gefcom_multiconv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our handy function for converting our dataset into the correct format\n",
    "\n",
    "def window_data(df, window, feature_col_number, target_col_number):\n",
    "    \"\"\"\n",
    "    This function accepts the column number for the features (X) and the target (y).\n",
    "    It chunks the data up with a rolling window of Xt - window to predict Xt.\n",
    "    It returns two numpy arrays of X and y.\n",
    "    \"\"\"\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(df) - window - 1):\n",
    "        features = df.iloc[i : (i + window), feature_col_number].values\n",
    "        target = df.iloc[(i + window), target_col_number]\n",
    "        #print(features)\n",
    "        #print(\"----\")\n",
    "        #print(target)\n",
    "        X.append(features)\n",
    "        y.append(target)\n",
    "    return np.array(X), np.array(y).astype(np.float64).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>RTDemand</th>\n",
       "      <th>DADemand</th>\n",
       "      <th>DALMP</th>\n",
       "      <th>DAEC</th>\n",
       "      <th>DACC</th>\n",
       "      <th>DAMLC</th>\n",
       "      <th>RTLMP</th>\n",
       "      <th>RTEC</th>\n",
       "      <th>RTCC</th>\n",
       "      <th>RTMLC</th>\n",
       "      <th>DryBulb</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>ZONE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>12055.0</td>\n",
       "      <td>11722.0</td>\n",
       "      <td>38.51</td>\n",
       "      <td>38.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>41.57</td>\n",
       "      <td>41.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>36.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>11430.0</td>\n",
       "      <td>11253.0</td>\n",
       "      <td>36.86</td>\n",
       "      <td>36.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>34.91</td>\n",
       "      <td>34.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>36.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>10966.0</td>\n",
       "      <td>10459.0</td>\n",
       "      <td>35.92</td>\n",
       "      <td>35.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>35.46</td>\n",
       "      <td>35.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>10725.0</td>\n",
       "      <td>10531.0</td>\n",
       "      <td>35.94</td>\n",
       "      <td>35.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>34.14</td>\n",
       "      <td>33.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>10672.0</td>\n",
       "      <td>10841.0</td>\n",
       "      <td>36.05</td>\n",
       "      <td>36.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>33.63</td>\n",
       "      <td>33.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 05:00:00</th>\n",
       "      <td>5</td>\n",
       "      <td>10852.0</td>\n",
       "      <td>10661.0</td>\n",
       "      <td>36.05</td>\n",
       "      <td>36.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>34.25</td>\n",
       "      <td>34.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 06:00:00</th>\n",
       "      <td>6</td>\n",
       "      <td>11255.0</td>\n",
       "      <td>11640.0</td>\n",
       "      <td>37.29</td>\n",
       "      <td>37.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>34.01</td>\n",
       "      <td>33.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>36.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 07:00:00</th>\n",
       "      <td>7</td>\n",
       "      <td>11583.0</td>\n",
       "      <td>11972.0</td>\n",
       "      <td>37.97</td>\n",
       "      <td>38.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>30.08</td>\n",
       "      <td>30.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 08:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>12238.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>39.36</td>\n",
       "      <td>44.27</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>32.28</td>\n",
       "      <td>32.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>35.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 09:00:00</th>\n",
       "      <td>9</td>\n",
       "      <td>12877.0</td>\n",
       "      <td>13503.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>39.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>32.79</td>\n",
       "      <td>32.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>39.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Unnamed: 0  RTDemand  DADemand  DALMP   DAEC  DACC  \\\n",
       "Date                                                                      \n",
       "2011-01-01 00:00:00           0   12055.0   11722.0  38.51  38.37  0.00   \n",
       "2011-01-01 01:00:00           1   11430.0   11253.0  36.86  36.75  0.00   \n",
       "2011-01-01 02:00:00           2   10966.0   10459.0  35.92  35.91  0.00   \n",
       "2011-01-01 03:00:00           3   10725.0   10531.0  35.94  35.91  0.00   \n",
       "2011-01-01 04:00:00           4   10672.0   10841.0  36.05  36.04  0.00   \n",
       "2011-01-01 05:00:00           5   10852.0   10661.0  36.05  36.12  0.00   \n",
       "2011-01-01 06:00:00           6   11255.0   11640.0  37.29  37.51  0.00   \n",
       "2011-01-01 07:00:00           7   11583.0   11972.0  37.97  38.68  0.00   \n",
       "2011-01-01 08:00:00           8   12238.0   12735.0  39.36  44.27 -4.15   \n",
       "2011-01-01 09:00:00           9   12877.0   13503.0  38.88  39.50  0.00   \n",
       "\n",
       "                     DAMLC  RTLMP   RTEC  RTCC  RTMLC  DryBulb  DewPoint  ZONE  \n",
       "Date                                                                            \n",
       "2011-01-01 00:00:00   0.14  41.57  41.48   0.0   0.09     36.0      29.0     1  \n",
       "2011-01-01 01:00:00   0.11  34.91  34.82   0.0   0.09     36.0      29.0     1  \n",
       "2011-01-01 02:00:00   0.01  35.46  35.29   0.0   0.17     35.0      29.0     1  \n",
       "2011-01-01 03:00:00   0.03  34.14  33.97   0.0   0.17     35.0      30.0     1  \n",
       "2011-01-01 04:00:00   0.01  33.63  33.49   0.0   0.14     36.0      31.0     1  \n",
       "2011-01-01 05:00:00  -0.07  34.25  34.11   0.0   0.14     36.0      31.0     1  \n",
       "2011-01-01 06:00:00  -0.22  34.01  33.90   0.0   0.11     36.0      32.0     1  \n",
       "2011-01-01 07:00:00  -0.71  30.08  30.02   0.0   0.06     35.0      31.0     1  \n",
       "2011-01-01 08:00:00  -0.76  32.28  32.23   0.0   0.05     35.0      32.0     1  \n",
       "2011-01-01 09:00:00  -0.62  32.79  32.71   0.0   0.08     39.0      34.0     1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc_df = pd.read_csv('zn1Data_big.csv', infer_datetime_format=True)\n",
    "gc_df.set_index('Date', inplace=True)\n",
    "gc_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gc_df.drop(columns=['Unnamed: 0'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gc_df['RTDemand'].replace(0.0, np.nan, inplace=True)\n",
    "gc_df['RTDemand'].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f14555973d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gc_df['RTDemand'][24:8760].plot()"
   ]
  },
  {
   "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>RTDemand</th>\n",
       "      <th>DADemand</th>\n",
       "      <th>DALMP</th>\n",
       "      <th>DAEC</th>\n",
       "      <th>DACC</th>\n",
       "      <th>DAMLC</th>\n",
       "      <th>RTLMP</th>\n",
       "      <th>RTEC</th>\n",
       "      <th>RTCC</th>\n",
       "      <th>RTMLC</th>\n",
       "      <th>DryBulb</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>ZONE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RTDemand</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977080</td>\n",
       "      <td>0.442104</td>\n",
       "      <td>0.445661</td>\n",
       "      <td>-0.023751</td>\n",
       "      <td>0.152479</td>\n",
       "      <td>0.402194</td>\n",
       "      <td>0.408048</td>\n",
       "      <td>-0.034435</td>\n",
       "      <td>0.116079</td>\n",
       "      <td>0.205051</td>\n",
       "      <td>0.082827</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DADemand</th>\n",
       "      <td>0.977080</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.428871</td>\n",
       "      <td>0.432052</td>\n",
       "      <td>-0.022464</td>\n",
       "      <td>0.169525</td>\n",
       "      <td>0.362882</td>\n",
       "      <td>0.368052</td>\n",
       "      <td>-0.029625</td>\n",
       "      <td>0.108818</td>\n",
       "      <td>0.214733</td>\n",
       "      <td>0.093497</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DALMP</th>\n",
       "      <td>0.442104</td>\n",
       "      <td>0.428871</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997859</td>\n",
       "      <td>0.031291</td>\n",
       "      <td>0.776977</td>\n",
       "      <td>0.782653</td>\n",
       "      <td>0.779834</td>\n",
       "      <td>0.115018</td>\n",
       "      <td>0.759487</td>\n",
       "      <td>-0.397712</td>\n",
       "      <td>-0.432217</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAEC</th>\n",
