notebooks/4.Entrenamiento.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entrenamiento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El entrenamiento contemplará el uso de los algoritmos supervisados CO y FHMM provistos por NILMTK, usando la señal de potencia activa de los dispositivos. Para cada algoritmo se entrenarán varios modelos usando diferentes periodos de muestreo entre 1 segundo y 10 minutos. Esto permitirá posteriormente comparar los modelos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Librerías y otros ajustes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "import warnings\n",
    "warnings. filterwarnings(\"ignore\")\n",
    "plt.style.use('bmh')\n",
    "\n",
    "from nilmtk import TimeFrame, MeterGroup, HDFDataStore, DataSet\n",
    "\n",
    "deps = DataSet('C:/data/DEPS.h5')\n",
    "elec = deps.buildings[1].elec\n",
    "labels = pd.DataFrame({'Instances': list(range(1,11)),\n",
    "                       'Meters': ['Main_RST', 'Main_R', 'Main_S', 'Main_T', 'Lights_1','Lights_2', 'HVAC_1', 'HVAC_2', 'HVAC_4', 'Rack']})\n",
    "Meters={}\n",
    "for x in range(len(labels)): Meters[elec[x+1]]=labels['Meters'][x]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set de Entrenamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])\n",
       "  ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set para entrenamiento\n",
    "train = DataSet('C:/data/DEPS.h5')\n",
    "train.set_window(start=\"2020-02-25 00:00:00\", end=\"2020-03-03 23:59:59\")\n",
    "train_elec = train.buildings[1].elec\n",
    "\n",
    "#Dispositivos para entrenamiento\n",
    "train_Devices = train_elec.select(instance=[5,6,7,8,9,10])\n",
    "train_Devices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 7)\n",
    "\n",
    "fig, ((ax1, ax2),(ax3, ax4),(ax5, ax6)) = plt.subplots(3, 2, sharex=True,\n",
    "                                                       gridspec_kw={'hspace': 0.2, 'wspace': 0.2})\n",
    "#colores\n",
    "colors=['tab:blue','tab:orange','tab:green','tab:olive','tab:purple','tab:cyan']\n",
    "m=5\n",
    "for ax in fig.get_axes(): \n",
    "    next(train_elec[m].load(ac_type='active', sample_period=60,\n",
    "                            resample_kwargs={'how':'first'})).plot(ax=ax, legend=False, color=colors[m-5])   \n",
    "    ax.set_ylabel('Power(W)')\n",
    "    ax1.set_ylim(auto=True)\n",
    "    ax.minorticks_off()\n",
    "    ax.set_xticklabels(labels=['Feb-25','Feb-26','Feb-27','Feb-28','Feb-29','Mar-01','Mar-02','Mar-03'], ha ='left')    \n",
    "    ax.set_title(labels['Meters'][m-1], fontsize=12).set_position([.11, 1])\n",
    "    c=['k','k','k','b','b','b','k','k']\n",
    "    [t.set_color(i) for (i,t) in zip(c, ax.xaxis.get_ticklabels())]\n",
    "    m=m+1\n",
    "\n",
    "ax6.set_ylim(90,130)   \n",
    "plt.savefig('C:/data/images/4.1_Plot_train.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n"
     ]
    }
   ],
   "source": [
    "from nilmtk.legacy.disaggregate import CombinatorialOptimisation\n",
    "CO=CombinatorialOptimisation() #variable del modelo\n",
    "CO.train(train_Devices) #entrenamiento del modelo CO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FHMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<nilmtk.legacy.disaggregate.fhmm_exact.FHMM at 0x26847b416d8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nilmtk.legacy.disaggregate import FHMM\n",
    "FHMM=FHMM() #variable del modelo\n",
    "FHMM.train(train_Devices) #entrenamiento del modelo FHMM\n",
    "FHMM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### Entrenamiento:  Modelo CO y  Modelo FHMM  - FIRST 1s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con el método first tiene sentido entrenar un solo modelo, cada 1 segundo, el que servirá para desagregar señales obtenidas cada 10s, 30s, 60s, 5min, 10min. 15min y 30min. Cabe recordar que con este modelo emula la adquisición de datos instantáneos en el periodo definido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************** CO********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "\n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "from nilmtk.legacy.disaggregate import CombinatorialOptimisation, FHMM\n",
    "\n",
    "dir_models = 'C:/data/NILM/Models/'\n",
    "times = {} #almacena tiempos de ejecución\n",
    "null={} #diccionario nulo\n",
    "    \n",
    "t = []\n",
    "models={'CO':CombinatorialOptimisation(), 'FHMM':FHMM()}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    print(\"*\"*20 + ' ' + model_name + \"*\"*20)\n",
    "    #Entrenamiento\n",
    "    model.MIN_CHUNK_LENGTH = 1\n",
    "    import time\n",
    "    start = time.time()\n",
    "    model.train(train_Devices,\n",
    "                num_states_dict={}, #diccionario de estados nulo, por defecto\n",
    "                ac_type='active', #potencia activa, por defecto\n",
    "                sample_period=1, #cambio de muestreo\n",
    "                resample_kwargs={'how':'first'})#método \n",
    "\n",
    "    t.append(round(time.time()-start,2)) #tiempo de simulación\n",
    "\n",
    "    #Guarda modelo\n",
    "    model_dir = dir_models + model_name + '_1s_first_model.pickle'\n",
    "    model.export_model(model_dir) \n",
    "\n",
    "times['1s'] = t #tiempo de simulación\n",
    "times_t1 = pd.DataFrame(times,index=['CO_first', 'FHMM_first'])\n",
    "    \n",
    "print('\\n\\nDone!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>CO_first</th>\n",
       "      <th>FHMM_first</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1s</th>\n",
       "      <td>4.12</td>\n",
       "      <td>49.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CO_first  FHMM_first\n",
       "1s      4.12       49.73"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tiempos de simulación: entrenamiento\n",
    "times_t1.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelos CO y  Modelos FHMM  - MEAN y MEDIAN\n",
