LSTM_TFt_MV_Guadalupe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM_tensorflow_tutorial [link](https://www.tensorflow.org/tutorials/structured_data/time_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tamarahuete/opt/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import datetime\n",
    "\n",
    "import IPython\n",
    "import IPython.display\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.metrics import r2_score, mean_absolute_error\n",
    "import math\n",
    "from scipy import stats\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (8, 6)\n",
    "mpl.rcParams['axes.grid'] = False\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Set fixed variables\n",
    "MAX_EPOCHS = 20\n",
    "### input files path\n",
    "input_files = 'data/PROCESSED/STATIONS_CLEAN'\n",
    "### output path\n",
    "path = 'data/PROCESSED/MODEL_OUTPUTS'\n",
    "## station name\n",
    "station = 'guadalupe'\n",
    "## sampling frequency\n",
    "sample_freq = 60 #(time in minutes)\n",
    "steps=int(sample_freq/5)\n",
    "time_steps = int(60/sample_freq)\n",
    "#60 - 12\n",
    "#30 - 6\n",
    "#15 - 3\n",
    "#5 - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(f'{input_files}/{station}_validation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Date_Time</th>\n",
       "      <th>ATAvg</th>\n",
       "      <th>ATMax</th>\n",
       "      <th>ATMin</th>\n",
       "      <th>RHAvg</th>\n",
       "      <th>RHMin</th>\n",
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       "      <th>WSAvg</th>\n",
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       "      <th>WMaxx</th>\n",
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       "      <th>WMiny</th>\n",
       "      <th>Day_sin</th>\n",
       "      <th>Day_cos</th>\n",
       "      <th>Year_sin</th>\n",
       "      <th>Year_cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-11 00:00:00</td>\n",
       "      <td>13.77</td>\n",
       "      <td>15.02</td>\n",
       "      <td>14.94</td>\n",
       "      <td>93.47</td>\n",
       "      <td>92.75</td>\n",
       "      <td>93.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.39</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.318835</td>\n",
       "      <td>-0.894899</td>\n",
       "      <td>-0.961311</td>\n",
       "      <td>-0.446634</td>\n",
       "      <td>-0.321348</td>\n",
       "      <td>0.315017</td>\n",
       "      <td>-8.709422e-12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.180875</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-11 00:05:00</td>\n",
       "      <td>13.70</td>\n",
       "      <td>15.02</td>\n",
       "      <td>14.94</td>\n",
       "      <td>93.19</td>\n",
       "      <td>92.75</td>\n",
       "      <td>93.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>234.54</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.127630</td>\n",
       "      <td>-0.179195</td>\n",
       "      <td>-0.961311</td>\n",
       "      <td>-0.446634</td>\n",
       "      <td>-0.321348</td>\n",
       "      <td>0.315017</td>\n",
       "      <td>2.181489e-02</td>\n",
       "      <td>0.999762</td>\n",
       "      <td>0.180934</td>\n",
       "      <td>0.983495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-11 00:10:00</td>\n",
       "      <td>13.74</td>\n",
       "      <td>15.02</td>\n",
       "      <td>14.94</td>\n",
       "      <td>92.52</td>\n",
       "      <td>92.75</td>\n",
       "      <td>93.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.09</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.126830</td>\n",
       "      <td>0.545449</td>\n",
       "      <td>-0.961311</td>\n",
       "      <td>-0.446634</td>\n",
       "      <td>-0.321348</td>\n",
       "      <td>0.315017</td>\n",
       "      <td>4.361939e-02</td>\n",
       "      <td>0.999048</td>\n",
       "      <td>0.180993</td>\n",
       "      <td>0.983484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-11 00:15:00</td>\n",
       "      <td>13.67</td>\n",
       "      <td>15.02</td>\n",
       "      <td>14.94</td>\n",
       "      <td>92.80</td>\n",
       "      <td>92.75</td>\n",
       "      <td>93.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.088661</td>\n",
       "      <td>0.522532</td>\n",
       "      <td>-0.961311</td>\n",
       "      <td>-0.446634</td>\n",
       "      <td>-0.321348</td>\n",
       "      <td>0.315017</td>\n",
       "      <td>6.540313e-02</td>\n",
       "      <td>0.997859</td>\n",
       "      <td>0.181052</td>\n",
       "      <td>0.983474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-11 00:20:00</td>\n",
       "      <td>13.56</td>\n",
       "      <td>15.02</td>\n",
       "      <td>14.94</td>\n",
       "      <td>92.80</td>\n",
       "      <td>92.75</td>\n",
       "      <td>93.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>248.92</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-0.100708</td>\n",
       "      <td>-0.261262</td>\n",
       "      <td>-0.961311</td>\n",
       "      <td>-0.446634</td>\n",
       "      <td>-0.321348</td>\n",
       "      <td>0.315017</td>\n",
       "      <td>8.715574e-02</td>\n",
       "      <td>0.996195</td>\n",
       "      <td>0.181110</td>\n",
       "      <td>0.983463</td>\n",
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       "             Date_Time  ATAvg  ATMax  ATMin  RHAvg  RHMin  RHMax  PAvg  \\\n",
       "0  2013-01-11 00:00:00  13.77  15.02  14.94  93.47  92.75  93.31   0.0   \n",
       "1  2013-01-11 00:05:00  13.70  15.02  14.94  93.19  92.75  93.31   0.0   \n",
       "2  2013-01-11 00:10:00  13.74  15.02  14.94  92.52  92.75  93.31   0.0   \n",
       "3  2013-01-11 00:15:00  13.67  15.02  14.94  92.80  92.75  93.31   0.0   \n",
       "4  2013-01-11 00:20:00  13.56  15.02  14.94  92.80  92.75  93.31   0.0   \n",
       "\n",
       "    WDAvg  WSAvg     WAvgx     WAvgy     WMaxx     WMaxy     WMinx     WMiny  \\\n",
       "0  250.39   0.95 -0.318835 -0.894899 -0.961311 -0.446634 -0.321348  0.315017   \n",
       "1  234.54   0.22 -0.127630 -0.179195 -0.961311 -0.446634 -0.321348  0.315017   \n",
       "2  103.09   0.56 -0.126830  0.545449 -0.961311 -0.446634 -0.321348  0.315017   \n",
       "3   80.37   0.53  0.088661  0.522532 -0.961311 -0.446634 -0.321348  0.315017   \n",
       "4  248.92   0.28 -0.100708 -0.261262 -0.961311 -0.446634 -0.321348  0.315017   \n",
       "\n",
       "        Day_sin   Day_cos  Year_sin  Year_cos  \n",
       "0 -8.709422e-12  1.000000  0.180875  0.983506  \n",
       "1  2.181489e-02  0.999762  0.180934  0.983495  \n",
       "2  4.361939e-02  0.999048  0.180993  0.983484  \n",
       "3  6.540313e-02  0.997859  0.181052  0.983474  \n",
       "4  8.715574e-02  0.996195  0.181110  0.983463  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date_Time    0\n",
       "ATAvg        0\n",
       "ATMax        0\n",
       "ATMin        0\n",
       "RHAvg        0\n",
       "RHMin        0\n",
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       "WSAvg        0\n",
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       "WAvgy        0\n",
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       "WMaxy        0\n",
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   "source": [
    "#### The data has already been cleaned, wind transformed to vector and added perodicity\n",
    "### Check for NAN values and delete before running models\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Subsample to get data for every hour (starting from index 0, get 12 steps)\n",
    "df = df[0::steps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>2013-01-11 00:00:00</td>\n",
       "      <td>13.77</td>\n",
       "      <td>15.02</td>\n",
       "      <td>14.94</td>\n",
       "      <td>93.47</td>\n",
       "      <td>92.75</td>\n",
       "      <td>93.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.39</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.318835</td>\n",
       "      <td>-0.894899</td>\n",
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       "      <td>-8.709422e-12</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-01-11 01:00:00</td>\n",
       "      <td>13.06</td>\n",
       "      <td>14.77</td>\n",
       "      <td>14.70</td>\n",
       "      <td>94.82</td>\n",
       "      <td>93.80</td>\n",
       "      <td>94.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.837708</td>\n",
