AOSC650RNNFinalProject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e73e3c4c-d628-487a-9220-e0537a94b96d",
   "metadata": {},
   "source": [
    "# LSTM\n",
    "\n",
    "Data being read includes precipitation, gage height, river discharge, and longitude/latitude\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9472a459-af62-40ac-8bf5-9db556fc279b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/umd-spring-2024/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n",
      "2024-05-09 13:15:47.184343: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-05-09 13:15:48.319185: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Dropout, Activation, TimeDistributed, RepeatVector\n",
    "import keras\n",
    "#from keras.src.preprocessing.sequence import TimeseriesGenerator\n",
    "#from google.colab import drive\n",
    "#drive.mount('/Colab_Notebooks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a49e0f5f-3782-4c63-826c-44c4928c76d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/share/bgramp'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84ac0507-98ef-4fe5-aaf8-7a9967f5ce86",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_122/3825804853.py:98: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  datasetthree11 = pd.read_csv(\n"
     ]
    }
   ],
   "source": [
    "# reading datasets\n",
    "dataset1 = pd.read_csv(\n",
    "    '01302020PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo1 = pd.read_csv(\n",
    "    '01302020Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree1 = pd.read_csv(\n",
    "    '01302020GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset2 = pd.read_csv(\n",
    "    '01303000PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo2 = pd.read_csv(\n",
    "    '01303000Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree2 = pd.read_csv(\n",
    "    '01303000GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset3 = pd.read_csv(\n",
    "    '01303500PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo3 = pd.read_csv(\n",
    "    '01303500Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree3 = pd.read_csv(\n",
    "    '01303500GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset4 = pd.read_csv(\n",
    "    '01305500PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo4 = pd.read_csv(\n",
    "    '01305500Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree4 = pd.read_csv(\n",
    "    '01305500GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset5 = pd.read_csv(\n",
    "    '01306460PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo5 = pd.read_csv(\n",
    "    '01306460Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree5 = pd.read_csv(\n",
    "    '01306460GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset6 = pd.read_csv(\n",
    "    '01308500PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo6 = pd.read_csv(\n",
    "    '01308500Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree6 = pd.read_csv(\n",
    "    '01308500GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset7 = pd.read_csv(\n",
    "    '01309500PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo7 = pd.read_csv(\n",
    "    '01309500Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree7 = pd.read_csv(\n",
    "    '01309500GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset8 = pd.read_csv(\n",
    "    '01309950PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo8 = pd.read_csv(\n",
    "    '01309950Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree8 = pd.read_csv(\n",
    "    '01309950GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset9 = pd.read_csv(\n",
    "    '01309990PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo9 = pd.read_csv(\n",
    "    '01309990Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree9 = pd.read_csv(\n",
    "    '01309990GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset10 = pd.read_csv(\n",
    "    '01310500PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo10 = pd.read_csv(\n",
    "    '01310500Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree10 = pd.read_csv(\n",
    "    '01310500GageHeight2010-2023.csv',\n",
    "    delimiter=',')\n",
    "dataset11 = pd.read_csv(\n",
    "    '01311500PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo11 = pd.read_csv(\n",
    "    '01311500Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree11 = pd.read_csv(\n",
    "    '01311500GageHeight2010-2023.csv',\n",
    "    delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be10da42-d2d0-41aa-b4e5-bb1423122166",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MO</th>\n",
       "      <th>DY</th>\n",
       "      <th>HR</th>\n",
       "      <th>PRECTOTCORR</th>\n",
       "      <th>Time2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>0.28</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>0.33</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>0.28</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   YEAR  MO  DY  HR  PRECTOTCORR  Time2\n",
       "0  2009  12  31  19         0.14      0\n",
       "1  2009  12  31  20         0.28      4\n",
       "2  2009  12  31  21         0.33      8\n",
       "3  2009  12  31  22         0.28     12\n",
       "4  2009  12  31  23         0.22     16"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "617582f7-4d7d-4aa4-8fe4-ce10703ef2ff",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agency_cd</th>\n",
       "      <th>site_no</th>\n",
       "      <th>datetime</th>\n",
       "      <th>tz_cd</th>\n",
       "      <th>106410_00060</th>\n",
       "      <th>106410_00060_cd</th>\n",
       "      <th>Time</th>\n",
       "      <th>Time2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 0:00</td>\n",
       "      <td>EST</td>\n",
       "      <td>94.9</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 0:15</td>\n",
       "      <td>EST</td>\n",
       "      <td>94.9</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.010416</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 0:30</td>\n",
       "      <td>EST</td>\n",
       "      <td>94.9</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.020833</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 0:45</td>\n",
       "      <td>EST</td>\n",
       "      <td>94.9</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.031250</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 1:00</td>\n",
       "      <td>EST</td>\n",
       "      <td>94.9</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.041666</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  agency_cd  site_no       datetime tz_cd  106410_00060 106410_00060_cd  \\\n",
       "0      USGS  1302020  1/1/2010 0:00   EST          94.9               A   \n",
       "1      USGS  1302020  1/1/2010 0:15   EST          94.9               A   \n",
       "2      USGS  1302020  1/1/2010 0:30   EST          94.9               A   \n",
       "3      USGS  1302020  1/1/2010 0:45   EST          94.9               A   \n",
       "4      USGS  1302020  1/1/2010 1:00   EST          94.9               A   \n",
       "\n",
       "           Time  Time2  \n",
       "0  40179.000000    0.0  \n",
       "1  40179.010416    1.0  \n",
       "2  40179.020833    2.0  \n",
       "3  40179.031250    3.0  \n",
       "4  40179.041666    4.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasettwo1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c5bac77-91e6-422c-8b53-cdd1243a4dec",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agency_cd</th>\n",
       "      <th>site_no</th>\n",
       "      <th>datetime</th>\n",
       "      <th>tz_cd</th>\n",
       "      <th>106409_00065</th>\n",
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       "      <th>Time</th>\n",
       "      <th>Time2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 0:00</td>\n",
       "      <td>EST</td>\n",
       "      <td>0.97</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 0:15</td>\n",
       "      <td>EST</td>\n",
       "      <td>0.97</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.010416</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 0:30</td>\n",
