{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "968154f8",
   "metadata": {},
   "source": [
    "# 20 — Contre-expertise : ce que la littérature reproche à ce travail\n",
    "\n",
    "Les chapitres précédents ont attaqué l'index, la norme, les jeux de données et l'algorithme. Ils\n",
    "n'ont jamais attaqué **les instruments de mesure eux-mêmes**, ni confronté les choix du dépôt à\n",
    "ce que la littérature du domaine sait déjà.\n",
    "\n",
    "Ce notebook le fait, en cinq contre-épreuves. Trois portent sur des hypothèses que le dépôt\n",
    "avait posées sans les éprouver ; une invalide une conclusion **publiée** ; la dernière n'est pas\n",
    "une erreur mais une amélioration de lecture.\n",
    "\n",
    "**Ce que ces contre-épreuves établissent :**\n",
    "\n",
    "* la remise d'attention $1/R$ n'est pas une propriété du monde mais **une convention**, et\n",
    "  toutes les conclusions sur l'enterrement en dépendent : à sévérité $0{,}1$ — celle mesurée sur\n",
    "  un bandeau de trois vignettes — **l'enterrement disparaît** ;\n",
    "* comparer deux mesures de diversité **à plancher nominal égal n'a pas de sens** : à diversité\n",
    "  réellement exposée égale, elles coûtent la même chose. La conclusion « la proximité à la cible\n",
    "  résiste le mieux », publiée deux fois par ce dépôt, est un **artefact d'échelle** ;\n",
    "* le **biais de confiance** — que la littérature documente et que l'estimateur du dépôt ignore —\n",
    "  gonfle la sévérité estimée de **+12,8 %**, sans que l'erreur type ne le voie ;\n",
    "* l'estimateur **doublement robuste ne sauve pas** la taille d'échantillon effective : le\n",
    "  problème est structurel, il demande de l'exploration, pas un meilleur estimateur ;\n",
    "* et l'indice se lit bien mieux en **nombre effectif de points de vue** qu'en entropie\n",
    "  normalisée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1df8ca5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:42:10.569062Z",
     "iopub.status.busy": "2026-08-23T13:42:10.568687Z",
     "iopub.status.idle": "2026-08-23T13:42:11.115267Z",
     "shell.execute_reply": "2026-08-23T13:42:11.114742Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65536 fils énumérés pour chaque norme et chaque sévérité\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ide.entropy import effective_viewpoints\n",
    "from ide.exposure import bucket_from_digest, load_digest, off_policy_check\n",
    "from ide.gaming import canonical_positions, position_entropy, target_divergence\n",
    "from ide.offpolicy import doubly_robust, estimate_position_bias\n",
    "from ide.plotting import PALETTE, save_figure, use_project_style\n",
    "from ide.ranking import all_rankings, aware_weights, optimal_ranking_under, ranking_engagement\n",
    "\n",
    "use_project_style()\n",
    "\n",
    "VIEWPOINTS, SLOTS, FLOOR = 4, 8, 0.70\n",
    "CATALOGUE = canonical_positions(VIEWPOINTS)\n",
    "RELEVANCE = np.array([0.90, 0.45, 0.25, 0.15])\n",
    "SEVERITIES = (0.1, 0.5, 1.0, 1.1, 2.0)\n",
    "\n",
    "MEASURES = {\n",
    "    \"entropie de position\": lambda w: position_entropy(w, CATALOGUE, CATALOGUE),\n",
    "    \"proximité à la cible\": lambda w: target_divergence(w, CATALOGUE, CATALOGUE),\n",
    "}\n",
    "\n",
    "print(f\"{VIEWPOINTS ** SLOTS} fils énumérés pour chaque norme et chaque sévérité\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692ade63",
   "metadata": {},
   "source": [
    "## 1. La remise $1/R$ est une convention, pas une mesure\n",
    "\n",
    "Tous les résultats d'enterrement du dépôt emploient $w_R = 1/R$ — la remise du rang réciproque,\n",
    "usuelle en évaluation de classement. Or le dépôt a lui-même mesuré que la sévérité de\n",
    "l'attention est une **propriété de la surface** : $\\eta \\approx 1{,}1$ sur une page de résultats,\n",
    "$0{,}04$ à $0{,}11$ sur un bandeau de trois vignettes horizontales.\n",
    "\n",
    "Employer $1/R$ — c'est-à-dire $\\eta = 1$ — sur un bandeau surpondère donc la tête d'un ordre de\n",
    "grandeur. La question est de savoir ce que les conclusions deviennent quand la remise est celle\n",