       "      <td>0.445661</td>\n",
       "      <td>0.432052</td>\n",
       "      <td>0.997859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.033794</td>\n",
       "      <td>0.772423</td>\n",
       "      <td>0.781217</td>\n",
       "      <td>0.778614</td>\n",
       "      <td>0.111314</td>\n",
       "      <td>0.754186</td>\n",
       "      <td>-0.395775</td>\n",
       "      <td>-0.431202</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DACC</th>\n",
       "      <td>-0.023751</td>\n",
       "      <td>-0.022464</td>\n",
       "      <td>0.031291</td>\n",
       "      <td>-0.033794</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>0.022644</td>\n",
       "      <td>0.020200</td>\n",
       "      <td>0.046754</td>\n",
       "      <td>0.042897</td>\n",
       "      <td>-0.009420</td>\n",
       "      <td>-0.000134</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAMLC</th>\n",
       "      <td>0.152479</td>\n",
       "      <td>0.169525</td>\n",
       "      <td>0.776977</td>\n",
       "      <td>0.772423</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.597040</td>\n",
       "      <td>0.589553</td>\n",
       "      <td>0.149013</td>\n",
       "      <td>0.816285</td>\n",
       "      <td>-0.428603</td>\n",
       "      <td>-0.425758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTLMP</th>\n",
       "      <td>0.402194</td>\n",
       "      <td>0.362882</td>\n",
       "      <td>0.782653</td>\n",
       "      <td>0.781217</td>\n",
       "      <td>0.022644</td>\n",
       "      <td>0.597040</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998726</td>\n",
       "      <td>0.142540</td>\n",
       "      <td>0.752172</td>\n",
       "      <td>-0.296412</td>\n",
       "      <td>-0.309828</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTEC</th>\n",
       "      <td>0.408048</td>\n",
       "      <td>0.368052</td>\n",
       "      <td>0.779834</td>\n",
       "      <td>0.778614</td>\n",
       "      <td>0.020200</td>\n",
       "      <td>0.589553</td>\n",
       "      <td>0.998726</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.092802</td>\n",
       "      <td>0.742827</td>\n",
       "      <td>-0.291708</td>\n",
       "      <td>-0.305749</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTCC</th>\n",
       "      <td>-0.034435</td>\n",
       "      <td>-0.029625</td>\n",
       "      <td>0.115018</td>\n",
       "      <td>0.111314</td>\n",
       "      <td>0.046754</td>\n",
       "      <td>0.149013</td>\n",
       "      <td>0.142540</td>\n",
       "      <td>0.092802</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.191479</td>\n",
       "      <td>-0.084378</td>\n",
       "      <td>-0.077857</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTMLC</th>\n",
       "      <td>0.116079</td>\n",
       "      <td>0.108818</td>\n",
       "      <td>0.759487</td>\n",
       "      <td>0.754186</td>\n",
       "      <td>0.042897</td>\n",
       "      <td>0.816285</td>\n",
       "      <td>0.752172</td>\n",
       "      <td>0.742827</td>\n",
       "      <td>0.191479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.453785</td>\n",
       "      <td>-0.440923</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DryBulb</th>\n",
       "      <td>0.205051</td>\n",
       "      <td>0.214733</td>\n",
       "      <td>-0.397712</td>\n",
       "      <td>-0.395775</td>\n",
       "      <td>-0.009420</td>\n",
       "      <td>-0.428603</td>\n",
       "      <td>-0.296412</td>\n",
       "      <td>-0.291708</td>\n",
       "      <td>-0.084378</td>\n",
       "      <td>-0.453785</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.915139</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DewPoint</th>\n",
       "      <td>0.082827</td>\n",
       "      <td>0.093497</td>\n",
       "      <td>-0.432217</td>\n",
       "      <td>-0.431202</td>\n",
       "      <td>-0.000134</td>\n",
       "      <td>-0.425758</td>\n",
       "      <td>-0.309828</td>\n",
       "      <td>-0.305749</td>\n",
       "      <td>-0.077857</td>\n",
       "      <td>-0.440923</td>\n",
       "      <td>0.915139</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZONE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RTDemand  DADemand     DALMP      DAEC      DACC     DAMLC  \\\n",
       "RTDemand  1.000000  0.977080  0.442104  0.445661 -0.023751  0.152479   \n",
       "DADemand  0.977080  1.000000  0.428871  0.432052 -0.022464  0.169525   \n",
       "DALMP     0.442104  0.428871  1.000000  0.997859  0.031291  0.776977   \n",
       "DAEC      0.445661  0.432052  0.997859  1.000000 -0.033794  0.772423   \n",
       "DACC     -0.023751 -0.022464  0.031291 -0.033794  1.000000  0.003497   \n",
       "DAMLC     0.152479  0.169525  0.776977  0.772423  0.003497  1.000000   \n",
       "RTLMP     0.402194  0.362882  0.782653  0.781217  0.022644  0.597040   \n",
       "RTEC      0.408048  0.368052  0.779834  0.778614  0.020200  0.589553   \n",
       "RTCC     -0.034435 -0.029625  0.115018  0.111314  0.046754  0.149013   \n",
       "RTMLC     0.116079  0.108818  0.759487  0.754186  0.042897  0.816285   \n",
       "DryBulb   0.205051  0.214733 -0.397712 -0.395775 -0.009420 -0.428603   \n",
       "DewPoint  0.082827  0.093497 -0.432217 -0.431202 -0.000134 -0.425758   \n",
       "ZONE           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "             RTLMP      RTEC      RTCC     RTMLC   DryBulb  DewPoint  ZONE  \n",
       "RTDemand  0.402194  0.408048 -0.034435  0.116079  0.205051  0.082827   NaN  \n",
       "DADemand  0.362882  0.368052 -0.029625  0.108818  0.214733  0.093497   NaN  \n",
       "DALMP     0.782653  0.779834  0.115018  0.759487 -0.397712 -0.432217   NaN  \n",
       "DAEC      0.781217  0.778614  0.111314  0.754186 -0.395775 -0.431202   NaN  \n",
       "DACC      0.022644  0.020200  0.046754  0.042897 -0.009420 -0.000134   NaN  \n",
       "DAMLC     0.597040  0.589553  0.149013  0.816285 -0.428603 -0.425758   NaN  \n",
       "RTLMP     1.000000  0.998726  0.142540  0.752172 -0.296412 -0.309828   NaN  \n",
       "RTEC      0.998726  1.000000  0.092802  0.742827 -0.291708 -0.305749   NaN  \n",
       "RTCC      0.142540  0.092802  1.000000  0.191479 -0.084378 -0.077857   NaN  \n",
       "RTMLC     0.752172  0.742827  0.191479  1.000000 -0.453785 -0.440923   NaN  \n",
       "DryBulb  -0.296412 -0.291708 -0.084378 -0.453785  1.000000  0.915139   NaN  \n",
       "DewPoint -0.309828 -0.305749 -0.077857 -0.440923  0.915139  1.000000   NaN  \n",
       "ZONE           NaN       NaN       NaN       NaN       NaN       NaN   NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc_df.corr()\n",
    "# heatmap next?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gc_df = gc_df.head(500) # Tiny dataset to be sure the models all compile/run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size = 72 # 3 days\n",
    "\n",
    "(X, y) = window_data(gc_df, window_size, 0, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77346, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77346, 72)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "(dry_bulb, _) = window_data(gc_df, window_size, 10, 1)\n",
    "(dew_pt, _) = window_data(gc_df, window_size, 11, 1)\n",
    "(DADemand, _) = window_data(gc_df, window_size, 1, 1)\n",
    "(DALMP, _) = window_data(gc_df, window_size, 2, 1)\n",
    "(DAEC, _) = window_data(gc_df, window_size, 3, 1)\n",
    "(DACC, _) = window_data(gc_df, window_size, 4, 1)\n",
    "(DAMLC, _) = window_data(gc_df, window_size, 5, 1)\n",
    "(RTLMP, _) = window_data(gc_df, window_size, 6, 1)\n",
    "(RTEC, _) = window_data(gc_df, window_size, 7, 1)\n",
    "(RTCC, _) = window_data(gc_df, window_size, 8, 1)\n",
    "(RTMLC, _) = window_data(gc_df, window_size, 9, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29., 29., 29., 30., 31., 31., 32., 31., 32., 34., 35., 37., 38.,\n",
       "       39., 39., 38., 37., 37., 37., 36., 37., 38., 38., 40., 41., 41.,\n",
       "       41., 41., 42., 42., 42., 42., 41., 42., 43., 43., 43., 43., 42.,\n",
       "       42., 39., 38., 35., 33., 31., 28., 26., 23., 21., 19., 17., 16.,\n",
       "       15., 15., 13., 12., 12., 11., 11., 10., 10., 10., 10.,  9., 10.,\n",
       "       10., 11., 11., 11., 12., 12., 12.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dew_pt[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "dry_bulb = scaler.fit_transform(dry_bulb)\n",
    "dew_pt = scaler.fit_transform(dew_pt)\n",
    "\n",
    "DADemand = scaler.fit_transform(DADemand)\n",
    "DALMP = scaler.fit_transform(DALMP)\n",
    "DAEC = scaler.fit_transform(DAEC)\n",
    "DACC = scaler.fit_transform(DACC)\n",
    "DAMLC = scaler.fit_transform(DAMLC)\n",
    "RTLMP = scaler.fit_transform(RTLMP)\n",
    "RTEC = scaler.fit_transform(RTEC)\n",
    "RTCC = scaler.fit_transform(RTCC)\n",
    "RTMLC = scaler.fit_transform(RTMLC)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DADemand\n",
    "DALMP\n",
    "DAEC\n",
    "DACC\n",
    "DAMLC\n",
    "RTLMP\n",
    "RTEC\n",
