{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df1317f6",
   "metadata": {},
   "source": [
    "# 14 — Le rang et le contrefactuel : deux angles morts de l'évaluation\n",
    "\n",
    "Le [test adverse](13_test_adverse_index.ipynb) a corrigé deux fois la définition de l'index :\n",
    "mesurer les **contenus** et non les étiquettes, puis ne pas mesurer par l'entropie de Rao, qui\n",
    "prescrivait la polarisation. Deux hypothèses restaient, implicites l'une et l'autre, et fausses\n",
    "l'une et l'autre.\n",
    "\n",
    "**Que la position d'un contenu dans le fil ne compte pas.** Elle compte : un lecteur consulte\n",
    "le premier élément bien plus souvent que le huitième, et une plateforme tenue à un plancher de\n",
    "diversité peut s'y conformer en plaçant les contenus divergents **en bas**. C'est un quatrième\n",
    "adversaire, et il n'avait pas été éprouvé.\n",
    "\n",
    "**Qu'un fil enregistré puisse servir à évaluer un filtre qui ne l'a pas produit.** Il ne le peut\n",
    "pas sans correction : les clics enregistrés portent l'exposition que la plateforme avait\n",
    "accordée, et un filtre de diversité fait précisément remonter ce qu'elle avait enterré.\n",
    "\n",
    "**Ce que ce notebook établit :**\n",
    "\n",
    "* l'enterrement fonctionne — à **composition rigoureusement identique**, une permutation seule\n",
    "  fait passer la divergence de 0,525 à 0,630 et rapporte 10 % d'engagement, sans qu'aucune\n",
    "  mesure ponctuelle n'y voie de différence ;\n",
    "* la remise de rang de [RADio](https://arxiv.org/abs/2209.13520) ferme cette échappatoire, et\n",
    "  remplace du même geste une valeur ponctuelle par une **divergence à une référence déclarée** ;\n",
    "* l'évaluation naïve d'un réordonnancement sur données enregistrées se trompe de **201 % en\n",
    "  médiane**, jusqu'à 851 % — et son **sens n'est pas garanti** ;\n",
    "* les estimateurs contrefactuels retrouvent la valeur vraie à moins d'un point."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e15bd7e",
   "metadata": {},
   "source": [
    "## 1. Le quatrième adversaire : se conformer en enterrant\n",
    "\n",
    "La remise de rang de RADio pondère chaque position par l'attention qu'elle reçoit :\n",
    "\n",
    "$$Q^*(x) = \\frac{\\sum_i w_{R_i}\\,\\mathbb{1}[i \\in x]}{\\sum_i w_{R_i}}\n",
    "  \\qquad w_{R_i} = \\frac{1}{R_i}$$\n",
    "\n",
    "La conséquence est immédiate : deux fils composés des **mêmes contenus** mais rangés\n",
    "différemment n'ont plus la même mesure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "075a9ae0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:16.912341Z",
     "iopub.status.busy": "2026-08-23T13:40:16.912140Z",
     "iopub.status.idle": "2026-08-23T13:40:17.478032Z",
     "shell.execute_reply": "2026-08-23T13:40:17.477556Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diversité remontée     composition [5 1 1 1]   entropie de position 0.774\n",
      "diversité enterrée     composition [5 1 1 1]   entropie de position 0.774\n",
      "\n",
      "Même composition, même entropie de position : la mesure ponctuelle est aveugle\n",
      "à l'ordre, donc à l'enterrement.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ide.gaming import position_entropy\n",
    "from ide.offpolicy import (\n",
    "    clipped_ips,\n",
    "    effective_sample_size,\n",
    "    ips,\n",
    "    naive,\n",
    "    naive_replay,\n",
    "    rank_propensities,\n",
    "    simulate_logged_feedback,\n",
    "    snips,\n",
    "    value_under_policy,\n",
    ")\n",
    "from ide.plotting import PALETTE, save_figure, use_project_style\n",
    "from ide.radio import (\n",
    "    calibration,\n",
    "    fragmentation,\n",
    "    rank_aware_distribution,\n",
    "    rank_weights,\n",
    "    representation,\n",
    ")\n",
    "\n",
    "use_project_style()\n",
    "\n",
    "VIEWPOINTS = 4\n",
    "SUPPLY = np.arange(VIEWPOINTS)          # l'offre disponible, équilibrée\n",
    "CATALOGUE = np.arange(VIEWPOINTS, dtype=float)\n",
    "\n",
    "# Deux fils de huit positions, de composition RIGOUREUSEMENT identique : cinq contenus du\n",
    "# point de vue que le lecteur préfère, et un de chacun des trois autres. Seul l'ordre diffère.\n",
    "diversified = np.array([0, 1, 0, 2, 0, 3, 0, 0])   # la diversité est remontée\n",
