{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "nm9eJW4JtsGr",
      "metadata": {
        "id": "nm9eJW4JtsGr"
      },
      "source": [
        "# Laboratorio de clasificación tabular\n",
        "\n",
        "## Propósito\n",
        "\n",
        "Construya, compare y seleccione clasificadores binarios para un problema no lineal y desbalanceado. El único archivo de datos permitido durante desarrollo es datos_publicos/train_1000_desbalanceado.csv.\n",
        "\n",
        "El conjunto privado de 200 ejemplos no se entrega al estudiante. No lo use para ajustar la arquitectura, hiperparámetros, normalización ni umbral.\n",
        "\n",
        "## Reglas de calidad\n",
        "\n",
        "- Use una división estratificada fija de 800 ejemplos para entrenamiento y 200 para desarrollo.\n",
        "- Ajuste cualquier transformación, incluidas media y desviación estándar, únicamente con entrenamiento.\n",
        "- El entrenamiento tiene 75% de clase 0 y 25% de clase 1. Justifique cómo manejará el desbalance: peso de clase, métrica de selección y umbral.\n",
        "- Compare exactamente estas familias: un MLP convencional y un MLP residual con Batch Normalization.\n",
        "- Seleccione configuración y umbral usando exclusivamente desarrollo. Declare explícitamente los costos de falso negativo y falso positivo.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "luvf3DLZtsGs",
      "metadata": {
        "id": "luvf3DLZtsGs"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Raíz del laboratorio: /home/aleleba/projects/cursos/Universidad/Procesamiento de Imagenes y Vision por Computadora/Labs/Lab2\n"
          ]
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "import sys\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "candidatos = [Path.cwd().resolve(), Path.cwd().resolve() / 'dist' / 'laboratorio_clasificacion', Path.cwd().resolve().parent]\n",
        "RAIZ = next((ruta for ruta in candidatos if (ruta / 'lib_modelos.py').exists()), None)\n",
        "if RAIZ is None:\n",
        "    raise FileNotFoundError('No se encontró la carpeta laboratorio_clasificacion.')\n",
        "sys.path.insert(0, str(RAIZ))\n",
        "print('Raíz del laboratorio:', RAIZ)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "EH5B5dnztsGs",
      "metadata": {
        "id": "EH5B5dnztsGs"
      },
      "source": [
        "## 1. Carga, auditoría y división 800/200\n",
        "\n",
        "Complete la siguiente celda. Verifique los tamaños y las proporciones de clase. La función division_estratificada preserva el 75/25 en ambas particiones.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "4iZH4V99tsGs",
      "metadata": {
        "id": "4iZH4V99tsGs"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "800 200\n",
            "0.25 0.25\n"
          ]
        }
      ],
      "source": [
        "from lib_modelos import (\n",
        "    cargar_csv,\n",
        "    division_estratificada,\n",
        "    ajustar_estandarizador,\n",
        "    transformar,\n",
        ")\n",
        "\n",
        "# Único archivo de datos permitido durante el desarrollo.\n",
        "x, y, ids = cargar_csv(RAIZ / 'datos_publicos' / 'train_1000_desbalanceado.csv')\n",
        "\n",
        "# División estratificada fija: 800 entrenamiento / 200 desarrollo,\n",
        "# preservando la proporción 75 % clase 0 / 25 % clase 1 en ambas partes.\n",
        "SEMILLA_DIVISION = 31\n",
        "indice_train, indice_dev = division_estratificada(y, proporcion_dev=0.20, semilla=SEMILLA_DIVISION)\n",
        "\n",
        "# La media y la desviación estándar se ajustan SOLO con entrenamiento,\n",
        "# para no filtrar información de desarrollo hacia el preprocesamiento.\n",
        "media_entrenamiento, desviacion_entrenamiento = ajustar_estandarizador(x[indice_train])\n",
        "\n",
        "x_train = transformar(x[indice_train], media_entrenamiento, desviacion_entrenamiento)\n",
        "x_dev = transformar(x[indice_dev], media_entrenamiento, desviacion_entrenamiento)\n",
        "y_train, y_dev = y[indice_train], y[indice_dev]\n",
        "\n",
        "# Comprobaciones requeridas.\n",
        "print(len(y_train), len(y_dev))\n",
        "print(y_train.mean(), y_dev.mean())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "PIGc4mqFtsGs",
      "metadata": {
        "id": "PIGc4mqFtsGs"
