{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "en-00",
   "metadata": {},
   "source": [
    "# 13. Ensembles e ensembles de híbridos\n",
    "\n",
    "Um ensemble combina **previsões de candidatas completas** — modelos isolados, híbridos residuais ou outros ensembles. Este notebook cobre a F2 do plano de ensembles:\n",
    "\n",
    "1. agregações simples e ponderadas (`equal`, `median`, `trimmed_mean`, `inverse_mse`, `nnls`);\n",
    "2. ensemble de híbridos e ensemble corrigido por um corretor residual;\n",
    "3. ensembles na busca automática, com a regra de aceite decidindo se valem a complexidade.\n",
    "\n",
    "Os controles são o melhor modelo isolado e a média simples: um ensemble mais elaborado precisa superá-los nas mesmas origens. Pesos aprendidos usam só o vetor de treino de cada origem (previsões fora da amostra de um passo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "en-01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:38:03.938463Z",
     "iopub.status.busy": "2026-10-13T14:38:03.937466Z",
     "iopub.status.idle": "2026-10-13T14:38:08.374612Z",
     "shell.execute_reply": "2026-10-13T14:38:08.370089Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoHybTS 0.0.1\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import autohybts\n",
    "from autohybts import AutoHybridExperiment\n",
    "from autohybts.datasets import get_data\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option(\"display.width\", 160)\n",
    "pd.set_option(\"display.max_columns\", 20)\n",
    "\n",
    "\n",
    "def clean(ax, grid_axis=\"both\"):\n",
    "    \"\"\"Recessive grid and axes; text in neutral ink.\"\"\"\n",
    "    ax.grid(axis=grid_axis, color=\"#e4e3df\", linewidth=0.8)\n",
    "    ax.set_axisbelow(True)\n",
    "    for side in (\"top\", \"right\"):\n",
    "        ax.spines[side].set_visible(False)\n",
    "    ax.tick_params(colors=\"#52514e\")\n",
    "\n",
    "\n",
    "print(\"AutoHybTS\", autohybts.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "en-02",
   "metadata": {},
   "source": [
    "## 1. Dados e controles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "en-03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:38:08.385146Z",
     "iopub.status.busy": "2026-10-13T14:38:08.383147Z",
     "iopub.status.idle": "2026-10-13T14:38:14.765890Z",
     "shell.execute_reply": "2026-10-13T14:38:14.759499Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MASE V_search</th>\n",
       "      <th>MASE V_select</th>\n",
       "      <th>ajustes/origem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>candidata</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seasonal_naive</th>\n",
       "      <td>1.288</td>\n",
       "      <td>1.522</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear</th>\n",
       "      <td>1.222</td>\n",
       "      <td>1.277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>0.997</td>\n",
       "      <td>1.827</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                MASE V_search  MASE V_select  ajustes/origem\n",
       "candidata                                                   \n",
       "seasonal_naive          1.288          1.522               1\n",
       "linear                  1.222          1.277               1\n",
       "knn                     0.997          1.827               1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = get_data(\"synthetic_daily\", n=500)\n",
    "exp = AutoHybridExperiment(fh=14, fold=3, session_id=42).fit(y)\n",
    "\n",
    "\n",
    "def summary(result, name=None):\n",
    "    frame = result.evaluation.to_frame()\n",
    "    return {\n",
    "        \"candidata\": name or result.candidate.name,\n",
    "        \"MASE V_search\": frame.loc[frame[\"layer\"] == \"search\", \"MASE\"].mean(),\n",
    "        \"MASE V_select\": frame.loc[frame[\"layer\"] == \"select\", \"MASE\"].mean(),\n",
    "        \"ajustes/origem\": result.evaluation.trials[0].n_fits,\n",
    "    }\n",
    "\n",
    "\n",
    "members = {name: exp.create_model(name) for name in [\"seasonal_naive\", \"linear\", \"knn\"]}\n",
    "pd.DataFrame([summary(m) for m in members.values()]).set_index(\"candidata\").round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "en-04",
   "metadata": {},
   "source": [