    "favouring = np.array([0, 0, 0, 1, 0, 2, 3, 0])     # la diversité est enterrée\n",
    "\n",
    "for name, feed in ((\"diversité remontée\", diversified), (\"diversité enterrée\", favouring)):\n",
    "    composition = np.bincount(feed, minlength=VIEWPOINTS) / feed.size\n",
    "    print(f\"{name:22s} composition {np.bincount(feed, minlength=VIEWPOINTS)}\"\n",
    "          f\"   entropie de position {position_entropy(composition, CATALOGUE, CATALOGUE):.3f}\")\n",
    "\n",
    "print(\"\\nMême composition, même entropie de position : la mesure ponctuelle est aveugle\")\n",
    "print(\"à l'ordre, donc à l'enterrement.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e166711d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.478832Z",
     "iopub.status.busy": "2026-08-23T13:40:17.478728Z",
     "iopub.status.idle": "2026-08-23T13:40:17.481581Z",
     "shell.execute_reply": "2026-08-23T13:40:17.481214Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "remise de rang réciproque : [1.    0.5   0.333 0.25  0.2   0.167 0.143 0.125] \n",
      "\n",
      "fil                                   distribution consciente du rang   divergence\n",
      "------------------------------------------------------------------------------------\n",
      "diversité remontée                          [0.663 0.184 0.092 0.061]        0.525\n",
      "diversité enterrée                          [0.794 0.092 0.061 0.053]        0.630\n",
      "\n",
      "composition : [5 1 1 1] et [5 1 1 1] — identiques.\n",
      "Seule la permutation change, et la divergence en rend compte.\n"
     ]
    }
   ],
   "source": [
    "print(\"remise de rang réciproque :\", np.round(rank_weights(8, \"mrr\"), 3), \"\\n\")\n",
    "\n",
    "print(f\"{'fil':34s} {'distribution consciente du rang':>34s} {'divergence':>12s}\")\n",
    "print(\"-\" * 84)\n",
    "for name, feed in ((\"diversité remontée\", diversified), (\"diversité enterrée\", favouring)):\n",
    "    distribution = rank_aware_distribution(feed, VIEWPOINTS, \"mrr\")\n",
    "    print(f\"{name:34s} {str(np.round(distribution, 3)):>34s}\"\n",
    "          f\" {representation(feed, SUPPLY, VIEWPOINTS):12.3f}\")\n",
    "\n",
    "print(f\"\\ncomposition : {np.bincount(diversified, minlength=VIEWPOINTS)}\"\n",
    "      f\" et {np.bincount(favouring, minlength=VIEWPOINTS)} — identiques.\")\n",
    "print(\"Seule la permutation change, et la divergence en rend compte.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6442428",
   "metadata": {},
   "source": [
    "### Pourquoi enterrer est rentable\n",
    "\n",
    "L'enterrement ne serait qu'une curiosité si le lecteur consultait tout le fil. Ce n'est pas le\n",
    "cas, et le modèle qui le dit — le biais de position $e(R) = R^{-\\eta}$ — est **le même** que\n",
    "celui dont les estimateurs contrefactuels de la seconde partie ont besoin. Ce n'est pas une\n",
    "coïncidence : c'est la même ignorance, vue depuis la mesure puis depuis l'évaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a31de3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.482302Z",
     "iopub.status.busy": "2026-08-23T13:40:17.482235Z",
     "iopub.status.idle": "2026-08-23T13:40:17.485174Z",
     "shell.execute_reply": "2026-08-23T13:40:17.484776Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fil                        engagement   divergence\n",
      "--------------------------------------------------\n",
      "diversité remontée              1.934        0.525\n",
      "diversité enterrée              2.118        0.630\n",
      "\n",
      "Enterrer rapporte 10 % d'engagement, à composition inchangée.\n",
      "Une norme aveugle au rang offre donc ce gain gratuitement.\n"
     ]
    }
   ],
   "source": [
    "SEVERITY = 1.0\n",
    "# Pertinence de chaque point de vue pour un lecteur qui préfère le point de vue 0.\n",
    "relevance_by_viewpoint = np.array([0.90, 0.45, 0.25, 0.15])\n",
    "\n",
    "def engagement_of(feed, severity=SEVERITY):\n",
    "    exposure = rank_propensities(np.arange(1, feed.size + 1), severity, normalise=False)\n",
    "    return float(np.sum(exposure * relevance_by_viewpoint[feed]))\n",
    "\n",
    "print(f\"{'fil':24s} {'engagement':>12s} {'divergence':>12s}\")\n",
    "print(\"-\" * 50)\n",
    "for name, feed in ((\"diversité remontée\", diversified), (\"diversité enterrée\", favouring)):\n",