    "qu'on a mesurée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34c9fb01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:42:11.116190Z",
     "iopub.status.busy": "2026-08-23T13:42:11.116081Z",
     "iopub.status.idle": "2026-08-23T13:42:19.053202Z",
     "shell.execute_reply": "2026-08-23T13:42:19.052632Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plancher aveugle de 0.7 sur l'entropie de position, selon la sévérité de l'attention\n",
      "\n",
      " sévérité                    surface   affiché   exposé   écart  coût aveugle  coût conscient\n",
      "--------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0.1     bandeau de 3 vignettes     0.774    0.747   0.028         24.1%           24.1%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0.5              intermédiaire     0.774    0.620   0.154         17.7%           21.1%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      1.0             convention MRR     0.774    0.443   0.331         10.7%           20.9%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      1.1          page de résultats     0.774    0.408   0.366          9.5%           20.9%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.0         fil très top-lourd     0.774    0.157   0.617          2.7%           20.8%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Plancher aveugle de {FLOOR} sur l'entropie de position, selon la sévérité de l'attention\\n\")\n",
    "print(f\"{'sévérité':>9s} {'surface':>26s} {'affiché':>9s} {'exposé':>8s} {'écart':>7s} \"\n",
    "      f\"{'coût aveugle':>13s} {'coût conscient':>15s}\")\n",
    "print(\"-\" * 92)\n",
    "discount_scan = {}\n",
    "for severity, surface in zip(SEVERITIES,\n",
    "                             (\"bandeau de 3 vignettes\", \"intermédiaire\", \"convention MRR\",\n",
    "                              \"page de résultats\", \"fil très top-lourd\"), strict=True):\n",
    "    unconstrained = ranking_engagement(np.zeros(SLOTS, dtype=int), RELEVANCE, discount=severity)\n",
    "    blind = optimal_ranking_under(MEASURES[\"entropie de position\"], RELEVANCE, SLOTS, FLOOR,\n",
    "                                  rank_aware=False, discount=severity)\n",
    "    aware = optimal_ranking_under(MEASURES[\"entropie de position\"], RELEVANCE, SLOTS, FLOOR,\n",
    "                                  rank_aware=True, discount=severity)\n",
    "    blind_cost = 100 * (1 - blind.engagement / unconstrained)\n",
    "    aware_cost = 100 * (1 - aware.engagement / unconstrained)\n",
    "    discount_scan[severity] = (blind.blind, blind.aware, blind_cost, aware_cost)\n",
    "    print(f\"{severity:9.1f} {surface:>26s} {blind.blind:9.3f} {blind.aware:8.3f} \"\n",
    "          f\"{blind.blind - blind.aware:7.3f} {blind_cost:12.1f}% {aware_cost:14.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5faa5fbf",
   "metadata": {},
   "source": [
    "### Lecture\n",
    "\n",
    "**L'enterrement n'existe que si l'attention est concentrée.** À sévérité $0{,}1$, une plateforme\n",
    "contrainte à $0{,}70$ par une mesure aveugle expose $0{,}747$ — l'échappatoire disparaît, et la\n",
    "norme consciente du rang ne coûte pas un point de plus que la norme aveugle. À sévérité $2$,\n",
    "elle expose $0{,}157$ et l'échappatoire coûte $2{,}7\\ \\%$ au lieu de $20{,}8\\ \\%$ : plus\n",
    "l'attention est concentrée, plus enterrer est **bon marché**.\n",
    "\n",
    "Le résultat publié par ce dépôt — « une plateforme certifiée à 0,70 n'expose que 0,36 » — est\n",
    "donc **une affirmation sur $\\eta = 1$**, présentée comme si elle valait partout. Elle vaut pour\n",
    "un fil vertical ; elle est fausse d'un ordre de grandeur pour un bandeau.\n",
    "\n",
    "**Ce que cela change à la norme.** Le plancher doit employer la remise **mesurée sur la\n",
    "surface**, non une convention d'évaluation. Et le régulateur ne peut pas fixer une remise unique\n",
    "pour toutes les plateformes : la remise fait partie de ce qui doit être mesuré, au même titre\n",