       "      <td>-0.448046</td>\n",
       "      <td>-0.350744</td>\n",
       "      <td>0.248754</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>0.181580</td>\n",
       "      <td>0.983376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013-01-11 02:00:00</td>\n",
       "      <td>12.63</td>\n",
       "      <td>14.54</td>\n",
       "      <td>14.48</td>\n",
       "      <td>94.79</td>\n",
       "      <td>94.43</td>\n",
       "      <td>94.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.99</td>\n",
       "      <td>0.47</td>\n",
       "      <td>-0.081534</td>\n",
       "      <td>0.462874</td>\n",
       "      <td>-0.870900</td>\n",
       "      <td>-0.427121</td>\n",
       "      <td>-0.344924</td>\n",
       "      <td>0.239640</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.182285</td>\n",
       "      <td>0.983246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2013-01-11 03:00:00</td>\n",
       "      <td>12.29</td>\n",
       "      <td>14.41</td>\n",
       "      <td>14.34</td>\n",
       "      <td>97.16</td>\n",
       "      <td>94.85</td>\n",
       "      <td>95.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>258.25</td>\n",
       "      <td>0.37</td>\n",
       "      <td>-0.075347</td>\n",
       "      <td>-0.362247</td>\n",
       "      <td>-0.820072</td>\n",
       "      <td>-0.438614</td>\n",
       "      <td>-0.320287</td>\n",
       "      <td>0.222522</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.182990</td>\n",
       "      <td>0.983115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2013-01-11 04:00:00</td>\n",
       "      <td>12.48</td>\n",
       "      <td>14.31</td>\n",
       "      <td>14.26</td>\n",
       "      <td>90.55</td>\n",
       "      <td>95.24</td>\n",
       "      <td>95.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.89</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.306654</td>\n",
       "      <td>0.803469</td>\n",
       "      <td>-0.853073</td>\n",
       "      <td>-0.440304</td>\n",
       "      <td>-0.345983</td>\n",
       "      <td>0.219991</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.183694</td>\n",
       "      <td>0.982983</td>\n",
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       "              Date_Time  ATAvg  ATMax  ATMin  RHAvg  RHMin  RHMax  PAvg  \\\n",
       "0   2013-01-11 00:00:00  13.77  15.02  14.94  93.47  92.75  93.31   0.0   \n",
       "12  2013-01-11 01:00:00  13.06  14.77  14.70  94.82  93.80  94.13   0.0   \n",
       "24  2013-01-11 02:00:00  12.63  14.54  14.48  94.79  94.43  94.76   0.0   \n",
       "36  2013-01-11 03:00:00  12.29  14.41  14.34  97.16  94.85  95.17   0.0   \n",
       "48  2013-01-11 04:00:00  12.48  14.31  14.26  90.55  95.24  95.50   0.0   \n",
       "\n",
       "     WDAvg  WSAvg     WAvgx     WAvgy     WMaxx     WMaxy     WMinx     WMiny  \\\n",
       "0   250.39   0.95 -0.318835 -0.894899 -0.961311 -0.446634 -0.321348  0.315017   \n",
       "12  302.45   0.00  0.000000 -0.000000 -0.837708 -0.448046 -0.350744  0.248754   \n",
       "24   99.99   0.47 -0.081534  0.462874 -0.870900 -0.427121 -0.344924  0.239640   \n",
       "36  258.25   0.37 -0.075347 -0.362247 -0.820072 -0.438614 -0.320287  0.222522   \n",
       "48  110.89   0.86 -0.306654  0.803469 -0.853073 -0.440304 -0.345983  0.219991   \n",
       "\n",
       "         Day_sin   Day_cos  Year_sin  Year_cos  \n",
       "0  -8.709422e-12  1.000000  0.180875  0.983506  \n",
       "12  2.588190e-01  0.965926  0.181580  0.983376  \n",
       "24  5.000000e-01  0.866025  0.182285  0.983246  \n",
       "36  7.071068e-01  0.707107  0.182990  0.983115  \n",
       "48  8.660254e-01  0.500000  0.183694  0.982983  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_time = pd.to_datetime(df.pop('Date_Time'), format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cols = ['ATAvg', 'RHAvg', 'WAvgx','WAvgy']\n",
    "plot_features = df[plot_cols]\n",
    "plot_features.index = date_time\n",
    "_ = plot_features.plot(subplots=True)\n",
    "\n",
    "plot_features = df[plot_cols][:480]\n",
    "plot_features.index = date_time[:480]\n",
    "_ = plot_features.plot(subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATAvg</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>1.694126e+01</td>\n",
       "      <td>3.385494</td>\n",
       "      <td>4.190000</td>\n",
       "      <td>14.530000</td>\n",
       "      <td>1.618600e+01</td>\n",
       "      <td>19.370000</td>\n",
       "      <td>29.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATMax</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>1.705264e+01</td>\n",
       "      <td>3.248350</td>\n",
       "      <td>5.160000</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>1.622000e+01</td>\n",
       "      <td>19.720000</td>\n",
       "      <td>29.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATMin</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>1.688573e+01</td>\n",
       "      <td>3.159448</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>14.520000</td>\n",
       "      <td>1.611000e+01</td>\n",
       "      <td>19.470000</td>\n",
       "      <td>29.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RHAvg</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>7.972151e+01</td>\n",
       "      <td>16.536240</td>\n",
       "      <td>5.120000</td>\n",
       "      <td>65.510000</td>\n",
       "      <td>8.379000e+01</td>\n",
       "      <td>94.980000</td>\n",
       "      <td>100.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RHMin</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>7.910131e+01</td>\n",
       "      <td>16.940392</td>\n",
       "      <td>5.690000</td>\n",
       "      <td>62.770000</td>\n",
       "      <td>8.385000e+01</td>\n",
       "      <td>95.200000</td>\n",
       "      <td>100.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RHMax</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>8.043544e+01</td>\n",
       "      <td>16.168787</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>65.050000</td>\n",
       "      <td>8.537000e+01</td>\n",
       "      <td>95.600000</td>\n",
       "      <td>100.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAvg</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>5.250550e-03</td>\n",
       "      <td>0.043080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WDAvg</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>1.740801e+02</td>\n",
       "      <td>58.407140</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>151.580000</td>\n",
       "      <td>1.796550e+02</td>\n",
       "      <td>199.500000</td>\n",
       "      <td>354.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WSAvg</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>2.464626e+00</td>\n",
       "      <td>2.110336</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>1.525000e+00</td>\n",
       "      <td>4.340000</td>\n",
       "      <td>9.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAvgx</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>-2.055486e+00</td>\n",
       "      <td>2.165681</td>\n",
       "      <td>-9.574509</td>\n",
       "      <td>-4.014477</td>\n",
       "      <td>-1.236939e+00</td>\n",
       "      <td>-0.198715</td>\n",
       "      <td>2.777757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAvgy</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>-7.617829e-02</td>\n",
       "      <td>1.267648</td>\n",
       "      <td>-6.470612</td>\n",
       "      <td>-0.610501</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.445041</td>\n",
       "      <td>7.992564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMaxx</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>-2.571026e+00</td>\n",
       "      <td>2.409542</td>\n",
       "      <td>-11.824311</td>\n",
       "      <td>-4.982809</td>\n",
       "      <td>-1.573343e+00</td>\n",
       "      <td>-0.739274</td>\n",
       "      <td>4.512922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMaxy</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>-9.040149e-01</td>\n",
       "      <td>1.372289</td>\n",
       "      <td>-10.509392</td>\n",
       "      <td>-1.729310</td>\n",
       "      <td>-5.443985e-01</td>\n",
       "      <td>-0.277040</td>\n",
       "      <td>8.897659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMinx</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>-1.522342e+00</td>\n",