    "    print(f\"{name:24s} {engagement_of(feed):12.3f}\"\n",
    "          f\" {representation(feed, SUPPLY, VIEWPOINTS):12.3f}\")\n",
    "\n",
    "gain = engagement_of(favouring) / engagement_of(diversified) - 1\n",
    "print(f\"\\nEnterrer rapporte {100 * gain:.0f} % d'engagement, à composition inchangée.\")\n",
    "print(\"Une norme aveugle au rang offre donc ce gain gratuitement.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24916da7",
   "metadata": {},
   "source": [
    "## 2. Ce que RADio déplace : de la valeur ponctuelle à la référence déclarée\n",
    "\n",
    "Le second apport de RADio importe autant que le premier. Les cinq mesures sont **la même\n",
    "divergence** appliquée à des paires de distributions différentes ; ce qui les distingue est le\n",
    "choix de la **référence**, et c'est lui qui porte la valeur normative.\n",
    "\n",
    "| Mesure | Distribution servie | Référence |\n",
    "|---|---|---|\n",
    "| calibration | catégories du fil | historique de lecture du lecteur |\n",
    "| fragmentation | fil d'un lecteur | fil d'un autre lecteur |\n",
    "| activation | intensité affective du fil | intensité dans l'offre |\n",
    "| représentation | points de vue du fil | points de vue dans l'offre |\n",
    "| voix alternatives | voix minoritaires du fil | voix minoritaires dans l'offre |\n",
    "\n",
    "Cela résout le défaut de principe relevé au notebook 13 : l'entropie suppose que l'uniforme est\n",
    "l'idéal, l'entropie de Rao suppose que l'écartement l'est, et aucune ne le dit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5207a42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.485806Z",
     "iopub.status.busy": "2026-08-23T13:40:17.485749Z",
     "iopub.status.idle": "2026-08-23T13:40:17.488431Z",
     "shell.execute_reply": "2026-08-23T13:40:17.488023Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fil servi                calibration   lecture\n",
      "------------------------------------------------------------------------\n",
      "conforme à l'historique         0.013   objectif d'un recommandeur libéral\n",
      "ouvrant sur d'autres           0.173   objectif d'un recommandeur délibératif\n",
      "\n",
      "La même mesure, deux verdicts opposés selon la valeur qu'on poursuit.\n",
      "La divergence mesure ; elle ne tranche pas — et c'est ce qu'elle a de mieux à offrir.\n"
     ]
    }
   ],
   "source": [
    "history = np.array([0, 0, 0, 0, 1, 0, 0, 1])     # un lecteur très polarisé\n",
    "conforming = np.array([0, 0, 0, 1, 0, 0, 1, 0])  # un fil qui l'épouse\n",
    "opening = np.array([0, 1, 2, 0, 3, 1, 2, 3])     # un fil qui l'ouvre\n",
    "\n",
    "print(f\"{'fil servi':22s} {'calibration':>13s}   lecture\")\n",
    "print(\"-\" * 72)\n",
    "for name, feed, reading in (\n",
    "    (\"conforme à l'historique\", conforming, \"objectif d'un recommandeur libéral\"),\n",
    "    (\"ouvrant sur d'autres\", opening, \"objectif d'un recommandeur délibératif\"),\n",
    "):\n",
    "    print(f\"{name:22s} {calibration(feed, history, VIEWPOINTS):13.3f}   {reading}\")\n",
    "\n",
    "print(\"\\nLa même mesure, deux verdicts opposés selon la valeur qu'on poursuit.\")\n",
    "print(\"La divergence mesure ; elle ne tranche pas — et c'est ce qu'elle a de mieux à offrir.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1f638d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.489097Z",
     "iopub.status.busy": "2026-08-23T13:40:17.489032Z",
     "iopub.status.idle": "2026-08-23T13:40:17.491624Z",
     "shell.execute_reply": "2026-08-23T13:40:17.491219Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "paire de lecteurs             fragmentation\n",
      "----------------------------------------------\n",
      "Alice et un Bob voisin                0.005\n",
      "Alice et un Bob distant               1.000\n",
      "\n",
      "Seule des cinq à ne pas comparer un fil à une référence globale : elle demande\n",
      "si deux lecteurs partagent encore un espace commun — ce que le reste du dépôt\n",
      "appelle un régime figé.\n"
     ]
    }
   ],
   "source": [
    "alice = np.array([0, 0, 1, 0, 1, 0, 0, 1])\n",
    "bob_similar = np.array([0, 1, 0, 0, 1, 1, 0, 0])\n",
    "bob_apart = np.array([3, 3, 2, 3, 2, 2, 3, 3])\n",
    "\n",
