{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a599b7b",
   "metadata": {},
   "source": [
    "# NutriFresh S.A.S. — Modelo predictivo de retrasos (Grupo 16)\n",
    "\n",
    "**Cuaderno académico reproducible · Unidad 3 · Evidencia de la sección 8 del informe**\n",
    "\n",
    "**Resultado ejecutivo:** El árbol de decisión y la regresión logística se evaluaron sobre registros futuros no utilizados en el entrenamiento. Ninguno mostró capacidad predictiva suficiente para utilizarse en operación. El resultado negativo también es un hallazgo válido del proceso de ciencia de datos.\n",
    "\n",
    "**Fuente:** `nutrifresh_pedidos_dataset (2).csv` (archivo académico del profesor, registros de 2023). Es distinto del archivo `(1)` de 2026.\n",
    "\n",
    "**Supuestos clave:** El objetivo es *retraso confirmado* antes del despacho. Los pedidos *Devuelto* se excluyen del clasificador binario y se mantienen en el análisis de incidencias. Se usa únicamente información disponible durante el registro del pedido; no se usan tiempos, costos, combustible ni clima/tránsito observado al finalizar la operación. Esta disponibilidad debe verificarse en una empresa real.\n",
    "\n",
    "**Cómo ejecutar:** Coloca el CSV original junto a este archivo y ejecuta las celdas en orden. En Google Colab, carga el CSV en el directorio de trabajo. El conjunto es de uso académico y **no** demuestra eficacia en producción."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d7678db",
   "metadata": {},
   "source": [
    "## 1. Bibliotecas y fuente de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b311a29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-08T05:51:52.049552Z",
     "iopub.status.busy": "2026-11-08T05:51:52.049444Z",
     "iopub.status.idle": "2026-11-08T05:51:52.894406Z",
     "shell.execute_reply": "2026-11-08T05:51:52.893790Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fuente: nutrifresh_pedidos_dataset (2).csv\n",
      "Dimensiones originales: (228, 18)\n",
      "Duplicados exactos: 8\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import (confusion_matrix, accuracy_score, precision_score, recall_score,\n",
    "                             f1_score, balanced_accuracy_score, roc_auc_score,\n",
    "                             average_precision_score, roc_curve, precision_recall_curve)\n",
    "\n",
    "ruta = Path('nutrifresh_pedidos_dataset (2).csv')\n",
    "if not ruta.exists():\n",
    "    raise FileNotFoundError('Debes situar nutrifresh_pedidos_dataset (2).csv junto al notebook o subirlo en Google Colab.')\n",
    "original = pd.read_csv(ruta)\n",
    "print('Fuente:', ruta.name)\n",
    "print('Dimensiones originales:', original.shape)\n",
    "print('Duplicados exactos:', int(original.duplicated().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a03325",
   "metadata": {},
   "source": [
    "## 2. Depuración, definición del objetivo y tratamiento de las devoluciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddba9147",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-08T05:51:52.895816Z",
     "iopub.status.busy": "2026-11-08T05:51:52.895698Z",
     "iopub.status.idle": "2026-11-08T05:51:52.914884Z",
     "shell.execute_reply": "2026-11-08T05:51:52.914327Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estados de entrega:\n",
      " estado_entrega\n",
      "Entregado a tiempo    168\n",
      "Retrasado              49\n",
      "Devuelto                3\n",
      "Devoluciones excluidas SOLO del clasificador: 3\n",
      "Muestra de clasificación: 217 | retrasos: 49 | puntuales: 168\n",