       "      <td>1.725943</td>\n",
       "      <td>-8.973281</td>\n",
       "      <td>-3.148087</td>\n",
       "      <td>-6.588208e-01</td>\n",
       "      <td>-0.173697</td>\n",
       "      <td>3.153126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMiny</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>3.262371e-01</td>\n",
       "      <td>0.807775</td>\n",
       "      <td>-4.843436</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.733155e-01</td>\n",
       "      <td>0.544978</td>\n",
       "      <td>7.017185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_sin</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>1.236993e-04</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>2.100214e-14</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_cos</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>2.994682e-16</td>\n",
       "      <td>0.707118</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>6.744632e-15</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year_sin</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>1.413415e-04</td>\n",
       "      <td>0.706877</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.706692</td>\n",
       "      <td>1.168355e-03</td>\n",
       "      <td>0.706697</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year_cos</th>\n",
       "      <td>61405.0</td>\n",
       "      <td>7.067635e-04</td>\n",
       "      <td>0.707347</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.706690</td>\n",
       "      <td>1.114596e-03</td>\n",
       "      <td>0.708306</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count          mean        std        min         25%  \\\n",
       "ATAvg     61405.0  1.694126e+01   3.385494   4.190000   14.530000   \n",
       "ATMax     61405.0  1.705264e+01   3.248350   5.160000   14.600000   \n",
       "ATMin     61405.0  1.688573e+01   3.159448   4.920000   14.520000   \n",
       "RHAvg     61405.0  7.972151e+01  16.536240   5.120000   65.510000   \n",
       "RHMin     61405.0  7.910131e+01  16.940392   5.690000   62.770000   \n",
       "RHMax     61405.0  8.043544e+01  16.168787   5.700000   65.050000   \n",
       "PAvg      61405.0  5.250550e-03   0.043080   0.000000    0.000000   \n",
       "WDAvg     61405.0  1.740801e+02  58.407140   0.130000  151.580000   \n",
       "WSAvg     61405.0  2.464626e+00   2.110336   0.000000    0.680000   \n",
       "WAvgx     61405.0 -2.055486e+00   2.165681  -9.574509   -4.014477   \n",
       "WAvgy     61405.0 -7.617829e-02   1.267648  -6.470612   -0.610501   \n",
       "WMaxx     61405.0 -2.571026e+00   2.409542 -11.824311   -4.982809   \n",
       "WMaxy     61405.0 -9.040149e-01   1.372289 -10.509392   -1.729310   \n",
       "WMinx     61405.0 -1.522342e+00   1.725943  -8.973281   -3.148087   \n",
       "WMiny     61405.0  3.262371e-01   0.807775  -4.843436    0.000000   \n",
       "Day_sin   61405.0  1.236993e-04   0.707107  -1.000000   -0.707107   \n",
       "Day_cos   61405.0  2.994682e-16   0.707118  -1.000000   -0.707107   \n",
       "Year_sin  61405.0  1.413415e-04   0.706877  -1.000000   -0.706692   \n",
       "Year_cos  61405.0  7.067635e-04   0.707347  -1.000000   -0.706690   \n",
       "\n",
       "                   50%         75%         max  \n",
       "ATAvg     1.618600e+01   19.370000   29.310000  \n",
       "ATMax     1.622000e+01   19.720000   29.670000  \n",
       "ATMin     1.611000e+01   19.470000   29.030000  \n",
       "RHAvg     8.379000e+01   94.980000  100.070000  \n",
       "RHMin     8.385000e+01   95.200000  100.050000  \n",
       "RHMax     8.537000e+01   95.600000  100.090000  \n",
       "PAvg      0.000000e+00    0.000000    5.600000  \n",
       "WDAvg     1.796550e+02  199.500000  354.330000  \n",
       "WSAvg     1.525000e+00    4.340000    9.950000  \n",
       "WAvgx    -1.236939e+00   -0.198715    2.777757  \n",
       "WAvgy    -0.000000e+00    0.445041    7.992564  \n",
       "WMaxx    -1.573343e+00   -0.739274    4.512922  \n",
       "WMaxy    -5.443985e-01   -0.277040    8.897659  \n",
       "WMinx    -6.588208e-01   -0.173697    3.153126  \n",
       "WMiny     2.733155e-01    0.544978    7.017185  \n",
       "Day_sin   2.100214e-14    0.707107    1.000000  \n",
       "Day_cos   6.744632e-15    0.707107    1.000000  \n",
       "Year_sin  1.168355e-03    0.706697    1.000000  \n",
       "Year_cos  1.114596e-03    0.708306    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Remove PAvg and WMaxx, WMiny, WDAvg and WSAvg\n",
    "df = df[['ATAvg','ATMax','ATMin',\n",
    "         'RHAvg','RHMin','RHMax',\n",
    "         'WAvgx','WAvgy', 'WMinx', 'WMiny',\n",
    "         'Day_sin', 'Day_cos', 'Year_sin', 'Year_cos']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Split data into 70%, 20%, 10% split for the training, validation, and test sets\n",
    "column_indices = {name: i for i, name in enumerate(df.columns)}\n",
    "\n",
    "n = len(df)\n",
    "train_df = df[0:int(n*0.7)]\n",
    "val_df = df[int(n*0.7):int(n*0.9)]\n",
    "test_df = df[int(n*0.9):]\n",
    "\n",
    "num_features = df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Normalize the data  ### ROOM TO MAKE TESTS (this is just an average)\n",
    "train_mean = train_df.mean()\n",
    "train_std = train_df.std()\n",
    "\n",
    "train_df = (train_df - train_mean) / train_std\n",
    "val_df = (val_df - train_mean) / train_std\n",
    "test_df = (test_df - train_mean) / train_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Feature distributions\n",
    "df_std = (df - train_mean) / train_std\n",
    "df_std = df_std.melt(var_name='Column', value_name='Normalized')\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.violinplot(x='Column', y='Normalized', data=df_std)\n",
    "_ = ax.set_xticklabels(df.keys(), rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Data windowing\n",
    "class WindowGenerator():\n",
    "  def __init__(self, input_width, label_width, shift,\n",
    "               train_df=train_df, val_df=val_df, test_df=test_df,\n",
    "               label_columns=None):\n",
    "    # Store the raw data.\n",
    "    self.train_df = train_df\n",
    "    self.val_df = val_df\n",
    "    self.test_df = test_df\n",
    "\n",
    "    # Work out the label column indices.\n",
    "    self.label_columns = label_columns\n",
    "    if label_columns is not None:\n",
    "      self.label_columns_indices = {name: i for i, name in\n",
    "                                    enumerate(label_columns)}\n",
    "    self.column_indices = {name: i for i, name in\n",
    "                           enumerate(train_df.columns)}\n",
    "\n",
    "    # Work out the window parameters.\n",
    "    self.input_width = input_width\n",
    "    self.label_width = label_width\n",
    "    self.shift = shift\n",
    "\n",
    "    self.total_window_size = input_width + shift\n",
    "\n",
    "    self.input_slice = slice(0, input_width)\n",
    "    self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n",
    "\n",
    "    self.label_start = self.total_window_size - self.label_width\n",
    "    self.labels_slice = slice(self.label_start, None)\n",
    "    self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n",
    "\n",
    "  def __repr__(self):\n",
    "    return '\\n'.join([\n",
    "        f'Total window size: {self.total_window_size}',\n",
    "        f'Input indices: {self.input_indices}',\n",
    "        f'Label indices: {self.label_indices}',\n",
    "        f'Label column name(s): {self.label_columns}'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Split window\n",
    "def split_window(self, features):\n",
    "  inputs = features[:, self.input_slice, :]\n",
    "  labels = features[:, self.labels_slice, :]\n",
    "  if self.label_columns is not None:\n",
    "    labels = tf.stack(\n",
    "        [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n",
    "        axis=-1)\n",
    "\n",
    "  # Slicing doesn't preserve static shape information, so set the shapes\n",
    "  # manually. This way the `tf.data.Datasets` are easier to inspect.\n",
    "  inputs.set_shape([None, self.input_width, None])\n",
    "  labels.set_shape([None, self.label_width, None])\n",
    "\n",
    "  return inputs, labels\n",
    "\n",