       "      <td>EST</td>\n",
       "      <td>0.97</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.020833</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 0:45</td>\n",
       "      <td>EST</td>\n",
       "      <td>0.97</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.031250</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USGS</td>\n",
       "      <td>1302020</td>\n",
       "      <td>1/1/2010 1:00</td>\n",
       "      <td>EST</td>\n",
       "      <td>0.97</td>\n",
       "      <td>A</td>\n",
       "      <td>40179.041666</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  agency_cd  site_no       datetime tz_cd  106409_00065 106409_00065_cd  \\\n",
       "0      USGS  1302020  1/1/2010 0:00   EST          0.97               A   \n",
       "1      USGS  1302020  1/1/2010 0:15   EST          0.97               A   \n",
       "2      USGS  1302020  1/1/2010 0:30   EST          0.97               A   \n",
       "3      USGS  1302020  1/1/2010 0:45   EST          0.97               A   \n",
       "4      USGS  1302020  1/1/2010 1:00   EST          0.97               A   \n",
       "\n",
       "           Time  Time2  \n",
       "0  40179.000000    0.0  \n",
       "1  40179.010416    1.0  \n",
       "2  40179.020833    2.0  \n",
       "3  40179.031250    3.0  \n",
       "4  40179.041666    4.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetthree1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "443ec77f-18c0-4c8d-a9cf-a70683146cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MO</th>\n",
       "      <th>DY</th>\n",
       "      <th>HR</th>\n",
       "      <th>PRECTOTCORR</th>\n",
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       "      <td>2023</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>122710</th>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122711</th>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        YEAR  MO  DY  HR  PRECTOTCORR   Time2\n",
       "122707  2023  12  31  14          0.0  490828\n",
       "122708  2023  12  31  15          0.0  490832\n",
       "122709  2023  12  31  16          0.0  490836\n",
       "122710  2023  12  31  17          0.0  490840\n",
       "122711  2023  12  31  18          0.0  490844"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8dfae6ab-8ab6-4128-9fa3-6993978e7a4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "489972    109.0\n",
       "489973    109.0\n",
       "489974    109.0\n",
       "489975    109.0\n",
       "489976    109.0\n",
       "Name: 106410_00060, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasettwo1[\"106410_00060\"].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82499a30-c9f6-4682-8af6-02b773e6458a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "490179    0.93\n",
       "490180    0.93\n",
       "490181    0.93\n",
       "490182    0.93\n",
       "490183    0.93\n",
       "Name: 106409_00065, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetthree1[\"106409_00065\"].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51aec7a3-2577-41e3-959a-4456049bb7f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop any nans\n",
    "dataset1 = dataset1.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset2 = dataset2.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset3 = dataset3.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset4 = dataset4.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset5 = dataset5.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset6 = dataset6.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset7 = dataset7.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset8 = dataset8.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset9 = dataset9.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset10 = dataset10.dropna(subset=[\"PRECTOTCORR\"])\n",
    "dataset11 = dataset11.dropna(subset=[\"PRECTOTCORR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07e3606c-700f-477d-83d7-9f7a1ec85085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>YEAR</th>\n",
       "      <th>MO</th>\n",
       "      <th>DY</th>\n",
       "      <th>HR</th>\n",
       "      <th>PRECTOTCORR</th>\n",
       "      <th>Time2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>122712.000000</td>\n",
       "      <td>122712.000000</td>\n",
       "      <td>122712.00000</td>\n",
       "      <td>122712.000000</td>\n",
       "      <td>122712.000000</td>\n",
       "      <td>122712.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.499136</td>\n",
       "      <td>6.523372</td>\n",
       "      <td>15.72834</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0.143851</td>\n",
       "      <td>245422.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.030828</td>\n",
       "      <td>3.448596</td>\n",
       "      <td>8.79958</td>\n",
       "      <td>6.922215</td>\n",
       "      <td>0.533112</td>\n",
       "      <td>141696.189815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>122711.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>245422.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>368133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2023.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.00000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>19.180000</td>\n",
       "      <td>490844.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                YEAR             MO            DY             HR  \\\n",
       "count  122712.000000  122712.000000  122712.00000  122712.000000   \n",
       "mean     2016.499136       6.523372      15.72834      11.500000   \n",
       "std         4.030828       3.448596       8.79958       6.922215   \n",
       "min      2009.000000       1.000000       1.00000       0.000000   \n",
       "25%      2013.000000       4.000000       8.00000       5.750000   \n",
       "50%      2016.000000       7.000000      16.00000      11.500000   \n",
       "75%      2020.000000      10.000000      23.00000      17.250000   \n",
       "max      2023.000000      12.000000      31.00000      23.000000   \n",
       "\n",
       "         PRECTOTCORR          Time2  \n",
       "count  122712.000000  122712.000000  \n",
       "mean        0.143851  245422.000000  \n",
       "std         0.533112  141696.189815  \n",
       "min         0.000000       0.000000  \n",
       "25%         0.000000  122711.000000  \n",
       "50%         0.000000  245422.000000  \n",
       "75%         0.030000  368133.000000  \n",
       "max        19.180000  490844.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bba20539-17f6-4a4f-9415-c527d455c481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>site_no</th>\n",
       "      <th>106410_00060</th>\n",
       "      <th>Time</th>\n",
       "      <th>Time2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>489977.0</td>\n",
       "      <td>489977.00000</td>\n",
       "      <td>489977.000000</td>\n",
       "      <td>489977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1302020.0</td>\n",
       "      <td>76.70612</td>\n",
       "      <td>42732.821357</td>\n",
       "      <td>245166.850337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>128.36378</td>\n",
       "      <td>1475.420189</td>\n",
       "      <td>141640.338186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1302020.0</td>\n",
       "      <td>7.10000</td>\n",
       "      <td>40179.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1302020.0</td>\n",
       "      <td>28.90000</td>\n",
       "      <td>41455.479166</td>\n",
       "      <td>122542.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1302020.0</td>\n",
       "      <td>45.80000</td>\n",
       "      <td>42731.750000</td>\n",
       "      <td>245064.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1302020.0</td>\n",
       "      <td>76.60000</td>\n",
       "      <td>44008.822916</td>\n",
       "      <td>367663.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1302020.0</td>\n",
       "      <td>3000.00000</td>\n",
       "      <td>45291.989583</td>\n",
       "      <td>490847.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         site_no  106410_00060           Time          Time2\n",
