GeothermalDatathon_Enthalpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PIVOT 2022 Geothermal Datathon: SPE - Gulf Coast Section\n",
    "## Data Modeling: Power Enthalpy\n",
    "\n",
    "\n",
    "#### - Team: \n",
    "GeotherML    \n",
    "    \n",
    "#### - Team Members:\n",
    "- Antonio Dagnino\n",
    "- Andres Calvo\n",
    "- Oliver Rojas\n",
    "- Yusuf Falola\n",
    "- Chandryl Paterne    \n",
    "    \n",
    "#### - Contents:\n",
    "\n",
    "   - **Data Modeling: Power Enthalpy**\n",
    "       - Import inputs and outputs\n",
    "       - Train / Test split\n",
    "       - Scale Data\n",
    "       - Create Neural Network\n",
    "       - Fit the Model\n",
    "       \n",
    "\n",
    "   - **Evaluation Metrics**\n",
    "       - MAE\n",
    "       - MSE\n",
    "       - RMSE\n",
    "       - Explained variance score\n",
    "       - Compare mean values per year: Predictions Vs Real\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Modeling: Power Enthalpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Data Modeling Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from pylab import rcParams\n",
    "\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error,explained_variance_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Customize Plots\n",
    "rcParams['figure.figsize'] = 14,8\n",
    "rcParams['axes.titlesize'] = 15\n",
    "rcParams['font.size']=10\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "#show all columns in Jupyter Notebook\n",
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = pd.read_csv('inputs.csv',index_col=0)\n",
    "df_Enthalpy = pd.read_csv('df_Enthalpy.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Train / Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(inputs.values,df_Enthalpy.values,test_size=0.2,random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Scale Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36388, 37)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9097, 37)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(37,activation='relu'))\n",
    "model.add(Dense(37,activation='relu'))\n",
    "model.add(Dense(37,activation='relu'))\n",
    "model.add(Dense(37,activation='relu'))\n",
    "model.add(Dense(20))\n",
    "\n",
    "\n",
    "model.compile(optimizer='adam',loss='mse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fit the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# - Hyperparameter Tunning:\n",
    "\n",
    "batchsize = 128\n",
    "epochs = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 14/400\n",
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      "Epoch 19/400\n",
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      "Epoch 22/400\n",
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      "Epoch 26/400\n",
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      "Epoch 27/400\n",
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      "Epoch 28/400\n",
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      "Epoch 75/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "285/285 [==============================] - 0s 2ms/step - loss: 11747.1318 - val_loss: 12029.0068\n",
      "Epoch 76/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 11790.2002 - val_loss: 12017.9922\n",
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      "Epoch 87/400\n",
      "285/285 [==============================] - ETA: 0s - loss: 11552.697 - 1s 2ms/step - loss: 11533.4014 - val_loss: 11823.9688\n",
      "Epoch 88/400\n",
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      "Epoch 100/400\n",
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      "Epoch 110/400\n",
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      "Epoch 111/400\n",
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      "Epoch 112/400\n",
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      "Epoch 113/400\n",
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      "Epoch 114/400\n",
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      "Epoch 115/400\n",
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      "Epoch 128/400\n",
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      "Epoch 129/400\n",
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      "Epoch 130/400\n",
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      "Epoch 131/400\n",
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      "Epoch 132/400\n",
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      "Epoch 133/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10902.0293 - val_loss: 11558.9453\n",
      "Epoch 134/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10867.6045 - val_loss: 12230.0508\n",
      "Epoch 135/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10823.0742 - val_loss: 11373.9795\n",
      "Epoch 136/400\n",
      "285/285 [==============================] - ETA: 0s - loss: 10890.654 - 0s 2ms/step - loss: 10850.7539 - val_loss: 11771.3486\n",
      "Epoch 137/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10812.3701 - val_loss: 11362.9473\n",
      "Epoch 138/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10832.0176 - val_loss: 11418.0615\n",
      "Epoch 139/400\n",
      "285/285 [==============================] - ETA: 0s - loss: 10819.711 - 1s 2ms/step - loss: 10786.2168 - val_loss: 11615.5352\n",