    "que le catalogue de points de vue est ce qui doit être déclaré.\n",
    "\n",
    "## 2. Comparer deux mesures à plancher égal n'a pas de sens\n",
    "\n",
    "Le [rang adverse](../docs/rang-adverse.md) a comparé quatre mesures de diversité **au même\n",
    "plancher nominal de 0,70** et a conclu que la « proximité à la cible » résistait le mieux à\n",
    "l'enterrement. Cette conclusion a été publiée deux fois.\n",
    "\n",
    "Elle est fausse — non parce que les chiffres seraient faux, mais parce que **les deux mesures ne\n",
    "vivent pas sur la même échelle**. Un plancher de 0,70 sur la proximité à la cible n'exige pas la\n",
    "même chose qu'un plancher de 0,70 sur l'entropie. La comparaison honnête fixe la **diversité\n",
    "réellement exposée** et demande à chaque mesure ce qu'elle coûte pour l'obtenir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15f3d161",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:42:19.054054Z",
     "iopub.status.busy": "2026-08-23T13:42:19.053980Z",
     "iopub.status.idle": "2026-08-23T13:42:32.765130Z",
     "shell.execute_reply": "2026-08-23T13:42:32.764563Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "À sévérité 1.0, chaque mesure jugée sur ce qu'elle EXPOSE, non sur son plancher\n",
      "\n",
      "                mesure  plancher  entropie exposée    coût  coût optimal\n",
      "--------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  entropie de position      0.50             0.512   12.9%         12.9%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  entropie de position      0.60             0.605   16.4%         16.4%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  entropie de position      0.70             0.702   20.9%         20.8%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  entropie de position      0.80             0.803   26.6%         26.2%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  entropie de position      0.90             0.901   34.0%         33.7%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  proximité à la cible      0.50             0.135    2.3%          2.3%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  proximité à la cible      0.60             0.281    5.9%          5.9%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  proximité à la cible      0.70             0.429   10.6%         10.5%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  proximité à la cible      0.80             0.591   15.8%         15.7%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  proximité à la cible      0.90             0.792   25.9%         25.5%\n"
     ]
    }
   ],
   "source": [
    "def exposed_entropy(assignment, severity):\n",
    "    # Yardstick commun : l'entropie réellement exposée par le fil retenu.\n",
    "    return position_entropy(aware_weights(assignment, VIEWPOINTS, severity), CATALOGUE, CATALOGUE)\n",
    "\n",
    "\n",
    "matched = {name: [] for name in MEASURES}\n",
    "severity = 1.0\n",
    "unconstrained = ranking_engagement(np.zeros(SLOTS, dtype=int), RELEVANCE, discount=severity)\n",
    "\n",
    "reachable = {}\n",
    "for assignment in all_rankings(SLOTS, VIEWPOINTS):\n",
    "    key = round(exposed_entropy(assignment, severity), 2)\n",
    "    reachable[key] = max(reachable.get(key, -np.inf),\n",
    "                         ranking_engagement(assignment, RELEVANCE, discount=severity))\n",
    "\n",
    "print(f\"À sévérité {severity}, chaque mesure jugée sur ce qu'elle EXPOSE, non sur son plancher\\n\")\n",
    "print(f\"{'mesure':>22s} {'plancher':>9s} {'entropie exposée':>17s} {'coût':>7s} \"\n",
    "      f\"{'coût optimal':>13s}\")\n",
    "print(\"-\" * 74)\n",
    "for name, measure in MEASURES.items():\n",
    "    for floor in (0.5, 0.6, 0.7, 0.8, 0.9):\n",
    "        best = optimal_ranking_under(measure, RELEVANCE, SLOTS, floor, rank_aware=True,\n",
    "                                     discount=severity)\n",
    "        if best is None:\n",
    "            continue\n",
    "        exposure = exposed_entropy(best.assignment, severity)\n",