    "RTCC\n",
    "RTMLC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = int(0.8 * len(X))\n",
    "X_train = X[: split - 1]\n",
    "X_test = X[split:]\n",
    "\n",
    "db_train = dry_bulb[: split - 1]\n",
    "db_test = dry_bulb[split:]\n",
    "\n",
    "dew_train = dew_pt[: split - 1]\n",
    "dew_test = dew_pt[split:]\n",
    "\n",
    "y_train = y[: split - 1]\n",
    "y_test = y[split:]\n",
    "\n",
    "DADemand_train = DADemand[: split - 1]\n",
    "DALMP_train = DALMP[: split - 1]\n",
    "DAEC_train = DAEC[: split - 1]\n",
    "DACC_train = DACC[: split - 1]\n",
    "DAMLC_train = DAMLC[: split - 1]\n",
    "RTLMP_train = RTLMP[: split - 1]\n",
    "RTEC_train = RTEC[: split - 1]\n",
    "RTCC_train = RTCC[: split - 1]\n",
    "RTMLC_train = RTMLC[: split - 1]\n",
    "\n",
    "DADemand_test = DADemand[split:]\n",
    "DALMP_test = DALMP[split:]\n",
    "DAEC_test = DAEC[split:]\n",
    "DACC_test = DACC[split:]\n",
    "DAMLC_test = DAMLC[split:]\n",
    "RTLMP_test = RTLMP[split:]\n",
    "RTEC_test = RTEC[split:]\n",
    "RTCC_test = RTCC[split:]\n",
    "RTMLC_test = RTMLC[split:]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "db_train = db_train.reshape((db_train.shape[0], db_train.shape[1], 1))\n",
    "dew_train = dew_train.reshape((dew_train.shape[0], dew_train.shape[1], 1))\n",
    "DADemand_train = DADemand_train.reshape((DADemand_train.shape[0], DADemand_train.shape[1], 1))\n",
    "DALMP_train = DALMP_train.reshape((DALMP_train.shape[0], DALMP_train.shape[1], 1))\n",
    "DAEC_train = DAEC_train.reshape((DAEC_train.shape[0], DAEC_train.shape[1], 1))\n",
    "DACC_train = DACC_train.reshape((DACC_train.shape[0], DACC_train.shape[1], 1))\n",
    "DAMLC_train = DAMLC_train.reshape((DAMLC_train.shape[0], DAMLC_train.shape[1], 1))\n",
    "RTLMP_train = RTLMP_train.reshape((RTLMP_train.shape[0], RTLMP_train.shape[1], 1))\n",
    "RTEC_train = RTEC_train.reshape((RTEC_train.shape[0], RTEC_train.shape[1], 1))\n",
    "RTCC_train = RTCC_train.reshape((RTCC_train.shape[0], RTCC_train.shape[1], 1))\n",
    "RTMLC_train = RTMLC_train.reshape((RTMLC_train.shape[0], RTMLC_train.shape[1], 1))\n",
    "data_train = np.concatenate((X_train, db_train, dew_train, DADemand_train, DALMP_train, DAEC_train, DACC_train, DAMLC_train, RTLMP_train, RTEC_train, RTCC_train, RTMLC_train), axis=2)\n",
    "#data_train = np.concatenate((X_train, DADemand_train), axis=2)\n",
    "\n",
    "\n",
    "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
    "db_test = db_test.reshape((db_test.shape[0], db_test.shape[1], 1))\n",
    "dew_test = dew_test.reshape((dew_test.shape[0], dew_test.shape[1], 1))\n",
    "DADemand_test = DADemand_test.reshape((DADemand_test.shape[0], DADemand_test.shape[1], 1))\n",
    "DALMP_test = DALMP_test.reshape((DALMP_test.shape[0], DALMP_test.shape[1], 1))\n",
    "DAEC_test = DAEC_test.reshape((DAEC_test.shape[0], DAEC_test.shape[1], 1))\n",
    "DACC_test = DACC_test.reshape((DACC_test.shape[0], DACC_test.shape[1], 1))\n",
    "DAMLC_test = DAMLC_test.reshape((DAMLC_test.shape[0], DAMLC_test.shape[1], 1))\n",
    "RTLMP_test = RTLMP_test.reshape((RTLMP_test.shape[0], RTLMP_test.shape[1], 1))\n",
    "RTEC_test = RTEC_test.reshape((RTEC_test.shape[0], RTEC_test.shape[1], 1))\n",
    "RTCC_test = RTCC_test.reshape((RTCC_test.shape[0], RTCC_test.shape[1], 1))\n",
    "RTMLC_test = RTMLC_test.reshape((RTMLC_test.shape[0], RTMLC_test.shape[1], 1))\n",
    "data_test = np.concatenate((X_test, db_test, dew_test, DADemand_test, DALMP_test, DAEC_test, DACC_test, DAMLC_test, RTLMP_test, RTEC_test, RTCC_test, RTMLC_test), axis=2)\n",
    "#data_test = np.concatenate((X_test, DADemand_test), axis=2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61875, 72, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How about a 1-variable LSTM?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def basic_LSTM(window_size=5, n_features=1):\n",
    "    new_model = keras.Sequential()\n",
    "    new_model.add(tf.keras.layers.LSTM(100, input_shape=(window_size, n_features), activation='relu'))\n",
    "    new_model.add(tf.keras.layers.Flatten())\n",
    "    new_model.add(tf.keras.layers.Dense(1500, activation='relu'))\n",
    "    new_model.add(tf.keras.layers.Dense(100, activation='linear'))\n",
    "    new_model.add(tf.keras.layers.Dense(1))\n",
    "    new_model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")    \n",
    "    return new_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls_model = basic_LSTM(window_size=window_size, n_features=X_train.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 100)               40800     \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1500)              151500    \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 100)               150100    \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 342,501\n",
      "Trainable params: 342,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "ls_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 61875 samples\n",
      "Epoch 1/10\n",
      "16600/61875 [=======>......................] - ETA: 33s - loss: nan"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-928ae476bf2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mls_history\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mls_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m    729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m   def evaluate(self,\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[1;32m    322\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m                 total_epochs=epochs)\n\u001b[0m\u001b[1;32m    325\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[0;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[1;32m    121\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[1;32m    122\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[0;34m(input_fn)\u001b[0m\n\u001b[1;32m     84\u001b[0m     \u001b[0;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[0;32m---> 86\u001b[0;31m                               distributed_function(input_fn))\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_counter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalled_without_tracing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    485\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 487\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    488\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1821\u001b[0m     \u001b[0;34m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1822\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1823\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1824\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1825\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1139\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1140\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1141\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1222\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[0;32m-> 1224\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1225\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    512\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[1;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m                                                num_outputs)\n\u001b[0m\u001b[1;32m     62\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "ls_history = ls_model.fit(X_train ,y_train, epochs=10, shuffle=False, batch_size=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a6GZWYcou0AFam+u9h25mFacsA31OSyPPbt7J1ld8xqiZVY6yDPTMwOgT3ks3swpSloG+94xRB7qZVY6yDPRDDhpDc+M4B7qZVZSyDHRI9tId6GZWSco30FsaWOuBUTOrIGUb6B4YNbNKU7aBnhkY9S3pzKxSlG2ge2DUzCpN2QY6JHvpPmPUzCpFeQd6ZmB0pwdGzaz8lXegZ668+Jz30s2s/FVEoLsf3cwqQVkH+sEHjaHlYA+MmlllKOtAh/SMUV8b3cwqwKCBLmm6pIWSnpT0hKSP9tPmTElbJS1NH1f2t6xCaG1u4A8vemDUzMrfoDeJBrqBKyJiiaSJwOOS7o+IJ/u0eygi3pH/EkdmTsvegdFTj5pc4GrMzEbPoHvoEbEhIpakz7cDq4Dm0S4sX1qnpWeMutvFzMrckPrQJc0ATgQe7Wf2KZKWSbpX0vEDvP9SSYslLe7o6BhyscORGRj1CUZmVu5yDnRJE4CfAh+LiG19Zi8BjoiIE4BvAD/rbxkRcX1EzIuIeU1NTcOtecjmtPhSumZW/nIKdEm1JGF+a0Tc2Xd+RGyLiB3p83uAWklF02GdGRjdsnN3oUsxMxs1uRzlIuAGYFVEfG2ANoel7ZA0P13u5nwWOhJ7zhhd3/eLhZlZ+cjlKJdTgfcDKyQtTad9GjgcICKuA94LfEhSN/AKcF5ExCjUOyzZZ4yednTRfHEwM8urQQM9Ih4GNEiba4Fr81VUvjWOH8P0Q8axYv2WQpdiZjZqyv5M0QzfY9TMyl0FBXoj6158xQOjZla2KijQfeVFMytvFRPorc31gAPdzMpXxQR64/gxHH7IeJ8xamZlq2ICHZJuF1/TxczKVUUFemtzA+0vvcJLL3tg1MzKT0UFuu8xamblrCID3QOjZlaOKirQG8bXcvgh431LOjMrSxUV6OAzRs2sfFVeoLd4YNTMylPlBbr70c2sTFVcoGfuMepAN7NyU3GB3jC+liMm+YxRMys/FRfokJxg5DNGzazcVGSgtzU3sH6LB0bNrLxUZKDP8cComZWhigz04x3oZlaGBg10SdMlLZT0pKQnJH20nzaS9HVJqyUtl/Sa0Sk3PxrGJQOjPmPUzMpJLnvo3cAVETEbOBn4sKTZfdq8FTg6fVwKfDuvVY4CnzFqZuVm0ECPiA0RsSR9vh1YBTT3aXYucHMkHgEaJU3Ne7V5lBkYfdEDo2ZWJobUhy5pBnAi8GifWc3AuqzX7bw69JF0qaTFkhZ3dHQMrdI88xmjZlZucg50SROAnwIfi4htw1lZRFwfEfMiYl5TU9NwFpE3mYFRn2BkZuUip0CXVEsS5rdGxJ39NFkPTM963ZJOK1oN42qZMWk8y9u3FLoUM7O8yOUoFwE3AKsi4msDNLsLuCA92uVkYGtEbMhjnaOitbmBleuH9WXDzKzo1OTQ5lTg/cAKSUvTaZ8GDgeIiOuAe4C3AauBncBF+S81/+a0NHD38g1s3rGLSRPGFrocM7MRGTTQI+JhQIO0CeDD+SrqQGnNGhg985hDC1yNmdnIVOSZohmtHhg1szJS0YFeX1fLzMkH+dBFMysLFR3o4IFRMysfFR/obc31rN/yCpt37Cp0KWZmI+JAb24EfMaomZW+ig/