    "Debido que los métodos mean y median realizan un cálculo sobre la señal original, solo tiene sentido entrenar modelos para periodos de muestreo diferentes a al original de 1 segundo. Por esto, para estos método solo se entrenarán modelos para los periodos 10s, 30s, 60s, 5min , 10min, 15min y 30 min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************** CO 10s mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 10s mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 10s median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 10s median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 30s mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 30s mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 30s median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 30s median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 60s mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 60s mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 60s median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 60s median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 5min mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 5min mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 5min median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 5min median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 10min mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 10min mean********************\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 10min median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 10min median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 15min mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 15min mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 15min median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 15min median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 30min mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 30min mean********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "******************** CO 30min median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "Done training!\n",
      "******************** FHMM 30min median********************\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=1)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DEPS', appliances=[Appliance(type='light', instance=2)])' with 2 states\n",
      "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=1)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=2)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='DEPS', appliances=[Appliance(type='air conditioner', instance=3)])' with 3 states\n",
      "Training model for submeter 'ElecMeter(instance=10, building=1, dataset='DEPS', appliances=[Appliance(type='server computer', instance=1)])' with 3 states\n",
      "\n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "from nilmtk.legacy.disaggregate import CombinatorialOptimisation, FHMM\n",
    "\n",
    "periods = {'10s':10 , '30s':30, '60s':60, '5min':60*5, '10min':60*10, '15min':60*15, '30min':60*30}\n",
    "methods = ['mean', 'median']\n",
    "dir_models = 'C:/data/NILM/Models/'\n",
    "times = {} #almacena tiempos de ejecución\n",
    "null= {} #diccionario nulo\n",
    "    \n",
    "for sp_name, sp in periods.items():\n",
    "    t = []\n",
    "    \n",
    "    for method in methods:\n",
    "        models={'CO':CombinatorialOptimisation(), 'FHMM':FHMM()}\n",
    "\n",
    "        for model_name, model in models.items():\n",
    "            print(\"*\"*20 + ' ' + model_name + ' ' + sp_name+ ' ' + method + \"*\"*20)\n",
    "            #Entrenamiento\n",
    "            model.MIN_CHUNK_LENGTH = 1\n",
    "            import time\n",
    "            start = time.time()\n",
    "            model.train(train_Devices,\n",
    "                        num_states_dict={ }, #diccionario de estados nulo, por defecto\n",
    "                        ac_type='active', #potencia activa, por defecto\n",
    "                        sample_period=sp, #cambio de muestreo\n",
    "                        resample_kwargs={'how':method}) #método\n",
    "\n",
    "            t.append(round(time.time()-start,2)) #tiempo de simulación\n",
    "\n",
    "            #Guarda modelo\n",
    "            model_dir = dir_models + model_name + '_' + sp_name + '_' + method + '_model.pickle'\n",
    "            model.export_model(model_dir) \n",
    "\n",
    "    times[sp_name] = t #tiempo de simulación\n",
    "\n",
    "times_t2 = pd.DataFrame(times,index=['CO_mean', 'FHMM_mean', 'CO_median', 'FHMM_median'])\n",
    "    \n",
    "print('\\n\\nDone!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>CO_mean</th>\n",
       "      <th>FHMM_mean</th>\n",
       "      <th>CO_median</th>\n",
       "      <th>FHMM_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10s</th>\n",
       "      <td>3.75</td>\n",
       "      <td>11.60</td>\n",
       "      <td>3.87</td>\n",
       "      <td>10.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30s</th>\n",
       "      <td>3.80</td>\n",
       "      <td>6.69</td>\n",
       "      <td>3.87</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60s</th>\n",
       "      <td>3.74</td>\n",
       "      <td>5.70</td>\n",
       "      <td>3.77</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5min</th>\n",
       "      <td>3.11</td>\n",
       "      <td>4.43</td>\n",
       "      <td>3.51</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10min</th>\n",
       "      <td>2.94</td>\n",
       "      <td>3.59</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15min</th>\n",
       "      <td>2.56</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30min</th>\n",
       "      <td>2.62</td>\n",
       "      <td>3.09</td>\n",
       "      <td>2.49</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CO_mean  FHMM_mean  CO_median  FHMM_median\n",
       "10s       3.75      11.60       3.87        10.43\n",
       "30s       3.80       6.69       3.87         6.26\n",
       "60s       3.74       5.70       3.77         5.11\n",
       "5min      3.11       4.43       3.51         4.23\n",
       "10min     2.94       3.59       2.98         3.44\n",
       "15min     2.56       3.33       2.65         3.34\n",
       "30min     2.62       3.09       2.49         2.88"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tiempos de simulación: entrenamiento\n",
    "times_t2.transpose()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}