      "Pesos faltantes: 6\n"
     ]
    }
   ],
   "source": [
    "sin_duplicados = original.drop_duplicates().copy()\n",
    "print('Estados de entrega:\\n', sin_duplicados['estado_entrega'].value_counts().to_string())\n",
    "print('Devoluciones excluidas SOLO del clasificador:', (sin_duplicados['estado_entrega']=='Devuelto').sum())\n",
    "\n",
    "df = sin_duplicados[sin_duplicados['estado_entrega'].isin(['Entregado a tiempo', 'Retrasado'])].copy()\n",
    "df['objetivo'] = (df['estado_entrega']=='Retrasado').astype(int)\n",
    "# No usar la columna «retraso» original, que incluye las devoluciones como valor 1.\n",
    "df['peso'] = pd.to_numeric(df['peso_kg'].astype(str)\n",
    "    .str.replace(r'\\s*kg\\s*$', '', regex=True).str.strip().str.replace(',','.',regex=False),errors='coerce')\n",
    "df['fecha'] = pd.to_datetime(df['fecha_registro'], format='%Y-%m-%d',errors='raise')\n",
    "df['hora'] = pd.to_numeric(df['hora_registro'].astype(str).str.slice(0,2),errors='coerce')\n",
    "df['dia_semana'] = df['fecha'].dt.day_name()\n",
    "df['ciudad'] = df['ciudad_destino'].str.strip().str.title()\n",
    "df = df.sort_values(['fecha','hora_registro','id_pedido']).reset_index(drop=True)\n",
    "assert len(original)==228 and len(sin_duplicados)==220 and len(df)==217\n",
    "assert (df['objetivo']==1).sum()==49 and df['id_pedido'].is_unique\n",
    "print('Muestra de clasificación:',len(df),'| retrasos:',sum(df['objetivo']), '| puntuales:',len(df)-sum(df['objetivo']))\n",
    "print('Pesos faltantes:', df['peso'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030344b5",
   "metadata": {},
   "source": [
    "## 3. Variables y separación temporal\n",
    "\n",
    "Los datos de **octubre y noviembre de 2023** se destinan al entrenamiento. Los registros de **diciembre de 2023** se reservan como prueba futura. Ninguna fecha se comparte entre ambos grupos.\n",
    "\n",
    "Predictores: ciudad, producto, peso, hora del registro y día de la semana. No se utilizan las variables registradas después de iniciar o terminar la ruta. Se excluyen los identificadores y las variables de resultado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d519e0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-08T05:51:52.916113Z",
     "iopub.status.busy": "2026-11-08T05:51:52.916003Z",
     "iopub.status.idle": "2026-11-08T05:51:52.920606Z",
     "shell.execute_reply": "2026-11-08T05:51:52.920141Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trenamiento: 174 pedidos, 38 retrasados\n",
      "Prueba futura: 43 pedidos, 11 retrasados\n",
      "Prevalencia prueba: 0.256\n"
     ]
    }
   ],
   "source": [
    "categoricas=['ciudad','tipo_producto','dia_semana']\n",
    "numericas=['peso','hora']\n",
    "features=categoricas+numericas\n",
    "X=df[features]\n",
    "y=df['objetivo']\n",
    "seleccion_train=df['fecha'] < pd.Timestamp('2023-12-01')\n",
    "X_train,X_test=X.loc[seleccion_train],X.loc[~seleccion_train]\n",
    "y_train,y_test=y.loc[seleccion_train],y.loc[~seleccion_train]\n",
    "print(f'Trenamiento: {len(y_train)} pedidos, {int(y_train.sum())} retrasados')\n",
    "print(f'Prueba futura: {len(y_test)} pedidos, {int(y_test.sum())} retrasados')\n",
    "print('Prevalencia prueba:',round(y_test.mean(),3))\n",
    "assert len(X_train)==174 and len(X_test)==43"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e06b6b",
   "metadata": {},
   "source": [
    "## 4. Preprocesamiento sin fuga de información y clasificadores\n",
    "\n",