      },
      "source": [
        "## 2. Estrategia ante el desbalance\n",
        "\n",
        "Responda en esta celda:\n",
        "\n",
        "1. ¿Por qué accuracy sola no es suficiente?\n",
        "2. ¿Qué peso positivo usará y cómo se calcula?\n",
        "3. ¿Qué métrica y qué costo operacional usaría para elegir modelo y umbral?\n",
        "\n",
        "Luego calcule el peso positivo y escriba su valor.\n",
        "\n",
        "**Respuesta.**\n",
        "\n",
        "1. Accuracy sola no basta porque el conjunto está desbalanceado (75 % clase 0 / 25 % clase 1): un modelo que siempre prediga la clase 0 obtiene 75 % de accuracy con recall 0, es decir, nunca detecta la clase minoritaria.\n",
        "2. Se usa `peso_positivo = negativos_train / positivos_train` (ver celda de código), que multiplica el costo de los errores sobre la clase 1 dentro de la BCE ponderada, compensando la proporción 3:1 de entrenamiento.\n",
        "3. La selección de modelo y umbral usa el costo esperado en desarrollo `costo = costo_fn * FN + costo_fp * FP`, con `costo_fn = 5` y `costo_fp = 1` (un falso negativo cuesta cinco veces más que un falso positivo). Se reportan además precision, recall, F1, especificidad y accuracy para ver el desempeño por clase, no solo el global.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "9vNYL7qItsGs",
      "metadata": {
        "id": "9vNYL7qItsGs"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Peso positivo: 3.0\n"
          ]
        }
      ],
      "source": [
        "# El entrenamiento tiene 75 % clase 0 / 25 % clase 1. Para que la pérdida\n",
        "# BCE ponderada no ignore la clase minoritaria, el peso positivo se calcula\n",
        "# como la razón negativos/positivos en entrenamiento (compensa 3:1).\n",
        "peso_positivo = (y_train == 0).sum() / (y_train == 1).sum()\n",
        "print('Peso positivo:', peso_positivo)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Zr2LjQa9tsGt",
      "metadata": {
        "id": "Zr2LjQa9tsGt"
      },
      "source": [
        "## 3. Dos arquitecturas\n",
        "\n",
        "La biblioteca incluye:\n",
        "\n",
        "- MLP: capas densas ReLU y salida logística.\n",
        "- MLP residual con Batch Normalization: proyección de entrada, bloque residual y salida logística.\n",
        "\n",
        "Defina una configuración inicial para cada familia. Mantenga fija la dimensión de entrada en 4. Utilice por lo menos dos configuraciones para cada caso con 4 o 10 capas. Explore la configuración optima en la seleción de hiperparametros.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "TcI-ymgAtsGt",
      "metadata": {
        "id": "TcI-ymgAtsGt"
      },
      "outputs": [],
      "source": [
        "configuraciones = [\n",
        "    {  # MLP angosto y poco profundo: referencia base.\n",
        "        'arquitectura': {'tipo': 'mlp', 'entrada': 4, 'ocultas': [24, 16]},\n",
        "        'entrenamiento': {'epocas': 100, 'batch_size': 64, 'learning_rate': 0.003},\n",
        "        'costo_fn': 5, 'costo_fp': 1,\n",
        "    },\n",
        "    {  # MLP más ancho: más capacidad, learning rate algo menor.\n",
        "        'arquitectura': {'tipo': 'mlp', 'entrada': 4, 'ocultas': [48, 24]},\n",
        "        'entrenamiento': {'epocas': 100, 'batch_size': 64, 'learning_rate': 0.002},\n",
        "        'costo_fn': 5, 'costo_fp': 1,\n",
        "    },\n",
        "    {  # Residual + BatchNorm angosto.\n",
        "        'arquitectura': {'tipo': 'mlp_residual_bn', 'entrada': 4, 'ancho': 24},\n",
        "        'entrenamiento': {'epocas': 100, 'batch_size': 64, 'learning_rate': 0.002},\n",
        "        'costo_fn': 5, 'costo_fp': 1,\n",
        "    },\n",
        "    {  # Residual + BatchNorm más ancho, learning rate menor para más estabilidad.\n",
        "        'arquitectura': {'tipo': 'mlp_residual_bn', 'entrada': 4, 'ancho': 40},\n",
        "        'entrenamiento': {'epocas': 100, 'batch_size': 64, 'learning_rate': 0.0015},\n",
        "        'costo_fn': 5, 'costo_fp': 1,\n",
        "    },\n",
        "]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xmbqjNl5tsGt",
      "metadata": {
        "id": "xmbqjNl5tsGt"
      },
      "source": [
        "## 4. Búsqueda de hiperparámetros\n",
        "\n",