    "WindowGenerator.split_window = split_window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Create sequence of arrays of all the dataset\n",
    "def dataset_stack(train_df, window):\n",
    "    stack = []\n",
    "    for i in range(0,len(train_df[0::window])):\n",
    "        start_array = window*i\n",
    "        end_array = window*i + window\n",
    "        if len(train_df[start_array:end_array]) < window:\n",
    "            continue\n",
    "        else:\n",
    "            stack.append(np.array(train_df[start_array:end_array]))\n",
    "    return stack\n",
    "#stack = dataset_stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(self, model=None, plot_col='ATAvg', max_subplots=5):\n",
    "    inputs, labels = self.example\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    plot_col_index = self.column_indices[plot_col]\n",
    "    max_n = min(max_subplots, len(inputs))\n",
    "    \n",
    "    for n in range(max_n):\n",
    "        plt.subplot(max_n, 1, n+1)\n",
    "        plt.ylabel(f'{plot_col} [normed]')\n",
    "        plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n",
    "             label='Inputs', marker='.', zorder=-10)\n",
    "\n",
    "        if self.label_columns:\n",
    "            label_col_index = self.label_columns_indices.get(plot_col, None)\n",
    "        else:\n",
    "            label_col_index = plot_col_index\n",
    "            \n",
    "        if label_col_index is None:\n",
    "            continue\n",
    "            \n",
    "        plt.scatter(self.label_indices, labels[n, :, label_col_index],\n",
    "                edgecolors='k', label='Labels', c='#2ca02c', s=64)\n",
    "        \n",
    "        if model is not None:\n",
    "            predictions = model(inputs)\n",
    "            if self.label_width == 1:\n",
    "                plt.scatter(self.label_indices, predictions[0, -1, label_col_index],\n",
    "                  marker='X', edgecolors='k', label='Predictions',\n",
    "                  c='#ff7f0e', s=64)\n",
    "            else:\n",
    "                plt.scatter(self.label_indices, predictions[n, :, label_col_index],\n",
    "                  marker='X', edgecolors='k', label='Predictions',\n",
    "                  c='#ff7f0e', s=64)\n",
    "        if n == 0:\n",
    "            plt.legend()\n",
    "            \n",
    "        plt.xlabel('Time [steps]')\n",
    "\n",
    "WindowGenerator.plot = plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_dataset(self, data):\n",
    "  data = np.array(data, dtype=np.float32)\n",
    "\n",
    "  ds = tf.keras.preprocessing.timeseries_dataset_from_array(\n",
    "      data=data,\n",
    "      targets=None,\n",
    "      sequence_length=self.total_window_size,\n",
    "      sequence_stride=1,\n",
    "      shuffle=True,\n",
    "      batch_size=32,)\n",
    "\n",
    "  ds = ds.map(self.split_window)\n",
    "\n",
    "  return ds\n",
    "\n",
    "WindowGenerator.make_dataset = make_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "@property\n",
    "def train(self):\n",
    "  return self.make_dataset(self.train_df)\n",
    "\n",
    "@property\n",
    "def val(self):\n",
    "  return self.make_dataset(self.val_df)\n",
    "\n",
    "@property\n",
    "def test(self):\n",
    "  return self.make_dataset(self.test_df)\n",
    "\n",
    "@property\n",
    "def example(self):\n",
    "  \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n",
    "  result = getattr(self, '_example', None)\n",
    "  if result is None:\n",
    "    # No example batch was found, so get one from the `.train` dataset\n",
    "    result = next(iter(self.train))\n",
    "    # And cache it for next time\n",
    "    self._example = result\n",
    "  return result\n",
    "\n",
    "WindowGenerator.train = train\n",
    "WindowGenerator.val = val\n",
    "WindowGenerator.test = test\n",
    "WindowGenerator.example = example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compile_and_fit(model, window,patience=2):\n",
    "  early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                                    patience=patience,\n",
    "                                                    mode='min')\n",
    "\n",
    "  model.compile(loss=tf.losses.MeanSquaredError(),\n",
    "                optimizer=tf.optimizers.Adam(),\n",
    "                metrics=[tf.metrics.MeanAbsoluteError()])\n",
    "\n",
    "  history = model.fit(window.train, epochs=MAX_EPOCHS,\n",
    "                      validation_data=window.val,\n",
    "                      callbacks=[early_stopping])\n",
    "\n",
    "  return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_predictions(self, model = None, plot_col = ['ATAvg','RHAvg'], plot=True):\n",
    "    \n",
    "    \n",
    "    plot_col_index_0 = self.column_indices[plot_col[0]]\n",
    "    plot_col_index_1 = self.column_indices[plot_col[1]]\n",
    "    all_preds_0=[]\n",
    "    all_labels_0 =[]\n",
    "    all_preds_1=[]\n",
    "    all_labels_1 =[]\n",
    "    \n",
    "    n_batches = len(tuple(self.test))\n",
    "    if self.shift==1:\n",
    "        for i in range(n_batches):\n",
    "            for inputs, labels in self.test.take(i):  # iterate over batches\n",
    "            \n",
    "                numpy_labels = labels.numpy() ### get labels\n",
    "                numpy_inputs = inputs.numpy() ### get inputs\n",
    "                preds = model(numpy_inputs) ### make prediction from trined model\n",
    "                numpy_preds = preds.numpy() ### get predictions\n",
    "                \n",
    "                batch_pred_0 =numpy_preds[:,-1,plot_col_index_0]*train_std[plot_col_index_0] + train_mean[plot_col_index_0]\n",
    "                batch_label_0 = numpy_labels[:,-1,plot_col_index_0]*train_std[plot_col_index_0] + train_mean[plot_col_index_0]\n",
    "                batch_pred_1 =numpy_preds[:,-1,plot_col_index_1]*train_std[plot_col_index_1] + train_mean[plot_col_index_1]\n",
    "                batch_label_1 = numpy_labels[:,-1,plot_col_index_1]*train_std[plot_col_index_1] + train_mean[plot_col_index_1]\n",
    "        \n",
    "                all_preds_0.extend(batch_pred_0)\n",
    "                all_labels_0.extend(batch_label_0)\n",
    "                all_preds_1.extend(batch_pred_1)\n",
    "                all_labels_1.extend(batch_label_1)\n",
    "                \n",
    "        r2_0=round(r2_score(all_labels_0, all_preds_0),3)\n",
    "        r2_1=round(r2_score(all_labels_1, all_preds_1),3)\n",
    "        mae_0 = mean_absolute_error(all_labels_0, all_preds_0)\n",
    "        mae_1 = mean_absolute_error(all_labels_1, all_preds_1)\n",
    "                \n",
    "        accuracy = {'r2':{plot_col[0]:r2_0,plot_col[1]:r2_1},\n",
    "                            'mae':{plot_col[0]:mae_0,plot_col[1]:mae_1}}\n",
    "\n",
    "        if plot:\n",
    "            ## var 0 (ATAvg)\n",
    "            slope, intercept, r_value_0, p_value, std_err = stats.linregress(all_labels_0, all_preds_0)\n",
    "            fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
    "            ax[0].scatter(all_labels_0, all_preds_0,edgecolors='k', c='#ff5555', s=32)\n",
    "            ax[0].set_xlabel(f'True Values {plot_col[0]} [de-normed]')\n",
    "            ax[0].set_ylabel(f'Predicted Values {plot_col[0]} [de-normed]')\n",
    "            lims = [math.floor(min(all_labels_0)-min(all_labels_0)*0.1), math.ceil(max(all_labels_0)+max(all_labels_0)*0.1)]\n",
    "            ax[0].set_xlim(lims)\n",
    "            ax[0].set_ylim(lims)\n",
    "            line = slope*np.array(all_labels_0)+intercept\n",
    "            ax[0].plot(all_labels_0, line, 'gray',label = f'r2 = {round(r_value_0**2,3)}')\n",
    "            ax[0].legend(loc = 'lower right')\n",
    "\n",
    "            ## var 1 (RHvg)\n",
    "            slope, intercept, r_value_1, p_value, std_err = stats.linregress(all_labels_1, all_preds_1)\n",
    "            ax[1].scatter(all_labels_1, all_preds_1,edgecolors='k', c='#0ca4b4', s=32)\n",
    "            ax[1].set_xlabel(f'True Values {plot_col[1]} [de-normed]')\n",
    "            ax[1].set_ylabel(f'Predicted Values {plot_col[1]} [de-normed]')\n",
    "            lims = [math.floor(min(all_labels_1)-min(all_labels_1)*0.1), math.ceil(max(all_labels_1)+max(all_labels_1)*0.1)]\n",
    "            ax[1].set_xlim(lims)\n",
    "            ax[1].set_ylim(lims)\n",
    "            line = slope*np.array(all_labels_1)+intercept\n",