045vrAQ+Mmlnpq/hAzwyM+pZ0ZlbqKj7QITMw6kA3s9LmQCe5Jd1zWzvZ5IFRMythDnT2PWPUzKxUOdDJGhh1P7qZlTAHOsnA6CyfMWpmJc6Bnmr1PUbNrMQ50FNtzQ1s8MComZUwB3qqrcUDo2ZW2hzoqeOnJQOjKzwwamYlyoGemuiBUTMrcQ70LG0tPmPUzEqXAz1LZmC0Y7sHRs2s9Awa6JJulLRR0soB5p8paaukpenjyvyXeWD4lnRmVspy2UP/PnDOIG0eioi56ePqkZdVGMdPq0fykS5mVpoGDfSIWAS8eABqKbiJvseomZWwfPWhnyJpmaR7JR0/UCNJl0paLGlxR0dHnladX23NDT500cxKUj4CfQlwREScAHwD+NlADSPi+oiYFxHzmpqa8rDq/GtrbuD5bR4YNbPSM+JAj4htEbEjfX4PUCtp8ogrK5A2D4yaWYkacaBLOkyS0ufz02VuHulyC+X45gYkfEs6Mys5NYM1kHQbcCYwWVI78DmgFiAirgPeC3xIUjfwCnBeRMSoVTzKJoyt8RmjZlaSBg30iDh/kPnXAtfmraIi0NbcwCNrKuLAHjMrIz5TtB+t6cDoxu2dhS7FzCxnDvR+zGlpBDwwamalxYHejz1njLZvK3QpZmY5c6D346A9A6NbCl2KmVnOHOgDaPM9Rs2sxDjQB9DW0sgL23axcZsHRs2sNDjQB5A5Y9R76WZWKhzoA/CldM2s1DjQB3DQ2BqObJrgQxfNrGQ40PejrbnB13Qxs5LhQN+P1uYGNm73wKiZlQYH+n7MafHAqJmVDgf6fsyeWu9L6ZpZyXCg74cHRs2slDjQBzHHZ4yaWYlwoA8iMzD6ggdGzazIOdAH0ZYZGHU/upkVOQf6IGZPrafKZ4yaWQlwoA/CA6NmVioGDXRJN0raKGnlAPMl6euSVktaLuk1+S+zsNqaG1juQDezIpfLHvr3gXP2M/+twNHp41Lg2yMvq7i0tTTQ4YFRMytygwZ6RCwCXtxPk3OBmyPxCNAoaWq+CiwGey6l64FRMyti+ehDbwbWZb1uT6e9iqRLJS2WtLijoyMPqz4wZk9LBkbd7WJmxeyADopGxPURMS8i5jU1NR3IVY/I+DE1HHWoB0bNrLjlI9DXA9OzXrek08pKa3rGaEQUuhQzs37lI9DvAi5Ij3Y5GdgaERvysNyi0tacGRjdVehSzMz6VTNYA0m3AWcCkyW1A58DagEi4jrgHuBtwGpgJ3DRaBVbSNmX0j2soa7A1ZiZvdqggR4R5w8yP4AP562iIjV7asOeM0bPnj2l0OWYmb2KzxTN0bgx1Rx16ARWtG8pdClmZv1yoA9BMjC6zQOjZlaUHOhDMKe5gU07PDBqZsXJgT4EmUvpLne3i5kVIQf6EGQGRn2CkZkVIwf6EIwbU83Rh070tdHNrCg50IfIA6NmVqwc6EPU1lzPph27eN6X0jWzIuNAH6K2lkbAl9I1s+LjQB+izD1GPTBqZsXGgT5EmYFRXxvdzIqNA30Y2loaWOlL6ZpZkXGgD0NbcwObduz2wKiZFRUH+jC0NmfOGHW3i5kVDwf6MMyeWk91lTwwamZFxYE+DMnA6ASfMWpmRcWBPkytzQ08vvYlHnx6owdHzawoONCH6YJTjqC+rpYF33uMc7/5K+5/8gUHu5kVlAN9mOa0NLLwb87kn/5HG1t2dvHBmxfz9q8/zL0rNtDb62A3swMvp0CXdI6kpyWtlvR3/cxfIKlD0tL0cUn+Sy0+Y2qqOG/+4TxwxRl89U9PoLOrhw/duoRz/nURdy17jh4Hu5kdQBqsm0BSNfBb4GygHXgMOD8insxqswCYFxGX5briefPmxeLFi4dTc9Hq6Q3uXv4c1z6wmmc27mBW00F8+MyjOHfuNGqq/WXIzEZO0uMRMa+/ebmkzHxgdUSsiYjdwI+Ac/NZYLmorhLnzm3mvo+dzrfe9xrG1lRzxU+W8aav/he3P/YHdnf3FrpEMytjuQR6M7Au63V7Oq2v90haLukOSdP7W5CkSyUtlrS4o6NjGOWWhqoq8ba2qdxz+Wl854J5NI6v5W9/uoI3/vOD3PLIWnZ19xS6RDMrQ/nqB/g5MCMi5gD3Azf11ygiro+IeRExr6mpKU+rLl6SOHv2FP7Ph0/lexedxJT6sfz9z1Zyxpcf5Hu/+j2dXQ52M8ufXAJ9PZC9x92STtsjIjZHxK705XeB1+anvPIgiTcecyg//dDrufWS13HEpPFc9fMnOe1LC/nOojXs3N1d6BLNrAzkEuiPAUdLmilpDHAecFd2A0lTs16+E1iVvxLLhyROPWoyt//lKdx+6ckce9hE/vGeVZz2pYV8c+Fqtnd2FbpEMythNYM1iIhuSZcB9wHVwI0R8YSkq4HFEXEXcLmkdwLdwIvAglGsuSy8btYkXjdrEo+vfYlrH3iGr9z3NNcvWsNFp87gotfPpGF8baFLNLMSM+hhi6OlHA9bHInl7Vv4xgOruf/JF5g4toYLXz+Di0+bycEHjSl0aWZWRPZ32KIDvcg8+dw2rl34DPeufJ5xtdW8/5Qj+OAbZjF5wthCl2ZmRcCBXoKeeWE71y5czc+XPceYmir+Yv4R/OUZs5hSX1fo0sysgBzoJWxNxw6+ufB3/GzpeqqrxHknTeevzjiSaY3jCl2amRWAA70M/GHzTr714Gp+uqQdgPe+toW/PvMoph8yvsCVmdmB5EAvI+u3vMJ1D/6O2x9bR08E7z6xmQtPmcGxUydS6+vFmJU9B3oZemFbJ//2X2u49dG17OruZWxNFcdPq2dOSyMnTG9gTksjMycdRFWVCl2qmeWRA72Mbdqxi1+t3sTy9q0sb9/CyvXbeCW9pMDEuhrampNwP6GlgTnTG5nWUIfkkDcrVQ70CtLd08vqjh0sX7eVZe1bWN6+laee30ZXT/LvPHnCGOa0NDKnpYET0p+TfEikWcnYX6APeqaolZaa6iqOPayeYw+r589OSi7B09nVw1PPb2d5+xaWrUv25Bc+vZHMtrzl4HF7wn1OSyNtLQ1MGOv/Gmalxn+1FaCutpq50xuZO70RTkmm7djVzYq0m2Z5e7I3/4sVGwCQ4MimCfvsxR83tZ662uoC/hZmNhgHeoWaMLaGU46cxClHTtozbfOOXSxfv5Xl6V78ot9u4s4lyYU1a6vFMYdN3Nsf39LI0YdO8J2YzIqI+9BtQBHBhq2dSVdN1t789s7kcr/jaqtpba7f0yc/rXEcY2uqqKutZmxNFWNrqqmrTX6OranyETdmeeA+dBsWSUxrHMe0xnGc05pcIbm3N3h288t7ummWt2/lB48kh04OZkx1VRL0mZCvzQ79gTcEe6bXDtxmbO3etrXVora6ippqUVtVRW1NFTVVybRqb1SsjDnQbUiqqsSspgnMaprAu05M7kTY3dPLMxt3sHnHbjq7etjV3cuu7h46u5Kfu7p7907v6qWzu2efn7vSny+9vHvftunzzq4eevP0RbJKycBxbZXSoK/qZwOgPtOT9jXp69rqdANRk5lelU5P31eTLGdMTfqoTjZGY6r3TstslDLzs6dnpvnwUhsqB7qNWE11FcdNrR/VdXT19KYbhB4605/Z4Z/9s7sn6Orppas36O7pTZ73RNb0Xrp7knm705/dvcHunt7keU/medL+5d09e6Znv3/vcpOfXb295LMHc6Cg33da9Z5vPgNvPKr3Pu8zf595fZYxtnrvPH+zKQ0OdCsJmT3jYj+csqc3CfrdPb10dSc/d3UlP3en3zp275nes2f67j5t97RLv+1k5vdtu/WVrnRaz6vWtbs72VDlQ02V+g/+PhuEgeZlb4yy29RmbXgyG5wxNX2mpxus7Gk1VfI3mH4U91+HWYmprhLVVdVFc4hnT2/s3TD09Oy7UdlnA9Ozz8Yku03fjcq+G6iePW137Opm845952WvJ18bF0gOrd3zLaT61RuNzEZhbD/za/dsTPZ2oSXts15Xp11n2a8z76np8zprI7TndXVhDgJwoJuVseoqMW5MNePGVAOFva1hb9qtlf0Noiv7m0s/0/t+W8menv2+rj7vz7xvx67ufaYl7TLdZXu740ZDdZX2CfjarI3E+ScdzgdPn5X3dTrQzeyAqKoSdUX07SUjIpIxkDTgd2fGRLr7vM7acOzTvrv31e/v7ru8vdN29/TSNHF0LreRU6BLOgf4V5KbRH83Iv6pz/yxwM3Aa4HNwJ9HxLP5LdXMLP8kMaYmGSModYP+BpKqgW8CbwVmA+dLmt2n2cXASxFxFHAN8KV8F2pmZvuXyyZpPrA6ItZExG7gR8C5fdqcC9yUPr8DOEsegjYzO6ByCfRmYF3W6/Z0Wr9tIqIb2ApM6tMGSZdKWixpcUdHx/AqNjOzfh3QTqOIuD4i5kXEvKampgO5ajOzspdLoK8Hpme9bkmn9dtGUg3QQDI4amZmB0gugf4YcLSkmZLGAOcBd/VpcxdwYfr8vcADUXE9Q5gAAAL1SURBVKjLOJqZVahBD1uMiG5JlwH3kRy2eGNEPCHpamBxRNwF3ADcImk18CJJ6JuZ2QGU03HoEXEPcE+faVdmPe8E/jS/pZmZ2VAU7AYXkjqAtcN8+2RgUx7LKXX+PPblz2Mvfxb7KofP44iI6PeokoIF+khIWjzQHTsqkT+Pffnz2Mufxb7K/fMo/XNdzcwMcKCbmZWNUg306wtdQJHx57Evfx57+bPYV1l/HiXZh25mZq9WqnvoZmbWhwPdzKxMlFygSzpH0tOSVkv6u0LXU0iSpktaKOlJSU9I+mihayo0SdWS/lvS3YWupdAkNUq6Q9JTklZJOqXQNRWKpI+nfyMrJd0mqa7QNY2Gkgr0HG+2UUm6gSsiYjZwMvDhCv88AD4KrCp0EUXiX4FfRsSxwAlU6OciqRm4HJgXEa0klzApy8uTlFSgk9vNNipGRGyIiCXp8+0kf7B9r1VfMSS1AG8HvlvoWgpNUgNwOsl1loiI3RGxpbBVFVQNMC69Gux44LkC1zMqSi3Qc7nZRkWSNAM4EXi0sJUU1L8A/wvoLXQhRWAm0AF8L+2C+q6kgwpdVCFExHrgn4E/ABuArRHxH4WtanSUWqBbPyRNAH4KfCwithW6nkKQ9A5gY0Q8XuhaikQN8Brg2xFxIvAyUJFjTpIOJvkmPxOYBhwk6X8WtqrRUWqBnsvNNiqKpFqSML81Iu4sdD0FdCrwTknPknTFvUnSDwpbUkG1A+0RkfnGdgdJwFeiPwZ+HxEdEdEF3Am8vsA1jYpSC/RcbrZRMdIbcd8ArIqIrxW6nkKKiE9FREtEzCD5f/FARJTlXlguIuJ5YJ2kY9JJZwFPFrCkQvoDcLKk8enfzFmU6QBxTtdDLxYD3WyjwGUV0qnA+4EVkpam0z6dXr/e7CPArenOzxrgogLXUxAR8aikO4AlJEeG/TdlegkAn/pvZlYmSq3LxczMBuBANzMrEw50M7My4UA3MysTDnQzszLhQDczKxMOdDOzMvH/AXkM8xuNVFo+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ls_history.history[\"loss\"])\n",
    "plt.title(\"loss_function - LSTM Basic\")\n",
    "plt.legend([\"loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1174830.0587427279"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls_model.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8451637094740732"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "predictions = ls_model.predict(X_test)\n",
    "test_r2 = r2_score(y_test, predictions)\n",
    "test_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc440ea25d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lstm_acc_df = pd.DataFrame()\n",
    "lstm_acc_df['Actual'] = y_test[:,0]\n",
    "lstm_acc_df['Predict'] = predictions[:,0]\n",
    "lstm_acc_df[48:240].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1D Convolution with 1 variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def basic_conv1D(n_filters=10, fsize=5, window_size=5, n_features=2):\n",
    "    new_model = keras.Sequential()\n",
    "    # Hypothetically, we could also tune the padding and activation here.\n",
    "    new_model.add(tf.keras.layers.Conv1D(n_filters, fsize, padding=\"same\", activation=\"relu\", input_shape=(window_size, n_features)))\n",
    "    # Flatten will take our convolution filters and lay them out end to end so our dense layer can predict based on the outcomes of each\n",
    "    new_model.add(tf.keras.layers.Flatten())\n",
    "    new_model.add(tf.keras.layers.Dense(1800, activation='relu'))\n",
    "    new_model.add(tf.keras.layers.Dense(100))\n",
    "    new_model.add(tf.keras.layers.Dense(1))\n",
    "    new_model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")    \n",
    "    return new_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "univar_model = basic_conv1D(n_filters=24, fsize=8, window_size=window_size, n_features=X_train.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d (Conv1D)              (None, 72, 24)            216       \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1728)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1800)              3112200   \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 100)               180100    \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 3,292,617\n",
      "Trainable params: 3,292,617\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "univar_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 61875 samples\n",
      "Epoch 1/10\n",
      "61875/61875 [==============================] - 110s 2ms/sample - loss: 3373622.6293\n",
      "Epoch 2/10\n",
      "61875/61875 [==============================] - 109s 2ms/sample - loss: 903452.3404\n",
      "Epoch 3/10\n",
      "61875/61875 [==============================] - 109s 2ms/sample - loss: 594056.2459\n",
      "Epoch 4/10\n",
      "61875/61875 [==============================] - 110s 2ms/sample - loss: 430210.8427\n",
      "Epoch 5/10\n",
      "61875/61875 [==============================] - 109s 2ms/sample - loss: 352022.9304\n",
      "Epoch 6/10\n",
      "61875/61875 [==============================] - 109s 2ms/sample - loss: 299500.0900\n",
      "Epoch 7/10\n",
      "61875/61875 [==============================] - 110s 2ms/sample - loss: 248175.8128\n",
      "Epoch 8/10\n",
      "61875/61875 [==============================] - 109s 2ms/sample - loss: 211657.6709\n",
      "Epoch 9/10\n",
      "61875/61875 [==============================] - 111s 2ms/sample - loss: 191893.8157\n",
      "Epoch 10/10\n",
      "61875/61875 [==============================] - 109s 2ms/sample - loss: 174983.3995\n"
     ]
    }
   ],
   "source": [