    "## 2. Agregações\n",
    "\n",
    "Os mesmos três membros, cinco formas de combinar. `nnls` encolhe os pesos para a média (`shrinkage=0.5`): com poucas linhas fora da amostra, pesos livres oscilam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "en-05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:38:14.774365Z",
     "iopub.status.busy": "2026-10-13T14:38:14.773785Z",
     "iopub.status.idle": "2026-10-13T14:38:28.731382Z",
     "shell.execute_reply": "2026-10-13T14:38:28.727029Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MASE V_search</th>\n",
       "      <th>MASE V_select</th>\n",
       "      <th>ajustes/origem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>candidata</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>equal</th>\n",
       "      <td>1.093</td>\n",
       "      <td>1.496</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>1.153</td>\n",
       "      <td>1.507</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trimmed_mean</th>\n",
       "      <td>1.153</td>\n",
       "      <td>1.507</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inverse_mse</th>\n",
       "      <td>1.176</td>\n",
       "      <td>1.341</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nnls</th>\n",
       "      <td>1.122</td>\n",
       "      <td>1.391</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MASE V_search  MASE V_select  ajustes/origem\n",
       "candidata                                                 \n",
       "equal                 1.093          1.496               3\n",
       "median                1.153          1.507               3\n",
       "trimmed_mean          1.153          1.507               3\n",
       "inverse_mse           1.176          1.341               6\n",
       "nnls                  1.122          1.391               6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensembles = {\n",
    "    method: exp.create_ensemble(list(members.values()), method=method)\n",
    "    for method in [\"equal\", \"median\", \"trimmed_mean\", \"inverse_mse\", \"nnls\"]\n",
    "}\n",
    "pd.DataFrame([summary(r, f\"{m}\") for m, r in ensembles.items()]).set_index(\"candidata\").round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "en-05b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:38:28.741829Z",
     "iopub.status.busy": "2026-10-13T14:38:28.739826Z",
     "iopub.status.idle": "2026-10-13T14:38:30.299025Z",
     "shell.execute_reply": "2026-10-13T14:38:30.294313Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = (\n",
    "    pd.DataFrame(\n",
    "        [summary(m) for m in members.values()]\n",
    "        + [summary(r, f\"ensemble {m}\") for m, r in ensembles.items()]\n",
    "    )\n",
    "    .set_index(\"candidata\")[\"MASE V_select\"]\n",
    "    .sort_values(ascending=False)\n",
    ")\n",
    "best_member = min(summary(m)[\"MASE V_select\"] for m in members.values())\n",
    "fig, ax = plt.subplots(figsize=(8, 3.8))\n",
    "is_ensemble = scores.index.str.startswith(\"ensemble\")\n",
    "ax.barh(\n",
    "    scores.index,\n",
    "    scores,\n",
    "    color=[\"#2a78d6\" if flag else \"#c3c2b7\" for flag in is_ensemble],\n",
    "    height=0.6,\n",
    ")\n",
    "for row, value in enumerate(scores):\n",
    "    ax.text(value + 0.02, row, f\"{value:.2f}\", va=\"center\", fontsize=8, color=\"#0b0b0b\")\n",
    "ax.axvline(best_member, color=\"#0b0b0b\", linewidth=1, linestyle=\"--\")\n",
    "ax.text(\n",
    "    best_member,\n",
    "    len(scores) - 0.4,\n",
    "    \" melhor membro\",\n",
    "    fontsize=8,\n",
    "    color=\"#0b0b0b\",\n",
    "    va=\"bottom\",\n",
    ")\n",
    "ax.set_xlabel(\"MASE média em V_select (cinza: membros; azul: ensembles)\", color=\"#52514e\")\n",
    "ax.set_title(\"Nenhuma agregação passou do melhor membro\", loc=\"left\", fontsize=10)\n",
    "clean(ax, grid_axis=\"x\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "en-06",
   "metadata": {},
   "source": [
    "Pesos aprendidos em cada origem, por membro. Ver pesos mudarem entre origens é sinal de instabilidade, não de sabedoria; a linha tracejada é a média simples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "en-07",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:38:30.308403Z",