    "            ax[1].plot(all_labels_1, line, 'gray',label = f'r2 = {round(r_value_1**2,3)}')\n",
    "            ax[1].legend(loc = 'lower right')\n",
    "\n",
    "    else:\n",
    "        for i in range(n_batches):\n",
    "            #print(f'i = {i}')\n",
    "            for inputs, labels in self.test.take(i):  # iterate over batches\n",
    "                \n",
    "                numpy_labels = labels.numpy() ### get labels\n",
    "                numpy_inputs = inputs.numpy() ### get inputs\n",
    "                preds = model(numpy_inputs) ### make prediction from trined model\n",
    "                numpy_preds = preds.numpy() ### get predictions\n",
    "                \n",
    "                all_preds_by_time_0 = []\n",
    "                all_labels_by_time_0 = []\n",
    "                all_preds_by_time_1 = []\n",
    "                all_labels_by_time_1 = []\n",
    "                \n",
    "                for j in range(numpy_labels.shape[1]): ## number of time steps\n",
    "                    ### get values for each bacth and time and de-normalize\n",
    "                    #print(f'j = {j}')\n",
    "                    batch_pred_0 =numpy_preds[:,j,plot_col_index_0]*train_std[plot_col_index_0] + train_mean[plot_col_index_0]\n",
    "                    batch_label_0 = numpy_labels[:,j,plot_col_index_0]*train_std[plot_col_index_0] + train_mean[plot_col_index_0]\n",
    "                    batch_pred_1 =numpy_preds[:,j,plot_col_index_1]*train_std[plot_col_index_1] + train_mean[plot_col_index_1]\n",
    "                    batch_label_1 = numpy_labels[:,j,plot_col_index_1]*train_std[plot_col_index_1] + train_mean[plot_col_index_1]\n",
    "        \n",
    "                    all_preds_by_time_0.extend(batch_pred_0)\n",
    "                    #print(f'all_preds_by_time = {len(all_preds_by_time_0)}')\n",
    "                    all_labels_by_time_0.extend(batch_label_0)\n",
    "                    all_preds_by_time_1.extend(batch_pred_1)\n",
    "                    all_labels_by_time_1.extend(batch_label_1)\n",
    "                \n",
    "            \n",
    "                all_preds_0.append(all_preds_by_time_0)\n",
    "                all_labels_0.append(all_labels_by_time_0)\n",
    "                all_preds_1.append(all_preds_by_time_1)\n",
    "                all_labels_1.append(all_labels_by_time_1)\n",
    "                if len(all_preds_0) >= i:\n",
    "                    break\n",
    "        \n",
    "                ## covert to array (shape= i,time*batch_size)\n",
    "        multi_preds_0 = np.vstack(all_preds_0)\n",
    "        multi_labels_0 = np.vstack(all_labels_0)\n",
    "        multi_preds_1 = np.vstack(all_preds_1)\n",
    "        multi_labels_1 = np.vstack(all_labels_1)\n",
    "        \n",
    "        mae_pred = []\n",
    "        r2_pred = []        \n",
    "        for a in np.arange(0,multi_labels_0.shape[1],step=32):\n",
    "            mae = mean_absolute_error(multi_labels_0[:,a:a+32], multi_preds_0[:,a:a+32])\n",
    "            mae_pred.append(mae)\n",
    "            r2 = round(r2_score(multi_labels_0[:,a:a+32], multi_preds_0[:,a:a+32]),3)\n",
    "            r2_pred.append(r2)\n",
    "        df = pd.DataFrame(mae_pred, columns=['mae'])\n",
    "        df['r2']=r2_pred\n",
    "        df['var']=plot_col[0]\n",
    "        df['time']=df.index\n",
    "\n",
    "        mae_pred = []\n",
    "        r2_pred = []\n",
    "        for a in np.arange(0,multi_labels_1.shape[1],step=32):\n",
    "            mae = mean_absolute_error(multi_labels_1[:,a:a+32], multi_preds_1[:,a:a+32])\n",
    "            mae_pred.append(mae)\n",
    "            r2 = round(r2_score(multi_labels_1[:,a:a+32], multi_preds_1[:,a:a+32]),3)\n",
    "            r2_pred.append(r2)\n",
    "        df1 = pd.DataFrame(mae_pred, columns=['mae'])\n",
    "        df1['r2']=r2_pred\n",
    "        df1['var']=plot_col[1]\n",
    "        df1['time']=df1.index\n",
    "        df2 = df.append(df1)\n",
    "        accuracy = df2.to_dict('records')  \n",
    "              \n",
    "        if plot:\n",
    "            fig, ax = plt.subplots(2, 2, figsize=(10, 5))\n",
    "            plt.suptitle(f'{model}, window: {self.input_width}_{self.shift}',fontsize = 14)\n",
    "            ax[0,0].plot(df.time, df.mae, c='#ff5555')\n",
    "            ax[0,0].scatter(df.time, df.mae,edgecolors='k', c='#ff5555', s=32 )\n",
    "            ax[0,0].set_xlabel(f'prediction times {plot_col[0]}')\n",
    "            ax[0,0].set_ylabel(f'MAE {plot_col[0]} [de-normed]')\n",
    "            ax[0,1].plot(df1.time, df1.mae, c='#0ca4b4')\n",
    "            ax[0,1].scatter(df1.time, df1.mae,edgecolors='k', c='#0ca4b4', s=32 )\n",
    "            ax[0,1].set_xlabel(f'prediction times {plot_col[1]}')\n",
    "            ax[0,1].set_ylabel(f'MAE {plot_col[1]} [de-normed]')\n",
    "            ax[1,0].plot(df.time, df.r2, c='#ff5555')\n",
    "            ax[1,0].scatter(df.time, df.r2,edgecolors='k', c='#ff5555', s=32 )\n",
    "            ax[1,0].set_xlabel(f'prediction times {plot_col[0]}')\n",
    "            ax[1,0].set_ylabel(f'R2 {plot_col[0]} [de-normed]')\n",
    "            ax[1,1].plot(df1.time, df1.r2, c='#0ca4b4')\n",
    "            ax[1,1].scatter(df1.time, df1.r2,edgecolors='k', c='#0ca4b4', s=32 )\n",
    "            ax[1,1].set_xlabel(f'prediction times {plot_col[1]}')\n",
    "            ax[1,1].set_ylabel(f'R2 {plot_col[1]} [de-normed]')\n",
    "            \n",
    "    return accuracy\n",
    "                       \n",
    "WindowGenerator.get_predictions = get_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MULTI-VARIATE + SINGLE STEP MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 24 hours prior --> predict 1 hour\n",
    "- 24 hours prior --> predict 6 hours\n",
    "- 48 hours prior --> predict 1 hours\n",
    "- 48 hours prior --> predict 6 hours\n",
    "- 48 hours prior --> predict 12 hours\n",
    "- 48 hours prior --> predict 24 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Baseline(tf.keras.Model):\n",
    "  def __init__(self, label_index=None):\n",
    "    super().__init__()\n",
    "    self.label_index = label_index\n",
    "\n",
    "  def call(self, inputs):\n",
    "    if self.label_index is None:\n",
    "      return inputs\n",
    "    result = inputs[:, :, self.label_index]\n",
    "    return result[:, :, tf.newaxis]\n",
    "\n",
    "class ResidualWrapper(tf.keras.Model):\n",
    "  def __init__(self, model):\n",
    "    super().__init__()\n",
    "    self.model = model\n",
    "\n",
    "  def call(self, inputs, *args, **kwargs):\n",
    "    delta = self.model(inputs, *args, **kwargs)\n",
    "\n",
    "    # The prediction for each timestep is the input\n",
    "    # from the previous time step plus the delta\n",
    "    # calculated by the model.\n",
    "    return inputs + delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def single_models(station,path,num_features,input_width, OUT_STEPS):\n",
    "    ### aggragte results\n",
    "    val_performance = {}\n",
    "    performance = {}\n",
    "    r2 ={}\n",
    "    \n",
    "    ## window\n",
    "    window = WindowGenerator(\n",
    "    input_width=input_width, label_width=1, shift=OUT_STEPS)\n",
    "    window.plot()\n",
    "    plt.savefig(f'{path}/{station}_{sample_freq}_w{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    \n",
    "    ### Baseline\n",
    "    baseline = Baseline()\n",
    "    baseline.compile(loss=tf.losses.MeanSquaredError(),\n",
    "                 metrics=[tf.metrics.MeanAbsoluteError()])\n",
    "    val_performance[f'Baseline_{sample_freq}_w{input_width}_{OUT_STEPS}'] = baseline.evaluate(window.val)\n",
    "    performance[f'Baseline_{sample_freq}_w{input_width}_{OUT_STEPS}'] = baseline.evaluate(window.test, verbose=0)\n",
    "    r2[f'Baseline_{sample_freq}_w{input_width}_{OUT_STEPS}']= window.get_predictions(model=baseline,plot=False)\n",
    "    #window.plot(baseline)\n",
    "    #plt.savefig(f'{path}/{station}_Single_Baseline_w{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    \n",
    "    ### Dense\n",
    "    dense = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(units=64, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=64, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=num_features)\n",
    "    ])\n",
    "    history = compile_and_fit(dense, window)\n",