    "univar_hist = univar_model.fit(X_train, y_train, epochs=10, shuffle=False, batch_size=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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11znxxBO56667uOGGG1i+fDkFBQVMnTqV1atXc/XVV/PEE09QWFh42PvXGUgcKsuLAVj8/nYmjhyW5NqISKqI90xhoJ1++um88MILPProo1x66aX84z/+I5dccgnLli3jySef5Oc//zkPPvggd95552HtR2cgcTh6bAHDcjJZoo50EUkhp512Gg888ACtra3U19fzwgsvMGfOHNauXcvYsWP53Oc+xxVXXMHixYvZsmULbW1tfOpTn+Kmm25i8eLFh71/nYHEISszg+PKitSRLiIp5ZOf/CQvv/wyxx9/PGbG97//fY444ggWLFjAD37wA7KzsxkxYgT33HMP69ev57LLLqOtrQ2A7373u4e9f93KJE7ff2IVt7+wmrduPIe87Mw+rJmIDCYrV678wO0+0kVnr023MukDleUltLQ5b9buTHZVRERSggIkTrNDR/oSNWOJiAAKkLiNHpHLpFHD1A8iIqRj038ir0kB0guV5SUsfn9HWv7xiEh88vLy2Lp1a1q9D7R/H0heXl6v1tNVWL1QUV7M75esZ/2OfZSVaDyIyFBUVlZGbW0t9fX1ya5Kn2r/RsLeUID0QmXMjRUVICJDU3Z2dq++tS+dqQmrF6YfUUB+diaL16ofREREAdIL7QMKdSWWiIgCpNcqyktYsWEXjc36hkIRGdoUIL1UWV5MS5vz1noNKBSRoU0B0ksHv6FQzVgiMrT1GCBmlmdmr5nZMjNbYWY3hvIpZvaqmdWY2QNmlhPKc8PzmjB/csy2rgvl75jZOTHl80JZjZldG1Pe6330t9KCXCaOzNedeUVkyIvnDKQJOMPdjwdmA/PM7GTge8Ct7n4ksB24PCx/ObA9lN8alsPMZgIXArOAecB/mFmmmWUCPwPOBWYCF4Vl6e0+Bko0oHB7Wg0kEhHprR4DxCO7w9Ps8HDgDOChUL4AuCBMnx+eE+afadH3wJ4P3O/uTe7+HlADzAmPGndf7e77gfuB88M6vd3HgKgsL2HzriY27GwcqF2KiKScuPpAwpnCUqAOeBr4C7DD3VvCIrXAhDA9AVgHEObvBEbFlndYp6vyUQnso2O9rzSzajOr7stRowcGFGo8iIgMYXEFiLu3uvtsoIzojGF6v9aqj7j77e5e5e5VpaWlfbbd6eMKyMvOUD+IiAxpvboKy913AM8CHwaKzaz9VihlwPowvR6YCBDmFwFbY8s7rNNV+dYE9jEgsjMzOG5Csa7EEpEhLZ6rsErNrDhM5wN/DawkCpJPh8XmA38I0wvDc8L8ZzzqbV4IXBiuoJoCTANeA14HpoUrrnKIOtoXhnV6u48BUzGpmBUbdmpAoYgMWfHcTHEcsCBcLZUBPOjuj5jZ28D9ZnYTsAS4Iyx/B/BrM6sBthEFAu6+wsweBN4GWoAvunsrgJldBTwJZAJ3uvuKsK2v92YfA6liYgnNratZsWEXJ0wqGejdi4gknb4TPUF1DY3M+c4i/vljM7jitKl9tl0RkVSi70TvB2MK8igryVc/iIgMWQqQw1BRXqIrsURkyFKAHIbK8mI27mxk4859ya6KiMiAU4AchoMDCnUWIiJDjwLkMMwYV0huVob6QURkSFKAHIacrAyOnaBvKBSRoUkBcpgqJ5Xw1vpdNLVoQKGIDC0KkMNUWV7M/tY2VmzYleyqiIgMKAXIYWr/hkJdzisiQ40C5DCNLcxjQrEGFIrI0KMA6QMV5cUs0XeDiMgQowDpA5XlJWzY2cgmfUOhiAwhCpA+UFFeDKDLeUVkSFGA9IFZ44vI0YBCERliFCB9oH1A4WJdiSUiQ4gCpI9UTCxm+fqd7G9pS3ZVREQGhAKkj1ROKmF/Sxtvb9SAQhEZGhQgfeTgnXnVDyIiQ4MCpI8cUZTH+KI8daSLyJChAOlD+oZCERlKFCB9qKK8mPU79lG3SwMKRST9KUD6UOWk0A+iZiwRGQJ6DBAzm2hmz5rZ22a2wsy+FMpvMLP1ZrY0PM6LWec6M6sxs3fM7JyY8nmhrMbMro0pn2Jmr4byB8wsJ5Tnhuc1Yf7knvaRTLPGF5KTmaFmLBEZEuI5A2kBrnH3mcDJwBfNbGaYd6u7zw6PxwDCvAuBWcA84D/MLNPMMoGfAecCM4GLYrbzvbCtI4HtwOWh/HJgeyi/NSzX5T4SPgp9JDcrk1kTCnUGIiJDQo8B4u4b3X1xmG4AVgITulnlfOB+d29y9/eAGmBOeNS4+2p33w/cD5xvZgacATwU1l8AXBCzrQVh+iHgzLB8V/tIusryEt6s1YBCEUl/veoDCU1IFcCroegqM3vTzO40s5JQNgFYF7NabSjrqnwUsMPdWzqUH7KtMH9nWL6rbXWs75VmVm1m1fX19b15qQmrLC+hqaWNVZs0oFBE0lvcAWJmI4DfAV92913AbcCHgNnARuDf+qWGh8Hdb3f3KnevKi0tHZB9tt+ZVwMKRSTdxRUgZpZNFB6/dfeHAdx9s7u3unsb8EsONiGtBybGrF4Wyroq3woUm1lWh/JDthXmF4Xlu9pW0o0vzueIwjzdWFFE0l48V2EZcAew0t1viSkfF7PYJ4G3wvRC4MJwBdUUYBrwGvA6MC1ccZVD1Am+0N0deBb4dFh/PvCHmG3ND9OfBp4Jy3e1j5RQOalYHekikvayel6EU4GLgeVmtjSUfYPoKqrZgANrgM8DuPsKM3sQeJvoCq4vunsrgJldBTwJZAJ3uvuKsL2vA/eb2U3AEqLAIvz8tZnVANuIQqfbfaSCioklPLZ8E3UNjYwpyEt2dURE+oVFH+jTX1VVlVdXVw/Ivt5Yu41P3fYyv7j4BM6ZdcSA7FNEpD+Y2RvuXtXZPI1E7wezxheRnWlqxhKRtKYA6Qd52ZnMGl+kEekiktYUIP2koryYN2t30NyqAYUikp4UIP2ksryExuY2Vm1sSHZVRET6hQKkn+jOvCKS7hQg/WR8UR5jCnJZogARkTSlAOknZkZleYlGpItI2lKA9KPKScW8v20vW3Y3JbsqIiJ9TgHSjyrLo34QXc4rIulIAdKPjplQRFaGBhSKSHpSgPSjaEBhoW7tLiJpSQHSzyrCNxS2aEChiKQZBUg/qygvZl9zK6s2aUChiKQXBUg/O9iRrmYsEUkvCpB+VlaST2lBrsaDiEjaUYD0s2hAYbHOQEQk7ShABkBFeQlrtu5lqwYUikgaUYAMAA0oFJF0pAAZAMeGAYVL1qkZS0TShwJkAOTnZDJjXCGL1+oMRETShwJkgFSWF7OsdocGFIpI2lCADJDKSSXs3d/KO5s1oFBE0kOPAWJmE83sWTN728xWmNmXQvlIM3vazN4NP0tCuZnZT8ysxszeNLPKmG3ND8u/a2bzY8pPMLPlYZ2fmJkluo9UpY50EUk38ZyBtADXuPtM4GTgi2Y2E7gWWOTu04BF4TnAucC08LgSuA2iMACuB04C5gDXtwdCWOZzMevNC+W92kcqKyvJZ/SIHN2ZV0TSRo8B4u4b3X1xmG4AVgITgPOBBWGxBcAFYfp84B6PvAIUm9k44BzgaXff5u7bgaeBeWFeobu/4u4O3NNhW73ZR8oyMyrKS3QGIiJpo1d9IGY2GagAXgXGuvvGMGsTMDZMTwDWxaxWG8q6K6/tpJwE9tGxvleaWbWZVdfX18f3IvtRZXkJ723Zw/Y9+5NdFRGRwxZ3gJjZCOB3wJfdfVfsvHDm4H1ct0Mksg93v93dq9y9qrS0tJ9qFr+K8mIAjQcRkbQQV4CYWTZRePzW3R8OxZvbm43Cz7pQvh6YGLN6WSjrrrysk/JE9pHSjisrIjPDNB5ERNJCPFdhGXAHsNLdb4mZtRBov5JqPvCHmPJLwpVSJwM7QzPUk8DZZlYSOs/PBp4M83aZ2clhX5d02FZv9pHShuVkMWNcgTrSRSQtZMWxzKnAxcByM1sayr4B3Aw8aGaXA2uBz4R5jwHnATXAXuAyAHffZmbfBl4Py33L3beF6S8AdwP5wOPhQW/3MRhUlpfwuzdqaW1zMjMs2dUREUmYRV0L6a+qqsqrq6uTXQ1+v6SWrzywjMe/dBozxhUmuzoiIt0yszfcvaqzeRqJPsDaBxSqGUtEBjsFyAArHzmMUcNzNB5ERAY9BcgAiwYUFusMREQGPQVIElSUl7C6fg879mpAoYgMXgqQJNCNFUUkHShAkuD4iUVkGCxRM5aIDGIKkCQYlpPF9CMKWawzEBEZxBQgSVI5qZil63bQ2jY0xuGISPpRgCRJZXkJu5taqKnbneyqiIgkRAGSJBUaUCgig5wCJEkmjxrGyOE5LF6rABGRwUkBkiRmRsVEDSgUkcFLAZJElZNK+Ev9HnbubU52VUREek0BkkQVE/UNhSIyeClAkuj4icVkGBoPIiKDkgIkiYbnZnH0EYUakS4ig5ICJMkqyqMBhW0aUCgig4wCJMkqy0toaGyhpl4DCkVkcFGAJFlleehIVzOWiAwyCpAkmzJ6OMXDslm8Vh3pIjK4KECSTAMKRWSwUoCkgMryEt6t283OfRpQKCKDR48BYmZ3mlmdmb0VU3aDma03s6XhcV7MvOvMrMbM3jGzc2LK54WyGjO7NqZ8ipm9GsofMLOcUJ4bnteE+ZN72sdgVTkpurHisnVqxhKRwSOeM5C7gXmdlN/q7rPD4zEAM5sJXAjMCuv8h5llmlkm8DPgXGAmcFFYFuB7YVtHAtuBy0P55cD2UH5rWK7LffTuZaeW48qKMNOdeUVkcOkxQNz9BWBbnNs7H7jf3Zvc/T2gBpgTHjXuvtrd9wP3A+ebmQFnAA+F9RcAF8Rsa0GYfgg4Myzf1T4GrYK8bI4eW6AR6SIyqBxOH8hVZvZmaOIqCWUTgHUxy9SGsq7KRwE73L2lQ/kh2wrzd4blu9rWB5jZlWZWbWbV9fX1ib3KAVJRXsLS97drQKGIDBqJBshtwIeA2cBG4N/6rEZ9yN1vd/cqd68qLS1NdnW6VVlezK7GFlZv0YBCERkcEgoQd9/s7q3u3gb8koNNSOuBiTGLloWyrsq3AsVmltWh/JBthflFYfmutjWoHfiGQo0HEZFBIqEAMbNxMU8/CbRfobUQuDBcQTUFmAa8BrwOTAtXXOUQdYIvdHcHngU+HdafD/whZlvzw/SngWfC8l3tY1CbOno4RfnZ6kgXkUEjq6cFzOw+YC4w2sxqgeuBuWY2G3BgDfB5AHdfYWYPAm8DLcAX3b01bOcq4EkgE7jT3VeEXXwduN/MbgKWAHeE8juAX5tZDVEn/oU97WMwy8gwKsqLWaKOdBEZJCz6UJ/+qqqqvLq6OtnV6NaP//guP1r0Z5ZdfzaFednJro6ICGb2hrtXdTZPI9FTSOWkYtw1oFBEBgcFSAqZPbEYM9SMJSKDggIkhRTkZXPUmAJ1pIvIoKAASTHtHekaUCgiqU4BkmIqy0vYua+Z1Vv2JLsqIiLdUoCkmMpJ+oZCERkcFCApZuroERTmZenGiiKS8hQgKSYjw5hdXqIzEBFJeQqQFFRZXsw7mxvY3dTS88IiIkmiAElBFeUlGlAoIilPAZKCZk+MOtIXr1UzloikLgVICirKz2bamBEs0RmIiKQwBUiKqgwd6UPlZpciMvgoQFJURXkx2/c2854GFIpIilKApKjKSeEbCjUeRERSlAIkRR1ZOoKC3CyNBxGRlKUASVHRgMJinYGISMpSgKSwivIS3tm0SwMKRSQlKUBSWGV5MW0Ob9bqLEREUo8CJIVVTIw60vUNhSKSihQgKaxoWDYfKh2uEekikpIUICmusryEJet2aEChiKScHgPEzO40szozeyumbKSZPW1m74afJaHczOwnZlZjZm+aWWXMOvPD8u+a2fyY8hPMbHlY5ydmZonuIx1VTiph2579rN26N9lVERE5RDxnIHcD8zqUXQsscvdpwKLwHOBcYFp4XAncBlEYANcDJwFzgOvbAyEs87mY9eYlso90VVEebqyo8SAikmJ6DBB3fwHY1qH4fGBBmF4AXBBTfo9HXgGKzWwccA7wtLtvc/ftwNPAvDCv0N1f8aiN5p4O2+rNPtLStDEFjMjNUoCISMpJtA9krLtvDNObgLFhegKwLma52lDWXXltJ+WJ7OMDzOxKM6s2s+r6+vo4X1pqycwwZk8s1uMb5PIAAA5ySURBVJVYIpJyDrsTPZw59GsPb6L7cPfb3b3K3atKS0v7oWYDo7K8mFWbGti7XwMKRSR1JBogm9ubjcLPulC+HpgYs1xZKOuuvKyT8kT2kbYqyktobXOWrduZ7KqIiByQaIAsBNqvpJoP/CGm/JJwpdTJwM7QDPUkcLaZlYTO87OBJ8O8XWZ2crj66pIO2+rNPtKWOtJFJBVl9bSAmd0HzAVGm1kt0dVUNwMPmtnlwFrgM2Hxx4DzgBpgL3AZgLtvM7NvA6+H5b7l7u0d818gutIrH3g8POjtPtJZ8bAcppYOVz+IiKQUGyoD1Kqqqry6ujrZ1UjYNQ8u46m3N3H7xVV8+EOjkl0dERkizOwNd6/qbJ5Gog8Sl506mcK8bC765StceU81a/RNhSKSZAqQQeKYCUUsuuajfPWco/lTzRb++tbn+fYjb7Nzb3OyqyYiQ5QCZBDJy87ki391JM9+dS6fqizjzj+9x9wfPsuC/15Dc2tbsqsnIkOMAmQQGlOQx82fOo5Hrz6NGeMKuX7hCub96AWeWbVZN10UkQGjABnEZo4v5LdXnMSvLqnCHT57dzWX3PkaqzbtSnbVRGQIUIAMcmbGWTPH8sSXT+dfPj6TN2t3ct6PX+S6h5dT39CU7OqJSBpTgKSJnKwMPvuRKTz/1bnMP2Uy/1m9jr/64XPc9txfaGxuTXb1RCQNKUDSTPGwHK7/xCye/MrpnDx1JN97YhVn3fI8j7y5Qf0jItKnFCBp6kOlI/jV/BP57RUnMSI3i6vuXcL//PnLLF2n0ewi0jcUIGnu1CNH8+g/nMbNf3Msa7bu5YKf/Ykv37+EDTv2JbtqIjLIKUCGgMwM48I55Tz31bl8Ye6HeOytTZzxb89xy1PvsKdJt4gXkcQoQIaQEblZfG3edJ655qP89cwj+MkzNfzVD5/jwep1tLWpf0REekcBMgSVlQzjpxdV8Lv/cwrji/P52kNv8ol/f4lXVm9NdtVEZBBRgAxhJ0wq4fdfOIUfXzib7Xv2c+Htr/D5X+tGjSISHwXIEGdmnD97As/801z+6eyjePHd6EaNNz3yNjv36UaNItI1BYgA0Y0arzpjGs/901z+pqKMO/70HnN/oBs1ikjXFCByiDGFeXzv08fxyNUfYfoRB2/U+OyqOg1EFJFDKECkU7PGF3Hv507il5dU0eZw2d2vc8mdr/HOpoZkV01EUoS+0lZ6tL+ljV+/spYf//HP7G5q4WPHjeecWWM5/ahSCvOyk109EelH3X2lrQJE4rZ9z35+8sy7/H7JenbsbSYrwzhp6kjOmD6Ws2aMYdKo4cmuooj0MQUICpC+1NLaxpJ1O/jjys0sWllHTd1uAI4cM4Izp4/hzBljqSwvJitTLaQig50CBAVIf1q7dQ+LVtaxaNVmXl29jZY2p3hYNnOPKuXMGWP56NFq6hIZrPotQMxsDdAAtAIt7l5lZiOBB4DJwBrgM+6+3cwM+DFwHrAXuNTdF4ftzAf+OWz2JndfEMpPAO4G8oHHgC+5u3e1j+7qqgAZGLsam3nxz1tYtHIzz75Tx/bQ1HXi5JGcOWMMZ80Yy+TRauoSGSz6O0Cq3H1LTNn3gW3ufrOZXQuUuPvXzew84GqiADkJ+LG7nxTCoBqoAhx4AzghhM5rwD8ArxIFyE/c/fGu9tFdXRUgA6+1zVny/nYWrapj0crN/Hlz1NQ1tXQ4Z80YyxnTx1A1qURNXSIpbKAD5B1grrtvNLNxwHPufrSZ/SJM3xe7XPvD3T8fyn8BPBcez7r79FB+UftyXe2ju7oqQJLv/a17eWbVZhatquOV1VtpbnWK8rOZe3QpZ0wfw9yjxlA0TE1dIqmkuwDJOsxtO/CUmTnwC3e/HRjr7hvD/E3A2DA9AVgXs25tKOuuvLaTcrrZxyHM7ErgSoDy8vJevzjpW+WjhnHpqVO49NQpNDQ289K7W/jjyjqefaeOPyzdQGaGceLkEs6cPpYzZ4xhaumIZFdZRLpxuAHyEXdfb2ZjgKfNbFXszNBf0a+99N3tIwTa7RCdgfRnPaR3CvKyOffYcZx77Dha25yl63awKFzV9Z3HVvKdx1YydfRwzghXdVVNLiFbTV0iKeWwAsTd14efdWb2e2AOsNnMxsU0L9WFxdcDE2NWLwtl64masWLLnwvlZZ0sTzf7kEEoM8M4YVIJJ0wq4WvzprNu216eWVXHH1du5p6X1/Krl96jMC+Ljx49hrNmqKlLJFUkHCBmNhzIcPeGMH028C1gITAfuDn8/ENYZSFwlZndT9SJvjMEwJPAv5pZSVjubOA6d99mZrvM7GSiTvRLgJ/GbKuzfUgamDhyGPNPmcz8Uyazu6mFl96tj5q6VtXxX8s2HBI4U0YNZ9KoYUwZPZzSglyii/1EZCAk3IluZlOB34enWcC97v4dMxsFPAiUA2uJLrHdFi7j/XdgHtFlvJe5e3XY1meBb4Rtfcfd7wrlVRy8jPdx4OrQZNXpPrqrrzrRB7+2Nmdp7Q6eWVnHM6vq+PPmBlpivklxWE4mk0YNZ8roYdFPhYvIYdNAQhQg6ailtY0NOxp5b+se1m7dw3tb9rBmyx7Wbt3L+9v2KlxE+kB/XoUlkjRZmRmUjxpG+ahhQOkh8zoLl7Vb97JqYwNPrdiscBHpAwoQSUsKF5H+pwCRIaevw2VcUR6lI3IZU5jLmIJcSgtyKS3IOzCdl505wK9QZGAoQERixBMua7buYU1MuGze1cjy9TvZuruJtk66FAvzsigtyGVMQV74GYVNx7Ki/Gyd0cigogARiVNsuJzeIVwguvfX1j1N1Dc0UdcQ/axvaKJuVyP1u5uo29XEstod1O1qYl9z6wfWz8nMCGcvuQdCpT1kDkwX5jJ6RK4GVUpKUICI9JHMDAtv9nnM6mY5d2d3U8shQVPX0ERdQ+OB0Fm3bS9vrN3Otj37O93GyOE5Mc1lh57NlI44WFaYl6WzGuk3ChCRAWZmFORlU5CX3eP9vppb29gSzl4ODZzGA9Or6/dQ39DE/ta2D6yfk5VxSKCUFkRnMLFBo74aSZQCRCSFZWdmMK4on3FF+d0u5+7s2tdC/e7GQ5rP6nc3HXJWs+T97Wzds5/Ohn8V5EZ9NaMLcj9wJtP+fExBLiOH5+gW/AIoQETSgplRNCybomHZHDmmoNtlW1rb2LZnfxQ0MQETGzgrN+zihYYmGppaOtkXjBqec/BMpkPgFOZnU9ThoT6b9KQAERlisjIzGFOYx5jCvB6X3be/NWpC6+SMpv15d01o7YbnZFKUn91puBTlR8HXWXmhwielKUBEpEv5OZlMHDmMiSOHdbvcwSa0Jnbua2bXvmZ2dnjs2Nt8YN7arXsPlHd2RVqsjuFT3EnYdAymgrxs8rIzyMvOVAD1IwWIiBy22Ca03trf0nZI0Ow6EDj72bmv5QPz1myJP3wAsjKMvOxM8rIzyM3KJD8nms7LygzlmQfCpr08WiaT3KyoPP8Dy3WYzsqI1snKJCNj6Fz1pgARkaTKyTo4/qW3mlpa2XVIyOxn575mdje20NjcRmNzK40trezb30ZjSyuNza00Nbexrzma3ru/hW179kfLNbfS2BKts6+5tdMLDeJ6PZkZ5GZnHAidnKwMcg88MsnNjpnOygjPMz8wPyd2mawMcrMzu95O+zqZGQN62bYCREQGrdysTEoLMhMKn+64O/tb22hsbqMpBMqBQIp53hRCad/+g+ETu1xTS7RMU3Pbgem9e1rCdLTtA9MtrTS3Hv7d0XOyPhhQfzunnCtOm9oHR+ZQChARkQ7MLLwBZ0L+wH37ZWubsz+Eyf6YYGmMCaAoeA5Oxy7X1MVyfR2w7RQgIiIpIjPDyM+J+mAGA12eICIiCVGAiIhIQhQgIiKSEAWIiIgkRAEiIiIJUYCIiEhCFCAiIpIQBYiIiCTEPNEbvgwyZlYPrE1w9dHAlj6szmCn43EoHY+DdCwOlQ7HY5K7l3Y2Y8gEyOEws2p3r0p2PVKFjsehdDwO0rE4VLofDzVhiYhIQhQgIiKSEAVIfG5PdgVSjI7HoXQ8DtKxOFRaHw/1gYiISEJ0BiIiIglRgIiISEIUID0ws3lm9o6Z1ZjZtcmuTzKZ2UQze9bM3jazFWb2pWTXKdnMLNPMlpjZI8muS7KZWbGZPWRmq8xspZl9ONl1ShYz+0r4H3nLzO4zs7xk16k/KEC6YWaZwM+Ac4GZwEVmNjO5tUqqFuAad58JnAx8cYgfD4AvASuTXYkU8WPgCXefDhzPED0uZjYB+Aegyt2PATKBC5Nbq/6hAOneHKDG3Ve7+37gfuD8JNcpadx9o7svDtMNRG8QE5Jbq+QxszLgY8Cvkl2XZDOzIuB04A4Ad9/v7juSW6ukygLyzSwLGAZsSHJ9+oUCpHsTgHUxz2sZwm+YscxsMlABvJrcmiTVj4CvAW3JrkgKmALUA3eFJr1fmdnwZFcqGdx9PfBD4H1gI7DT3Z9Kbq36hwJEes3MRgC/A77s7ruSXZ9kMLOPA3Xu/kay65IisoBK4DZ3rwD2AEOyz9DMSohaKqYA44HhZva/k1ur/qEA6d56YGLM87JQNmSZWTZRePzW3R9Odn2S6FTgf5jZGqKmzTPM7DfJrVJS1QK17t5+RvoQUaAMRWcB77l7vbs3Aw8DpyS5Tv1CAdK914FpZjbFzHKIOsIWJrlOSWNmRtTGvdLdb0l2fZLJ3a9z9zJ3n0z0d/GMu6flp8x4uPsmYJ2ZHR2KzgTeTmKVkul94GQzGxb+Z84kTS8oyEp2BVKZu7eY2VXAk0RXUtzp7iuSXK1kOhW4GFhuZktD2Tfc/bEk1klSx9XAb8OHrdXAZUmuT1K4+6tm9hCwmOjKxSWk6S1NdCsTERFJiJqwREQkIQoQERFJiAJEREQSogAREZGEKEBERCQhChAREUmIAkRERBLy/wFocXFsd2fmoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(univar_hist.history[\"loss\"])\n",
    "plt.title(\"loss_function - Conv1D Deep, w/ 1 variable\")\n",
    "plt.legend([\"loss\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "921088.9656229799"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univar_model.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8786054224398805"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "predictions = univar_model.predict(X_test)\n",
    "test_r2 = r2_score(y_test, predictions)\n",
    "test_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Actual</th>\n",
       "      <th>Predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16092.869</td>\n",
       "      <td>15189.262695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15745.950</td>\n",
       "      <td>14561.523438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15488.247</td>\n",
       "      <td>14086.809570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15252.608</td>\n",
       "      <td>13980.620117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15253.593</td>\n",
       "      <td>13994.477539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15363.811</td>\n",
       "      <td>14063.493164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15835.579</td>\n",
       "      <td>14401.088867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16778.363</td>\n",
       "      <td>15448.152344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18007.694</td>\n",
       "      <td>16595.259766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18130.813</td>\n",
       "      <td>16833.191406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual       Predict\n",
       "0  16092.869  15189.262695\n",
       "1  15745.950  14561.523438\n",
       "2  15488.247  14086.809570\n",
       "3  15252.608  13980.620117\n",
       "4  15253.593  13994.477539\n",
       "5  15363.811  14063.493164\n",
       "6  15835.579  14401.088867\n",
       "7  16778.363  15448.152344\n",
       "8  18007.694  16595.259766\n",
       "9  18130.813  16833.191406"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_acc_df = pd.DataFrame()\n",
    "conv_acc_df['Actual'] = y_test[:,0]\n",
    "conv_acc_df['Predict'] = predictions[:,0]\n",
    "conv_acc_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c326a4d50>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_acc_df[24:480].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-variate 1D conv net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61875, 72, 12)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_model = basic_conv1D(n_filters=25, fsize=8, window_size=window_size, n_features=data_train.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 72, 25)            2425      \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1800)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1800)              3241800   \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 100)               180100    \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 3,424,426\n",
      "Trainable params: 3,424,426\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "test_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 61875 samples\n",
      "Epoch 1/10\n",
      "61875/61875 [==============================] - 112s 2ms/sample - loss: 6757805.7699\n",
      "Epoch 2/10\n",
      "61875/61875 [==============================] - 112s 2ms/sample - loss: 2430849.9738\n",
      "Epoch 3/10\n",
      "61875/61875 [==============================] - 112s 2ms/sample - loss: 1534002.6363\n",
      "Epoch 4/10\n",
      "61875/61875 [==============================] - 112s 2ms/sample - loss: 883716.0229\n",
      "Epoch 5/10\n",
      "61875/61875 [==============================] - 112s 2ms/sample - loss: 762074.4609\n",
      "Epoch 6/10\n",
      "61875/61875 [==============================] - 112s 2ms/sample - loss: 593377.1990\n",
      "Epoch 7/10\n",
      "61875/61875 [==============================] - 111s 2ms/sample - loss: 505852.3352\n",
      "Epoch 8/10\n",
      "61875/61875 [==============================] - 112s 2ms/sample - loss: 420105.5836\n",
      "Epoch 9/10\n",
      "61875/61875 [==============================] - 111s 2ms/sample - loss: 381965.5248\n",
      "Epoch 10/10\n",
      "61875/61875 [==============================] - 112s 2ms/sample - loss: 392245.8856\n"
     ]
    }
   ],
   "source": [
    "train_history = test_model.fit(data_train, y_train, epochs=10, shuffle=False, batch_size=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_history.history[\"loss\"])\n",
    "plt.title(\"loss_function - Conv1D Deep, w/ 12 variable\")\n",
    "plt.legend([\"loss\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1190440.5917905623"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_model.evaluate(data_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8431063253046929"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "predictions = test_model.predict(data_test)\n",
    "test_r2 = r2_score(y_test, predictions)\n",
    "test_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Actual</th>\n",
       "      <th>Predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16092.869</td>\n",
       "      <td>17164.814453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15745.950</td>\n",
       "      <td>17168.632812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15488.247</td>\n",
       "      <td>17025.683594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15252.608</td>\n",
       "      <td>16717.927734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15253.593</td>\n",
       "      <td>16480.197266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15363.811</td>\n",
       "      <td>16336.948242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15835.579</td>\n",
       "      <td>16719.464844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16778.363</td>\n",
       "      <td>17872.382812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18007.694</td>\n",
       "      <td>18845.275391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18130.813</td>\n",
       "      <td>18925.900391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual       Predict\n",
       "0  16092.869  17164.814453\n",
       "1  15745.950  17168.632812\n",
       "2  15488.247  17025.683594\n",
       "3  15252.608  16717.927734\n",
       "4  15253.593  16480.197266\n",
       "5  15363.811  16336.948242\n",
       "6  15835.579  16719.464844\n",
       "7  16778.363  17872.382812\n",
       "8  18007.694  18845.275391\n",
       "9  18130.813  18925.900391"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_acc_df = pd.DataFrame()\n",
    "conv_acc_df['Actual'] = y_test[:,0]\n",
    "conv_acc_df['Predict'] = predictions[:,0]\n",
    "conv_acc_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c2dc41c50>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_acc_df[24:480].plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-variate LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls_model = basic_LSTM(window_size=window_size, n_features=data_train.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_3 (LSTM)                (None, 72, 100)           45200     \n",
