{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\M S I\\AppData\\Local\\Temp\\ipykernel_26736\\1588026616.py:23: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  df_all.fillna(method='ffill', inplace=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔝 Top 15 variables más importantes:\n",
      "    Feature  Importance\n",
      "0    PO_net    0.096043\n",
      "1   PO_feed    0.090828\n",
      "2   TRC_sub    0.044884\n",
      "3       TCA    0.040790\n",
      "4       TCI    0.033075\n",
      "5       FWC    0.032078\n",
      "6       TSI    0.029370\n",
      "7       FWE    0.028078\n",
      "8        VE    0.025102\n",
      "9       TCO    0.022290\n",
      "10     TWCI    0.021140\n",
      "11   TR_dis    0.020998\n",
      "12  TO_sump    0.020621\n",
      "13     TWCO    0.020063\n",
      "14       VW    0.018665\n"
     ]
    }
   ],
   "source": [
    "folder = \"Data select\"\n",
    "moods = os.listdir(folder)\n",
    "\n",
    "dfs = []\n",
    "for mood in moods:\n",
    "    files = os.listdir(os.path.join(folder, mood))\n",
    "    for fn in files:\n",
    "        path = os.path.join(folder, mood, fn)\n",
    "        df = pd.read_excel(path, sheet_name=\"Complete Data Set\")\n",
    "        \n",
    "        # Si la primera fila es de nombres, eliminarla\n",
    "        if not df.columns[0].startswith(\"Time\"):\n",
    "            df.columns = df.iloc[0]\n",
    "            df = df.drop(index=0)\n",
    "        \n",
    "        df[\"Label\"] = mood\n",
    "        dfs.append(df)\n",
    "\n",
    "# Concatenar todos los datos\n",
    "df_all = pd.concat(dfs, ignore_index=True)\n",
    "\n",
    "# Llenar valores faltantes\n",
    "df_all.fillna(method='ffill', inplace=True)\n",
    "\n",
    "# Eliminar columnas no numéricas\n",
    "df_all_numeric = df_all.select_dtypes(include=[\"float64\", \"int64\"])\n",
    "\n",
    "# Guardar la lista original de features\n",
    "features_originales = df_all_numeric.columns.tolist()\n",
    "\n",
    "# Extraer etiquetas binaria: 1 = Normal, -1 = Falla\n",
    "df_all[\"BinaryLabel\"] = df_all[\"Label\"].apply(lambda x: 1 if \"Normal\" in x else -1)\n",
    "y_bin = df_all[\"BinaryLabel\"]\n",
    "\n",
    "# X = todas las variables numéricas originales (sin la columna 'BinaryLabel')\n",
    "X_bin = df_all_numeric\n",
    "\n",
    "# Estandarización\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_bin)\n",
    "\n",
    "# Entrenar modelo Random Forest\n",
    "rf_bin = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "rf_bin.fit(X_scaled, y_bin)\n",
    "\n",
    "# Importancia de características\n",
    "importances = rf_bin.feature_importances_\n",
    "sorted_idx = np.argsort(importances)\n",
    "\n",
    "# Gráfica\n",
    "plt.figure(figsize=(10, 15))\n",
    "plt.barh(np.array(features_originales)[sorted_idx], importances[sorted_idx])\n",
    "plt.title(\"Importancia de características - Clasificación Binaria (66 features)\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Mostrar top 15\n",
    "top_features = pd.DataFrame({\n",
    "    'Feature': np.array(features_originales)[sorted_idx][::-1],\n",
    "    'Importance': importances[sorted_idx][::-1]\n",
    "}).head(15)\n",
    "\n",
    "print(\"🔝 Top 15 variables más importantes:\")\n",
    "print(top_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\M S I\\AppData\\Local\\Temp\\ipykernel_26736\\1073213539.py:24: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  df_faults.fillna(method='ffill', inplace=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔝 Top 15 variables más importantes para clasificación multiclase:\n",
      "         Feature  Importance\n",
      "0         PO_net    0.099475\n",
      "1        PO_feed    0.094998\n",
      "2            FWC    0.089940\n",
      "3            TCA    0.075035\n",
      "4   Heat Balance    0.073564\n",
      "5            FWE    0.071844\n",
      "6             VE    0.067465\n",
      "7             VC    0.059821\n",
      "8        TRC_sub    0.049019\n",
      "9        TO_sump    0.038700\n",
      "10       TO_feed    0.038620\n",
      "11          TWCD    0.015936\n",
      "12        TR_dis    0.011185\n",
      "13          TWED    0.009027\n",
      "14     Evap Tons    0.008689\n"
     ]
    }
   ],
   "source": [
    "folder = \"Data select\"\n",
    "moods = os.listdir(folder)\n",
    "\n",
    "dfs = []\n",
    "for mood in moods:\n",
    "    if \"Normal\" in mood:\n",
    "        continue  # saltar normales\n",
    "    \n",
    "    files = os.listdir(os.path.join(folder, mood))\n",
    "    for fn in files:\n",
    "        path = os.path.join(folder, mood, fn)\n",
    "        df = pd.read_excel(path, sheet_name=\"Complete Data Set\")\n",