    "print(f\"{'paire de lecteurs':28s} {'fragmentation':>14s}\")\n",
    "print(\"-\" * 46)\n",
    "print(f\"{'Alice et un Bob voisin':28s} {fragmentation(alice, bob_similar, VIEWPOINTS):14.3f}\")\n",
    "print(f\"{'Alice et un Bob distant':28s} {fragmentation(alice, bob_apart, VIEWPOINTS):14.3f}\")\n",
    "print(\"\\nSeule des cinq à ne pas comparer un fil à une référence globale : elle demande\")\n",
    "print(\"si deux lecteurs partagent encore un espace commun — ce que le reste du dépôt\")\n",
    "print(\"appelle un régime figé.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a639e76d",
   "metadata": {},
   "source": [
    "## 3. Le piège de l'évaluation hors ligne\n",
    "\n",
    "La [feuille de route §3.1](../docs/feuille-de-route.md) annonce d'évaluer l'ADE sur un jeu de\n",
    "données public : réordonner des fils enregistrés, mesurer le gain de diversité et la perte de\n",
    "pertinence. Prise au pied de la lettre, cette mesure est fausse.\n",
    "\n",
    "Les clics enregistrés n'ont pas été produits par le filtre qu'on évalue. Un clic dépend de la\n",
    "pertinence du contenu **et** de l'exposition qu'on lui a donnée : un article que la plateforme\n",
    "avait enterré a peu de clics — non parce qu'il n'intéressait personne, mais parce que personne\n",
    "ne l'a vu.\n",
    "\n",
    "Ici, la valeur vraie est connue : c'est une simulation. Sur données réelles, c'est exactement\n",
    "la quantité qu'on cherche et qu'on n'a pas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a73a98a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.492279Z",
     "iopub.status.busy": "2026-08-23T13:40:17.492222Z",
     "iopub.status.idle": "2026-08-23T13:40:17.514016Z",
     "shell.execute_reply": "2026-08-23T13:40:17.513531Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valeur vraie de la politique évaluée        : 0.6488\n",
      "valeur vraie de la politique d'enregistrement : 0.6947\n",
      "\n",
      "estimateur              estimation     écart\n",
      "----------------------------------------------\n",
      "moyenne naïve               0.6942      7.0 %\n",
      "IPS                         0.6486     -0.0 %\n",
      "SNIPS                       0.6481     -0.1 %\n",
      "IPS plafonné à 5            0.6486     -0.0 %\n",
      "\n",
      "La moyenne naïve n'est pas imprécise : elle répond à une autre question.\n",
      "Elle estime la valeur de la politique d'enregistrement, non celle qu'on évalue.\n"
     ]
    }
   ],
   "source": [
    "ITEMS = 20\n",
    "IMPRESSIONS = 400_000\n",
    "\n",
    "def experiment(seed=11, severity=1.0, diversity_weight=0.6, items=ITEMS,\n",
    "               impressions=IMPRESSIONS):\n",
    "    rng = np.random.default_rng(seed)\n",
    "    relevance = rng.uniform(0.05, 0.95, items)\n",
    "    diversity = rng.uniform(0.0, 1.0, items)\n",
    "\n",
    "    # La plateforme classe par pertinence ; le filtre de diversité contrarie ce classement.\n",
    "    logged_ranks = np.argsort(np.argsort(-relevance)) + 1\n",
    "    score = (1 - diversity_weight) * relevance + diversity_weight * diversity\n",
    "    target_ranks = np.argsort(np.argsort(-score)) + 1\n",
    "\n",
    "    logged = rank_propensities(logged_ranks, severity)\n",
    "    target = rank_propensities(target_ranks, severity)\n",
    "    examined, clicks = simulate_logged_feedback(relevance, logged, impressions, rng)\n",
    "    rates = np.bincount(examined, weights=clicks, minlength=items) / impressions\n",
    "\n",
    "    return {\n",
    "        \"relevance\": relevance, \"logged\": logged, \"target\": target,\n",
    "        \"examined\": examined, \"clicks\": clicks, \"rates\": rates,\n",
    "    }\n",
    "\n",
    "run = experiment()\n",
    "truth = value_under_policy(run[\"relevance\"], run[\"target\"])\n",
    "logged_value = value_under_policy(run[\"relevance\"], run[\"logged\"])\n",
    "weights = (run[\"target\"][run[\"examined\"]], run[\"logged\"][run[\"examined\"]])\n",
    "\n",
    "print(f\"valeur vraie de la politique évaluée        : {truth:.4f}\")\n",
    "print(f\"valeur vraie de la politique d'enregistrement : {logged_value:.4f}\\n\")\n",
    "print(f\"{'estimateur':22s} {'estimation':>11s} {'écart':>9s}\")\n",
    "print(\"-\" * 46)\n",
    "for name, estimate in (\n",
    "    (\"moyenne naïve\", naive(run[\"clicks\"])),\n",