    "        cost = 100 * (1 - best.engagement / unconstrained)\n",
    "        ideal = max(value for key, value in reachable.items() if key >= round(exposure, 2) - 1e-9)\n",
    "        matched[name].append((exposure, cost))\n",
    "        print(f\"{name:>22s} {floor:9.2f} {exposure:17.3f} {cost:6.1f}% \"\n",
    "              f\"{100 * (1 - ideal / unconstrained):12.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1946d44",
   "metadata": {},
   "source": [
    "### Ce qu'il faut retirer\n",
    "\n",
    "À diversité exposée égale, les deux mesures coûtent **la même chose**, et toutes deux se tiennent\n",
    "sur la borne exacte à moins de $0{,}6\\ \\%$. La proximité à la cible ne « résiste » pas mieux :\n",
    "elle est simplement **moins exigeante au même chiffre**.\n",
    "\n",
    "!!! failure \"Conclusion retirée\"\n",
    "    « La proximité à la cible résiste le mieux à l'enterrement » était un **artefact d'échelle**.\n",
    "    Ce qui compte n'est pas le choix de la mesure mais **le niveau exigé et la conscience du\n",
    "    rang**. Le dépôt avait pourtant déjà appliqué la bonne méthode ailleurs — la comparaison des\n",
    "    [lignes de base](../docs/lignes-de-base.md) fixe le plancher précisément pour rendre les\n",
    "    méthodes comparables — et ne l'avait pas appliquée à ses propres mesures.\n",
    "\n",
    "## 3. Le biais de confiance, que l'estimateur ignore\n",
    "\n",
    "La littérature sur l'apprentissage de classement non biaisé documente un effet que le modèle de\n",
    "ce dépôt ne contient pas : le **biais de confiance**. Un lecteur qui fait confiance au moteur\n",
    "clique davantage sur les premiers résultats *même lorsqu'ils ne sont pas pertinents*. Le modèle\n",
    "qui en rend compte est **affine** plutôt que multiplicatif (Vardasbi, Oosterhuis & de Rijke,\n",
    "CIKM 2020) :\n",
    "\n",
    "$$P(\\text{clic} \\mid k, \\gamma) = \\theta_k\\big(\\varepsilon^+_k\\,\\gamma + \\varepsilon^-_k\\,(1-\\gamma)\\big)\n",
    "= \\alpha_k \\gamma + \\beta_k$$\n",
    "\n",
    "Les mêmes auteurs démontrent que la pondération par l'inverse de la propension **ne peut pas**\n",
    "corriger un tel biais : une transformation linéaire ne corrige pas une transformation affine.\n",
    "\n",
    "L'estimateur du dépôt suppose la forme multiplicative $\\mathrm{CTR}(i, R) = g(i)\\,R^{-\\eta}$.\n",
    "Question : de combien se trompe-t-il quand le monde est affine ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ba676d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:42:32.765996Z",
     "iopub.status.busy": "2026-08-23T13:42:32.765920Z",
     "iopub.status.idle": "2026-08-23T13:42:33.948052Z",
     "shell.execute_reply": "2026-08-23T13:42:33.947585Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sévérité vraie du biais de position : 1.0\n",
      "\n",
      " ε⁻ (confiance)   η estimé  erreur type    erreur\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0.00      1.011       0.0095      1.1%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0.02      1.025       0.0096      2.5%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0.05      1.045       0.0102      4.5%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0.10      1.076       0.0114      7.6%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0.20      1.128       0.0132     12.8%\n"
     ]
    }
   ],
   "source": [
    "def affine_feedback(items_count, impressions, severity, epsilon_minus, epsilon_plus=1.0,\n",
    "                    trust_decay=1.0, exploration=0.5, rng=None):\n",
    "    # Modèle affine : la confiance dans le moteur décroît avec le rang, ce qui brise la\n",
    "    # séparabilité entre contenu et rang que suppose l'estimateur à effets fixes.\n",
    "    generator = rng or np.random.default_rng(0)\n",
    "    relevance = generator.uniform(0.05, 0.6, items_count)\n",
    "    noise = generator.normal(0.0, exploration, size=(impressions, items_count))\n",
    "    orders = np.argsort(-(relevance + noise), axis=1)\n",
    "    ranks = np.tile(np.arange(1, items_count + 1), (impressions, 1))\n",