    "        \n",
    "        # Validar que la primera fila no sea una fila de nombres duplicada\n",
    "        if not df.columns[0].startswith(\"Time\"):\n",
    "            df.columns = df.iloc[0]\n",
    "            df = df.drop(index=0)\n",
    "        \n",
    "        df[\"Label\"] = mood\n",
    "        dfs.append(df)\n",
    "\n",
    "# Concatenar todos los datos de fallas\n",
    "df_faults = pd.concat(dfs, ignore_index=True)\n",
    "df_faults.fillna(method='ffill', inplace=True)\n",
    "\n",
    "# Extraer solo columnas numéricas\n",
    "df_faults_numeric = df_faults.select_dtypes(include=[\"float64\", \"int64\"])\n",
    "features_originales = df_faults_numeric.columns.tolist()\n",
    "\n",
    "X_multi = df_faults_numeric\n",
    "y_multi = df_faults[\"Label\"]  # cada tipo de falla es una clase\n",
    "\n",
    "# Estandarización\n",
    "scaler = StandardScaler()\n",
    "X_scaled_multi = scaler.fit_transform(X_multi)\n",
    "\n",
    "# Modelo Random Forest Multiclase\n",
    "rf_multi = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "rf_multi.fit(X_scaled_multi, y_multi)\n",
    "\n",
    "# Importancia de características\n",
    "importances = rf_multi.feature_importances_\n",
    "sorted_idx = np.argsort(importances)\n",
    "\n",
    "# Gráfica\n",
    "plt.figure(figsize=(10, 15))\n",
    "plt.barh(np.array(features_originales)[sorted_idx], importances[sorted_idx])\n",
    "plt.title(\"Importancia de características - Clasificación Multiclase (66 features)\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Mostrar top 15\n",
    "top_features = pd.DataFrame({\n",
    "    'Feature': np.array(features_originales)[sorted_idx][::-1],\n",
    "    'Importance': importances[sorted_idx][::-1]\n",
    "}).head(15)\n",
    "\n",
    "print(\"🔝 Top 15 variables más importantes para clasificación multiclase:\")\n",
    "print(top_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "CompileException",
     "evalue": "nvrtc: error: failed to open nvrtc-builtins64_129.dll.\n  Make sure that nvrtc-builtins64_129.dll is installed correctly.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNVRTCError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\M S I\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\cupy\\cuda\\compiler.py:737\u001b[0m, in \u001b[0;36m_NVRTCProgram.compile\u001b[1;34m(self, options, log_stream)\u001b[0m\n\u001b[0;32m    736\u001b[0m         nvrtc\u001b[38;5;241m.\u001b[39maddNameExpression(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mptr, ker)\n\u001b[1;32m--> 737\u001b[0m \u001b[43mnvrtc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompileProgram\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mptr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    738\u001b[0m mapping \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mcupy_backends\\\\cuda\\\\libs\\\\nvrtc.pyx:125\u001b[0m, in \u001b[0;36mcupy_backends.cuda.libs.nvrtc.compileProgram\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mcupy_backends\\\\cuda\\\\libs\\\\nvrtc.pyx:138\u001b[0m, in \u001b[0;36mcupy_backends.cuda.libs.nvrtc.compileProgram\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mcupy_backends\\\\cuda\\\\libs\\\\nvrtc.pyx:53\u001b[0m, in \u001b[0;36mcupy_backends.cuda.libs.nvrtc.check_status\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mNVRTCError\u001b[0m: NVRTC_ERROR_BUILTIN_OPERATION_FAILURE (7)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mCompileException\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcupy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mcp\u001b[39;00m\n\u001b[0;32m      2\u001b[0m x \u001b[38;5;241m=\u001b[39m cp\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m])\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mcp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqrt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[1;32mcupy\\\\_core\\\\_kernel.pyx:1374\u001b[0m, in \u001b[0;36mcupy._core._kernel.ufunc.__call__\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mcupy\\\\_core\\\\_kernel.pyx:1401\u001b[0m, in \u001b[0;36mcupy._core._kernel.ufunc._get_ufunc_kernel\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mcupy\\\\_core\\\\_kernel.pyx:1082\u001b[0m, in \u001b[0;36mcupy._core._kernel._get_ufunc_kernel\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mcupy\\\\_core\\\\_kernel.pyx:94\u001b[0m, in \u001b[0;36mcupy._core._kernel._get_simple_elementwise_kernel\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mcupy\\\\_core\\\\_kernel.pyx:82\u001b[0m, in \u001b[0;36mcupy._core._kernel._get_simple_elementwise_kernel_from_code\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mcupy\\\\_core\\\\core.pyx:2330\u001b[0m, in \u001b[0;36mcupy._core.core.compile_with_cache\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\M S I\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\cupy\\cuda\\compiler.py:542\u001b[0m, in \u001b[0;36m_compile_module_with_cache\u001b[1;34m(source, options, arch, cache_dir, extra_source, backend, enable_cooperative_groups, name_expressions, log_stream, jitify)\u001b[0m\n\u001b[0;32m    538\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _compile_with_cache_hip(\n\u001b[0;32m    539\u001b[0m         source, options, arch, cache_dir, extra_source, backend,\n\u001b[0;32m    540\u001b[0m         name_expressions, log_stream, cache_in_memory)\n\u001b[0;32m    541\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 542\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_compile_with_cache_cuda\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    543\u001b[0m \u001b[43m        \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43march\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_source\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    544\u001b[0m \u001b[43m        \u001b[49m\u001b[43menable_cooperative_groups\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname_expressions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_stream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    545\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_in_memory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjitify\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\M S I\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\cupy\\cuda\\compiler.py:586\u001b[0m, in \u001b[0;36m_compile_with_cache_cuda\u001b[1;34m(source, options, arch, cache_dir, extra_source, backend, enable_cooperative_groups, name_expressions, log_stream, cache_in_memory, jitify)\u001b[0m\n\u001b[0;32m    583\u001b[0m base \u001b[38;5;241m=\u001b[39m _empty_file_preprocess_cache\u001b[38;5;241m.\u001b[39mget(env, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m base \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    585\u001b[0m     \u001b[38;5;66;03m# This is for checking NVRTC/NVCC compiler internal version\u001b[39;00m\n\u001b[1;32m--> 586\u001b[0m     base \u001b[38;5;241m=\u001b[39m \u001b[43m_preprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43march\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    587\u001b[0m     _empty_file_preprocess_cache[env] \u001b[38;5;241m=\u001b[39m base\n\u001b[0;32m    589\u001b[0m key_src \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (\n\u001b[0;32m    590\u001b[0m     env, base, source, extra_source, _get_cupy_cache_key())\n",
      "File \u001b[1;32mc:\\Users\\M S I\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\cupy\\cuda\\compiler.py:480\u001b[0m, in \u001b[0;36m_preprocess\u001b[1;34m(source, options, arch, backend)\u001b[0m\n\u001b[0;32m    478\u001b[0m prog \u001b[38;5;241m=\u001b[39m _NVRTCProgram(source)\n\u001b[0;32m    479\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 480\u001b[0m     result, _ \u001b[38;5;241m=\u001b[39m \u001b[43mprog\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    481\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m CompileException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    482\u001b[0m     dump \u001b[38;5;241m=\u001b[39m _get_bool_env_variable(\n\u001b[0;32m    483\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCUPY_DUMP_CUDA_SOURCE_ON_ERROR\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\M S I\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\cupy\\cuda\\compiler.py:756\u001b[0m, in \u001b[0;36m_NVRTCProgram.compile\u001b[1;34m(self, options, log_stream)\u001b[0m\n\u001b[0;32m    754\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m nvrtc\u001b[38;5;241m.\u001b[39mNVRTCError:\n\u001b[0;32m    755\u001b[0m     log \u001b[38;5;241m=\u001b[39m nvrtc\u001b[38;5;241m.\u001b[39mgetProgramLog(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mptr)\n\u001b[1;32m--> 756\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CompileException(log, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msrc, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, options,\n\u001b[0;32m    757\u001b[0m                            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnvrtc\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m runtime\u001b[38;5;241m.\u001b[39mis_hip \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhiprtc\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mCompileException\u001b[0m: nvrtc: error: failed to open nvrtc-builtins64_129.dll.\n  Make sure that nvrtc-builtins64_129.dll is installed correctly."
     ]
    }
   ],
   "source": []
  }
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