     "iopub.status.busy": "2026-10-13T14:38:30.307384Z",
     "iopub.status.idle": "2026-10-13T14:38:31.667978Z",
     "shell.execute_reply": "2026-10-13T14:38:31.662861Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 3.6), sharey=True)\n",
    "member_colors = [\"#2a78d6\", \"#eb6834\", \"#1baf7a\"]  # fixed order of the components\n",
    "for ax, method in zip(axes, [\"inverse_mse\", \"nnls\"], strict=True):\n",
    "    result = ensembles[method]\n",
    "    names = [component.name for component in result.candidate.components]\n",
    "    trials = result.evaluation.trials\n",
    "    origins = [t.fold_id for t in trials]\n",
    "    for k, (name, color) in enumerate(zip(names, member_colors, strict=True)):\n",
    "        values = [t.diagnostics[f\"weight_{k}\"] for t in trials]\n",
    "        ax.plot(origins, values, marker=\"o\", color=color, linewidth=2, label=name)\n",
    "    ax.axhline(1 / len(names), color=\"#c3c2b7\", linewidth=1, linestyle=\"--\")\n",
    "    ax.set_title(f\"{method}\", loc=\"left\", fontsize=10)\n",
    "    ax.set_xlabel(\"origem\", color=\"#52514e\")\n",
    "    ax.set_ylim(0, 1)\n",
    "    clean(ax, grid_axis=\"y\")\n",
    "axes[0].set_ylabel(\"peso (tracejado: pesos iguais)\", color=\"#52514e\")\n",
    "axes[0].legend(frameon=False, title=\"membro\", loc=\"upper left\", ncol=3)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "en-08",
   "metadata": {},
   "source": [
    "## 3. Ensemble de híbridos e ensemble corrigido\n",
    "\n",
    "* **Ensemble de híbridos:** combina híbridos independentes; só ajuda se as correções forem complementares.\n",
    "* **Ensemble corrigido:** um corretor aprende os resíduos fora da amostra **do ensemble**, não a soma de resíduos de cada membro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "en-09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:38:31.681628Z",
     "iopub.status.busy": "2026-10-13T14:38:31.681222Z",
     "iopub.status.idle": "2026-10-13T14:41:24.025480Z",
     "shell.execute_reply": "2026-10-13T14:41:24.020497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MASE V_search</th>\n",
       "      <th>MASE V_select</th>\n",
       "      <th>ajustes/origem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>candidata</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seasonal_naive-linear</th>\n",
       "      <td>0.949</td>\n",
       "      <td>1.409</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear-knn</th>\n",
       "      <td>1.310</td>\n",
       "      <td>1.339</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nnls[linear-knn,seasonal_naive-linear]</th>\n",
       "      <td>1.047</td>\n",
       "      <td>1.325</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(equal[knn,linear,seasonal_naive])-linear</th>\n",
       "      <td>1.128</td>\n",
       "      <td>1.643</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           MASE V_search  MASE V_select  ajustes/origem\n",
       "candidata                                                                              \n",
       "seasonal_naive-linear                              0.949          1.409               3\n",
       "linear-knn                                         1.310          1.339               3\n",
       "nnls[linear-knn,seasonal_naive-linear]             1.047          1.325              12\n",
       "(equal[knn,linear,seasonal_naive])-linear          1.128          1.643               7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hybrids = [\n",
    "    exp.create_hybrid(\"seasonal_naive\", \"linear\", window=14),\n",
    "    exp.create_hybrid(\"linear\", \"knn\", window=14),\n",
    "]\n",
    "of_hybrids = exp.create_ensemble(hybrids, method=\"nnls\")\n",
    "corrected = exp.create_hybrid(ensembles[\"equal\"], \"linear\", window=14)\n",
    "rows = [summary(h) for h in hybrids] + [summary(of_hybrids), summary(corrected)]\n",
    "pd.DataFrame(rows).set_index(\"candidata\").round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "en-10",
   "metadata": {},
   "source": [
    "## 4. Ensembles na busca automática\n",
    "\n",
