: Path, cache_path: Path) -> int:\n", + " \"\"\"Parse one .cdx.gz into a 3-column .csv.gz cache. Returns the row count.\"\"\"\n", + " if cache_path.exists() and not FORCE_REBUILD:\n", + " return 0 # cache hit; row count unknown without reading, but we don't need it here\n", + " cache_path.parent.mkdir(parents=True, exist_ok=True)\n", + " rows: list[tuple[str, int, str]] = []\n", + " with gzip.open(cdx_path, \"rt\", encoding=\"utf-8\", errors=\"replace\") as fh:\n", + " for line in fh:\n", + " # Each line is: \" \"\n", + " parts = line.split(\" \", 2)\n", + " if len(parts) != 3:\n", + " continue\n", + " try:\n", + " rec = json.loads(parts[2])\n", + " except json.JSONDecodeError:\n", + " continue\n", + " mime = (rec.get(\"mime-detected\") or \"\").split(\";\", 1)[0].strip().lower() or \"unknown\"\n", + " rows.append((rec[\"filename\"], int(rec[\"offset\"]), mime))\n", + " pd.DataFrame(rows, columns=[\"warc_filename\", \"offset\", \"mime_detected\"]).to_csv(\n", + " cache_path, index=False, compression=\"gzip\"\n", + " )\n", + " return len(rows)\n", + "\n", + "\n", + "# Collect every (cdx_path, cache_path) pair across all segments.\n", + "todo: list[tuple[Path, Path]] = []\n", + "for _, seg_dir in segments:\n", + " for cdx_path in sorted((seg_dir / \"cdx\" / \"warc\").glob(\"*.cdx.gz\")):\n", + " rel = cdx_path.relative_to(CDX_ROOT) # e.g. 20260521074042/cdx/warc/foo.cdx.gz\n", + " cache_path = (CACHE_ROOT / rel).with_suffix(\"\").with_suffix(\".csv.gz\")\n", + " todo.append((cdx_path, cache_path))\n", + "\n", + "print(f\"{len(todo)} CDX files to consider.\")\n", + "\n", + "t0 = time.time()\n", + "built = 0\n", + "built_rows = 0\n", + "for i, (cdx_path, cache_path) in enumerate(todo, start=1):\n", + " if cache_path.exists() and not FORCE_REBUILD:\n", + " continue\n", + " n = build_cdx_cache(cdx_path, cache_path)\n", + " built += 1\n", + " built_rows += n\n", + " if built % 50 == 0 or built == 1:\n", + " print(f\" [{i:>5}/{len(todo)}] built {built} caches — latest: {cache_path.relative_to(CACHE_ROOT)} ({n:,} rows)\")\n", + "\n", + "elapsed = time.time() - t0\n", + "print(\n", + " f\"Built {built} new cache files ({built_rows:,} rows) in {elapsed:.1f}s; \"\n", + " f\"{len(todo) - built} were already cached.\"\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-06-aggregate-md", + "metadata": {}, + "source": [ + "## 3. Aggregate MIME counts per segment\n", + "\n", + "For each segment we concatenate all its cache files (just the `mime_detected` column) and count occurrences. The result is one `pd.Series` per segment, indexed by MIME string.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cell-07-aggregate", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:01.236203Z", + "iopub.status.busy": "2026-05-28T13:12:01.236137Z", + "iopub.status.idle": "2026-05-28T13:12:14.940926Z", + "shell.execute_reply": "2026-05-28T13:12:14.940568Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-05-21 07:40:42 6975\n", + "2026-05-22 07:20:19 3407671\n", + "2026-05-22 20:48:39 3403506\n", + "2026-05-23 07:11:17 3014988\n", + "2026-05-23 15:38:27 2986003\n", + "2026-05-23 23:53:50 2687931\n", + "2026-05-24 07:38:39 2446855\n", + "2026-05-24 15:05:44 2240532\n", + "2026-05-24 21:53:35 2109066\n", + "2026-05-25 04:13:49 1887040\n", + "2026-05-25 09:56:52 1819881\n", + "2026-05-25 15:32:27 1791073\n", + "2026-05-25 21:15:36 1724453\n", + "2026-05-26 02:47:21 1675723\n", + "2026-05-26 08:18:25 1581544\n", + "2026-05-26 13:42:33 1484653\n", + "2026-05-26 19:04:52 1508879\n", + "2026-05-27 00:17:43 1450413\n", + "2026-05-27 05:40:02 1477566\n", + "2026-05-27 11:00:13 1443381\n", + "2026-05-27 16:22:06 1406055\n", + "2026-05-27 21:50:03 1353335\n", + "2026-05-28 03:12:42 