       "count   489977.0  489977.00000  489977.000000  489977.000000\n",
       "mean   1302020.0      76.70612   42732.821357  245166.850337\n",
       "std          0.0     128.36378    1475.420189  141640.338186\n",
       "min    1302020.0       7.10000   40179.000000       0.000000\n",
       "25%    1302020.0      28.90000   41455.479166  122542.000000\n",
       "50%    1302020.0      45.80000   42731.750000  245064.000000\n",
       "75%    1302020.0      76.60000   44008.822916  367663.000000\n",
       "max    1302020.0    3000.00000   45291.989583  490847.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasettwo1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7f681a92-244c-44b6-9b0c-b78aadafac2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4ec34e5540>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(122712), dataset1[\"PRECTOTCORR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fca77d64-3146-4a7f-91ac-37e25e83c4a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4ec33c2d70>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(489977), datasettwo1[\"106410_00060\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e217d717-44f7-40e1-90f1-502249fb1df3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4ec2458760>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(490184), datasetthree1[\"106409_00065\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec79da90-c449-448d-bc60-6ead1f9bfbad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122395\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train = []\n",
    "target = []\n",
    "for x in dataset1[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset1[\"PRECTOTCORR\"][np.where(dataset1[\"Time2\"]==x)[0][0]], datasettwo1[\"106410_00060\"][np.where(datasettwo1[\"Time2\"]==x)[0][0]], datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x)[0][0]], 40.8623, -73.8744])\n",
    "    check.append([dataset1[\"PRECTOTCORR\"][np.where(dataset1[\"Time2\"]==x)[0][0]], datasettwo1[\"106410_00060\"][np.where(datasettwo1[\"Time2\"]==x+1)[0][0]], datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x+1)[0][0]], 40.8623, -73.8744])\n",
    "    check.append([dataset1[\"PRECTOTCORR\"][np.where(dataset1[\"Time2\"]==x)[0][0]], datasettwo1[\"106410_00060\"][np.where(datasettwo1[\"Time2\"]==x+2)[0][0]], datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x+2)[0][0]], 40.8623, -73.8744])\n",
    "    check.append([dataset1[\"PRECTOTCORR\"][np.where(dataset1[\"Time2\"]==x)[0][0]], datasettwo1[\"106410_00060\"][np.where(datasettwo1[\"Time2\"]==x+3)[0][0]], datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x+3)[0][0]], 40.8623, -73.8744])\n",
    "    check.append(datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x+5)[0][0]])\n",
    "    train.append(check[0:4])\n",
    "    target.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8815f6e7-7bb5-4f66-8aa5-0b40418aecc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30027\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train2 = []\n",
    "target2 = []\n",
    "for x in dataset2[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset2[\"PRECTOTCORR\"][np.where(dataset2[\"Time2\"]==x)[0][0]], datasettwo2[\"201840_0060\"][np.where(datasettwo2[\"Time2\"]==x)[0][0]], datasetthree2[\"201841_00065\"][np.where(datasetthree2[\"Time2\"]==x)[0][0]], 40.8874, -73.5632])\n",
    "    check.append([dataset2[\"PRECTOTCORR\"][np.where(dataset2[\"Time2\"]==x)[0][0]], datasettwo2[\"201840_0060\"][np.where(datasettwo2[\"Time2\"]==x+1)[0][0]], datasetthree2[\"201841_00065\"][np.where(datasetthree2[\"Time2\"]==x+1)[0][0]], 40.8874, -73.5632])\n",
    "    check.append([dataset2[\"PRECTOTCORR\"][np.where(dataset2[\"Time2\"]==x)[0][0]], datasettwo2[\"201840_0060\"][np.where(datasettwo2[\"Time2\"]==x+2)[0][0]], datasetthree2[\"201841_00065\"][np.where(datasetthree2[\"Time2\"]==x+2)[0][0]], 40.8874, -73.5632])\n",
    "    check.append([dataset2[\"PRECTOTCORR\"][np.where(dataset2[\"Time2\"]==x)[0][0]], datasettwo2[\"201840_0060\"][np.where(datasettwo2[\"Time2\"]==x+3)[0][0]], datasetthree2[\"201841_00065\"][np.where(datasetthree2[\"Time2\"]==x+3)[0][0]], 40.8874, -73.5632])\n",
    "    check.append(datasetthree2[\"201841_00065\"][np.where(datasetthree2[\"Time2\"]==x+5)[0][0]])\n",
    "    train2.append(check[0:4])\n",
    "    target2.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33e43e16-1f16-4f49-b8d7-cb6d94f7ddc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105000\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train3 = []\n",
    "target3 = []\n",
    "for x in dataset3[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset3[\"PRECTOTCORR\"][np.where(dataset3[\"Time2\"]==x)[0][0]], datasettwo3[\"106444_00060\"][np.where(datasettwo3[\"Time2\"]==x)[0][0]], datasetthree3[\"241896_00065\"][np.where(datasetthree3[\"Time2\"]==x)[0][0]], 40.8572, -73.4636])\n",
    "    check.append([dataset3[\"PRECTOTCORR\"][np.where(dataset3[\"Time2\"]==x)[0][0]], datasettwo3[\"106444_00060\"][np.where(datasettwo3[\"Time2\"]==x+1)[0][0]], datasetthree3[\"241896_00065\"][np.where(datasetthree3[\"Time2\"]==x+1)[0][0]], 40.8572, -73.4636])\n",
    "    check.append([dataset3[\"PRECTOTCORR\"][np.where(dataset3[\"Time2\"]==x)[0][0]], datasettwo3[\"106444_00060\"][np.where(datasettwo3[\"Time2\"]==x+2)[0][0]], datasetthree3[\"241896_00065\"][np.where(datasetthree3[\"Time2\"]==x+2)[0][0]], 40.8572, -73.4636])\n",
    "    check.append([dataset3[\"PRECTOTCORR\"][np.where(dataset3[\"Time2\"]==x)[0][0]], datasettwo3[\"106444_00060\"][np.where(datasettwo3[\"Time2\"]==x+3)[0][0]], datasetthree3[\"241896_00065\"][np.where(datasetthree3[\"Time2\"]==x+3)[0][0]], 40.8572, -73.4636])\n",
    "    check.append(datasetthree3[\"241896_00065\"][np.where(datasetthree3[\"Time2\"]==x+5)[0][0]])\n",
    "    train3.append(check[0:4])\n",
    "    target3.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7d22ae0c-349c-46ec-9508-7f5d149a97eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107449\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train4 = []\n",
    "target4 = []\n",
    "for x in dataset4[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset4[\"PRECTOTCORR\"][np.where(dataset4[\"Time2\"]==x)[0][0]], datasettwo4[\"172577_00060\"][np.where(datasettwo4[\"Time2\"]==x)[0][0]], datasetthree4[\"250916_00065\"][np.where(datasetthree4[\"Time2\"]==x)[0][0]], 40.7672, -72.9938])\n",
    "    check.append([dataset4[\"PRECTOTCORR\"][np.where(dataset4[\"Time2\"]==x)[0][0]], datasettwo4[\"172577_00060\"][np.where(datasettwo4[\"Time2\"]==x+1)[0][0]], datasetthree4[\"250916_00065\"][np.where(datasetthree4[\"Time2\"]==x+1)[0][0]], 40.7672, -72.9938])\n",
    "    check.append([dataset4[\"PRECTOTCORR\"][np.where(dataset4[\"Time2\"]==x)[0][0]], datasettwo4[\"172577_00060\"][np.where(datasettwo4[\"Time2\"]==x+2)[0][0]], datasetthree4[\"250916_00065\"][np.where(datasetthree4[\"Time2\"]==x+2)[0][0]], 40.7672, -72.9938])\n",
    "    check.append([dataset4[\"PRECTOTCORR\"][np.where(dataset4[\"Time2\"]==x)[0][0]], datasettwo4[\"172577_00060\"][np.where(datasettwo4[\"Time2\"]==x+3)[0][0]], datasetthree4[\"250916_00065\"][np.where(datasetthree4[\"Time2\"]==x+3)[0][0]], 40.7672, -72.9938])\n",
    "    check.append(datasetthree4[\"250916_00065\"][np.where(datasetthree4[\"Time2\"]==x+5)[0][0]])\n",
    "    train4.append(check[0:4])\n",
    "    target4.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c98dcec-ae72-4323-8ec9-deea6a0a0e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121945\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train5 = []\n",
    "target5 = []\n",
    "for x in dataset5[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset5[\"PRECTOTCORR\"][np.where(dataset5[\"Time2\"]==x)[0][0]], datasettwo5[\"106542_00060\"][np.where(datasettwo5[\"Time2\"]==x)[0][0]], datasetthree5[\"106543_00065\"][np.where(datasetthree5[\"Time2\"]==x)[0][0]], 40.7723, -73.1587])\n",
    "    check.append([dataset5[\"PRECTOTCORR\"][np.where(dataset5[\"Time2\"]==x)[0][0]], datasettwo5[\"106542_00060\"][np.where(datasettwo5[\"Time2\"]==x+1)[0][0]], datasetthree5[\"106543_00065\"][np.where(datasetthree5[\"Time2\"]==x+1)[0][0]], 40.7723, -73.1587])\n",