    "La imputación del peso, el escalado y la codificación de categorías se ajustan **únicamente con los datos de entrenamiento**, dentro de un `Pipeline`. El árbol aplica pre-poda mediante `max_depth=3` y `min_samples_leaf=10`. Se equilibran pesos de clase para no favorecer por defecto a la clase mayoritaria. Se utiliza un umbral 0,5 sin optimizarlo mirando la prueba."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f517600",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-08T05:51:52.921711Z",
     "iopub.status.busy": "2026-11-08T05:51:52.921607Z",
     "iopub.status.idle": "2026-11-08T05:51:52.924558Z",
     "shell.execute_reply": "2026-11-08T05:51:52.924094Z"
    }
   },
   "outputs": [],
   "source": [
    "def crear_pipeline(estimator):\n",
    "    cat = Pipeline([('imputacion',SimpleImputer(strategy='most_frequent')),\n",
    "                    ('codificacion',OneHotEncoder(handle_unknown='ignore'))])\n",
    "    num = Pipeline([('imputacion',SimpleImputer(strategy='median')),\n",
    "                    ('escalado',StandardScaler())])\n",
    "    transformar = ColumnTransformer([('cat',cat,categoricas),('num',num,numericas)])\n",
    "    return Pipeline([('transformar',transformar),('clasificador',estimator)])\n",
    "\n",
    "modelos = {\n",
    "    'Línea base': DummyClassifier(strategy='prior'),\n",
    "    'Árbol de decisión': DecisionTreeClassifier(criterion='gini',max_depth=3,\n",
    "                    min_samples_leaf=10,class_weight='balanced',random_state=42),\n",
    "    'Regresión logística': LogisticRegression(class_weight='balanced',C=1.0,\n",
    "                    solver='lbfgs',max_iter=2000,random_state=42)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f3553ef",
   "metadata": {},
   "source": [
    "## 5. Validación interna en tres ventanas cronológicas\n",
    "\n",
    "Cada ventana utiliza exclusivamente fechas anteriores para entrenar. No es validación aleatoria: se comprueba si el método funciona en periodos posteriores dentro del conjunto de entrenamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67b1b0d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-08T05:51:52.925589Z",
     "iopub.status.busy": "2026-11-08T05:51:52.925492Z",
     "iopub.status.idle": "2026-11-08T05:51:53.240821Z",
     "shell.execute_reply": "2026-11-08T05:51:53.240384Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-16 train=51, val=50, positivos_val=10\n",
      "2023-11-01 train=101, val=39, positivos_val=6\n",
      "2023-11-15 train=140, val=34, positivos_val=7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        f1  recall  roc_auc     ap\n",
      "Línea base           0.000   0.000    0.500  0.187\n",
      "Árbol de decisión    0.306   0.476    0.581  0.229\n",
      "Regresión logística  0.259   0.410    0.507  0.262\n"
     ]
    }
   ],
   "source": [
    "ventanas=[('2023-10-16','2023-11-01'),('2023-11-01','2023-11-15'),('2023-11-15','2023-12-01')]\n",
    "fechas_train=df.loc[seleccion_train,'fecha'].to_numpy()\n",
    "cv_bloques=[]\n",
    "for inicio,fin in ventanas:\n",
    "    idx_ent=np.flatnonzero(fechas_train < np.datetime64(inicio))\n",
    "    idx_val=np.flatnonzero((fechas_train >= np.datetime64(inicio)) & (fechas_train < np.datetime64(fin)))\n",
    "    cv_bloques.append((idx_ent,idx_val))\n",
    "    print(inicio, f'train={len(idx_ent)}, val={len(idx_val)}, positivos_val={int(y_train.iloc[idx_val].sum())}')\n",
    "\n",
    "cv_resultados={}\n",
    "for nombre,estimador in modelos.items():\n",
    "    pipe=crear_pipeline(estimador)\n",
    "    valores=cross_validate(pipe,X_train,y_train,cv=cv_bloques,\n",