1313857\n" + ] + } + ], + "source": [ + "segment_mime_counts: dict[datetime, pd.Series] = {}\n", + "total_per_segment: dict[datetime, int] = {}\n", + "\n", + "for dt, seg_dir in segments:\n", + " rel_seg = seg_dir.relative_to(CDX_ROOT)\n", + " cache_files = sorted((CACHE_ROOT / rel_seg / \"cdx\" / \"warc\").glob(\"*.csv.gz\"))\n", + " if not cache_files:\n", + " print(f\" {dt.isoformat()}: no cache files (skipping)\")\n", + " continue\n", + " series_list = [\n", + " pd.read_csv(p, usecols=[\"mime_detected\"], compression=\"gzip\")[\"mime_detected\"]\n", + " for p in cache_files\n", + " ]\n", + " mimes = pd.concat(series_list, ignore_index=True)\n", + " counts = mimes.value_counts()\n", + " segment_mime_counts[dt] = counts\n", + " total_per_segment[dt] = int(counts.sum())\n", + "\n", + "totals = pd.Series(total_per_segment, name=\"n_records\").sort_index()\n", + "print(totals.to_string())\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-08-filter-md", + "metadata": {}, + "source": [ + "## 4. Drop under-sized segments\n", + "\n", + "Segments with very few records are likely probe / aborted runs; their MIME shares are too noisy to be informative and they swamp the absolute-counts plot with a hairline bar. Real focus-crawl segments hold 1–3 million records, so we drop anything below `MIN_RECORDS = 100_000` (compare with the score notebook, which sets `MIN_RECORDS = 1000` because it counts already-scored text/html records, ~10 k per segment).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cell-09-filter", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:14.942101Z", + "iopub.status.busy": "2026-05-28T13:12:14.942043Z", + "iopub.status.idle": "2026-05-28T13:12:14.944145Z", + "shell.execute_reply": "2026-05-28T13:12:14.943793Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropping 1 segment(s) below MIN_RECORDS=100000:\n", + " 2026-05-21T07:40:42 n=6975\n", + "\n", + "22 segments kept for plotting.\n" + ] + } + ], + "source": [ + "dropped = [(dt, n) for dt, n in total_per_segment.items() if n < MIN_RECORDS]\n", + "kept_dts = [dt for dt, n in sorted(total_per_segment.items()) if n >= MIN_RECORDS]\n", + "\n", + "if dropped:\n", + " print(f\"Dropping {len(dropped)} segment(s) below MIN_RECORDS={MIN_RECORDS}:\")\n", + " for dt, n in dropped:\n", + " print(f\" {dt.isoformat()} n={n}\")\n", + "else:\n", + " print(f\"All {len(total_per_segment)} segments are ≥ MIN_RECORDS={MIN_RECORDS}.\")\n", + "\n", + "print(f\"\\n{len(kept_dts)} segments kept for plotting.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-10-topn-md", + "metadata": {}, + "source": [ + "## 5. Pick the top-N MIME types, bucket the rest as `other`\n", + "\n", + "We pool global counts across all kept segments and rank MIME types by total record share. The top `TOP_N = 8` keep their identity; everything else (including the `unknown` bucket and the long tail) goes into a single `other` series.\n", + "\n", + "Selecting the top-N **globally** — rather than per-segment — keeps the legend stable across the x-axis so the same colour always means the same MIME type in both plots.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cell-11-topn", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:14.945096Z", + "iopub.status.busy": "2026-05-28T13:12:14.945050Z", + "iopub.status.idle": "2026-05-28T13:12:14.954566Z", + "shell.execute_reply": "2026-05-28T13:12:14.954294Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Global record total (kept segments): 44,214,405\n", + "\n", + "Top 8 MIME types by global share:\n", + " text/html 37,163,761 (84.05%)\n", + " application/pdf 2,992,829 ( 6.77%)\n", + " application/xhtml+xml 2,235,845 ( 5.06%)\n", + " text/plain 788,862 ( 1.78%)\n", + " application/x-bibtex-text-file 140,197 ( 0.32%)\n", + " application/xml 99,664 ( 0.23%)\n", + " application/atom+xml 85,376 ( 0.19%)\n", + " text/calendar 82,332 ( 0.19%)\n", + " other 625,539 ( 1.41%) ← covers 363 long-tail MIME types\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