    "    check.append([dataset5[\"PRECTOTCORR\"][np.where(dataset5[\"Time2\"]==x)[0][0]], datasettwo5[\"106542_00060\"][np.where(datasettwo5[\"Time2\"]==x+2)[0][0]], datasetthree5[\"106543_00065\"][np.where(datasetthree5[\"Time2\"]==x+2)[0][0]], 40.7723, -73.1587])\n",
    "    check.append([dataset5[\"PRECTOTCORR\"][np.where(dataset5[\"Time2\"]==x)[0][0]], datasettwo5[\"106542_00060\"][np.where(datasettwo5[\"Time2\"]==x+3)[0][0]], datasetthree5[\"106543_00065\"][np.where(datasetthree5[\"Time2\"]==x+3)[0][0]], 40.7723, -73.1587])\n",
    "    check.append(datasetthree5[\"106543_00065\"][np.where(datasetthree5[\"Time2\"]==x+5)[0][0]])\n",
    "    train5.append(check[0:4])\n",
    "    target5.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "caa5f73b-2651-4ad4-b284-fa1b906cf151",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86955\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train6 = []\n",
    "target6 = []\n",
    "for x in dataset6[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset6[\"PRECTOTCORR\"][np.where(dataset6[\"Time2\"]==x)[0][0]], datasettwo6[\"106546_00060\"][np.where(datasettwo6[\"Time2\"]==x)[0][0]], datasetthree6[\"106547_00065\"][np.where(datasetthree6[\"Time2\"]==x)[0][0]], 40.709, -73.3288])\n",
    "    check.append([dataset6[\"PRECTOTCORR\"][np.where(dataset6[\"Time2\"]==x)[0][0]], datasettwo6[\"106546_00060\"][np.where(datasettwo6[\"Time2\"]==x+1)[0][0]], datasetthree6[\"106547_00065\"][np.where(datasetthree6[\"Time2\"]==x+1)[0][0]], 40.709, -73.3288])\n",
    "    check.append([dataset6[\"PRECTOTCORR\"][np.where(dataset6[\"Time2\"]==x)[0][0]], datasettwo6[\"106546_00060\"][np.where(datasettwo6[\"Time2\"]==x+2)[0][0]], datasetthree6[\"106547_00065\"][np.where(datasetthree6[\"Time2\"]==x+2)[0][0]], 40.709, -73.3288])\n",
    "    check.append([dataset6[\"PRECTOTCORR\"][np.where(dataset6[\"Time2\"]==x)[0][0]], datasettwo6[\"106546_00060\"][np.where(datasettwo6[\"Time2\"]==x+3)[0][0]], datasetthree6[\"106547_00065\"][np.where(datasetthree6[\"Time2\"]==x+3)[0][0]], 40.709, -73.3288])\n",
    "    check.append(datasetthree6[\"106547_00065\"][np.where(datasetthree6[\"Time2\"]==x+5)[0][0]])\n",
    "    train6.append(check[0:4])\n",
    "    target6.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e3210e55-190a-468f-a0fc-d5a0f70b00b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102967\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train7 = []\n",
    "target7 = []\n",
    "for x in dataset7[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset7[\"PRECTOTCORR\"][np.where(dataset7[\"Time2\"]==x)[0][0]], datasettwo7[\"106556_00060\"][np.where(datasettwo7[\"Time2\"]==x)[0][0]], datasetthree7[\"106557_00065\"][np.where(datasetthree7[\"Time2\"]==x)[0][0]], 40.689, -73.4554])\n",
    "    check.append([dataset7[\"PRECTOTCORR\"][np.where(dataset7[\"Time2\"]==x)[0][0]], datasettwo7[\"106556_00060\"][np.where(datasettwo7[\"Time2\"]==x+1)[0][0]], datasetthree7[\"106557_00065\"][np.where(datasetthree7[\"Time2\"]==x+1)[0][0]], 40.689, -73.4554])\n",
    "    check.append([dataset7[\"PRECTOTCORR\"][np.where(dataset7[\"Time2\"]==x)[0][0]], datasettwo7[\"106556_00060\"][np.where(datasettwo7[\"Time2\"]==x+2)[0][0]], datasetthree7[\"106557_00065\"][np.where(datasetthree7[\"Time2\"]==x+2)[0][0]], 40.689, -73.4554])\n",
    "    check.append([dataset7[\"PRECTOTCORR\"][np.where(dataset7[\"Time2\"]==x)[0][0]], datasettwo7[\"106556_00060\"][np.where(datasettwo7[\"Time2\"]==x+3)[0][0]], datasetthree7[\"106557_00065\"][np.where(datasetthree7[\"Time2\"]==x+3)[0][0]], 40.689, -73.4554])\n",
    "    check.append(datasetthree7[\"106557_00065\"][np.where(datasetthree7[\"Time2\"]==x+5)[0][0]])\n",
    "    train7.append(check[0:4])\n",
    "    target7.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fb16c9d5-99f4-4015-a35d-a639b87a02d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86678\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train8 = []\n",
    "target8 = []\n",
    "for x in dataset8[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset8[\"PRECTOTCORR\"][np.where(dataset8[\"Time2\"]==x)[0][0]], datasettwo8[\"106560_00060\"][np.where(datasettwo8[\"Time2\"]==x)[0][0]], datasetthree8[\"106561_00065\"][np.where(datasetthree8[\"Time2\"]==x)[0][0]], 40.6787, -73.5159])\n",
    "    check.append([dataset8[\"PRECTOTCORR\"][np.where(dataset8[\"Time2\"]==x)[0][0]], datasettwo8[\"106560_00060\"][np.where(datasettwo8[\"Time2\"]==x+1)[0][0]], datasetthree8[\"106561_00065\"][np.where(datasetthree8[\"Time2\"]==x+1)[0][0]], 40.6787, -73.5159])\n",
    "    check.append([dataset8[\"PRECTOTCORR\"][np.where(dataset8[\"Time2\"]==x)[0][0]], datasettwo8[\"106560_00060\"][np.where(datasettwo8[\"Time2\"]==x+2)[0][0]], datasetthree8[\"106561_00065\"][np.where(datasetthree8[\"Time2\"]==x+2)[0][0]], 40.6787, -73.5159])\n",
    "    check.append([dataset8[\"PRECTOTCORR\"][np.where(dataset8[\"Time2\"]==x)[0][0]], datasettwo8[\"106560_00060\"][np.where(datasettwo8[\"Time2\"]==x+3)[0][0]], datasetthree8[\"106561_00065\"][np.where(datasetthree8[\"Time2\"]==x+3)[0][0]], 40.6787, -73.5159])\n",
    "    check.append(datasetthree8[\"106561_00065\"][np.where(datasetthree8[\"Time2\"]==x+5)[0][0]])\n",
    "    train8.append(check[0:4])\n",
    "    target8.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "40557e75-e2b1-4325-92e2-964a6197d078",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64119\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train9 = []\n",
    "target9 = []\n",
    "for x in dataset9[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset9[\"PRECTOTCORR\"][np.where(dataset9[\"Time2\"]==x)[0][0]], datasettwo9[\"172625_00060\"][np.where(datasettwo9[\"Time2\"]==x)[0][0]], datasetthree9[\"172626_00065\"][np.where(datasetthree9[\"Time2\"]==x)[0][0]], 40.680, -73.513])\n",
    "    check.append([dataset9[\"PRECTOTCORR\"][np.where(dataset9[\"Time2\"]==x)[0][0]], datasettwo9[\"172625_00060\"][np.where(datasettwo9[\"Time2\"]==x+1)[0][0]], datasetthree9[\"172626_00065\"][np.where(datasetthree9[\"Time2\"]==x+1)[0][0]], 40.680, -73.513])\n",
    "    check.append([dataset9[\"PRECTOTCORR\"][np.where(dataset9[\"Time2\"]==x)[0][0]], datasettwo9[\"172625_00060\"][np.where(datasettwo9[\"Time2\"]==x+2)[0][0]], datasetthree9[\"172626_00065\"][np.where(datasetthree9[\"Time2\"]==x+2)[0][0]], 40.680, -73.513])\n",
    "    check.append([dataset9[\"PRECTOTCORR\"][np.where(dataset9[\"Time2\"]==x)[0][0]], datasettwo9[\"172625_00060\"][np.where(datasettwo9[\"Time2\"]==x+3)[0][0]], datasetthree9[\"172626_00065\"][np.where(datasetthree9[\"Time2\"]==x+3)[0][0]], 40.680, -73.513])\n",
    "    check.append(datasetthree9[\"172626_00065\"][np.where(datasetthree9[\"Time2\"]==x+5)[0][0]])\n",
    "    train9.append(check[0:4])\n",
    "    target9.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4fe9abd9-e48d-4426-b17a-993ad284132f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53287\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train10 = []\n",
    "target10 = []\n",
    "for x in dataset10[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset10[\"PRECTOTCORR\"][np.where(dataset10[\"Time2\"]==x)[0][0]], datasettwo10[\"172627_00060\"][np.where(datasettwo10[\"Time2\"]==x)[0][0]], datasetthree10[\"251639_00065\"][np.where(datasetthree10[\"Time2\"]==x)[0][0]], 40.6656, -73.5701])\n",
    "    check.append([dataset10[\"PRECTOTCORR\"][np.where(dataset10[\"Time2\"]==x)[0][0]], datasettwo10[\"172627_00060\"][np.where(datasettwo10[\"Time2\"]==x+1)[0][0]], datasetthree10[\"251639_00065\"][np.where(datasetthree10[\"Time2\"]==x+1)[0][0]], 40.6656, -73.5701])\n",
    "    check.append([dataset10[\"PRECTOTCORR\"][np.where(dataset10[\"Time2\"]==x)[0][0]], datasettwo10[\"172627_00060\"][np.where(datasettwo10[\"Time2\"]==x+2)[0][0]], datasetthree10[\"251639_00065\"][np.where(datasetthree10[\"Time2\"]==x+2)[0][0]], 40.6656, -73.5701])\n",
    "    check.append([dataset10[\"PRECTOTCORR\"][np.where(dataset10[\"Time2\"]==x)[0][0]], datasettwo10[\"172627_00060\"][np.where(datasettwo10[\"Time2\"]==x+3)[0][0]], datasetthree10[\"251639_00065\"][np.where(datasetthree10[\"Time2\"]==x+3)[0][0]], 40.6656, -73.5701])\n",