      "Epoch 140/400\n",
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      "Epoch 141/400\n",
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      "Epoch 142/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10846.4199 - val_loss: 11490.3555\n",
      "Epoch 143/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10803.6025 - val_loss: 11684.9990\n",
      "Epoch 144/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10772.2715 - val_loss: 11492.8662\n",
      "Epoch 145/400\n",
      "285/285 [==============================] - ETA: 0s - loss: 10730.744 - 0s 2ms/step - loss: 10747.3486 - val_loss: 11371.9307\n",
      "Epoch 146/400\n",
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      "Epoch 147/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "285/285 [==============================] - 0s 2ms/step - loss: 10837.0801 - val_loss: 11492.3096\n",
      "Epoch 148/400\n",
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      "Epoch 149/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10716.5518 - val_loss: 11583.4404\n",
      "Epoch 150/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10738.7979 - val_loss: 11931.9502\n",
      "Epoch 151/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10715.1719 - val_loss: 11362.5947\n",
      "Epoch 152/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10665.1152 - val_loss: 11269.4326\n",
      "Epoch 153/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10674.7031 - val_loss: 11961.8936\n",
      "Epoch 154/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10669.5996 - val_loss: 11488.8975\n",
      "Epoch 155/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10655.8877 - val_loss: 11617.3301\n",
      "Epoch 156/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10693.1602 - val_loss: 11350.8906\n",
      "Epoch 157/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10647.6455 - val_loss: 11957.7725\n",
      "Epoch 158/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10598.1260 - val_loss: 11328.3535\n",
      "Epoch 159/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10752.8398 - val_loss: 11372.3477\n",
      "Epoch 160/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10635.3838 - val_loss: 11346.7090\n",
      "Epoch 161/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10650.9756 - val_loss: 11397.9658\n",
      "Epoch 162/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10599.5293 - val_loss: 11394.8418\n",
      "Epoch 163/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10583.7861 - val_loss: 11375.6494\n",
      "Epoch 164/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10639.3535 - val_loss: 11246.3018\n",
      "Epoch 165/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10585.1709 - val_loss: 11343.9678\n",
      "Epoch 166/400\n",
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      "Epoch 167/400\n",
      "285/285 [==============================] - ETA: 0s - loss: 10558.673 - 0s 2ms/step - loss: 10540.9971 - val_loss: 11323.6152\n",
      "Epoch 168/400\n",
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      "Epoch 169/400\n",
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      "Epoch 170/400\n",
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      "Epoch 171/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10537.1436 - val_loss: 11236.0088\n",
      "Epoch 172/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10475.5938 - val_loss: 11374.0361\n",
      "Epoch 173/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10506.5342 - val_loss: 11378.3438\n",
      "Epoch 174/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10530.5361 - val_loss: 11288.2031\n",
      "Epoch 175/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10468.7910 - val_loss: 11230.7949\n",
      "Epoch 176/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10468.0449 - val_loss: 11254.6904\n",
      "Epoch 177/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10450.1650 - val_loss: 11273.3848\n",
      "Epoch 178/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10490.7354 - val_loss: 11326.5850\n",
      "Epoch 179/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10483.1797 - val_loss: 11239.0186\n",
      "Epoch 180/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10465.3691 - val_loss: 11287.5283\n",
      "Epoch 181/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10452.9238 - val_loss: 11379.3369\n",
      "Epoch 182/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10420.8047 - val_loss: 11368.5820\n",
      "Epoch 183/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10467.3623 - val_loss: 11188.0322\n",
      "Epoch 184/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10397.5234 - val_loss: 11438.8213\n",
      "Epoch 185/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10409.7881 - val_loss: 11180.5195\n",
      "Epoch 186/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10393.2275 - val_loss: 11647.8984\n",
      "Epoch 187/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10437.2109 - val_loss: 11279.5713\n",
      "Epoch 188/400\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 10440.6211 - val_loss: 11224.7910\n",
      "Epoch 189/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10364.2217 - val_loss: 11218.9092\n",