    "    (\"IPS\", ips(run[\"clicks\"], *weights)),\n",
    "    (\"SNIPS\", snips(run[\"clicks\"], *weights)),\n",
    "    (\"IPS plafonné à 5\", clipped_ips(run[\"clicks\"], *weights, cap=5.0)),\n",
    "):\n",
    "    print(f\"{name:22s} {estimate:11.4f} {100 * (estimate - truth) / truth:8.1f} %\")\n",
    "\n",
    "print(\"\\nLa moyenne naïve n'est pas imprécise : elle répond à une autre question.\")\n",
    "print(\"Elle estime la valeur de la politique d'enregistrement, non celle qu'on évalue.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2525f3a9",
   "metadata": {},
   "source": [
    "### L'estimateur qu'on emploie réellement, et son erreur\n",
    "\n",
    "La moyenne naïve ne dépend pas du filtre évalué : personne ne s'en sert pour comparer deux\n",
    "filtres. L'estimateur réellement employé est le **replay** — on réordonne les candidats, puis on\n",
    "somme les clics observés pondérés par l'exposition que le nouveau classement leur donnerait.\n",
    "\n",
    "Son biais est structurel : le taux de clic observé porte déjà l'exposition que la plateforme\n",
    "avait accordée, si bien que l'estimateur **l'applique deux fois**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c396c8e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.514697Z",
     "iopub.status.busy": "2026-08-23T13:40:17.514632Z",
     "iopub.status.idle": "2026-08-23T13:40:17.521564Z",
     "shell.execute_reply": "2026-08-23T13:40:17.521116Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coût réel du filtre de diversité   :   6.6 %\n",
      "  estimé par replay naïf           :   5.0 %\n",
      "  estimé par SNIPS                 :   6.6 %\n"
     ]
    }
   ],
   "source": [
    "def costs(run):\n",
    "    # Coût relatif du filtre de diversité : vrai, puis estimé de deux façons.\n",
    "    truth = 1 - value_under_policy(run[\"relevance\"], run[\"target\"]) / value_under_policy(\n",
    "        run[\"relevance\"], run[\"logged\"]\n",
    "    )\n",
    "    replay = 1 - naive_replay(run[\"rates\"], run[\"target\"]) / naive_replay(\n",
    "        run[\"rates\"], run[\"logged\"]\n",
    "    )\n",
    "    corrected = 1 - snips(\n",
    "        run[\"clicks\"], run[\"target\"][run[\"examined\"]], run[\"logged\"][run[\"examined\"]]\n",
    "    ) / naive(run[\"clicks\"])\n",
    "    return truth, replay, corrected\n",
    "\n",
    "true_cost, replay_cost, corrected_cost = costs(run)\n",
    "print(f\"coût réel du filtre de diversité   : {100 * true_cost:5.1f} %\")\n",
    "print(f\"  estimé par replay naïf           : {100 * replay_cost:5.1f} %\")\n",
    "print(f\"  estimé par SNIPS                 : {100 * corrected_cost:5.1f} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eccdd9c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.522259Z",
     "iopub.status.busy": "2026-08-23T13:40:17.522197Z",
     "iopub.status.idle": "2026-08-23T13:40:17.619326Z",
     "shell.execute_reply": "2026-08-23T13:40:17.618982Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sur 60 jeux de contenus, à configuration identique :\n",
      "\n",
      "  erreur relative médiane du replay : 201 %\n",
      "  pire cas                          : +851 %\n",
      "  surestime le coût                 : 56/60\n",
      "  le sous-estime                    : 4/60\n",
      "\n",
      "On aimerait pouvoir dire que le biais est conservateur — qu'il surestime toujours\n",
      "le coût, et qu'un résultat favorable resterait donc défendable. Il ne l'est pas.\n"
     ]
    }
   ],
   "source": [
    "errors, overestimates = [], 0\n",
    "draws = 0\n",
    "for seed in range(60):\n",
    "    sample = experiment(seed=seed, items=12, impressions=60_000)\n",
    "    drawn_true, drawn_replay, _ = costs(sample)\n",
    "    if drawn_true <= 0:\n",
    "        continue\n",
    "    draws += 1\n",
    "    errors.append((drawn_replay - drawn_true) / drawn_true)\n",
    "    overestimates += drawn_replay > drawn_true\n",
    "\n",
    "errors = np.array(errors)\n",
    "print(f\"sur {draws} jeux de contenus, à configuration identique :\\n\")\n",
    "print(f\"  erreur relative médiane du replay : {100 * np.median(np.abs(errors)):.0f} %\")\n",
    "print(f\"  pire cas                          : {100 * errors.max():+.0f} %\")\n",
    "print(f\"  surestime le coût                 : {overestimates}/{draws}\")\n",