    "    check.append(datasetthree10[\"251639_00065\"][np.where(datasetthree10[\"Time2\"]==x+5)[0][0]])\n",
    "    train10.append(check[0:4])\n",
    "    target10.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ce1397f4-3ff2-4dbe-b924-ef76d1d2798e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119267\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train11 = []\n",
    "target11 = []\n",
    "for x in dataset11[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset11[\"PRECTOTCORR\"][np.where(dataset11[\"Time2\"]==x)[0][0]], datasettwo11[\"106624_00060\"][np.where(datasettwo11[\"Time2\"]==x)[0][0]], datasetthree11[\"106625_00065\"][np.where(datasetthree11[\"Time2\"]==x)[0][0]], 40.6638, -73.7045])\n",
    "    check.append([dataset11[\"PRECTOTCORR\"][np.where(dataset11[\"Time2\"]==x)[0][0]], datasettwo11[\"106624_00060\"][np.where(datasettwo11[\"Time2\"]==x+1)[0][0]], datasetthree11[\"106625_00065\"][np.where(datasetthree11[\"Time2\"]==x+1)[0][0]], 40.6638, -73.7045])\n",
    "    check.append([dataset11[\"PRECTOTCORR\"][np.where(dataset11[\"Time2\"]==x)[0][0]], datasettwo11[\"106624_00060\"][np.where(datasettwo11[\"Time2\"]==x+2)[0][0]], datasetthree11[\"106625_00065\"][np.where(datasetthree11[\"Time2\"]==x+2)[0][0]], 40.6638, -73.7045])\n",
    "    check.append([dataset11[\"PRECTOTCORR\"][np.where(dataset11[\"Time2\"]==x)[0][0]], datasettwo11[\"106624_00060\"][np.where(datasettwo11[\"Time2\"]==x+3)[0][0]], datasetthree11[\"106625_00065\"][np.where(datasetthree11[\"Time2\"]==x+3)[0][0]], 40.6638, -73.7045])\n",
    "    check.append(datasetthree11[\"106625_00065\"][np.where(datasetthree11[\"Time2\"]==x+5)[0][0]])\n",
    "    train11.append(check[0:4])\n",
    "    target11.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9c9792a7-60b9-4f54-bb6b-67e96edc93e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.16'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetthree11[\"106625_00065\"][np.where(datasetthree11[\"Time2\"]==490000)[0][0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ffbf5fc3-5380-430b-b589-db2d1f5bfe18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.7"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasettwo10[\"172627_00060\"][np.where(datasettwo10[\"Time2\"]==490000)[0][0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f8a61094-a06b-4ab9-aede-1b9a9b97b1f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==490000)[0][0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4ae68426-bffc-46d8-af9c-019733cae8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#free up memory\n",
    "del dataset1\n",
    "del datasettwo1\n",
    "del datasetthree1\n",
    "del dataset2\n",
    "del datasettwo2\n",
    "del datasetthree2\n",
    "del dataset3\n",
    "del datasettwo3\n",
    "del datasetthree3\n",
    "del dataset4\n",
    "del datasettwo4\n",
    "del datasetthree4\n",
    "del dataset5\n",
    "del datasettwo5\n",
    "del datasetthree5\n",
    "del dataset6\n",
    "del datasettwo6\n",
    "del datasetthree6\n",
    "del dataset7\n",
    "del datasettwo7\n",
    "del datasetthree7\n",
    "del dataset8\n",
    "del datasettwo8\n",
    "del datasetthree8\n",
    "del dataset9\n",
    "del datasettwo9\n",
    "del datasetthree9\n",
    "del dataset10\n",
    "del datasettwo10\n",
    "del datasetthree10\n",
    "del dataset11\n",
    "del datasettwo11\n",
    "del datasetthree11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1dfb020b-2d01-463b-bb12-2f85611426ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "122395"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "26a03029-6043-4270-8b2f-9fe67273ecc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30027"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b728ec11-0b2c-4571-af0b-83e84420d3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "122395"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9d6fdfc1-63cd-4826-8447-8613f37a15ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30027"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(target2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9dde38a3-5cf5-4acd-813c-a127df016c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test split by years\n",
    "trainds = train[0:math.ceil(0.75*len(train))]\n",
    "traintargetds = target[0:math.ceil(0.75*len(target))]\n",
    "testds = train[math.ceil(0.75*len(train)):]\n",
    "testtargetds = target[math.ceil(0.75*len(target)):]\n",
    "trainds2 = train2[0:math.ceil(0.75*len(train2))]\n",
    "traintargetds2 = target2[0:math.ceil(0.75*len(target2))]\n",
    "testds2 = train2[math.ceil(0.75*len(train2)):]\n",
    "testtargetds2 = target2[math.ceil(0.75*len(target2)):]\n",
    "trainds3 = train3[0:math.ceil(0.75*len(train3))]\n",
    "traintargetds3 = target3[0:math.ceil(0.75*len(target3))]\n",
    "testds3 = train3[math.ceil(0.75*len(train3)):]\n",
    "testtargetds3 = target3[math.ceil(0.75*len(target3)):]\n",
    "trainds4 = train4[0:math.ceil(0.75*len(train4))]\n",
    "traintargetds4 = target4[0:math.ceil(0.75*len(target4))]\n",
    "testds4 = train4[math.ceil(0.75*len(train4)):]\n",
    "testtargetds4 = target4[math.ceil(0.75*len(target4)):]\n",
    "trainds5 = train5[0:math.ceil(0.75*len(train5))]\n",
    "traintargetds5 = target5[0:math.ceil(0.75*len(target5))]\n",
    "testds5 = train5[math.ceil(0.75*len(train5)):]\n",
    "testtargetds5 = target5[math.ceil(0.75*len(target5)):]\n",
    "trainds6 = train6[0:math.ceil(0.75*len(train6))]\n",
    "traintargetds6 = target6[0:math.ceil(0.75*len(target6))]\n",
    "testds6 = train6[math.ceil(0.75*len(train6)):]\n",
    "testtargetds6 = target6[math.ceil(0.75*len(target6)):]\n",
    "trainds7 = train7[0:math.ceil(0.75*len(train7))]\n",
    "traintargetds7 = target7[0:math.ceil(0.75*len(target7))]\n",
    "testds7 = train7[math.ceil(0.75*len(train7)):]\n",
    "testtargetds7 = target7[math.ceil(0.75*len(target7)):]\n",
    "trainds8 = train8[0:math.ceil(0.75*len(train8))]\n",
    "traintargetds8 = target8[0:math.ceil(0.75*len(target8))]\n",
    "testds8 = train8[math.ceil(0.75*len(train8)):]\n",
    "testtargetds8 = target8[math.ceil(0.75*len(target8)):]\n",
    "trainds9 = train9[0:math.ceil(0.75*len(train9))]\n",
    "traintargetds9 = target9[0:math.ceil(0.75*len(target9))]\n",
    "testds9 = train9[math.ceil(0.75*len(train9)):]\n",
    "testtargetds9 = target9[math.ceil(0.75*len(target9)):]\n",
    "trainds10 = train10[0:math.ceil(0.75*len(train10))]\n",
    "traintargetds10 = target10[0:math.ceil(0.75*len(target10))]\n",
    "testds10 = train10[math.ceil(0.75*len(train10)):]\n",
    "testtargetds10 = target10[math.ceil(0.75*len(target10)):]\n",
    "trainds11 = train11[0:math.ceil(0.75*len(train11))]\n",
    "traintargetds11 = target11[0:math.ceil(0.75*len(target11))]\n",
    "testds11 = train11[math.ceil(0.75*len(train11)):]\n",
    "testtargetds11 = target11[math.ceil(0.75*len(target11)):]\n",
    "for n in range(len(trainds2)):\n",
    "  trainds.append(trainds2[n])\n",
    "for n in range(len(trainds3)):\n",
    "  trainds.append(trainds3[n])\n",
    "for n in range(len(trainds4)):\n",
    "  trainds.append(trainds4[n])\n",
    "for n in range(len(trainds5)):\n",
    "  trainds.append(trainds5[n])\n",
    "for n in range(len(trainds6)):\n",
    "  trainds.append(trainds6[n])\n",
    "for n in range(len(trainds7)):\n",
    "  trainds.append(trainds7[n])\n",
    "for n in range(len(trainds8)):\n",
    "  trainds.append(trainds8[n])\n",
    "for n in range(len(trainds9)):\n",
    "  trainds.append(trainds9[n])\n",
    "for n in range(len(trainds10)):\n",
    "  trainds.append(trainds10[n])\n",
    "for n in range(len(trainds11)):\n",
    "  trainds.append(trainds11[n])\n",
    "for n in range(len(traintargetds2)):\n",
    "  traintargetds.append(traintargetds2[n])\n",
    "for n in range(len(traintargetds3)):\n",
    "  traintargetds.append(traintargetds3[n])\n",
    "for n in range(len(traintargetds4)):\n",
    "  traintargetds.append(traintargetds4[n])\n",
    "for n in range(len(traintargetds5)):\n",
    "  traintargetds.append(traintargetds5[n])\n",
    "for n in range(len(traintargetds6)):\n",
    "  traintargetds.append(traintargetds6[n])\n",
    "for n in range(len(traintargetds7)):\n",
    "  traintargetds.append(traintargetds7[n])\n",