    "         scoring={'f1':'f1','recall':'recall','roc_auc':'roc_auc','ap':'average_precision'},error_score='raise')\n",
    "    cv_resultados[nombre]={k.replace('test_',''):np.mean(v) for k,v in valores.items() if k.startswith('test_')}\n",
    "print(pd.DataFrame(cv_resultados).T.round(3).to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f92e8245",
   "metadata": {},
   "source": [
    "## 6. Evaluación final con diciembre de 2023\n",
    "\n",
    "**Interpretación de la matriz:** TN = puntuales correctamente identificados; FP = alarmas innecesarias; FN = retrasos no detectados; TP = retrasos identificados correctamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "799e0c7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-08T05:51:53.249382Z",
     "iopub.status.busy": "2026-11-08T05:51:53.249258Z",
     "iopub.status.idle": "2026-11-08T05:51:53.339245Z",
     "shell.execute_reply": "2026-11-08T05:51:53.338730Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       TN    FP    FN   TP  Accuracy  Precision  Recall     F1  ROC-AUC     AP\n",
      "Línea base           32.0   0.0  11.0  0.0     0.744      0.000   0.000  0.000    0.500  0.256\n",
      "Árbol de decisión    17.0  15.0   8.0  3.0     0.465      0.167   0.273  0.207    0.402  0.241\n",
      "Regresión logística  19.0  13.0   8.0  3.0     0.512      0.188   0.273  0.222    0.375  0.215\n"
     ]
    }
   ],
   "source": [
    "def metricas(y_real, pred, prob):\n",
    "    tn,fp,fn,tp=confusion_matrix(y_real,pred,labels=[0,1]).ravel()\n",
    "    return {'TN':int(tn),'FP':int(fp),'FN':int(fn),'TP':int(tp),\n",
    "            'Accuracy':accuracy_score(y_real,pred),\n",
    "            'Precision':precision_score(y_real,pred,zero_division=0),\n",
    "            'Recall':recall_score(y_real,pred,zero_division=0),\n",
    "            'F1':f1_score(y_real,pred,zero_division=0),\n",
    "            'ROC-AUC':roc_auc_score(y_real,prob),\n",
    "            'AP':average_precision_score(y_real,prob)}\n",
    "\n",
    "resultados={}\n",
    "probabilidades={}\n",
    "for nombre,estimador in modelos.items():\n",
    "    pipe=crear_pipeline(estimador)\n",
    "    pipe.fit(X_train,y_train)\n",
    "    pred=pipe.predict(X_test)\n",
    "    prob=pipe.predict_proba(X_test)[:,1]\n",
    "    resultados[nombre]=metricas(y_test,pred,prob)\n",
    "    probabilidades[nombre]=prob\n",
    "\n",
    "resumen=pd.DataFrame(resultados).T\n",
    "print(resumen.round(3).to_string())\n",
    "assert resultados['Árbol de decisión']['TP']==3\n",
    "assert resultados['Árbol de decisión']['FN']==8\n",
    "assert resultados['Árbol de decisión']['FP']==15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f047b8",
   "metadata": {},
   "source": [
    "## 7. Visualización: matrices de confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d87b5e10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-08T05:51:53.344506Z",
     "iopub.status.busy": "2026-11-08T05:51:53.344393Z",
     "iopub.status.idle": "2026-11-08T05:51:53.533437Z",
     "shell.execute_reply": "2026-11-08T05:51:53.532743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x340 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(1,3,figsize=(12,3.4))\n",
    "for ax,(nombre,met) in zip(axes,resultados.items()):\n",
    "    arr=np.array([[met['TN'],met['FP']],[met['FN'],met['TP']]])\n",
    "    ax.imshow(arr,cmap='Purples',vmin=0,vmax=32)\n",
    "    for (i,j),num in np.ndenumerate(arr):\n",
    "        ax.text(j,i,str(num),ha='center',va='center',fontsize=15,fontweight='bold')\n",
    "    ax.set_xticks([0,1],['Puntual','Retrasado'])\n",