text/htmlapplication/pdfapplication/xhtml+xmltext/plainapplication/x-bibtex-text-fileapplication/xmlapplication/atom+xmltext/calendarother
segment_datetime
2026-05-22 07:20:193290671109549346725023815773173433707
2026-05-22 20:48:392815439357496138005244999041824499961327427512
2026-05-23 07:11:172450435310425162494258131060093752158538938299
2026-05-23 15:38:272425967300646159026404181155833663305781233905
2026-05-23 23:53:50219647424670214846443714995630732351540431793
2026-05-24 07:38:39198131922232114919743539547444341161699532415
2026-05-24 15:05:44183175217130313159949460760954121097674235558
2026-05-24 21:53:3517608941412181142094033778433958725489434988
2026-05-25 04:13:4915786581216431057143283266064554720245133862
2026-05-25 09:56:52149856511321810947441329717649923413239039324
2026-05-25 15:32:27147134711495510354143370586153974099223240271
2026-05-25 21:15:3614251721032289954646307511237515995192233420
2026-05-26 02:47:211415180849188176645303624723247734203530216
2026-05-26 08:18:251323585813368175146202743546066930120428495
2026-05-26 13:42:331244273787167774239034609940997676222624788
2026-05-26 19:04:521267939780117808037967527944797788231427022
2026-05-27 00:17:431227975721227113131262600040177479216428263
2026-05-27 05:40:021260012750627045330668548538886741223223025
2026-05-27 11:00:131225615805426886032602550446332715274520165
2026-05-27 16:22:06119549480115674883202138873217594277820461
2026-05-27 21:50:03115483375724632593080432652685445283119489
2026-05-28 03:12:421122162721746057928879377933872081225518561
\n", + "
" + ], + "text/plain": [ + " text/html application/pdf application/xhtml+xml \\\n", + "segment_datetime \n", + "2026-05-22 07:20:19 3290671 10954 93467 \n", + "2026-05-22 20:48:39 2815439 357496 138005 \n", + "2026-05-23 07:11:17 2450435 310425 162494 \n", + "2026-05-23 15:38:27 2425967 300646 159026 \n", + "2026-05-23 23:53:50 2196474 246702 148464 \n", + "2026-05-24 07:38:39 1981319 222321 149197 \n", + "2026-05-24 15:05:44 1831752 171303 131599 \n", + "2026-05-24 21:53:35 1760894 141218 114209 \n", + "2026-05-25 04:13:49 1578658 121643 105714 \n", + "2026-05-25 09:56:52 1498565 113218 109474 \n", + "2026-05-25 15:32:27 1471347 114955 103541 \n", + "2026-05-25 21:15:36 1425172 103228 99546 \n", + "2026-05-26 02:47:21 1415180 84918 81766 \n", + "2026-05-26 08:18:25 1323585 81336 81751 \n", + "2026-05-26 13:42:33 1244273 78716 77742 \n", + "2026-05-26 19:04:52 1267939 78011 78080 \n", + "2026-05-27 00:17:43 1227975 72122 71131 \n", + "2026-05-27 05:40:02 1260012 75062 70453 \n", + "2026-05-27 11:00:13 1225615 80542 68860 \n", + "2026-05-27 16:22:06 1195494 80115 67488 \n", + "2026-05-27 21:50:03 1154833 75724 63259 \n", + "2026-05-28 03:12:42 1122162 72174 60579 \n", + "\n", + " text/plain application/x-bibtex-text-file \\\n", + "segment_datetime \n", + "2026-05-22 07:20:19 2502 381 \n", + "2026-05-22 20:48:39 24499 9041 \n", + "2026-05-23 07:11:17 25813 10600 \n", + "2026-05-23 15:38:27 40418 11558 \n", + "2026-05-23 23:53:50 43714 9956 \n", + "2026-05-24 07:38:39 43539 5474 \n", + "2026-05-24 15:05:44 49460 7609 \n", + "2026-05-24 21:53:35 40337 7843 \n", + "2026-05-25 04:13:49 32832 6606 \n", + "2026-05-25 09:56:52 41329 7176 \n", + "2026-05-25 15:32:27 43370 5861 \n", + "2026-05-25 21:15:36 46307 5112 \n", + "2026-05-26 02:47:21 45303 6247 \n", + "2026-05-26 08:18:25 46202 7435 \n", + "2026-05-26 13:42:33 39034 6099 \n", + "2026-05-26 19:04:52 37967 5279 \n", + "2026-05-27 00:17:43 31262 6000 \n", + "2026-05-27 05:40:02 30668 5485 \n", + "2026-05-27 11:00:13 32602 5504 \n", + "2026-05-27 16:22:06 32021 3887 \n", + "2026-05-27 21:50:03 30804 3265 \n", + "2026-05-28 03:12:42 28879 3779 \n", + "\n", + " application/xml application/atom+xml text/calendar \\\n", + "segment_datetime \n", + "2026-05-22 07:20:19 5773 173 43 \n", + "2026-05-22 20:48:39 8244 9996 13274 \n", + "2026-05-23 07:11:17 9375 2158 5389 \n", + "2026-05-23 15:38:27 3366 3305 7812 \n", + "2026-05-23 23:53:50 3073 2351 5404 \n", + "2026-05-24 07:38:39 4434 1161 6995 \n", + "2026-05-24 15:05:44 5412 1097 6742 \n", + "2026-05-24 21:53:35 3958 725 4894 \n", + "2026-05-25 04:13:49 4554 720 2451 \n", + "2026-05-25 09:56:52 4992 3413 2390 \n", + "2026-05-25 15:32:27 5397 4099 2232 \n", + "2026-05-25 21:15:36 3751 5995 1922 \n", + "2026-05-26 02:47:21 2324 7734 2035 \n", + "2026-05-26 08:18:25 4606 6930 1204 \n", + "2026-05-26 13:42:33 4099 7676 2226 \n", + "2026-05-26 19:04:52 4479 7788 2314 \n", + "2026-05-27 00:17:43 4017 7479 2164 \n", + "2026-05-27 05:40:02 3888 6741 2232 \n", + "2026-05-27 11:00:13 4633 2715 2745 \n", + "2026-05-27 16:22:06 3217 594 2778 \n", + "2026-05-27 21:50:03 2685 445 2831 \n", + "2026-05-28 03:12:42 3387 2081 2255 \n", + "\n", + " other \n", + "segment_datetime \n", + "2026-05-22 07:20:19 3707 \n", + "2026-05-22 20:48:39 27512 \n", + "2026-05-23 07:11:17 38299 \n", + "2026-05-23 15:38:27 33905 \n", + "2026-05-23 23:53:50 31793 \n", + "2026-05-24 07:38:39 32415 \n", + "2026-05-24 15:05:44 35558 \n", + "2026-05-24 21:53:35 34988 \n", + "2026-05-25 04:13:49 33862 \n", + "2026-05-25 09:56:52 39324 \n", + "2026-05-25 15:32:27 40271 \n", + "2026-05-25 21:15:36 33420 \n", + "2026-05-26 02:47:21 30216 \n", + "2026-05-26 08:18:25 28495 \n", + "2026-05-26 13:42:33 24788 \n", + "2026-05-26 19:04:52 27022 \n", + "2026-05-27 00:17:43 28263 \n", + "2026-05-27 05:40:02 23025 \n", + "2026-05-27 11:00:13 20165 \n", + "2026-05-27 16:22:06 20461 \n", + "2026-05-27 21:50:03 19489 \n", + "2026-05-28 03:12:42 18561 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pool counts across all kept segments.\n", + "global_counts = (\n", + " pd.concat([segment_mime_counts[dt] for dt in kept_dts], axis=1)\n", + " .fillna(0)\n", + " .sum(axis=1)\n", + " .astype(int)\n", + " .sort_values(ascending=False)\n", + ")\n", + "global_total = int(global_counts.sum())\n", + "\n", + "top_n_mimes = global_counts.head(TOP_N).index.tolist()\n", + "other_count = int(global_counts.iloc[TOP_N:].sum()) if len(global_counts) > TOP_N else 0\n", + "\n", + "print(f\"Global record total (kept segments): {global_total:,}\")\n", + "print(f\"\\nTop {TOP_N} MIME types by global share:\")\n", + "for mime in top_n_mimes:\n", + " c = int(global_counts[mime])\n", + " print(f\" {mime:<35} {c:>12,} ({c / global_total:6.2%})\")\n", + "print(f\" {'other':<35} {other_count:>12,} ({other_count / global_total:6.2%}) \"\n", + " f\"← covers {max(0, len(global_counts) - TOP_N)} long-tail MIME types\")\n", + "\n", + "# Build the wide per-segment frame: rows = segment datetime, cols = top-N + 'other'.\n", + "records = []\n", + "for dt in kept_dts:\n", + " counts = segment_mime_counts[dt]\n", + " row = {mime: int(counts.get(mime, 0)) for mime in top_n_mimes}\n", + " row[\"other\"] = int(counts.sum()) - sum(row.values())\n", + " records.append(row)\n", + "\n", + "mime_df = pd.DataFrame(records, index=pd.Index(kept_dts, name=\"segment_datetime\"))\n", + "mime_df = mime_df[top_n_mimes + [\"other\"]] # fix column order\n", + "mime_df.round(0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-12-plot1-md", + "metadata": {}, + "source": [ + "## 6. Plot 1 — stacked bar of absolute counts\n", + "\n", + "Absolute bars show both **composition** *and* **segment size**: taller bar = larger segment. Useful for spotting a segment that's just smaller (which would otherwise look like a composition shift in the normalized plot).