      "_________________________________________________________________\n",
      "flatten_7 (Flatten)          (None, 7200)              0         \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 1500)              10801500  \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 100)               150100    \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 10,996,901\n",
      "Trainable params: 10,996,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "ls_model.summary()\n",
    "# Note: if we're getting nan here, re-check the dataset to see if we're getting any bad data there\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will be true if data_train has any nans\n",
    "summa = np.sum(data_train)\n",
    "check_nan = np.isnan(summa)\n",
    "check_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 61875 samples\n",
      "Epoch 1/10\n",
      "61875/61875 [==============================] - 98s 2ms/sample - loss: 17353243.5944\n",
      "Epoch 2/10\n",
      "61875/61875 [==============================] - 102s 2ms/sample - loss: 3894199.4620\n",
      "Epoch 3/10\n",
      "61875/61875 [==============================] - 104s 2ms/sample - loss: 2039165.5416\n",
      "Epoch 4/10\n",
      "61875/61875 [==============================] - 104s 2ms/sample - loss: 1440865.4328\n",
      "Epoch 5/10\n",
      "61875/61875 [==============================] - 108s 2ms/sample - loss: 1172870.2937\n",
      "Epoch 6/10\n",
      "61875/61875 [==============================] - 106s 2ms/sample - loss: 1497879.3899\n",
      "Epoch 7/10\n",
      "61875/61875 [==============================] - 100s 2ms/sample - loss: 1344339.4115\n",
      "Epoch 8/10\n",
      "61875/61875 [==============================] - 103s 2ms/sample - loss: 1321593.1008\n",
      "Epoch 9/10\n",
      "61875/61875 [==============================] - 104s 2ms/sample - loss: 1539892.5339\n",
      "Epoch 10/10\n",
      "61875/61875 [==============================] - 104s 2ms/sample - loss: 1360416.4545\n"
     ]
    }
   ],
   "source": [
    "ls_history = ls_model.fit(data_train ,y_train, epochs=10, shuffle=False, batch_size=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ls_history.history[\"loss\"])\n",
    "plt.title(\"loss_function - LSTM Multivariate\")\n",
    "plt.legend([\"loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1439643.2088255493\n"
     ]
    }
   ],
   "source": [
    "\n",
    "test = ls_model.evaluate(data_test, y_test, verbose=0)\n",
    "if(not np.isnan(test)):\n",
    "    print(test)\n",
    "else:\n",
    "    print(\"nan failure. :(\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8102627579239303\n"
     ]
    }
   ],
   "source": [
    "\n",
    "if(not np.isnan(test)):\n",
    "    from sklearn.metrics import r2_score\n",
    "    predictions = ls_model.predict(data_test)\n",
    "    test_r2 = r2_score(y_test, predictions)\n",
    "    print(test_r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if(not np.isnan(test)):\n",
    "    lstm_acc_df = pd.DataFrame()\n",
    "    lstm_acc_df['Actual'] = y_test[:,0]\n",
    "    lstm_acc_df['Predict'] = predictions[:,0]\n",
    "    lstm_acc_df[24:480].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONV 2D: Game changer\n",
    "Here we create a conv2d with a filter shape of 1 timestep by fsize features, basically turning the convolution window on the Z-axis in our slides. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def basic_conv2D(n_filters=10, fsize=5, window_size=5, n_features=2):\n",
    "    new_model = keras.Sequential()\n",
    "    # Hypothetically, we could also tune the padding and activation here.\n",
    "    new_model.add(tf.keras.layers.Conv2D(n_filters, (1,fsize), padding=\"same\", activation=\"relu\", input_shape=(window_size, n_features, 1)))\n",
    "    # Flatten will take our convolution filters and lay them out end to end so our dense layer can predict based on the outcomes of each\n",
    "    new_model.add(tf.keras.layers.Flatten())\n",
    "    new_model.add(tf.keras.layers.Dense(1000, activation='relu'))\n",
    "    new_model.add(tf.keras.layers.Dense(100))\n",
    "    new_model.add(tf.keras.layers.Dense(1))\n",
    "    new_model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")    \n",
    "    return new_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_wide = data_train.reshape((data_train.shape[0], data_train.shape[1], data_train.shape[2], 1))\n",
    "data_test_wide = data_test.reshape((data_test.shape[0], data_test.shape[1], data_test.shape[2], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61875, 72, 12, 1)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_wide.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = basic_conv2D(n_filters=24, fsize=2, window_size=window_size, n_features=data_train_wide.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 72, 12, 24)        72        \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 20736)             0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 1000)              20737000  \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 100)               100100    \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 20,837,273\n",
      "Trainable params: 20,837,273\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "m2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 61875 samples\n",
      "Epoch 1/10\n",
      "61875/61875 [==============================] - 239s 4ms/sample - loss: 3333101.1274\n",
      "Epoch 2/10\n",
      "61875/61875 [==============================] - 234s 4ms/sample - loss: 300454.4922\n",
      "Epoch 3/10\n",
      "61875/61875 [==============================] - 258s 4ms/sample - loss: 234770.6481\n",
      "Epoch 4/10\n",
      "61875/61875 [==============================] - 291s 5ms/sample - loss: 185101.6956\n",
      "Epoch 5/10\n",
      "61875/61875 [==============================] - 297s 5ms/sample - loss: 161944.8792\n",
      "Epoch 6/10\n",
      "61875/61875 [==============================] - 275s 4ms/sample - loss: 143251.4362\n",
      "Epoch 7/10\n",
      "61875/61875 [==============================] - 236s 4ms/sample - loss: 127758.4972\n",
      "Epoch 8/10\n",
      "61875/61875 [==============================] - 268s 4ms/sample - loss: 119297.8298\n",
      "Epoch 9/10\n",
      "61875/61875 [==============================] - 243s 4ms/sample - loss: 112657.1073\n",
      "Epoch 10/10\n",
      "61875/61875 [==============================] - 234s 4ms/sample - loss: 108458.5306\n"
     ]
    }
   ],
   "source": [
    "m2_hist = m2.fit(data_train_wide, y_train, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(m2_hist.history[\"loss\"])\n",
    "plt.title(\"loss_function - Conv2D Deep, w/ 12 variable\")\n",
    "plt.legend([\"loss\"])\n",
    "# \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68627.36885150695"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.evaluate(data_test_wide, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9909552814481323"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "predictions = m2.predict(data_test_wide)\n",
    "test_r2 = r2_score(y_test, predictions)\n",
    "test_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Actual</th>\n",
       "      <th>Predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16092.869</td>\n",
       "      <td>16453.220703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15745.950</td>\n",
       "      <td>16298.168945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15488.247</td>\n",
       "      <td>15888.483398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15252.608</td>\n",
       "      <td>15474.940430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15253.593</td>\n",
       "      <td>15228.213867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15363.811</td>\n",
       "      <td>15060.584961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15835.579</td>\n",
       "      <td>15249.165039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16778.363</td>\n",
       "      <td>16405.300781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18007.694</td>\n",
       "      <td>17606.994141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18130.813</td>\n",
       "      <td>18313.033203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual       Predict\n",
       "0  16092.869  16453.220703\n",
       "1  15745.950  16298.168945\n",
       "2  15488.247  15888.483398\n",
       "3  15252.608  15474.940430\n",
       "4  15253.593  15228.213867\n",
       "5  15363.811  15060.584961\n",
       "6  15835.579  15249.165039\n",
       "7  16778.363  16405.300781\n",
       "8  18007.694  17606.994141\n",
       "9  18130.813  18313.033203"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_acc_df = pd.DataFrame()\n",
    "conv_acc_df['Actual'] = y_test[:,0]\n",
    "conv_acc_df['Predict'] = predictions[:,0]\n",