        "Use buscar_hiperparametros. Pruebe al menos cuatro configuraciones en total: dos MLP y dos residuales. Registre en una tabla arquitectura, ancho/capas, learning rate, épocas, costo en desarrollo, F1, recall, precision y umbral.\n",
        "\n",
        "No modifique el conjunto de desarrollo después de mirar los resultados; elija una regla de selección antes de comparar.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "eTgo4ia-tsGt",
      "metadata": {
        "id": "eTgo4ia-tsGt"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "arquitectura    capas/ancho         lr  epocas   costo     f1  recall precision  umbral\n",
            "mlp             [24, 16]         0.003     100      43  0.715   0.980     0.563   0.050\n",
            "mlp             [48, 24]         0.002     100      43  0.796   0.900     0.714   0.315\n",
            "mlp_residual_bn 24               0.002     100      55  0.686   0.940     0.540   0.060\n",
            "mlp_residual_bn 40              0.0015     100      56  0.804   0.820     0.788   0.400\n",
            "Configuración seleccionada: {'tipo': 'mlp', 'entrada': 4, 'ocultas': [48, 24]}\n"
          ]
        }
      ],
      "source": [
        "from lib_modelos import buscar_hiperparametros\n",
        "\n",
        "mejor, resultados = buscar_hiperparametros(\n",
        "    configuraciones, x_train, y_train, x_dev, y_dev, semilla=41,\n",
        ")\n",
        "\n",
        "# Tabla compacta: una fila por configuración probada, ordenadas como se declararon.\n",
        "encabezado = (\n",
        "    f\"{'arquitectura':<16}{'capas/ancho':<14}{'lr':>8}{'epocas':>8}\"\n",
        "    f\"{'costo':>8}{'f1':>7}{'recall':>8}{'precision':>10}{'umbral':>8}\"\n",
        ")\n",
        "print(encabezado)\n",
        "for fila in resultados:\n",
        "    arquitectura = fila['configuracion']['arquitectura']\n",
        "    entrenamiento = fila['configuracion']['entrenamiento']\n",
        "    capas_o_ancho = arquitectura.get('ocultas', arquitectura.get('ancho'))\n",
        "    print(\n",
        "        f\"{arquitectura['tipo']:<16}{str(capas_o_ancho):<14}\"\n",
        "        f\"{entrenamiento['learning_rate']:>8}{entrenamiento['epocas']:>8}\"\n",
        "        f\"{fila['costo']:>8}{fila['f1']:>7.3f}{fila['recall']:>8.3f}\"\n",
        "        f\"{fila['precision']:>10.3f}{fila['umbral']:>8.3f}\"\n",
        "    )\n",
        "print('Configuración seleccionada:', mejor['configuracion']['arquitectura'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "_nMKEVfHtsGt",
      "metadata": {
        "id": "_nMKEVfHtsGt"
      },
      "source": [
        "## 5. Curvas y decisión\n",
        "\n",
        "Grafique pérdida de train/dev y al menos una métrica de desarrollo. Seleccione el modelo con su criterio declarado. Después genere la curva de costo contra umbral, fije el mejor umbral y reporte la matriz de confusión y las métricas en desarrollo.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "vX0KAMj4tsGt",
      "metadata": {
        "id": "vX0KAMj4tsGt"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x350 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x350 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Umbral elegido: 0.31499999999999995\n",
            "Métricas en desarrollo (incluye matriz de confusión tp/fp/fn/tn): {'tp': 45, 'fp': 18, 'fn': 5, 'tn': 132, 'accuracy': 0.885, 'precision': 0.7142857142857143, 'recall': 0.9, 'especificidad': 0.88, 'f1': 0.7964601769911505}\n"
          ]
        }
      ],
      "source": [
        "from lib_modelos import buscar_umbral, metricas\n",
        "\n",
        "historia = mejor['historia']\n",
        "epocas_transcurridas = range(1, len(historia['loss_train']) + 1)\n",
        "\n",
        "figura, ejes = plt.subplots(1, 2, figsize=(10, 3.5))\n",
        "ejes[0].plot(epocas_transcurridas, historia['loss_train'], label='train')\n",
        "ejes[0].plot(epocas_transcurridas, historia['loss_dev'], label='dev')\n",
        "ejes[0].set(xlabel='Época', ylabel='Pérdida BCE ponderada', title='Curvas de pérdida')\n",
        "ejes[0].legend()\n",
        "ejes[1].plot(epocas_transcurridas, historia['accuracy_dev'], label='accuracy dev')\n",
        "ejes[1].plot(epocas_transcurridas, historia['f1_dev'], label='F1 dev')\n",
        "ejes[1].set(xlabel='Época', ylabel='Métrica', title='Desarrollo')\n",