    "for n in range(len(traintargetds8)):\n",
    "  traintargetds.append(traintargetds8[n])\n",
    "for n in range(len(traintargetds9)):\n",
    "  traintargetds.append(traintargetds9[n])\n",
    "for n in range(len(traintargetds10)):\n",
    "  traintargetds.append(traintargetds10[n])\n",
    "for n in range(len(traintargetds11)):\n",
    "  traintargetds.append(traintargetds11[n])\n",
    "traintargetds = [traintargetds]\n",
    "for n in range(len(testds2)):\n",
    "  testds.append(testds2[n])\n",
    "for n in range(len(testds3)):\n",
    "  testds.append(testds3[n])\n",
    "for n in range(len(testds4)):\n",
    "  testds.append(testds4[n])\n",
    "for n in range(len(testds5)):\n",
    "  testds.append(testds5[n])\n",
    "for n in range(len(testds6)):\n",
    "  testds.append(testds6[n])\n",
    "for n in range(len(testds7)):\n",
    "  testds.append(testds7[n])\n",
    "for n in range(len(testds8)):\n",
    "  testds.append(testds8[n])\n",
    "for n in range(len(testds9)):\n",
    "  testds.append(testds9[n])\n",
    "for n in range(len(testds10)):\n",
    "  testds.append(testds10[n])\n",
    "for n in range(len(testds11)):\n",
    "  testds.append(testds11[n])\n",
    "for n in range(len(testtargetds2)):\n",
    "  testtargetds.append(testtargetds2[n])\n",
    "for n in range(len(testtargetds3)):\n",
    "  testtargetds.append(testtargetds3[n])\n",
    "for n in range(len(testtargetds4)):\n",
    "  testtargetds.append(testtargetds4[n])\n",
    "for n in range(len(testtargetds5)):\n",
    "  testtargetds.append(testtargetds5[n])\n",
    "for n in range(len(testtargetds6)):\n",
    "  testtargetds.append(testtargetds6[n])\n",
    "for n in range(len(testtargetds7)):\n",
    "  testtargetds.append(testtargetds7[n])\n",
    "for n in range(len(testtargetds8)):\n",
    "  testtargetds.append(testtargetds8[n])\n",
    "for n in range(len(testtargetds9)):\n",
    "  testtargetds.append(testtargetds9[n])\n",
    "for n in range(len(testtargetds10)):\n",
    "  testtargetds.append(testtargetds10[n])\n",
    "for n in range(len(testtargetds11)):\n",
    "  testtargetds.append(testtargetds11[n])\n",
    "#testtargetds = [testtargetds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1d23f289-c15a-4464-bfc2-f18be9d95bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250016"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(testds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "347f82ca-72d6-49bc-9bb3-3e153dd47eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "testds=testds[0:245000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "462204a1-acb6-4672-b9ac-0e0fe13df418",
   "metadata": {},
   "outputs": [],
   "source": [
    "testtargetds=testtargetds[0:245000]\n",
    "testtargetds = [testtargetds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3485d4c7-19ff-48b3-b0f3-cbb08f8114c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in range(len(trainds)):\n",
    "  for n in range(4):\n",
    "    trainds[m][n][1] = (trainds[m][n][1])/3000\n",
    "\n",
    "for m in range(len(testds)):\n",
    "  for n in range(4):\n",
    "    testds[m][n][1] = (testds[m][n][1])/3000\n",
    "\n",
    "for m in range(len(trainds)):\n",
    "  for n in range(4):\n",
    "    trainds[m][n][3] = (trainds[m][n][3] - 40.6638)/(40.8874-40.6638)\n",
    "\n",
    "for m in range(len(testds)):\n",
    "  for n in range(4):\n",
    "    testds[m][n][3] = (testds[m][n][3] - 40.6638)/(40.8874-40.6638)\n",
    "\n",
    "for m in range(len(trainds)):\n",
    "  for n in range(4):\n",
    "    trainds[m][n][4] = (trainds[m][n][4] + 73.8744)/(-72.9938+73.8744)\n",
    "\n",
    "for m in range(len(testds)):\n",
    "  for n in range(4):\n",
    "    testds[m][n][4] = (testds[m][n][4] + 73.8744)/(-72.9938+73.8744)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dbfbc45b-7f5f-4111-ab55-7df336bcedf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test split by years\n",
    "#trainds = train[0:90000]\n",
    "#traintargetds = [target[0:90000]]\n",
    "#testds = train[90000:]\n",
    "#testtargetds = [target[90000:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e08dcffb-21a1-4604-b459-20f497b8fba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainds = np.array(trainds)\n",
    "traintargetds = np.array(traintargetds).T\n",
    "testds = np.array(testds)\n",
    "testtargetds = np.array(testtargetds).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e7613638-49fe-4be6-b45a-a85763009a92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 1.83333333e-04, 2.12000000e+00, 7.24508050e-02,\n",
       "        4.10401999e-01],\n",
       "       [0.00000000e+00, 1.83333333e-04, 2.12000000e+00, 7.24508050e-02,\n",
       "        4.10401999e-01],\n",
       "       [0.00000000e+00, 1.83333333e-04, 2.12000000e+00, 7.24508050e-02,\n",
       "        4.10401999e-01],\n",
       "       [0.00000000e+00, 1.83333333e-04, 2.12000000e+00, 7.24508050e-02,\n",
       "        4.10401999e-01]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testds[200000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b88e2b4d-6687-42b0-8077-103a6c983635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(750073, 4, 5)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d6ae5547-d5e6-469e-ad2c-0c27021f1866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(750073, 1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traintargetds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "de7f9e18-96d7-4178-8261-43fe4b545a70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.16, 0.0308, 0.96, 0.8877459749552675, 0.0],\n",
       " [0.16, 0.0308, 0.96, 0.8877459749552675, 0.0],\n",
       " [0.16, 0.0308, 0.96, 0.8877459749552675, 0.0],\n",
       " [0.16, 0.0308, 0.96, 0.8877459749552675, 0.0]]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c949bbd0-9729-4566-8143-bbd35a07bf02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f7fca6d0-b21f-454e-8ade-037b85d47ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245000, 4, 5)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3ad2513c-4136-4941-ac5f-cd42931939a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(750073, 4, 5) (245000, 4, 5)\n"
     ]
    }
   ],
   "source": [
    "print(trainds.shape, testds.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4f4470dc-e7ec-4b6b-bc6d-975227377b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for help with data manipulation for time series rnn training; we can use the time series generator from keras\n",
    "\n",
    "# dummy input for understanding\n",
    "# data = np.array([[i] for i in range(50)])\n",
    "\n",
    "# here goes our training data\n",
    "data = trainds\n",
    "\n",
    "# the shape of your target is your prediction shape (e.g., here we make it 1)\n",
    "#target = data\n",
    "\n",
    "# the length of the input feature\n",
    "n_input=4 # you can change this! doesn't HAVE to be 4 months input\n",
    "n_features=data.shape[1]*data.shape[2]\n",
    "\n",
    "# this is the generator that will go into our rnn for training\n",
    "#data_gen = TimeseriesGenerator(data,\n",
    "#                               traintargetds,\n",
    "#                               length=n_input,\n",
    "#                               sampling_rate=1,\n",
    "#                               batch_size=1000, # you can try different batch sizes\n",
    "#                               shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b41c6800-f8ca-4392-b10a-a07332c2c0a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(750073, 4, 5) (750073, 1) 20\n"
     ]
    }
   ],
   "source": [
    "# check shapes\n",
    "print(data.shape, traintargetds.shape, n_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "91c36532-31c5-44d4-8998-30db55d76767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">68,608</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">520</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m68,608\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation (\u001b[38;5;33mActivation\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │           \u001b[38;5;34m520\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m9\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m0\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">77,393</span> (302.32 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m77,393\u001b[0m (302.32 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">77,393</span> (302.32 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m77,393\u001b[0m (302.32 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# many-to-one example\n",