    "conv_acc_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c17000490>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_acc_df[24:240].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deeper conv2D\n",
    "\n",
    "\n",
    "\n",
    "I convolved over 2-feature/1-timestep, now I want to convolve over 1-feature/8-timestep, but the 1-feature is actually a 2-feature convolution. \n",
    "\n",
    "<img src=\"resources/deeper.jpg\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deeper_conv2D(n_filters=10, fsize=5, window_size=5, n_features=2, hour_filter=8):\n",
    "    new_model = keras.Sequential()\n",
    "    new_model.add(tf.keras.layers.Conv2D(n_filters, (1,fsize), padding=\"same\", activation=\"linear\", input_shape=(window_size, n_features, 1)))\n",
    "    new_model.add(tf.keras.layers.Conv2D(n_filters, (hour_filter, 1), padding=\"same\", activation=\"relu\"))\n",
    "    # Flatten will take our convolution filters and lay them out end to end so our dense layer can predict based on the outcomes of each\n",
    "    new_model.add(tf.keras.layers.Flatten())\n",
    "    new_model.add(tf.keras.layers.Dense(1000, activation='relu'))\n",
    "    new_model.add(tf.keras.layers.Dense(100))\n",
    "    new_model.add(tf.keras.layers.Dense(1))\n",
    "    new_model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")    \n",
    "    return new_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "m3 = deeper_conv2D(n_filters=24, fsize=2, window_size=window_size, n_features=data_train_wide.shape[2], hour_filter=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 72, 12, 24)        72        \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 72, 12, 24)        4632      \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 20736)             0         \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 1000)              20737000  \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 100)               100100    \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 20,841,905\n",
      "Trainable params: 20,841,905\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "m3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 61875 samples\n",
      "Epoch 1/10\n",
      "61875/61875 [==============================] - 270s 4ms/sample - loss: 2702396.0686\n",
      "Epoch 2/10\n",
      "61875/61875 [==============================] - 260s 4ms/sample - loss: 259545.2844\n",
      "Epoch 3/10\n",
      "61875/61875 [==============================] - 275s 4ms/sample - loss: 208541.9392\n",
      "Epoch 4/10\n",
      "61875/61875 [==============================] - 276s 4ms/sample - loss: 145264.1884\n",
      "Epoch 5/10\n",
      "61875/61875 [==============================] - 266s 4ms/sample - loss: 126733.4120\n",
      "Epoch 6/10\n",
      "61875/61875 [==============================] - 277s 4ms/sample - loss: 107967.2880\n",
      "Epoch 7/10\n",
      "61875/61875 [==============================] - 265s 4ms/sample - loss: 99217.9270\n",
      "Epoch 8/10\n",
      "61875/61875 [==============================] - 268s 4ms/sample - loss: 83188.1179\n",
      "Epoch 9/10\n",
      "61875/61875 [==============================] - 270s 4ms/sample - loss: 79428.3213\n",
      "Epoch 10/10\n",
      "61875/61875 [==============================] - 282s 5ms/sample - loss: 75911.6892\n"
     ]
    }
   ],
   "source": [
    "m3_hist = m3.fit(data_train_wide, y_train, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(m3_hist.history[\"loss\"])\n",
    "plt.title(\"loss_function - Conv2D->conv2D, w/ 12 variable\")\n",
    "plt.legend([\"loss\"])\n",
    "# \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47929.50422238607"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.evaluate(data_test_wide, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9936831487638945"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "predictions = m3.predict(data_test_wide)\n",
    "test_r2 = r2_score(y_test, predictions)\n",
    "test_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Actual</th>\n",
       "      <th>Predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16092.869</td>\n",
       "      <td>16398.658203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15745.950</td>\n",
       "      <td>15983.678711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15488.247</td>\n",
       "      <td>15390.015625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15252.608</td>\n",
       "      <td>15160.970703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15253.593</td>\n",
       "      <td>15124.511719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15363.811</td>\n",
       "      <td>15215.758789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15835.579</td>\n",
       "      <td>15586.958008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16778.363</td>\n",
       "      <td>16709.880859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18007.694</td>\n",
       "      <td>17714.212891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18130.813</td>\n",
       "      <td>17939.414062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual       Predict\n",
       "0  16092.869  16398.658203\n",
       "1  15745.950  15983.678711\n",
       "2  15488.247  15390.015625\n",
       "3  15252.608  15160.970703\n",
       "4  15253.593  15124.511719\n",
       "5  15363.811  15215.758789\n",
       "6  15835.579  15586.958008\n",
       "7  16778.363  16709.880859\n",
       "8  18007.694  17714.212891\n",
       "9  18130.813  17939.414062"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_acc_df = pd.DataFrame()\n",
    "conv_acc_df['Actual'] = y_test[:,0]\n",
    "conv_acc_df['Predict'] = predictions[:,0]\n",
    "conv_acc_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c685b03d0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_acc_df[24:240].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does it do vs dumb model?\n",
    "\n",
    "Here, my dumb model is to simply predict the previous value as the next value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([411577.17812475])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dumb model\n",
    "prev_val = y_test[0]\n",
    "sse = 0\n",
    "\n",
    "for n in range(0, len(y_test)-1):\n",
    "    err = y_test[n] - prev_val\n",
    "    sq_err = err ** 2\n",
    "    sse = sse + sq_err\n",
    "    prev_val = y_test[n]\n",
    "\n",
    "mse = sse / n\n",
    "mse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>actual</th>\n",
       "      <th>dumb_predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16092.869</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15745.950</td>\n",
       "      <td>16092.869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15488.247</td>\n",
       "      <td>15745.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15252.608</td>\n",
       "      <td>15488.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15253.593</td>\n",
       "      <td>15252.608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15465</th>\n",
       "      <td>14342.179</td>\n",
       "      <td>14399.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15466</th>\n",
       "      <td>13908.957</td>\n",
       "      <td>14342.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15467</th>\n",
       "      <td>13504.615</td>\n",
       "      <td>13908.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15468</th>\n",
       "      <td>12695.970</td>\n",
       "      <td>13504.615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15469</th>\n",
       "      <td>11708.896</td>\n",
       "      <td>12695.970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15470 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          actual  dumb_predict\n",
       "0      16092.869         0.000\n",
       "1      15745.950     16092.869\n",
       "2      15488.247     15745.950\n",
       "3      15252.608     15488.247\n",
       "4      15253.593     15252.608\n",
       "...          ...           ...\n",
       "15465  14342.179     14399.190\n",
       "15466  13908.957     14342.179\n",
       "15467  13504.615     13908.957\n",
       "15468  12695.970     13504.615\n",
       "15469  11708.896     12695.970\n",
       "\n",
       "[15470 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict = pd.DataFrame(y_test, columns = [\"dumb_predict\"])\n",
    "predict = predict.shift(1)\n",
    "predict[\"actual\"] = y_test\n",
    "predict.fillna(value=0, inplace=True)\n",
    "correct_cols = [\"actual\", \"dumb_predict\"]\n",
    "predict = predict.reindex(columns=correct_cols)\n",
    "predict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1433c7a1d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict[24:96].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9435487010187062"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "predictions = predict[\"dumb_predict\"].values\n",
    "test_r2 = r2_score(y_test, predictions)\n",
    "test_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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