    "    IPython.display.clear_output()\n",
    "    val_performance[f'Dense_{sample_freq}_w{input_width}_{OUT_STEPS}'] = dense.evaluate(window.val)\n",
    "    performance[f'Dense_{sample_freq}_w{input_width}_{OUT_STEPS}'] = dense.evaluate(window.test, verbose=0)\n",
    "    losses = pd.DataFrame(history.history)\n",
    "    losses.plot()\n",
    "    plt.savefig(f'{path}/{station}_Single_Dense_{sample_freq}_w{input_width}_{OUT_STEPS}_losses.png',dpi=100)\n",
    "    #window.plot(dense)\n",
    "    #plt.savefig(f'{path}/{station}_Single_Dense_w{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    r2[f'Dense_{sample_freq}_w{input_width}_{OUT_STEPS}']= window.get_predictions(model=dense,plot=False)\n",
    "    \n",
    "    #RNN\n",
    "    lstm_model = tf.keras.models.Sequential([\n",
    "    # Shape [batch, time, features] => [batch, time, lstm_units]\n",
    "    tf.keras.layers.LSTM(32, return_sequences=True),\n",
    "    # Shape => [batch, time, features]\n",
    "    tf.keras.layers.Dense(units=num_features)\n",
    "    ])\n",
    "    history = compile_and_fit(lstm_model, window)\n",
    "    IPython.display.clear_output()\n",
    "    val_performance[f'LSTM_{sample_freq}_w{input_width}_{OUT_STEPS}'] = lstm_model.evaluate(window.val)\n",
    "    performance[f'LSTM_{sample_freq}_w{input_width}_{OUT_STEPS}'] = lstm_model.evaluate(window.test, verbose=0)\n",
    "    losses = pd.DataFrame(history.history)\n",
    "    losses.plot()\n",
    "    plt.savefig(f'{path}/{station}_Single_LSTM_{sample_freq}_w{input_width}_{OUT_STEPS}_losses.png',dpi=100)\n",
    "    #window.plot(lstm_model)\n",
    "    #plt.savefig(f'{path}/{station}_Single_LSTM_w{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    r2[f'LSTM_{sample_freq}_w{input_width}_{OUT_STEPS}']= window.get_predictions(model=lstm_model,plot=False)\n",
    "    \n",
    "    ### Autoregressive RNN\n",
    "    residual_lstm = ResidualWrapper(\n",
    "    tf.keras.Sequential([\n",
    "    tf.keras.layers.LSTM(32, return_sequences=True),\n",
    "    tf.keras.layers.Dense(\n",
    "        num_features,\n",
    "        # The predicted deltas should start small\n",
    "        # So initialize the output layer with zeros\n",
    "        kernel_initializer=tf.initializers.zeros())\n",
    "    ]))\n",
    "\n",
    "    history = compile_and_fit(residual_lstm, window)\n",
    "\n",
    "    IPython.display.clear_output()\n",
    "    val_performance[f'Residual_LSTM_{sample_freq}_w{input_width}_{OUT_STEPS}'] = residual_lstm.evaluate(window.val)\n",
    "    performance[f'Residual_LSTM_{sample_freq}_w{input_width}_{OUT_STEPS}'] = residual_lstm.evaluate(window.test, verbose=0)\n",
    "    losses = pd.DataFrame(history.history)\n",
    "    losses.plot()\n",
    "    plt.savefig(f'{path}/{station}_Single_Residual_LSTM_{sample_freq}_w{input_width}_{OUT_STEPS}_losses.png',dpi=100)\n",
    "    #window.plot(lstm_model)\n",
    "    #plt.savefig(f'{path}/{station}_Single_Residual_LSTM_w{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    plt.close()\n",
    "    r2[f'Residual_LSTM_{sample_freq}_w{input_width}_{OUT_STEPS}']= window.get_predictions(model=residual_lstm,plot=False)\n",
    "    \n",
    "\n",
    "    ### Performance\n",
    "    fig, ax = plt.subplots()\n",
    "    x = np.arange(len(performance))\n",
    "    width = 0.3\n",
    "    metric_name = 'mean_absolute_error'\n",
    "    metric_index = baseline.metrics_names.index('mean_absolute_error')\n",
    "    val_mae = [v[metric_index] for v in val_performance.values()]\n",
    "    test_mae = [v[metric_index] for v in performance.values()]\n",
    "\n",
    "    ax.bar(x - 0.17, val_mae, width, label='Validation')\n",
    "    ax.bar(x + 0.17, test_mae, width, label='Test')\n",
    "    ax.set_xticks(ticks=x)\n",
    "           \n",
    "    ax.set_xticklabels(labels=performance.keys())\n",
    "    ax.set_ylabel('MAE (average over all outputs)')\n",
    "    ax.legend()\n",
    "    plt.savefig(f'{path}/Performance_Single_{sample_freq}_{station}_w{input_width}_{OUT_STEPS}.png', dpi = 100,bbox_inches='tight')\n",
    "    \n",
    "    pd.concat({k: pd.DataFrame(v).T for k, v in r2.items()}, axis=0).to_csv(f'{path}/{station}_single_{sample_freq}_w{input_width}_{OUT_STEPS}_performance_times.csv')\n",
    "    pd.DataFrame.from_dict(val_performance, orient='index',columns=['loss','mae']).to_csv(f'{path}/{station}_single_{sample_freq}_w{input_width}_{OUT_STEPS}_performance_val.csv')\n",
    "    pd.DataFrame.from_dict(performance, orient='index',columns=['loss','mae']).to_csv(f'{path}/{station}_single_{sample_freq}_w{input_width}_{OUT_STEPS}_performance_test.csv')\n",
    "    \n",
    "    return val_performance, performance, r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_features = df.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24 h + 1h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 24*time_steps\n",
    "OUT_STEPS = 1*time_steps\n",
    "val_performance, performance, r2 = single_models(num_features=num_features,\n",
    "                                                 station=station,\n",
    "                                                 path=path,\n",
    "                                                 input_width=input_width, \n",
    "                                                 OUT_STEPS = OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24 h + 6h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 24*time_steps\n",
    "OUT_STEPS = 6*time_steps\n",
    "val_performance, performance, r2 = single_models(num_features=num_features,\n",
    "                                                 station=station,\n",
    "                                                 path=path,\n",
    "                                                 input_width=input_width, \n",
    "                                                 OUT_STEPS = OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24 h + 24h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 24*time_steps\n",
    "OUT_STEPS = 24*time_steps\n",
    "val_performance, performance, r2 = single_models(num_features=num_features,\n",
    "                                                 station=station,\n",
    "                                                 path=path,\n",
    "                                                 input_width=input_width, \n",
    "                                                 OUT_STEPS = OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48 h + 6h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 48*time_steps\n",
    "OUT_STEPS = 6*time_steps\n",
    "val_performance, performance, r2 = single_models(num_features=num_features,\n",
    "                                                 station=station,\n",
    "                                                 path=path,\n",
    "                                                 input_width=input_width, \n",
    "                                                 OUT_STEPS = OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48 h + 12h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 48*time_steps\n",
    "OUT_STEPS = 12*time_steps\n",
    "val_performance, performance, r2 = single_models(num_features=num_features,\n",
    "                                                 station=station,\n",
    "                                                 path=path,\n",
    "                                                 input_width=input_width, \n",
    "                                                 OUT_STEPS = OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48 h + 24h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 48*time_steps\n",
    "OUT_STEPS = 24*time_steps\n",
    "val_performance, performance, r2 = single_models(num_features=num_features,\n",
    "                                                 station=station,\n",
    "                                                 path=path,\n",
    "                                                 input_width=input_width, \n",
    "                                                 OUT_STEPS = OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48 h + 48h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 48*time_steps\n",
    "OUT_STEPS = 48*time_steps\n",
    "val_performance, performance, r2 = single_models(num_features=num_features,\n",
    "                                                 station=station,\n",
    "                                                 path=path,\n",