      "Epoch 190/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10326.8076 - val_loss: 11441.1221\n",
      "Epoch 191/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10359.1396 - val_loss: 11306.0176\n",
      "Epoch 192/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10313.9424 - val_loss: 11554.1201\n",
      "Epoch 193/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10363.1934 - val_loss: 11451.6699\n",
      "Epoch 194/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10323.7393 - val_loss: 11219.4873\n",
      "Epoch 195/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10318.1660 - val_loss: 11343.6592\n",
      "Epoch 196/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10304.7100 - val_loss: 11355.7168\n",
      "Epoch 197/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10305.5342 - val_loss: 11197.2227\n",
      "Epoch 198/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10266.7920 - val_loss: 11788.7188\n",
      "Epoch 199/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10284.3936 - val_loss: 11420.1553\n",
      "Epoch 200/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10236.4590 - val_loss: 11392.9893\n",
      "Epoch 201/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10249.0195 - val_loss: 11329.2363\n",
      "Epoch 202/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10272.9961 - val_loss: 11354.7715\n",
      "Epoch 203/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10275.9658 - val_loss: 11335.6533\n",
      "Epoch 204/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10268.4297 - val_loss: 11207.8643\n",
      "Epoch 205/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10246.9238 - val_loss: 11320.4619\n",
      "Epoch 206/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10213.0430 - val_loss: 11306.6270\n",
      "Epoch 207/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10228.7275 - val_loss: 11433.2139\n",
      "Epoch 208/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10229.4336 - val_loss: 11645.2627\n",
      "Epoch 209/400\n",
      "285/285 [==============================] - 1s 2ms/step - loss: 10275.4287 - val_loss: 11481.4238\n",
      "Epoch 210/400\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 10192.3857 - val_loss: 11295.8418\n",
      "Epoch 00210: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1350942b700>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train,\n",
    "        y=y_train,\n",
    "        validation_data=(X_test,y_test),\n",
    "        batch_size=batchsize,\n",
    "        epochs=epochs,\n",
    "        verbose=1,\n",
    "        callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_metrics_timeseries(real_values,pred,name='Variable'):\n",
    "    print(f\"\"\"\n",
    "        ------- {name} ------\n",
    "        \n",
    "MAE: {mean_absolute_error(real_values,pred):.3f},\n",
    "MSE: {mean_squared_error(real_values,pred):.3f},\n",
    "RMSE: {np.sqrt(mean_squared_error(real_values,pred)):.3f},\n",
    "Explained Variance Score: {explained_variance_score(real_values,pred)*100:.3f}%,\n",
    "\n",
    "Compare Mean values per Year: Predictions VS Real:\n",
    "\n",
    "{pd.concat([pd.DataFrame(real_values).mean(),pd.DataFrame(pred).mean()],axis=1,keys=['Avg real',' Avg Pred'],names='Year')}\n",
    "    \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_enthalpy = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        ------- Power Enthalpy (Kj/Kg) ------\n",
      "        \n",
      "MAE: 68.826,\n",
      "MSE: 11295.843,\n",
      "RMSE: 106.282,\n",
      "Explained Variance Score: 76.311%,\n",
      "\n",
      "Compare Mean values per Year: Predictions VS Real:\n",
      "\n",
      "Year    Avg real    Avg Pred\n",
      "0     720.673024  727.023254\n",
      "1     685.503591  693.649292\n",
      "2     669.999661  678.117432\n",
      "3     659.121084  667.273560\n",
      "4     650.643491  658.646667\n",
      "5     643.678181  651.510315\n",
      "6     637.751328  645.419678\n",
      "7     632.605190  640.116699\n",
      "8     628.050429  635.330566\n",
      "9     623.965584  631.186340\n",
      "10    620.241453  627.290100\n",
      "11    616.822240  623.812683\n",
      "12    613.660062  620.654175\n",
      "13    610.725212  617.656555\n",
      "14    608.000128  614.906677\n",
      "15    605.442206  612.135742\n",
      "16    603.023039  609.653931\n",
      "17    600.744027  607.285156\n",
      "18    598.577764  605.018372\n",
      "19    596.526776  602.882385\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "eval_metrics_timeseries(y_test,predictions_enthalpy,name='Power Enthalpy (Kj/Kg)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Avg Power Enthalpy: Predicted Vs Real values'}>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(predictions_enthalpy,columns=df_Enthalpy.columns).mean().plot(legend=True,label='Avg Enthalpy PRED',\n",
    "                                                                           title='Avg Power Enthalpy: Predicted Vs Real values')\n",
    "pd.DataFrame(y_test,columns=df_Enthalpy.columns).mean().plot(legend=True,label='Avg Enthalpy REAL')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### END"
   ]
  }
 ],
 "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
}