    "Com `include=(..., \"ensemble\")`, a etapa S4 propõe ensembles das melhores bases de famílias diferentes, das duas melhores estruturas residuais e o ensemble de pesos iguais corrigido. A família `ensemble` desafia o campeão por último; a regra de aceite (ganho mediano ≥ 5%, vitórias ≥ 60%; ADR-0008) decide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "en-11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:41:24.035751Z",
     "iopub.status.busy": "2026-10-13T14:41:24.034221Z",
     "iopub.status.idle": "2026-10-13T14:46:31.282568Z",
     "shell.execute_reply": "2026-10-13T14:46:31.279113Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>status</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ensemble[equal: random_forest(lags=7) | knn(la...</td>\n",
       "      <td>EVALUATED</td>\n",
       "      <td>1.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ensemble[equal: seasonal_naive(season_length=7...</td>\n",
       "      <td>EVALUATED</td>\n",
       "      <td>0.956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(ensemble[equal: random_forest(lags=7) | knn(l...</td>\n",
       "      <td>EVALUATED</td>\n",
       "      <td>1.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ensemble[median: random_forest(lags=7) | knn(l...</td>\n",
       "      <td>EVALUATED</td>\n",
       "      <td>1.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ensemble[inverse_mse: seasonal_naive(season_le...</td>\n",
       "      <td>EVALUATED</td>\n",
       "      <td>0.959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(ensemble[equal: random_forest(lags=7) | knn(l...</td>\n",
       "      <td>EVALUATED</td>\n",
       "      <td>1.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ensemble[inverse_mse: random_forest(lags=7) | ...</td>\n",
       "      <td>EVALUATED</td>\n",
       "      <td>1.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ensemble[nnls, shrinkage=0.5: seasonal_naive(s...</td>\n",
       "      <td>EVALUATED</td>\n",
       "      <td>0.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ensemble[nnls, shrinkage=0.5: random_forest(la...</td>\n",
       "      <td>EVALUATED</td>\n",
       "      <td>1.091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                label     status  score\n",
       "19  ensemble[equal: random_forest(lags=7) | knn(la...  EVALUATED  1.026\n",
       "20  ensemble[equal: seasonal_naive(season_length=7...  EVALUATED  0.956\n",
       "21  (ensemble[equal: random_forest(lags=7) | knn(l...  EVALUATED  1.801\n",
       "22  ensemble[median: random_forest(lags=7) | knn(l...  EVALUATED  1.092\n",
       "23  ensemble[inverse_mse: seasonal_naive(season_le...  EVALUATED  0.959\n",
       "24  (ensemble[equal: random_forest(lags=7) | knn(l...  EVALUATED  1.612\n",
       "25  ensemble[inverse_mse: random_forest(lags=7) | ...  EVALUATED  1.150\n",
       "26  ensemble[nnls, shrinkage=0.5: seasonal_naive(s...  EVALUATED  0.987\n",
       "27  ensemble[nnls, shrinkage=0.5: random_forest(la...  EVALUATED  1.091"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = AutoHybridExperiment(\n",
    "    fh=14,\n",
    "    fold=3,\n",
    "    session_id=42,\n",
    "    budget=\"small\",\n",
    "    base_models=[\"naive\", \"seasonal_naive\", \"linear\", \"knn\", \"random_forest\"],\n",
    "    correctors=[\"linear\", \"knn\"],\n",
    "    residual_windows=[14],\n",
    "    alphas=[1.0],\n",
    ").fit(y)\n",
    "result = search.compare_models(include=(\"standalone\", \"residual\", \"ensemble\"))\n",
    "ledger = result.search.ledger_frame()\n",
    "ledger[ledger[\"family\"] == \"ensemble\"][[\"label\", \"status\", \"score\"]].round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "en-12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:46:31.291157Z",
     "iopub.status.busy": "2026-10-13T14:46:31.291157Z",
     "iopub.status.idle": "2026-10-13T14:46:31.323357Z",
     "shell.execute_reply": "2026-10-13T14:46:31.321223Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ordem da cadeia: ('statistical', 'linear', 'kernel', 'tree', 'neural', 'residual', 'decomposition', 'combination', 'ensemble', 'dynamic')\n",
      "ajustes na busca: 315; campeão: linear(lags=7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>family</th>\n",
       "      <th>champion_before</th>\n",
       "      <th>challenger</th>\n",
       "      <th>accepted</th>\n",
       "      <th>reason_codes</th>\n",
       "      <th>median_gain</th>\n",