    "    examination = ranks.astype(float) ** (-severity)\n",
    "    false_positive = epsilon_minus * ranks.astype(float) ** (-trust_decay)\n",
    "    quality = relevance[orders]\n",
    "    probability = examination * (epsilon_plus * quality + false_positive * (1.0 - quality))\n",
    "    clicks = (generator.random((impressions, items_count)) < np.clip(probability, 0, 1))\n",
    "    return orders.ravel(), ranks.ravel(), clicks.astype(float).ravel()\n",
    "\n",
    "\n",
    "TRUE_SEVERITY = 1.0\n",
    "print(f\"Sévérité vraie du biais de position : {TRUE_SEVERITY}\\n\")\n",
    "print(f\"{'ε⁻ (confiance)':>15s} {'η estimé':>10s} {'erreur type':>12s} {'erreur':>9s}\")\n",
    "print(\"-\" * 50)\n",
    "trust_scan = []\n",
    "for epsilon_minus in (0.0, 0.02, 0.05, 0.1, 0.2):\n",
    "    items, ranks, clicks = affine_feedback(12, 40_000, TRUE_SEVERITY, epsilon_minus,\n",
    "                                           rng=np.random.default_rng(4))\n",
    "    estimate = estimate_position_bias(items, ranks, clicks)\n",
    "    trust_scan.append((epsilon_minus, estimate.severity, estimate.standard_error))\n",
    "    print(f\"{epsilon_minus:15.2f} {estimate.severity:10.3f} {estimate.standard_error:12.4f} \"\n",
    "          f\"{100 * (estimate.severity / TRUE_SEVERITY - 1):8.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acfa9d4b",
   "metadata": {},
   "source": [
    "### Lecture\n",
    "\n",
    "L'estimateur **surestime** la sévérité de $12{,}8\\ \\%$ sous un biais de confiance marqué, et\n",
    "son erreur type — $0{,}013$ — ne le voit pas : l'estimation reste « significative » et fausse.\n",
    "\n",
    "Deux nuances, dans les deux sens :\n",
    "\n",
    "* c'est **beaucoup moins grave** que de poser $\\eta$ au jugé, qui coûte jusqu'à $+179\\ \\%$. La\n",
    "  correction reste largement rentable ;\n",
    "* mais l'intervalle publié est **trop étroit**, et il faut le dire : l'incertitude réelle sur\n",
    "  $\\eta$ inclut une part de misspécification que l'erreur type n'estime pas.\n",
    "\n",
    "Un détail technique mérite d'être signalé, parce qu'il explique pourquoi l'estimateur s'en tire\n",
    "si bien : un biais de confiance **indépendant du rang** ne le dérange pas du tout — la\n",
    "transformation reste séparable et l'effet fixe de contenu l'absorbe. Ce qui le biaise est la\n",
    "**dépendance au rang** de la confiance, et elle seule.\n",
    "\n",
    "## 4. Le doublement robuste ne sauve pas la taille effective\n",
    "\n",
    "Le dépôt publie une taille d'échantillon effective de $1\\,513$ pour $4\\,077\\,727$ impressions et\n",
    "en tire une recommandation : exiger une fraction d'exploration aléatoire. Une objection naturelle\n",
    "est qu'un meilleur estimateur suffirait — le doublement robuste, par exemple, qui combine un\n",
    "modèle de récompense et la repondération."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8fa28bc1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:42:33.948856Z",
     "iopub.status.busy": "2026-08-23T13:42:33.948785Z",
     "iopub.status.idle": "2026-08-23T13:42:34.212790Z",
     "shell.execute_reply": "2026-08-23T13:42:34.212299Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vérité terrain : 0.005124 ± 0.000106\n",
      "\n",
      "   naïf                   0.006743   écart   +31.6%\n",
      "   IPS                    0.005253   écart    +2.5%\n",
      "   auto-normalisé         0.004768   écart    -7.0%\n",
      "   IPS plafonné à 100     0.005238   écart    +2.2%\n",
      "   doublement robuste     0.004871   écart    -4.9%\n",
      "\n",
      "taille d'échantillon effective : 1513 sur 4077727\n",
      "Elle ne dépend que des poids d'importance : aucun estimateur ne la change.\n"
     ]
    }
   ],
   "source": [
    "digest = load_digest()\n",
    "target_bucket = bucket_from_digest(digest, \"obd_random_men\")\n",
    "logged_bucket = bucket_from_digest(digest, \"obd_bts_men\")\n",
    "check = off_policy_check(target_bucket, logged_bucket)\n",
    "\n",
    "items = logged_bucket[\"items\"]\n",
    "clicks = logged_bucket[\"clicks\"]\n",