    "# lets make our rnn!\n",
    "regressor = Sequential()\n",
    "\n",
    "# LSTM; input shape is 1 (for one time step at a time) and n_features which is also 1 for our toy example\n",
    "regressor.add(keras.layers.Input(shape=(1, 5)))\n",
    "\n",
    "# don't return sequence; just the last output of the input sequence\n",
    "regressor.add(LSTM(units=128, return_sequences=False))\n",
    "\n",
    "regressor.add(Dense(64))\n",
    "regressor.add(Activation('linear'))\n",
    "\n",
    "regressor.add(Dense(8))\n",
    "regressor.add(Activation('linear'))\n",
    "\n",
    "# one neuron for output prediction\n",
    "regressor.add(Dense(1))\n",
    "regressor.add(Activation('linear'))\n",
    "\n",
    "METRICS = [\n",
    "    keras.metrics.MeanSquaredError(name='mean_squared_error'),\n",
    "    keras.metrics.RootMeanSquaredError(name='root_mean_squared_error'),\n",
    "]\n",
    "\n",
    "# compile model\n",
    "regressor.compile(keras.optimizers.Adam(learning_rate=0.002), loss='mean_squared_error', metrics=METRICS)\n",
    "\n",
    "keras.utils.set_random_seed(1010)\n",
    "\n",
    "# what does graph look like?\n",
    "regressor.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3b0599a6-102a-4e5f-a9e4-e7f8b1a9c1a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 135ms/step - loss: 0.1184 - mean_squared_error: 0.1184 - root_mean_squared_error: 0.2892 - val_loss: 0.0010 - val_mean_squared_error: 0.0011 - val_root_mean_squared_error: 0.0325\n",
      "Epoch 2/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 131ms/step - loss: 5.8311e-04 - mean_squared_error: 5.8311e-04 - root_mean_squared_error: 0.0241 - val_loss: 9.6981e-04 - val_mean_squared_error: 9.8246e-04 - val_root_mean_squared_error: 0.0313\n",
      "Epoch 3/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 124ms/step - loss: 5.4139e-04 - mean_squared_error: 5.4139e-04 - root_mean_squared_error: 0.0233 - val_loss: 9.3382e-04 - val_mean_squared_error: 9.4593e-04 - val_root_mean_squared_error: 0.0308\n",
      "Epoch 4/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 144ms/step - loss: 5.1837e-04 - mean_squared_error: 5.1837e-04 - root_mean_squared_error: 0.0228 - val_loss: 8.9319e-04 - val_mean_squared_error: 9.0475e-04 - val_root_mean_squared_error: 0.0301\n",
      "Epoch 5/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 133ms/step - loss: 4.9711e-04 - mean_squared_error: 4.9712e-04 - root_mean_squared_error: 0.0223 - val_loss: 8.4659e-04 - val_mean_squared_error: 8.5753e-04 - val_root_mean_squared_error: 0.0293\n",
      "Epoch 6/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 136ms/step - loss: 4.6586e-04 - mean_squared_error: 4.6587e-04 - root_mean_squared_error: 0.0216 - val_loss: 7.8101e-04 - val_mean_squared_error: 7.9093e-04 - val_root_mean_squared_error: 0.0281\n",
      "Epoch 7/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 131ms/step - loss: 4.2476e-04 - mean_squared_error: 4.2476e-04 - root_mean_squared_error: 0.0206 - val_loss: 6.8716e-04 - val_mean_squared_error: 6.9592e-04 - val_root_mean_squared_error: 0.0264\n",
      "Epoch 8/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 131ms/step - loss: 3.7505e-04 - mean_squared_error: 3.7506e-04 - root_mean_squared_error: 0.0194 - val_loss: 5.8953e-04 - val_mean_squared_error: 5.9698e-04 - val_root_mean_squared_error: 0.0244\n",
      "Epoch 9/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 128ms/step - loss: 3.4130e-04 - mean_squared_error: 3.4130e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.3036e-04 - val_mean_squared_error: 5.3691e-04 - val_root_mean_squared_error: 0.0232\n",
      "Epoch 10/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 124ms/step - loss: 3.2104e-04 - mean_squared_error: 3.2104e-04 - root_mean_squared_error: 0.0179 - val_loss: 4.8638e-04 - val_mean_squared_error: 4.9238e-04 - val_root_mean_squared_error: 0.0222\n",
      "Epoch 11/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 140ms/step - loss: 2.7918e-04 - mean_squared_error: 2.7918e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.5887e-04 - val_mean_squared_error: 4.6457e-04 - val_root_mean_squared_error: 0.0216\n",
      "Epoch 12/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 130ms/step - loss: 2.7040e-04 - mean_squared_error: 2.7040e-04 - root_mean_squared_error: 0.0164 - val_loss: 4.3650e-04 - val_mean_squared_error: 4.4190e-04 - val_root_mean_squared_error: 0.0210\n",
      "Epoch 13/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 132ms/step - loss: 2.5206e-04 - mean_squared_error: 2.5207e-04 - root_mean_squared_error: 0.0158 - val_loss: 4.2391e-04 - val_mean_squared_error: 4.2907e-04 - val_root_mean_squared_error: 0.0207\n",
      "Epoch 14/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 148ms/step - loss: 2.3844e-04 - mean_squared_error: 2.3845e-04 - root_mean_squared_error: 0.0154 - val_loss: 4.1287e-04 - val_mean_squared_error: 4.1806e-04 - val_root_mean_squared_error: 0.0204\n",
      "Epoch 15/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 124ms/step - loss: 2.2697e-04 - mean_squared_error: 2.2698e-04 - root_mean_squared_error: 0.0150 - val_loss: 4.0421e-04 - val_mean_squared_error: 4.0934e-04 - val_root_mean_squared_error: 0.0202\n",
      "Epoch 16/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 135ms/step - loss: 2.3703e-04 - mean_squared_error: 2.3703e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.9790e-04 - val_mean_squared_error: 4.0297e-04 - val_root_mean_squared_error: 0.0201\n",
      "Epoch 17/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 133ms/step - loss: 2.2880e-04 - mean_squared_error: 2.2880e-04 - root_mean_squared_error: 0.0151 - val_loss: 4.0066e-04 - val_mean_squared_error: 4.0580e-04 - val_root_mean_squared_error: 0.0201\n",
      "Epoch 18/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 133ms/step - loss: 2.0819e-04 - mean_squared_error: 2.0819e-04 - root_mean_squared_error: 0.0144 - val_loss: 3.9396e-04 - val_mean_squared_error: 3.9902e-04 - val_root_mean_squared_error: 0.0200\n",
      "Epoch 19/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 134ms/step - loss: 2.0322e-04 - mean_squared_error: 2.0323e-04 - root_mean_squared_error: 0.0142 - val_loss: 3.8360e-04 - val_mean_squared_error: 3.8851e-04 - val_root_mean_squared_error: 0.0197\n",
      "Epoch 20/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 143ms/step - loss: 2.0833e-04 - mean_squared_error: 2.0833e-04 - root_mean_squared_error: 0.0144 - val_loss: 3.6794e-04 - val_mean_squared_error: 3.7251e-04 - val_root_mean_squared_error: 0.0193\n",
      "Epoch 21/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 158ms/step - loss: 2.2092e-04 - mean_squared_error: 2.2093e-04 - root_mean_squared_error: 0.0149 - val_loss: 3.6790e-04 - val_mean_squared_error: 3.7257e-04 - val_root_mean_squared_error: 0.0193\n",
      "Epoch 22/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 161ms/step - loss: 1.9496e-04 - mean_squared_error: 1.9496e-04 - root_mean_squared_error: 0.0139 - val_loss: 3.6231e-04 - val_mean_squared_error: 3.6686e-04 - val_root_mean_squared_error: 0.0192\n",
      "Epoch 23/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 161ms/step - loss: 1.9281e-04 - mean_squared_error: 1.9281e-04 - root_mean_squared_error: 0.0139 - val_loss: 3.5918e-04 - val_mean_squared_error: 3.6358e-04 - val_root_mean_squared_error: 0.0191\n",
      "Epoch 24/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 161ms/step - loss: 1.9074e-04 - mean_squared_error: 1.9074e-04 - root_mean_squared_error: 0.0138 - val_loss: 3.5738e-04 - val_mean_squared_error: 3.6171e-04 - val_root_mean_squared_error: 0.0190\n",
      "Epoch 25/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 152ms/step - loss: 1.8896e-04 - mean_squared_error: 1.8896e-04 - root_mean_squared_error: 0.0137 - val_loss: 3.5500e-04 - val_mean_squared_error: 3.5927e-04 - val_root_mean_squared_error: 0.0190\n",