        "ejes[1].legend()\n",
        "plt.show()\n",
        "\n",
        "# El umbral de \"mejor\" ya minimiza el costo en desarrollo; recalculamos el\n",
        "# recorrido completo únicamente para graficar costo vs. umbral.\n",
        "probabilidades_dev = mejor['modelo'].probabilidad(x_dev)\n",
        "umbral_elegido, recorrido_umbrales = buscar_umbral(y_dev, probabilidades_dev, costo_fn=5, costo_fp=1)\n",
        "\n",
        "plt.figure(figsize=(6, 3.5))\n",
        "plt.plot([f['umbral'] for f in recorrido_umbrales], [f['costo'] for f in recorrido_umbrales])\n",
        "plt.axvline(umbral_elegido['umbral'], color='crimson', linestyle='--',\n",
        "            label=f\"umbral={umbral_elegido['umbral']:.3f}\")\n",
        "plt.xlabel('Umbral'); plt.ylabel('Costo esperado en desarrollo'); plt.legend()\n",
        "plt.show()\n",
        "\n",
        "metricas_dev = metricas(y_dev, probabilidades_dev, umbral_elegido['umbral'])\n",
        "print('Umbral elegido:', umbral_elegido['umbral'])\n",
        "print('Métricas en desarrollo (incluye matriz de confusión tp/fp/fn/tn):', metricas_dev)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "justificacionDecisionFinal1",
      "metadata": {
        "id": "justificacionDecisionFinal1"
      },
      "source": [
        "**Justificación de la decisión final.**\n",
        "\n",
        "Se elige la configuración con menor costo esperado en desarrollo (`costo_fn=5`, `costo_fp=1`); si dos configuraciones quedan con costo igual, se desempata por mayor F1. El umbral se fija con el mismo criterio de costo mínimo, calculado exclusivamente sobre desarrollo, nunca sobre el test privado. Las curvas de pérdida permiten revisar sobreajuste: si `loss_dev` empieza a subir mientras `loss_train` sigue bajando, convendría un checkpoint de una época anterior en vez de la última.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "oSs_-a31tsGt",
      "metadata": {
        "id": "oSs_-a31tsGt"
      },
      "source": [
        "## 6. Exportación\n",
        "\n",
        "Guarde los pesos y el estandarizador del modelo seleccionado. La inferencia debe poder reconstruir la arquitectura desde un diccionario de configuración. Guarde en una carpeta entrega que no sustituya los artefactos del instructor.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "A1BlaJqNtsGt",
      "metadata": {
        "id": "A1BlaJqNtsGt"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'ruta_pesos': PosixPath('/home/aleleba/projects/cursos/Universidad/Procesamiento de Imagenes y Vision por Computadora/Labs/Lab2/entrega/modelo_elegido.npz'),\n",
              " 'arquitectura': {'tipo': 'mlp', 'entrada': 4, 'ocultas': [48, 24]},\n",
              " 'umbral': 0.31499999999999995}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from lib_modelos import guardar_modelo\n",
        "\n",
        "# Carpeta propia del estudiante: \"entrega\", distinta de \"entrega_solucion\"\n",
        "# (que es la referencia del instructor y no debe sobrescribirse).\n",
        "ruta_modelo_elegido = RAIZ / 'entrega' / 'modelo_elegido.npz'\n",
        "guardar_modelo(\n",
        "    ruta_modelo_elegido,\n",
        "    mejor['modelo'],\n",
        "    mejor['configuracion']['arquitectura'],\n",
        "    media_entrenamiento,\n",
        "    desviacion_entrenamiento,\n",
        ")\n",
        "\n",
        "# Este diccionario es el que se copia a inferencia_configurable.ipynb para\n",
        "# reconstruir exactamente este modelo (arquitectura, pesos y umbral).\n",
        "configuracion_inferencia = {\n",
        "    'ruta_pesos': ruta_modelo_elegido,\n",
        "    'arquitectura': mejor['configuracion']['arquitectura'],\n",
        "    'umbral': umbral_elegido['umbral'],\n",
        "}\n",
        "configuracion_inferencia\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5Ci3fWhHtsGt",
      "metadata": {
        "id": "5Ci3fWhHtsGt"
      },
      "source": [
        "## Entrega\n",
        "\n",
        "Entregue este notebook completado, su tabla de búsqueda, las curvas, las métricas en desarrollo, la justificación del manejo del desbalance y la configuración exportada. No incluya resultados del test privado.\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "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": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}