    "    ax.set_yticks([0,1],['Puntual','Retrasado'])\n",
    "    ax.set(title=nombre,xlabel='Predicho',ylabel='Real')\n",
    "fig.suptitle('Prueba temporal: diciembre de 2023 (43 pedidos)',fontweight='bold')\n",
    "plt.tight_layout();plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56153421",
   "metadata": {},
   "source": [
    "## 8. Visualización: curvas ROC y Precision–Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15756f8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-08T05:51:53.534875Z",
     "iopub.status.busy": "2026-11-08T05:51:53.534749Z",
     "iopub.status.idle": "2026-11-08T05:51:53.725553Z",
     "shell.execute_reply": "2026-11-08T05:51:53.725033Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(1,2,figsize=(10,3.6))\n",
    "for nombre in ['Árbol de decisión','Regresión logística']:\n",
    "    prob=probabilidades[nombre]\n",
    "    fpr,tpr,_=roc_curve(y_test,prob)\n",
    "    prec,rec,_=precision_recall_curve(y_test,prob)\n",
    "    axs[0].plot(fpr,tpr,label=f'{nombre} (AUC {roc_auc_score(y_test,prob):.3f})')\n",
    "    axs[1].plot(rec,prec,label=f'{nombre} (AP {average_precision_score(y_test,prob):.3f})')\n",
    "axs[0].plot([0,1],[0,1],'k--',alpha=.5,label='Azar')\n",
    "axs[1].axhline(y_test.mean(),linestyle='--',color='gray',label='Prevalencia en prueba')\n",
    "for ax in axs:\n",
    "    ax.set_xlim(0,1);ax.set_ylim(0,1.05);ax.legend(fontsize=8);ax.grid(alpha=.2)\n",
    "axs[0].set(title='ROC',xlabel='Tasa de falsos positivos',ylabel='Tasa de verdaderos positivos')\n",
    "axs[1].set(title='Precision–Recall',xlabel='Recall',ylabel='Precision')\n",
    "plt.tight_layout();plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee471385",
   "metadata": {},
   "source": [
    "## 9. Interpretación crítica y conclusiones\n",
    "\n",
    "En el conjunto de prueba de diciembre (43 pedidos, 11 retrasos), el **árbol detectó 3 retrasos y dejó pasar 8**; además, generó 15 alertas falsas. Su **recall = 0,273**, **precision = 0,167**, **F1 = 0,207** y **ROC-AUC = 0,402**. La regresión logística detectó igualmente solo 3 retrasos y tampoco superó la línea base en discriminación.\n",
    "\n",
    "El clasificador ingenuo que predice siempre «a tiempo» alcanza una exactitud de **74,4 %**, aunque **nunca detecta un retraso (recall = 0)**. Esto muestra por qué accuracy aislada sería engañosa.\n",
    "\n",
    "En validación interna cronológica, el árbol tuvo **F1 medio cercano a 0,306** y **ROC-AUC medio cercano a 0,581**. Su empeoramiento sobre diciembre señala inestabilidad y debilidades de generalización. **No hay fundamento para implantar este modelo en decisiones automáticas de despacho.**\n",
    "\n",
    "**Restricciones:** base académica pequeña, solo 49 positivos, 3 devoluciones excluidas, falta distancia planificada, pronóstico de tráfico y clima previo, metas de entrega y series históricas extensas. Las probabilidades no están calibradas y la separación por fecha no compensa una muestra reducida. El rendimiento negativo no prueba que la ciencia de datos sea inútil, sino que **esta combinación de datos y modelo no demuestra capacidad predictiva suficiente**.\n",
    "\n",
    "**Siguiente prueba técnicamente válida:** recolectar más pedidos con identificadores de ruta, tiempo prometido, hora de despacho, distancia planificada, meteorología y tráfico previstos, manteniendo particiones temporales y métricas por clase. Nunca presentar las métricas de la regresión lineal anterior (datos simulados) como si validaran este clasificador."
   ]
  }
 ],
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