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cell-13-plot1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:14.955473Z", + "iopub.status.busy": "2026-05-28T13:12:14.955428Z", + "iopub.status.idle": "2026-05-28T13:12:15.065177Z", + "shell.execute_reply": "2026-05-28T13:12:15.064889Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Stable colour palette: tab10 + 'lightgrey' for 'other' so it visually recedes.\n", + "palette = list(plt.get_cmap(\"tab10\").colors[:TOP_N])\n", + "colors = palette + [(0.78, 0.78, 0.78, 1.0)]\n", + "\n", + "x_labels = [dt.strftime(\"%Y-%m-%d %H:%M\") for dt in mime_df.index]\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "mime_df.plot.bar(stacked=True, ax=ax, color=colors, width=0.85, edgecolor=\"white\", linewidth=0.4)\n", + "ax.set_xticklabels(x_labels, rotation=30, ha=\"right\")\n", + "ax.set_xlabel(\"segment datetime (UTC, oldest left)\")\n", + "ax.set_ylabel(\"record count\")\n", + "ax.set_title(f\"{FOCUS_NAME}: per-segment MIME composition (absolute counts)\")\n", + "ax.grid(True, axis=\"y\", alpha=0.3)\n", + "ax.legend(loc=\"upper left\", bbox_to_anchor=(1.02, 1.0), title=\"mime-detected\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-13b-plot1b-md", + "metadata": {}, + "source": [ + "### Plot 1b — same as Plot 1 but with `text/html` excluded (linear y-axis)\n", + "\n", + "`text/html` occupies ~84 % of every bar in Plot 1, which crushes every other MIME type into a sliver against the top edge. This zoomed view drops `text/html` and re-plots the remaining categories on a fresh **linear** y-axis — same data, same colours, but you can now actually read the absolute counts for `application/pdf`, `application/xhtml+xml`, `text/plain`, and the long tail.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cell-13b-plot1b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:15.066287Z", + "iopub.status.busy": "2026-05-28T13:12:15.066207Z", + "iopub.status.idle": "2026-05-28T13:12:15.147565Z", + "shell.execute_reply": "2026-05-28T13:12:15.147259Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "non_html_cols = [c for c in mime_df.columns if c != \"text/html\"]\n", + "non_html_df = mime_df[non_html_cols]\n", + "non_html_colors = [colors[mime_df.columns.get_loc(c)] for c in non_html_cols]\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "non_html_df.plot.bar(stacked=True, ax=ax, color=non_html_colors, width=0.85, edgecolor=\"white\", linewidth=0.4)\n", + "ax.set_xticklabels(x_labels, rotation=30, ha=\"right\")\n", + "ax.set_xlabel(\"segment datetime (UTC, oldest left)\")\n", + "ax.set_ylabel(\"record count\")\n", + "ax.set_yscale(\"linear\") # explicit: linear, not log\n", + "ax.set_title(f\"{FOCUS_NAME}: per-segment MIME composition (absolute counts, excluding text/html)\")\n", + "ax.grid(True, axis=\"y\", alpha=0.3)\n", + "ax.legend(loc=\"upper left\", bbox_to_anchor=(1.02, 1.0), title=\"mime-detected\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-13c-plot1c-md", + "metadata": {}, + "source": [ + "### Plot 1c — total record count per segment\n", + "\n", + "Strips away MIME type entirely. One bar per segment, height = total records. Useful for spotting fetch-volume drift independently of composition (a steady decline like the one here can foreshadow an under-fetched tail).