    "                                                 input_width=input_width, \n",
    "                                                 OUT_STEPS = OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MULTI-VARIATE + MULTI-STEP MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Windows\n",
    "- 24 hours prior --> predict 1-12 hour\n",
    "- 24 hours prior --> predict 1-24 hours\n",
    "- 48 hours prior --> predict 1-12 hours\n",
    "- 48 hours prior --> predict 1-24 hours\n",
    "- 48 hours prior --> predict 1-36 hours\n",
    "- 48 hours prior --> predict 1-48 hours\n",
    "- 72 hours prior --> predict 1-24 hours\n",
    "- 72 hours prior --> predict 1-48 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiStepLastBaseline(tf.keras.Model):\n",
    "  def call(self, inputs):\n",
    "    return tf.tile(inputs[:, -1:, :], [1, OUT_STEPS, 1])\n",
    "\n",
    "class RepeatBaseline(tf.keras.Model):\n",
    "  def call(self, inputs):\n",
    "    return inputs\n",
    "\n",
    "class FeedBack(tf.keras.Model):\n",
    "  def __init__(self, units, out_steps):\n",
    "    super().__init__()\n",
    "    self.out_steps = out_steps\n",
    "    self.units = units\n",
    "    self.lstm_cell = tf.keras.layers.LSTMCell(units)\n",
    "    # Also wrap the LSTMCell in an RNN to simplify the `warmup` method.\n",
    "    self.lstm_rnn = tf.keras.layers.RNN(self.lstm_cell, return_state=True)\n",
    "    self.dense = tf.keras.layers.Dense(num_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def warmup(self, inputs):\n",
    "  # inputs.shape => (batch, time, features)\n",
    "  # x.shape => (batch, lstm_units)\n",
    "  x, *state = self.lstm_rnn(inputs)\n",
    "\n",
    "  # predictions.shape => (batch, features)\n",
    "  prediction = self.dense(x)\n",
    "  return prediction, state\n",
    "FeedBack.warmup = warmup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def call(self, inputs, training=None):\n",
    "  # Use a TensorArray to capture dynamically unrolled outputs.\n",
    "  predictions = []\n",
    "  # Initialize the lstm state\n",
    "  prediction, state = self.warmup(inputs)\n",
    "\n",
    "  # Insert the first prediction\n",
    "  predictions.append(prediction)\n",
    "\n",
    "  # Run the rest of the prediction steps\n",
    "  for n in range(1, self.out_steps):\n",
    "    # Use the last prediction as input.\n",
    "    x = prediction\n",
    "    # Execute one lstm step.\n",
    "    x, state = self.lstm_cell(x, states=state,\n",
    "                              training=training)\n",
    "    # Convert the lstm output to a prediction.\n",
    "    prediction = self.dense(x)\n",
    "    # Add the prediction to the output\n",
    "    predictions.append(prediction)\n",
    "\n",
    "  # predictions.shape => (time, batch, features)\n",
    "  predictions = tf.stack(predictions)\n",
    "  # predictions.shape => (batch, time, features)\n",
    "  predictions = tf.transpose(predictions, [1, 0, 2])\n",
    "  return predictions\n",
    "\n",
    "FeedBack.call = call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi_models(station, path, num_features,input_width= 24, OUT_STEPS=12):\n",
    "    ### aggragte results\n",
    "    multi_val_performance = {}\n",
    "    multi_performance = {}\n",
    "    r2 ={}\n",
    "    \n",
    "    ## window\n",
    "    window = WindowGenerator(\n",
    "    input_width=input_width, label_width=OUT_STEPS, shift=OUT_STEPS)\n",
    "    window.plot()\n",
    "    plt.savefig(f'{path}/{station}_{sample_freq}_wm{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    \n",
    "    ### Baseline last\n",
    "    print('Baseline last')\n",
    "    last_baseline = MultiStepLastBaseline()\n",
    "    last_baseline.compile(loss=tf.losses.MeanSquaredError(),\n",
    "                      metrics=[tf.metrics.MeanAbsoluteError()])\n",
    "    multi_val_performance[f'BaselineLast_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = last_baseline.evaluate(window.val)\n",
    "    multi_performance[f'BaselineLast_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = last_baseline.evaluate(window.test, verbose=0)\n",
    "    r2[f'BaselineLast_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = window.get_predictions(model=last_baseline)\n",
    "#     window.plot(last_baseline)\n",
    "#     plt.savefig(f'{path}/{station}_BaselineLast_{sample_freq}_wm{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    \n",
    "    ### Baseline repeat\n",
    "    if input_width == OUT_STEPS:\n",
    "        print(f'Baseline repeat')\n",
    "        repeat_baseline = RepeatBaseline()\n",
    "        repeat_baseline.compile(loss=tf.losses.MeanSquaredError(),\n",
    "                        metrics=[tf.metrics.MeanAbsoluteError()])\n",
    "\n",
    "        multi_val_performance[f'BaselineRepeat_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = repeat_baseline.evaluate(window.val)\n",
    "        multi_performance[f'BaselineRepeat_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = repeat_baseline.evaluate(window.test, verbose=0)\n",
    "        r2[f'BaselineRepeat_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = window.get_predictions(model=repeat_baseline)\n",
    "#         window.plot(repeat_baseline)\n",
    "#         plt.savefig(f'{path}/{station}_BaselineRepeat_{sample_freq}_wm{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    else:\n",
    "        print('Skipping Repeat baseline')\n",
    "    \n",
    "    ### Single-Shot\n",
    "    print(f'Single-shot')\n",
    "    multi_linear_model = tf.keras.Sequential([\n",
    "    # Take the last time-step.\n",
    "    # Shape [batch, time, features] => [batch, 1, features]\n",
    "    tf.keras.layers.Lambda(lambda x: x[:, -1:, :]),\n",
    "    # Shape => [batch, 1, out_steps*features]\n",
    "    tf.keras.layers.Dense(OUT_STEPS*num_features,\n",
    "                          kernel_initializer=tf.initializers.zeros()),\n",
    "    # Shape => [batch, out_steps, features]\n",
    "    tf.keras.layers.Reshape([OUT_STEPS, num_features])\n",
    "    ])\n",
    "\n",
    "    history = compile_and_fit(multi_linear_model, window)\n",
    "    IPython.display.clear_output()\n",
    "    multi_val_performance[f'MultiLinear_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = multi_linear_model.evaluate(window.val)\n",
    "    multi_performance[f'MultiLinear_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = multi_linear_model.evaluate(window.test, verbose=0)\n",
    "    r2[f'MultiLinear_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = window.get_predictions(model=multi_linear_model)\n",
    "    losses = pd.DataFrame(history.history)\n",
    "    losses.plot()\n",
    "    #plt.savefig(f'{path}/{station}_MultiLinear_wm{input_width}_{OUT_STEPS}_losses.png',dpi=100)\n",
    "    #window.plot(multi_linear_model)\n",
    "    plt.savefig(f'{path}/{station}_MultiLinear_{sample_freq}_wm{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    \n",
    "    ### Dense\n",
    "    print(f'Dense')\n",
    "    multi_dense_model = tf.keras.Sequential([\n",
    "    # Take the last time step.\n",
    "    # Shape [batch, time, features] => [batch, 1, features]\n",
    "    tf.keras.layers.Lambda(lambda x: x[:, -1:, :]),\n",
    "    # Shape => [batch, 1, dense_units]\n",
    "    tf.keras.layers.Dense(512, activation='relu'),\n",
    "    # Shape => [batch, out_steps*features]\n",
    "    tf.keras.layers.Dense(OUT_STEPS*num_features,\n",
    "                          kernel_initializer=tf.initializers.zeros()),\n",
    "    # Shape => [batch, out_steps, features]\n",
    "    tf.keras.layers.Reshape([OUT_STEPS, num_features])\n",
    "    ])\n",
    "\n",
    "    history = compile_and_fit(multi_dense_model, window)\n",
    "    IPython.display.clear_output()\n",
    "    multi_val_performance[f'MultiDense_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = multi_dense_model.evaluate(window.val)\n",
    "    multi_performance[f'MultiDense_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = multi_dense_model.evaluate(window.test, verbose=0)\n",
    "    r2[f'MultiDense_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = window.get_predictions(model=multi_dense_model)\n",
    "    losses = pd.DataFrame(history.history)\n",
    "    losses.plot()\n",
    "    plt.savefig(f'{path}/{station}_MultiDense_{sample_freq}_wm{input_width}_{OUT_STEPS}_losses.png',dpi=100)\n",