    "print(f\"  le sous-estime                    : {draws - overestimates}/{draws}\")\n",
    "print(\"\\nOn aimerait pouvoir dire que le biais est conservateur — qu'il surestime toujours\")\n",
    "print(\"le coût, et qu'un résultat favorable resterait donc défendable. Il ne l'est pas.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1684d0",
   "metadata": {},
   "source": [
    "### Lecture\n",
    "\n",
    "**L'erreur médiane est de 201 %.** Ce n'est pas une imprécision, c'est un ordre de grandeur : la\n",
    "mesure naïve donne typiquement le triple du coût réel.\n",
    "\n",
    "**Et son sens n'est pas garanti.** Elle surestime le coût dans cinquante-six cas sur soixante,\n",
    "ce qui inviterait à la tenir pour prudente — mais elle le sous-estime dans les quatre autres,\n",
    "à configuration pourtant identique. Le sens dépend du jeu de contenus, donc de données qu'on ne\n",
    "choisit pas.\n",
    "\n",
    "> **Un chiffre naïf n'est pas une borne supérieure. C'est un chiffre faux d'un montant\n",
    "> considérable et d'un sens que rien ne garantit.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a07f55",
   "metadata": {},
   "source": [
    "## 4. Ce qu'il faut publier à côté du chiffre\n",
    "\n",
    "Un estimateur sans biais ne suffit pas : il faut dire ce sur quoi il repose, et combien\n",
    "d'observations le portent réellement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ce4ba8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.620037Z",
     "iopub.status.busy": "2026-08-23T13:40:17.619969Z",
     "iopub.status.idle": "2026-08-23T13:40:17.629302Z",
     "shell.execute_reply": "2026-08-23T13:40:17.628944Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "écart des politiques         taille effective      sur\n",
      "--------------------------------------------------------\n",
      "poids de diversité 0.0                  60000    60000\n",
      "poids de diversité 0.3                  55547    60000\n",
      "poids de diversité 0.6                  47660    60000\n",
      "poids de diversité 0.9                  10026    60000\n",
      "\n",
      "Plus la politique évaluée s'éloigne de celle qui a produit les données, moins il\n",
      "reste d'observations pour l'estimer. Une estimation sans biais adossée à quelques\n",
      "centaines d'observations effectives n'est pas une mesure, c'est un chiffre.\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'écart des politiques':26s} {'taille effective':>18s} {'sur':>8s}\")\n",
    "print(\"-\" * 56)\n",
    "for weight in (0.0, 0.3, 0.6, 0.9):\n",
    "    sample = experiment(diversity_weight=weight, impressions=60_000)\n",
    "    size = effective_sample_size(\n",
    "        sample[\"target\"][sample[\"examined\"]], sample[\"logged\"][sample[\"examined\"]]\n",
    "    )\n",
    "    print(f\"{f'poids de diversité {weight:.1f}':26s} {size:18.0f} {sample['clicks'].size:8d}\")\n",
    "\n",
    "print(\"\\nPlus la politique évaluée s'éloigne de celle qui a produit les données, moins il\")\n",
    "print(\"reste d'observations pour l'estimer. Une estimation sans biais adossée à quelques\")\n",
    "print(\"centaines d'observations effectives n'est pas une mesure, c'est un chiffre.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d6b3123",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.629951Z",
     "iopub.status.busy": "2026-08-23T13:40:17.629883Z",
     "iopub.status.idle": "2026-08-23T13:40:17.639323Z",
     "shell.execute_reply": "2026-08-23T13:40:17.638979Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  plafond   estimation   écart à la vérité\n",
      "--------------------------------------------\n",
      "      1.2       0.6064               -6.5 %\n",
      "      2.0       0.6412               -1.2 %\n",
      "      5.0       0.6486               -0.0 %\n",
      "     20.0       0.6486               -0.0 %\n",
      "    aucun       0.6486               -0.0 %\n",
      "\n",
      "Le plafond doit être publié avec le résultat : son choix suffit à déplacer\n",
      "l'estimation, et sans lui le chiffre n'est pas reproductible.\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'plafond':>9s} {'estimation':>12s} {'écart à la vérité':>19s}\")\n",
    "print(\"-\" * 44)\n",
    "truth_value = value_under_policy(run[\"relevance\"], run[\"target\"])\n",