       "      <th>win_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>statistical</td>\n",
       "      <td>knn</td>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>True</td>\n",
       "      <td>(accepted,)</td>\n",
       "      <td>0.142373</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>linear</td>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>linear</td>\n",
       "      <td>True</td>\n",
       "      <td>(accepted,)</td>\n",
       "      <td>0.186763</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kernel</td>\n",
       "      <td>linear</td>\n",
       "      <td>knn</td>\n",
       "      <td>False</td>\n",
       "      <td>(median_gain_below_threshold, win_rate_below_t...</td>\n",
       "      <td>-0.433787</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tree</td>\n",
       "      <td>linear</td>\n",
       "      <td>random_forest</td>\n",
       "      <td>False</td>\n",
       "      <td>(median_gain_below_threshold, win_rate_below_t...</td>\n",
       "      <td>-0.581919</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>residual</td>\n",
       "      <td>linear</td>\n",
       "      <td>seasonal_naive-linear</td>\n",
       "      <td>False</td>\n",
       "      <td>(median_gain_below_threshold, win_rate_below_t...</td>\n",
       "      <td>-0.076187</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ensemble</td>\n",
       "      <td>linear</td>\n",
       "      <td>equal[linear-linear,seasonal_naive-linear]</td>\n",
       "      <td>False</td>\n",
       "      <td>(median_gain_below_threshold,)</td>\n",
       "      <td>0.010859</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        family champion_before                                  challenger  accepted                                       reason_codes  median_gain  win_rate\n",
       "0  statistical             knn                              seasonal_naive      True                                        (accepted,)     0.142373  1.000000\n",
       "1       linear  seasonal_naive                                      linear      True                                        (accepted,)     0.186763  1.000000\n",
       "2       kernel          linear                                         knn     False  (median_gain_below_threshold, win_rate_below_t...    -0.433787  0.000000\n",
       "3         tree          linear                               random_forest     False  (median_gain_below_threshold, win_rate_below_t...    -0.581919  0.000000\n",
       "4     residual          linear                       seasonal_naive-linear     False  (median_gain_below_threshold, win_rate_below_t...    -0.076187  0.333333\n",
       "5     ensemble          linear  equal[linear-linear,seasonal_naive-linear]     False                     (median_gain_below_threshold,)     0.010859  0.666667"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"ordem da cadeia:\", search.policy_.family_order)\n",
    "print(f\"ajustes na busca: {result.search.fit_count}; campeão: {result.best.label}\")\n",
    "result.decisions[\n",
    "    [\n",
    "        \"family\",\n",
    "        \"champion_before\",\n",
    "        \"challenger\",\n",
    "        \"accepted\",\n",
    "        \"reason_codes\",\n",
    "        \"median_gain\",\n",
    "        \"win_rate\",\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "en-12m",
   "metadata": {},
   "source": [
    "Estrutura e previsões fora da amostra do campeão nas origens de validação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "en-12b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:46:31.347683Z",
     "iopub.status.busy": "2026-10-13T14:46:31.347683Z",
     "iopub.status.idle": "2026-10-13T14:46:31.592452Z",
     "shell.execute_reply": "2026-10-13T14:46:31.590583Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "PlotArtifact(kind='model_structure', figure=<Figure size 800x240 with 1 Axes>, source_split='validation', candidate_id='e9e747a9648ad8d377f20122dce611a43dd3bf85a64c950a8e1ef246ebc032b4', origin_range=(Timestamp('2025-02-05 00:00:00'), Timestamp('2025-04-16 00:00:00')), provenance={'metric': 'MASE', 'units': 6, 'coverage': 1.0, 'layers': ['search', 'select'], 'series': 1}, backend='matplotlib')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.plot_model(result.best, \"model_structure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "en-12c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:46:31.601102Z",