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cell-13c-plot1c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:15.148540Z", + "iopub.status.busy": "2026-05-28T13:12:15.148491Z", + "iopub.status.idle": "2026-05-28T13:12:15.194925Z", + "shell.execute_reply": "2026-05-28T13:12:15.194601Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/cv/ww0kym4x5b98njk9g9yg9kxw0000gn/T/ipykernel_95328/1809985819.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_xticklabels(x_labels, rotation=30, ha=\"right\")\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.ticker import FuncFormatter\n", + "\n", + "totals_series = mime_df.sum(axis=1)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "ax.bar(x_labels, totals_series.values, width=0.85, color=\"#4878a6\", edgecolor=\"white\", linewidth=0.4)\n", + "ax.set_xticklabels(x_labels, rotation=30, ha=\"right\")\n", + "ax.set_xlabel(\"segment datetime (UTC, oldest left)\")\n", + "ax.set_ylabel(\"total record count\")\n", + "ax.set_yscale(\"linear\") # explicit: linear scale, no log\n", + "ax.yaxis.set_major_formatter(FuncFormatter(lambda v, _pos: f\"{v / 1_000_000:.1f}M\"))\n", + "ax.set_title(f\"{FOCUS_NAME}: total records per segment (linear y)\")\n", + "ax.grid(True, axis=\"y\", alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-14-plot2-md", + "metadata": {}, + "source": [ + "## 7. Plot 2 — stacked bar of normalized shares (the drift plot)\n", + "\n", + "Normalizing each bar to 100 % isolates **composition drift** from segment-size effects. This is the plot to read for \"did the MIME mix change over time?\".\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cell-15-plot2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T13:12:15.195928Z", + "iopub.status.busy": "2026-05-28T13:12:15.195868Z", + "iopub.status.idle": "2026-05-28T13:12:15.304781Z", + "shell.execute_reply": "2026-05-28T13:12:15.304473Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "share_df = mime_df.div(mime_df.sum(axis=1), axis=0)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "share_df.plot.bar(stacked=True, ax=ax, color=colors, width=0.85, edgecolor=\"white\", linewidth=0.4)\n", + "ax.set_xticklabels(x_labels, rotation=30, ha=\"right\")\n", + "ax.set_xlabel(\"segment datetime (UTC, oldest left)\")\n", + "ax.set_ylabel(\"share of records\")\n", + "ax.set_ylim(0, 1)\n", + "ax.set_title(f\"{FOCUS_NAME}: per-segment MIME composition (normalized share)\")\n", + "ax.grid(True, axis=\"y\", alpha=0.3)\n", + "ax.legend(loc=\"upper left\", bbox_to_anchor=(1.02, 1.0), title=\"mime-detected\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-16-conclusion-md", + "metadata": {}, + "source": [ + "## 8. How to read these plots\n", + "\n", + "- **Flat 100 % stack across the x-axis** in Plot 2 → stable MIME composition; no drift.\n", + "- **A slice growing or shrinking monotonically** → systematic drift (e.g. fetch policy change, source-list change).\n", + "- **A single bar that looks different from its neighbours** → batch-level anomaly; investigate the corresponding 14-digit segment timestamp in the fetch logs.\n", + "- **Ballooning `other`** → either real long-tail growth or `mime-detected` failing for a batch. Bump `TOP_N` to see what's hiding in `other`, or inspect the cache directly with `zcat data/cache/.../foo.csv.gz | cut -d',' -f3 | sort | uniq -c | sort -rn | head`.\n", + "- Plot 1 differs from Plot 2 → the segment had a different *size*, not a different *mix* — e.g. a partial fetch.\n", + "- Plot 1b (text/html excluded) makes absolute drift in PDF, xhtml+xml, plain text and the long tail readable — Plot 1 hides them under the html dominance.\n", + "- Plot 1c isolates *fetch volume* from composition: a steady downward trend means later segments simply contain fewer records, which is worth tracking on its own.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}