    "for cap in (1.2, 2.0, 5.0, 20.0, 1e6):\n",
    "    estimate = clipped_ips(run[\"clicks\"], *weights, cap=cap)\n",
    "    label = \"aucun\" if cap > 1e5 else f\"{cap:.1f}\"\n",
    "    print(f\"{label:>9s} {estimate:12.4f} {100 * (estimate - truth_value) / truth_value:18.1f} %\")\n",
    "\n",
    "print(\"\\nLe plafond doit être publié avec le résultat : son choix suffit à déplacer\")\n",
    "print(\"l'estimation, et sans lui le chiffre n'est pas reproductible.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ec0cf1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:17.639987Z",
     "iopub.status.busy": "2026-08-23T13:40:17.639919Z",
     "iopub.status.idle": "2026-08-23T13:40:18.010722Z",
     "shell.execute_reply": "2026-08-23T13:40:18.009924Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1380x936 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(2, 2, figsize=(11.5, 7.8))\n",
    "burial, references, estimators, sample_size = axes.ravel()\n",
    "\n",
    "# (a) L'enterrement : même composition, deux distributions conscientes du rang.\n",
    "width = 0.38\n",
    "labels = [f\"pdv {i}\" for i in range(VIEWPOINTS)]\n",
    "positions = np.arange(VIEWPOINTS)\n",
    "up = rank_aware_distribution(diversified, VIEWPOINTS, \"mrr\")\n",
    "down = rank_aware_distribution(favouring, VIEWPOINTS, \"mrr\")\n",
    "burial.bar(positions - width / 2, up, width, color=PALETTE[\"remedy\"],\n",
    "           label=f\"diversité remontée · D = {representation(diversified, SUPPLY, VIEWPOINTS):.2f}\")\n",
    "burial.bar(positions + width / 2, down, width, color=PALETTE[\"field\"],\n",
    "           label=f\"diversité enterrée · D = {representation(favouring, SUPPLY, VIEWPOINTS):.2f}\")\n",
    "burial.axhline(1 / VIEWPOINTS, color=PALETTE[\"neutral\"], linestyle=\"--\", linewidth=1.2)\n",
    "burial.text(VIEWPOINTS - 1.4, 1 / VIEWPOINTS + 0.02, \"offre disponible\", fontsize=7.5,\n",
    "            color=PALETTE[\"neutral\"])\n",
    "burial.set_xticks(positions)\n",
    "burial.set_xticklabels(labels)\n",
    "burial.set_ylabel(\"part de l'attention\")\n",
    "burial.set_title(\"Même composition, deux fils différents\", fontsize=10)\n",
    "burial.set_ylim(0, 0.95)\n",
    "burial.legend(fontsize=7.5, loc=\"upper right\")\n",
    "\n",
    "# (b) La remise de rang, et ce qu'elle change selon sa forme.\n",
    "# La composition reste rigoureusement fixe : seul l'emplacement du bloc divergent glisse.\n",
    "offsets = np.arange(0, 6)\n",
    "for discount, colour in ((\"mrr\", PALETTE[\"field\"]), (\"log\", PALETTE[\"order\"]),\n",
    "                         (\"none\", PALETTE[\"neutral\"])):\n",
    "    curve = []\n",
    "    for offset in offsets:\n",
    "        feed = np.zeros(8, dtype=int)\n",
    "        feed[offset:offset + 3] = np.array([1, 2, 3])\n",
    "        curve.append(representation(feed, SUPPLY, VIEWPOINTS, discount=discount))\n",
    "    references.plot(offsets, curve, marker=\"o\", markersize=3.5, linewidth=1.7, color=colour,\n",
    "                    label=f\"remise « {discount} »\")\n",
    "references.set_xlabel(\"position du bloc divergent, du haut vers le bas du fil\")\n",
    "references.set_ylabel(\"divergence à l'offre\")\n",
    "references.set_title(\"Sans remise de rang, enterrer ne coûte rien\", fontsize=10)\n",
    "references.legend(fontsize=8)\n",
    "\n",
    "# (c) Les estimateurs, contre la valeur vraie.\n",
    "names = [\"replay\\nnaïf\", \"IPS\", \"SNIPS\", \"IPS\\nplafonné\"]\n",
    "values = [replay_cost, 1 - ips(run[\"clicks\"], *weights) / naive(run[\"clicks\"]),\n",
    "          corrected_cost, 1 - clipped_ips(run[\"clicks\"], *weights, cap=5.0) / naive(run[\"clicks\"])]\n",
    "colours = [PALETTE[\"disorder\"]] + [PALETTE[\"remedy\"]] * 2 + [PALETTE[\"neutral\"]]\n",
    "estimators.bar(np.arange(len(names)), [100 * v for v in values], 0.6, color=colours, alpha=0.9)\n",
    "estimators.axhline(100 * true_cost, color=PALETTE[\"order\"], linestyle=\"--\", linewidth=1.6)\n",
    "estimators.set_ylim(0, 100 * true_cost * 1.35)\n",
    "estimators.text(-0.42, 100 * true_cost * 1.06, \"coût réel\", fontsize=8,\n",
    "                color=PALETTE[\"order\"])\n",
    "estimators.set_xticks(np.arange(len(names)))\n",
    "estimators.set_xticklabels(names, fontsize=8)\n",
    "estimators.set_ylabel(\"coût estimé du filtre  [%]\")\n",