    "logged = logged_bucket[\"propensities\"]\n",
    "catalogue = int(logged_bucket[\"item_count\"])\n",
    "target = np.full(clicks.size, 1.0 / catalogue)\n",
    "\n",
    "# modèle de récompense le plus simple : taux de clic par contenu sous la politique enregistrée\n",
    "rates = np.array([clicks[items == item].mean() if (items == item).any() else 0.0\n",
    "                  for item in range(catalogue)])\n",
    "robust = doubly_robust(clicks, target, logged, rates[items], np.full(clicks.size, rates.mean()))\n",
    "\n",
    "print(f\"vérité terrain : {check.truth:.6f} ± {check.truth_error:.6f}\\n\")\n",
    "estimates = [(\"naïf\", check.naive), (\"IPS\", check.importance_sampling),\n",
    "             (\"auto-normalisé\", check.self_normalised),\n",
    "             (\"IPS plafonné à 100\", check.clipped[100.0]),\n",
    "             (\"doublement robuste\", robust)]\n",
    "for name, value in estimates:\n",
    "    print(f\"   {name:22s} {value:.6f}   écart {100 * (value / check.truth - 1):+7.1f}%\")\n",
    "print(f\"\\ntaille d'échantillon effective : {check.effective_size:.0f} sur {check.logged_size}\")\n",
    "print(\"Elle ne dépend que des poids d'importance : aucun estimateur ne la change.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f8579a3",
   "metadata": {},
   "source": [
    "### Lecture\n",
    "\n",
    "Le doublement robuste fait **moins bien** que l'IPS simple ici — $-4{,}9\\ \\%$ contre\n",
    "$+2{,}5\\ \\%$ — parce que son modèle de récompense, estimé sur le seau d'enregistrement, hérite\n",
    "du biais qu'on cherchait à corriger.\n",
    "\n",
    "Et surtout, la taille d'échantillon effective **ne change pas** : elle ne dépend que de la\n",
    "distribution des poids d'importance, c'est-à-dire de la distance entre les deux politiques.\n",
    "Aucun estimateur ne la fabrique. Le problème est structurel, et la recommandation du mémorandum\n",
    "tient : **il faut de l'exploration, pas un meilleur estimateur**.\n",
    "\n",
    "## 5. Lire l'indice en nombre effectif de points de vue\n",
    "\n",
    "Une entropie normalisée n'est pas une diversité : elle n'est pas linéaire en ce qu'on entend par\n",
    "« deux fois plus divers ». Sa conversion en **nombre effectif** l'est — c'est le nombre de points\n",
    "de vue également servis qui produirait la même entropie (Jost, *Entropy and diversity*, 2006)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "370a185a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:42:34.213639Z",
     "iopub.status.busy": "2026-08-23T13:42:34.213557Z",
     "iopub.status.idle": "2026-08-23T13:42:34.216038Z",
     "shell.execute_reply": "2026-08-23T13:42:34.215711Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  indice   points de vue effectifs   lecture\n",
      "------------------------------------------------------------------------------\n",
      "   0.774                      2.92   affiché par le fil enterrant\n",
      "   0.700                      2.64   plancher réglementaire proposé\n",
      "   0.443                      1.85   réellement exposé à sévérité 1,0\n",
      "   0.157                      1.24   réellement exposé à sévérité 2,0\n",
      "\n",
      "« Certifiée à 2,6 points de vue effectifs sur 4, elle en expose 1,9 » se comprend\n",
      "sans formation ; « certifiée à 0,70, elle expose 0,44 » ne se comprend pas.\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'indice':>8s} {'points de vue effectifs':>25s}   lecture\")\n",
    "print(\"-\" * 78)\n",
    "readings = [\n",
    "    (0.774, \"affiché par le fil enterrant\"),\n",
    "    (0.700, \"plancher réglementaire proposé\"),\n",
    "    (0.443, \"réellement exposé à sévérité 1,0\"),\n",
    "    (0.157, \"réellement exposé à sévérité 2,0\"),\n",
    "]\n",
    "for index, reading in readings:\n",
    "    print(f\"{index:8.3f} {effective_viewpoints(index, VIEWPOINTS):25.2f}   {reading}\")\n",
    "\n",
    "print(\"\\n« Certifiée à 2,6 points de vue effectifs sur 4, elle en expose 1,9 » se comprend\")\n",