    "    #window.plot(multi_dense_model)\n",
    "    #plt.savefig(f'{path}/{station}_MultiDense_{sample_freq}_wm{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    \n",
    "    ### CNN\n",
    "    print(f'CNN')\n",
    "    CONV_WIDTH = 3\n",
    "    multi_conv_model = tf.keras.Sequential([\n",
    "    # Shape [batch, time, features] => [batch, CONV_WIDTH, features]\n",
    "    tf.keras.layers.Lambda(lambda x: x[:, -CONV_WIDTH:, :]),\n",
    "    # Shape => [batch, 1, conv_units]\n",
    "    tf.keras.layers.Conv1D(256, activation='relu', kernel_size=(CONV_WIDTH)),\n",
    "    # Shape => [batch, 1,  out_steps*features]\n",
    "    tf.keras.layers.Dense(OUT_STEPS*num_features,\n",
    "                          kernel_initializer=tf.initializers.zeros()),\n",
    "    # Shape => [batch, out_steps, features]\n",
    "    tf.keras.layers.Reshape([OUT_STEPS, num_features])\n",
    "    ])\n",
    "\n",
    "    history = compile_and_fit(multi_conv_model, window)\n",
    "    IPython.display.clear_output()\n",
    "    multi_val_performance[f'MultiConv_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = multi_conv_model.evaluate(window.val)\n",
    "    multi_performance[f'MultiConv_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = multi_conv_model.evaluate(window.test, verbose=0)\n",
    "    r2[f'MultiConv_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = window.get_predictions(model=multi_conv_model)\n",
    "    losses = pd.DataFrame(history.history)\n",
    "    losses.plot()\n",
    "    plt.savefig(f'{path}/{station}_MultiConv_{sample_freq}_wm{input_width}_{OUT_STEPS}_losses.png',dpi=100)\n",
    "    #window.plot(multi_conv_model)\n",
    "    #plt.savefig(f'{path}/{station}_MultiConv_{sample_freq}_wm{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    \n",
    "    ### RNN\n",
    "    print(f'RNN')\n",
    "    multi_lstm_model = tf.keras.Sequential([\n",
    "    # Shape [batch, time, features] => [batch, lstm_units]\n",
    "    # Adding more `lstm_units` just overfits more quickly.\n",
    "    tf.keras.layers.LSTM(32, return_sequences=False),\n",
    "    # Shape => [batch, out_steps*features]\n",
    "    tf.keras.layers.Dense(OUT_STEPS*num_features,\n",
    "                          kernel_initializer=tf.initializers.zeros()),\n",
    "    # Shape => [batch, out_steps, features]\n",
    "    tf.keras.layers.Reshape([OUT_STEPS, num_features])\n",
    "    ])\n",
    "\n",
    "    history = compile_and_fit(multi_lstm_model, window)\n",
    "    IPython.display.clear_output()\n",
    "    multi_val_performance[f'MultiLSTM_model_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = multi_lstm_model.evaluate(window.val)\n",
    "    multi_performance[f'MultiLSTM_model_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = multi_lstm_model.evaluate(window.test, verbose=0)\n",
    "    r2[f'MultiConv_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = window.get_predictions(model=multi_lstm_model)\n",
    "    losses = pd.DataFrame(history.history)\n",
    "    losses.plot()\n",
    "    plt.savefig(f'{path}/{station}_MultiLSTM_{sample_freq}_wm{input_width}_{OUT_STEPS}_losses.png',dpi=100)\n",
    "    #window.plot(multi_lstm_model)\n",
    "    #plt.savefig(f'{path}/{station}_MultiLSTM_{sample_freq}_wm{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    \n",
    "    ### Autoregressive RNN\n",
    "    print(f'Autoregressive RNN')\n",
    "    feedback_model = FeedBack(units=32, out_steps=OUT_STEPS)\n",
    "    prediction, state = feedback_model.warmup(window.example[0])\n",
    "    history = compile_and_fit(feedback_model, window)\n",
    "    IPython.display.clear_output()\n",
    "    multi_val_performance[f'AR_LSTM_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = feedback_model.evaluate(window.val)\n",
    "    multi_performance[f'AR_LSTM_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = feedback_model.evaluate(window.test, verbose=0)\n",
    "    r2[f'AR_LSTM_{sample_freq}_wm{input_width}_{OUT_STEPS}'] = window.get_predictions(model=multi_lstm_model)\n",
    "    losses = pd.DataFrame(history.history)\n",
    "    losses.plot()\n",
    "    plt.savefig(f'{path}/{station}_MultiAR_LSTM_{sample_freq}_wm{input_width}_{OUT_STEPS}_losses.png',dpi=100)\n",
    "    #window.plot(feedback_model)\n",
    "    #plt.savefig(f'{path}/{station}_MultiAR_LSTM_wm{input_width}_{OUT_STEPS}_window.png',dpi=100)\n",
    "    plt.close()\n",
    "    \n",
    "    ### Plot Performance\n",
    "    fig, ax = plt.subplots()\n",
    "    x = np.arange(len(multi_performance))\n",
    "    width = 0.3\n",
    "    metric_name = 'mean_absolute_error'\n",
    "    metric_index = last_baseline.metrics_names.index('mean_absolute_error')\n",
    "    val_mae = [v[metric_index] for v in multi_val_performance.values()]\n",
    "    test_mae = [v[metric_index] for v in multi_performance.values()]\n",
    "\n",
    "    ax.bar(x - 0.17, val_mae, width, label='Validation')\n",
    "    ax.bar(x + 0.17, test_mae, width, label='Test')\n",
    "    ax.set_xticks(ticks=x)\n",
    "           \n",
    "    ax.set_xticklabels(labels=multi_performance.keys())\n",
    "    ax.set_ylabel('MAE (average over all outputs)')\n",
    "    ax.legend()\n",
    "    plt.savefig(f'{path}/Performance_Multi_{station}_{sample_freq}_w{input_width}_{OUT_STEPS}.png', dpi = 100,bbox_inches='tight')\n",
    "    \n",
    "    pd.concat({k: pd.DataFrame(v).T for k, v in r2.items()}, axis=0).to_csv(f'{path}/{station}_multi_{sample_freq}_wm{input_width}_{OUT_STEPS}_performance_times.csv')\n",
    "    pd.DataFrame.from_dict(multi_val_performance, orient='index',columns=['loss','mae']).to_csv(f'{path}/{station}_multi_{sample_freq}_wm{input_width}_{OUT_STEPS}_performance_val.csv')\n",
    "    pd.DataFrame.from_dict(multi_performance, orient='index',columns=['loss','mae']).to_csv(f'{path}/{station}_multi_{sample_freq}_wm{input_width}_{OUT_STEPS}_performance_test.csv')\n",
    "    \n",
    "    return multi_val_performance, multi_performance, r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24 h + 12h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 24*time_steps\n",
    "OUT_STEPS =12*time_steps\n",
    "multi_val_performance, multi_performance, r2 = multi_models(station, path, num_features,input_width, OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24 h + 24h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 24*time_steps\n",
    "OUT_STEPS = 24*time_steps\n",
    "multi_val_performance, multi_performance, r2 = multi_models(station, path, num_features,input_width= input_width, OUT_STEPS=OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48 h + 12h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 48*time_steps\n",
    "OUT_STEPS = 12*time_steps\n",
    "multi_val_performance, multi_performance, r2 = multi_models(station, path, num_features,input_width= input_width, OUT_STEPS=OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48 h + 24h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 48*time_steps\n",
    "OUT_STEPS = 24*time_steps\n",
    "multi_val_performance, multi_performance, r2 = multi_models(station, path, num_features,input_width= input_width, OUT_STEPS=OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48 h + 48h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 48*time_steps\n",
    "OUT_STEPS =48*time_steps\n",
    "multi_val_performance, multi_performance, r2 = multi_models(station, path, num_features,input_width= input_width, OUT_STEPS=OUT_STEPS)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 72 h + 12h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 72*time_steps\n",
    "OUT_STEPS = 12*time_steps\n",
    "multi_val_performance, multi_performance, r2 = multi_models(station, path, num_features,input_width= input_width, OUT_STEPS=OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 72 h + 24h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 72*time_steps\n",
    "OUT_STEPS = 24*time_steps\n",
    "multi_val_performance, multi_performance, r2 = multi_models(station, path, num_features,input_width= input_width, OUT_STEPS=OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 72 h + 48h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_width = 72*time_steps\n",
    "OUT_STEPS = 48*time_steps\n",
    "multi_val_performance, multi_performance, r2 = multi_models(station, path, num_features,input_width= input_width, OUT_STEPS=OUT_STEPS)"
   ]
  }
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