    "estimators.set_title(\"Le replay se trompe, les estimateurs non\", fontsize=10)\n",
    "\n",
    "# (d) La distribution des erreurs du replay, sur soixante jeux de contenus.\n",
    "sample_size.hist(100 * errors, bins=18, color=PALETTE[\"disorder\"], alpha=0.7)\n",
    "sample_size.axvline(0, color=PALETTE[\"order\"], linestyle=\"--\", linewidth=1.6)\n",
    "sample_size.text(4, sample_size.get_ylim()[1] * 0.88, \"estimation exacte\", fontsize=8,\n",
    "                 color=PALETTE[\"order\"])\n",
    "sample_size.set_xlabel(\"erreur relative du replay naïf  [%]\")\n",
    "sample_size.set_ylabel(\"jeux de contenus\")\n",
    "sample_size.set_title(f\"Médiane {100 * np.median(np.abs(errors)):.0f} %, et un signe non garanti\",\n",
    "                      fontsize=10)\n",
    "\n",
    "figure.suptitle(\"Rang et contrefactuel : deux corrections avant toute évaluation\", fontsize=12)\n",
    "figure.tight_layout(rect=(0, 0, 1, 0.96))\n",
    "save_figure(figure, \"fig14_rang_et_contrefactuel\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f65c1a",
   "metadata": {},
   "source": [
    "## 5. Ce que le notebook établit\n",
    "\n",
    "**Le quatrième adversaire fonctionne, et la remise de rang le ferme.** À composition\n",
    "rigoureusement identique, enterrer la diversité au bas du fil rapporte de l'engagement et\n",
    "n'était vu par aucune des mesures retenues jusqu'ici. Une mesure consciente du rang en rend\n",
    "compte ; sans remise, la courbe est plate et l'échappatoire est gratuite.\n",
    "\n",
    "**La divergence à une référence déclarée résout le défaut de principe du notebook 13.** Les cinq\n",
    "mesures de RADio sont la même formule appliquée à cinq références, et c'est la référence qui\n",
    "porte la valeur. Une calibration nulle est l'objectif d'un recommandeur libéral et la définition\n",
    "d'une bulle pour un recommandeur délibératif : la mesure oblige à choisir au lieu de choisir en\n",
    "silence.\n",
    "\n",
    "**L'évaluation naïve d'un réordonnancement est fausse de 201 % en médiane**, jusqu'à 851 %, et\n",
    "son sens n'est pas garanti. Les estimateurs contrefactuels retrouvent la valeur vraie à moins\n",
    "d'un point — à trois conditions, qui doivent être publiées avec le chiffre : le **modèle de\n",
    "propension** employé, la **taille d'échantillon effective**, et le **plafond** s'il y en a un.\n",
    "\n",
    "> **Ce n'est pas un raffinement à apporter après l'évaluation sur données réelles. C'est ce qui\n",
    "> décide si cette évaluation mesurera quoi que ce soit.**\n",
    "\n",
    "### Les hypothèses qui restent\n",
    "\n",
    "Le modèle de biais de position — l'exposition ne dépend que du rang — est une **hypothèse**, non\n",
    "une mesure. Sur un jeu de données public, la politique d'enregistrement n'est pas fournie : la\n",
    "plateforme n'a pas publié ses probabilités de service. Tout ce qui précède déplace donc le\n",
    "problème d'un cran, de « les clics sont des étiquettes » vers « l'exposition se modélise par le\n",
    "rang ». Le second énoncé est bien meilleur que le premier, et il reste un énoncé."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37fbb48f",
   "metadata": {},
   "source": [
    "## Pistes ouvertes\n",
    "\n",
    "1. **Estimer la sévérité du biais de position** plutôt que la poser. Les méthodes\n",
    "   d'*intervention harvesting* et les modèles de position par maximum de vraisemblance\n",
    "   l'estiment à partir des données enregistrées elles-mêmes.\n",
    "2. **Instancier les cinq références de RADio sur des données réelles.** Trois d'entre elles —\n",
    "   activation, représentation, voix alternatives — demandent des attributs que ce dépôt n'a\n",
    "   pas, et le [corpus étendu](corpus-etendu.md) a montré ce que coûte de prendre une étiquette\n",
    "   disponible pour l'attribut qu'on voudrait mesurer.\n",
    "3. **Comparer à des lignes de base réglées.** Un filtre de diversité doit être opposé à un MMR\n",
    "   et à un réordonnancement aléatoire, non au seul filtre d'engagement pur qui est un homme de\n",
    "   paille.\n",
    "4. **Reprendre le test adverse sous mesure consciente du rang.** Les quatre mesures du\n",
    "   notebook 13 ont été éprouvées sans rang ; l'enterrement les concerne toutes."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}