    "print(\"sans formation ; « certifiée à 0,70, elle expose 0,44 » ne se comprend pas.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1ffb094",
   "metadata": {},
   "source": [
    "## 6. Ce que la littérature ajoute, et que la mesure ne dit pas\n",
    "\n",
    "**Corriger le biais de position n'améliore pas forcément le classement.** Hager, Deffayet,\n",
    "Renders, Zoeter et de Rijke (SIGIR 2024) reprennent Baidu-ULTR — le jeu même où ce dépôt mesure\n",
    "$\\hat\\eta = 1{,}10$ — et constatent que les corrections standard *améliorent la prédiction des\n",
    "clics sans améliorer la qualité du classement* évaluée par des annotateurs experts. Ils\n",
    "confirment la présence du biais par quatre méthodes concordantes, ce qui corrobore notre chiffre ;\n",
    "mais ils montrent que l'étape suivante — celle que ce dépôt n'a pas encore franchie — ne suit\n",
    "pas mécaniquement.\n",
    "\n",
    "**Une norme de diversité est une norme éditoriale.** Les métriques normatives de Vrijenhoek et\n",
    "Helberger (RecSys 2022) découpent la diversité en cinq dimensions dérivées de théories\n",
    "démocratiques explicites. L'indice de ce dépôt en occupe une seule — la répartition entre points\n",
    "de vue — et une plateforme peut la satisfaire en servant des contenus divergents et vides. Aucune\n",
    "mesure automatique ne distingue la pluralité de la fausse balance.\n",
    "\n",
    "**Ce que le dépôt mesure est l'exposition, pas la réception.** La littérature sur la diversité\n",
    "médiatique distingue l'offre, l'exposition et la réception. L'indice porte sur la deuxième : ce\n",
    "qui est servi, pondéré par l'attention *présumée* d'un rang. Ce qui est réellement lu, compris ou\n",
    "retenu n'est pas mesuré, et la remise d'attention n'en est qu'un substitut.\n",
    "\n",
    "**Et notre conclusion sur les jeux de données n'est pas neuve.** Un travail publié en octobre\n",
    "2025 établit que les jeux publics sont le goulot d'étranglement de la recommandation\n",
    "diversifiée, et que le droit européen est la voie d'accès à envisager — exactement la conclusion\n",
    "à laquelle ce dépôt est parvenu indépendamment. Nous **corroborons**, nous ne découvrons pas.\n",
    "\n",
    "## 7. La figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50d92602",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:42:34.216770Z",
     "iopub.status.busy": "2026-08-23T13:42:34.216700Z",
     "iopub.status.idle": "2026-08-23T13:42:34.698163Z",
     "shell.execute_reply": "2026-08-23T13:42:34.697793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1488x1032 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(2, 2, figsize=(12.4, 8.6))\n",
    "\n",
    "# (a) l'enterrement selon la sévérité de l'attention\n",
    "ax = axes[0, 0]\n",
    "severities = list(discount_scan)\n",
    "ax.plot(severities, [discount_scan[s][0] for s in severities], \"o-\",\n",
    "        color=PALETTE[\"neutral\"], markersize=5, label=\"diversité affichée (plancher)\")\n",
    "ax.plot(severities, [discount_scan[s][1] for s in severities], \"o-\",\n",
    "        color=PALETTE[\"disorder\"], markersize=5, label=\"diversité réellement exposée\")\n",
    "ax.axvline(1.0, color=PALETTE[\"order\"], linestyle=\":\", linewidth=1.2)\n",
    "ax.annotate(\"convention $1/R$\\nemployée par le dépôt\", xy=(1.0, 0.30), xytext=(1.25, 0.22),\n",
    "            fontsize=8, color=PALETTE[\"order\"],\n",
    "            arrowprops={\"arrowstyle\": \"->\", \"color\": PALETTE[\"order\"], \"linewidth\": 1.0})\n",
    "ax.annotate(\"bandeau mesuré\\n(η ≈ 0,1)\", xy=(0.1, 0.747), xytext=(0.15, 0.60), fontsize=8,\n",
    "            color=PALETTE[\"remedy\"],\n",
    "            arrowprops={\"arrowstyle\": \"->\", \"color\": PALETTE[\"remedy\"], \"linewidth\": 1.0})\n",
    "ax.set_xlabel(\"sévérité $\\\\eta$ de l'attention (mesurée sur la surface)\")\n",
    "ax.set_ylabel(\"diversité\")\n",
    "ax.set_title(\"(a) l'enterrement n'existe que si l'attention est concentrée\")\n",
    "ax.legend(loc=\"lower left\", fontsize=8)\n",
    "\n",
    "# (b) le prix des deux normes selon la sévérité\n",