     "iopub.status.busy": "2026-10-13T14:46:31.601102Z",
     "iopub.status.idle": "2026-10-13T14:46:32.427556Z",
     "shell.execute_reply": "2026-10-13T14:46:32.425500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PlotArtifact(kind='cv_forecasts', figure=<Figure size 1000x360 with 1 Axes>, source_split='validation', candidate_id='e9e747a9648ad8d377f20122dce611a43dd3bf85a64c950a8e1ef246ebc032b4', origin_range=(Timestamp('2025-02-05 00:00:00'), Timestamp('2025-04-16 00:00:00')), provenance={'metric': 'MASE', 'units': 6, 'coverage': 1.0, 'layers': ['search', 'select'], 'series': 1}, backend='matplotlib')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.plot_model(result.best, \"cv_forecasts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "en-13",
   "metadata": {},
   "source": [
    "## 5. Avaliação externa do campeão\n",
    "\n",
    "Uma única leitura do teste selado, depois de congelar a escolha."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "en-14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T14:46:32.427556Z",
     "iopub.status.busy": "2026-10-13T14:46:32.427556Z",
     "iopub.status.idle": "2026-10-13T14:46:32.513031Z",
     "shell.execute_reply": "2026-10-13T14:46:32.509183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MASE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>sMAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>linear(lags=7)</th>\n",
       "      <td>1.109</td>\n",
       "      <td>1.736</td>\n",
       "      <td>2.043</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MASE    MAE   RMSE  sMAPE\n",
       "model                                     \n",
       "linear(lags=7)  1.109  1.736  2.043  0.027"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = search.finalize_model(result.best)\n",
    "search.evaluate_external(final).metrics.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "en-99",
   "metadata": {},
   "source": [
    "## 6. Leitura\n",
    "\n",
    "Nesta série (500 pontos, `fh=14`, 3 origens por camada):\n",
    "\n",
    "* **Nenhuma agregação dos três membros superou o melhor membro** em `V_select`: `linear` sozinho tem MASE 1,28; o melhor ensemble (`inverse_mse`) fica em 1,34, e a média simples em 1,50. Os pesos aprendidos dão ~0,65 ao `linear` e quase não mudam entre origens — estáveis, mas ainda dividem peso com membros piores.\n",
    "* Com 3 membros, `trimmed_mean` é igual à `median` (descarta um de cada ponta).\n",
    "* O **ensemble de híbridos** (`nnls`, 1,33) ficou melhor que cada híbrido sozinho (1,41 e 1,34), sinal de alguma complementaridade entre as correções; o **ensemble corrigido** (1,64) ficou pior que o ensemble sem correção (1,50): aqui o corretor aprendeu ruído.\n",
    "* **Custo:** um ensemble `nnls` de 3 membros ajusta 6 modelos por origem; o ensemble de dois híbridos, 12; o ensemble corrigido, 7.\n",
    "* Na **busca**, o ensemble de híbridos venceu 2 de 3 unidades, mas com ganho mediano de 1,09%, abaixo do mínimo de 5%: recusado, e o `linear` ficou campeão (MASE 1,11 no teste selado). Com a regra anterior (ganho ≥ 1%) ele teria sido aceito — exatamente o tipo de aceite que a calibração mostrou ser frequente em séries sem estrutura a explorar (notebook 11, ADR-0008). Mesmo com a regra nova, `\"ensemble\"` fica fora do `include` padrão: ele ainda não passou pelo portão de calibração com essa regra.\n",
    "\n",
    "Ensembles não são uma melhoria automática: o resultado depende dos membros, da série e de quantas origens sustentam a decisão."
   ]
  }
 ],
 "metadata": {
  "autohybts": {
   "extras": [
    "plot"
   ],
   "guide": [
    "user_guide/ensembles"
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   "heavy": false,
   "network": false,
   "requires": [
    "autohybts.AutoHybridExperiment.create_ensemble",
    "autohybts.AutoHybridExperiment.create_hybrid",
    "autohybts.AutoHybridExperiment.compare_models",
    "autohybts.datasets.get_data"
   ],
   "seeds": [
    42
   ],
   "template": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
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   "file_extension": ".py",
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