    "ax = axes[0, 1]\n",
    "ax.plot(severities, [discount_scan[s][2] for s in severities], \"o-\",\n",
    "        color=PALETTE[\"disorder\"], markersize=5, label=\"plancher aveugle au rang\")\n",
    "ax.plot(severities, [discount_scan[s][3] for s in severities], \"o-\",\n",
    "        color=PALETTE[\"remedy\"], markersize=5, label=\"plancher conscient du rang\")\n",
    "ax.set_xlabel(\"sévérité $\\\\eta$ de l'attention\")\n",
    "ax.set_ylabel(\"coût d'engagement (%)\")\n",
    "ax.set_title(\"(b) plus l'attention est concentrée, moins enterrer coûte cher\")\n",
    "ax.legend(loc=\"center right\", fontsize=8)\n",
    "\n",
    "# (c) à diversité exposée égale, les deux mesures coïncident\n",
    "ax = axes[1, 0]\n",
    "for name, colour in ((\"entropie de position\", PALETTE[\"order\"]),\n",
    "                     (\"proximité à la cible\", PALETTE[\"field\"])):\n",
    "    points = sorted(matched[name])\n",
    "    ax.plot([p[0] for p in points], [p[1] for p in points], \"o-\", color=colour, markersize=5,\n",
    "            label=name)\n",
    "bound = sorted((key, 100 * (1 - value / unconstrained)) for key, value in reachable.items()\n",
    "               if key >= 0.05)\n",
    "ax.plot([p[0] for p in bound], [p[1] for p in bound], \"-\", color=\"black\", linewidth=1.4,\n",
    "        label=\"borne exacte\")\n",
    "ax.set_xlabel(\"entropie réellement exposée\")\n",
    "ax.set_ylabel(\"coût d'engagement (%)\")\n",
    "ax.set_title(\"(c) à diversité exposée égale, le choix de la mesure s'efface\")\n",
    "ax.legend(loc=\"upper left\", fontsize=8)\n",
    "\n",
    "# (d) le biais de confiance\n",
    "ax = axes[1, 1]\n",
    "epsilons = [row[0] for row in trust_scan]\n",
    "estimated = [row[1] for row in trust_scan]\n",
    "errors = [1.96 * row[2] for row in trust_scan]\n",
    "ax.errorbar(epsilons, estimated, yerr=errors, fmt=\"o-\", color=PALETTE[\"field\"], markersize=5,\n",
    "            capsize=4, label=\"sévérité estimée (± 2 erreurs types)\")\n",
    "ax.axhline(TRUE_SEVERITY, color=PALETTE[\"neutral\"], linestyle=\":\", linewidth=1.3)\n",
    "ax.text(0.135, TRUE_SEVERITY + 0.004, \"sévérité vraie\", fontsize=8,\n",
    "        color=PALETTE[\"neutral\"])\n",
    "ax.set_xlabel(\"$\\\\varepsilon^-$ : clics de confiance sur des contenus non pertinents\")\n",
    "ax.set_ylabel(\"sévérité $\\\\hat\\\\eta$ estimée\")\n",
    "ax.set_title(\"(d) le biais de confiance gonfle l'estimation, l'erreur type ne le voit pas\")\n",
    "ax.legend(loc=\"upper left\", fontsize=8)\n",
    "\n",
    "save_figure(figure, \"fig20_contre_expertise\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca966075",
   "metadata": {},
   "source": [
    "## 8. Ce que cette contre-expertise change\n",
    "\n",
    "**Une conclusion retirée.** « La proximité à la cible résiste le mieux » était un artefact\n",
    "d'échelle. À diversité exposée égale, le choix de la mesure ne compte presque pas ; ce qui compte\n",
    "est le **niveau** exigé et la **conscience du rang**.\n",
    "\n",
    "**Une conclusion restreinte.** « Certifiée à 0,70, elle expose 0,36 » vaut pour $\\eta = 1$. Sur\n",
    "une surface à attention plate, l'enterrement disparaît ; sur une surface très concentrée, il\n",
    "empire. La remise d'attention doit être **mesurée**, et elle fait partie de ce qu'un régulateur\n",
    "doit exiger — au même titre que le rang servi.\n",
    "\n",
    "**Une incertitude élargie.** L'erreur type publiée sur $\\hat\\eta$ ne couvre pas la\n",
    "misspécification du modèle de clic : sous biais de confiance, l'estimation dérive de $+12{,}8\\ \\%$\n",
    "sans que rien ne le signale.\n",
    "\n",
    "**Une recommandation confirmée.** Aucun estimateur ne remplace l'exploration : le doublement\n",
    "robuste ne rattrape pas une taille effective de $1\\,513$, et fait même moins bien.\n",
    "\n",
    "**Une amélioration de lecture.** L'indice se publie en **nombre effectif de points de vue**.\n",
    "« 2,6 sur 4 » se comprend ; « 0,70 » ne se comprend pas."
   ]
  }
 ],
 "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
}