      "Epoch 26/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 166ms/step - loss: 1.8744e-04 - mean_squared_error: 1.8744e-04 - root_mean_squared_error: 0.0137 - val_loss: 3.5210e-04 - val_mean_squared_error: 3.5634e-04 - val_root_mean_squared_error: 0.0189\n",
      "Epoch 27/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 163ms/step - loss: 1.8642e-04 - mean_squared_error: 1.8642e-04 - root_mean_squared_error: 0.0136 - val_loss: 3.4977e-04 - val_mean_squared_error: 3.5403e-04 - val_root_mean_squared_error: 0.0188\n",
      "Epoch 28/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 154ms/step - loss: 1.8570e-04 - mean_squared_error: 1.8570e-04 - root_mean_squared_error: 0.0136 - val_loss: 3.4980e-04 - val_mean_squared_error: 3.5411e-04 - val_root_mean_squared_error: 0.0188\n",
      "Epoch 29/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 140ms/step - loss: 1.8564e-04 - mean_squared_error: 1.8564e-04 - root_mean_squared_error: 0.0136 - val_loss: 3.5249e-04 - val_mean_squared_error: 3.5688e-04 - val_root_mean_squared_error: 0.0189\n",
      "Epoch 30/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 127ms/step - loss: 1.8696e-04 - mean_squared_error: 1.8696e-04 - root_mean_squared_error: 0.0137 - val_loss: 3.5680e-04 - val_mean_squared_error: 3.6126e-04 - val_root_mean_squared_error: 0.0190\n",
      "Epoch 31/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 143ms/step - loss: 1.9167e-04 - mean_squared_error: 1.9167e-04 - root_mean_squared_error: 0.0138 - val_loss: 3.5631e-04 - val_mean_squared_error: 3.6077e-04 - val_root_mean_squared_error: 0.0190\n",
      "Epoch 32/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 145ms/step - loss: 1.9406e-04 - mean_squared_error: 1.9407e-04 - root_mean_squared_error: 0.0139 - val_loss: 3.4752e-04 - val_mean_squared_error: 3.5189e-04 - val_root_mean_squared_error: 0.0188\n",
      "Epoch 33/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 140ms/step - loss: 1.8584e-04 - mean_squared_error: 1.8584e-04 - root_mean_squared_error: 0.0136 - val_loss: 3.4622e-04 - val_mean_squared_error: 3.5058e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 34/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 134ms/step - loss: 1.8350e-04 - mean_squared_error: 1.8350e-04 - root_mean_squared_error: 0.0135 - val_loss: 3.4634e-04 - val_mean_squared_error: 3.5071e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 35/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 135ms/step - loss: 1.8188e-04 - mean_squared_error: 1.8188e-04 - root_mean_squared_error: 0.0135 - val_loss: 3.4664e-04 - val_mean_squared_error: 3.5101e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 36/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 132ms/step - loss: 1.8070e-04 - mean_squared_error: 1.8070e-04 - root_mean_squared_error: 0.0134 - val_loss: 3.4686e-04 - val_mean_squared_error: 3.5123e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 37/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 130ms/step - loss: 1.7975e-04 - mean_squared_error: 1.7975e-04 - root_mean_squared_error: 0.0134 - val_loss: 3.4708e-04 - val_mean_squared_error: 3.5145e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 38/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 134ms/step - loss: 1.7902e-04 - mean_squared_error: 1.7902e-04 - root_mean_squared_error: 0.0134 - val_loss: 3.4762e-04 - val_mean_squared_error: 3.5199e-04 - val_root_mean_squared_error: 0.0188\n",
      "Epoch 39/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 133ms/step - loss: 1.7862e-04 - mean_squared_error: 1.7862e-04 - root_mean_squared_error: 0.0134 - val_loss: 3.4850e-04 - val_mean_squared_error: 3.5288e-04 - val_root_mean_squared_error: 0.0188\n",
      "Epoch 40/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 128ms/step - loss: 1.7856e-04 - mean_squared_error: 1.7856e-04 - root_mean_squared_error: 0.0133 - val_loss: 3.4902e-04 - val_mean_squared_error: 3.5341e-04 - val_root_mean_squared_error: 0.0188\n",
      "Epoch 41/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 134ms/step - loss: 1.7856e-04 - mean_squared_error: 1.7857e-04 - root_mean_squared_error: 0.0134 - val_loss: 3.4840e-04 - val_mean_squared_error: 3.5277e-04 - val_root_mean_squared_error: 0.0188\n",
      "Epoch 42/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 144ms/step - loss: 1.7800e-04 - mean_squared_error: 1.7800e-04 - root_mean_squared_error: 0.0133 - val_loss: 3.4669e-04 - val_mean_squared_error: 3.5103e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 43/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 132ms/step - loss: 1.7699e-04 - mean_squared_error: 1.7699e-04 - root_mean_squared_error: 0.0133 - val_loss: 3.4417e-04 - val_mean_squared_error: 3.4845e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 44/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 143ms/step - loss: 1.7575e-04 - mean_squared_error: 1.7575e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.4359e-04 - val_mean_squared_error: 3.4782e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 45/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 137ms/step - loss: 1.7440e-04 - mean_squared_error: 1.7440e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.4368e-04 - val_mean_squared_error: 3.4789e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 46/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 129ms/step - loss: 1.7382e-04 - mean_squared_error: 1.7382e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.4371e-04 - val_mean_squared_error: 3.4792e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 47/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 125ms/step - loss: 1.7352e-04 - mean_squared_error: 1.7352e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.4369e-04 - val_mean_squared_error: 3.4789e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 48/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 133ms/step - loss: 1.7326e-04 - mean_squared_error: 1.7327e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.4359e-04 - val_mean_squared_error: 3.4778e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 49/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 155ms/step - loss: 1.7304e-04 - mean_squared_error: 1.7304e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4338e-04 - val_mean_squared_error: 3.4756e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 50/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 153ms/step - loss: 1.7284e-04 - mean_squared_error: 1.7284e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4309e-04 - val_mean_squared_error: 3.4726e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 51/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 155ms/step - loss: 1.7266e-04 - mean_squared_error: 1.7266e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4277e-04 - val_mean_squared_error: 3.4694e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 52/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 146ms/step - loss: 1.7254e-04 - mean_squared_error: 1.7255e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4248e-04 - val_mean_squared_error: 3.4664e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 53/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 130ms/step - loss: 1.7251e-04 - mean_squared_error: 1.7252e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4220e-04 - val_mean_squared_error: 3.4637e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 54/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 131ms/step - loss: 1.7252e-04 - mean_squared_error: 1.7252e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4194e-04 - val_mean_squared_error: 3.4610e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 55/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 132ms/step - loss: 1.7238e-04 - mean_squared_error: 1.7239e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4168e-04 - val_mean_squared_error: 3.4584e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 56/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 136ms/step - loss: 1.7211e-04 - mean_squared_error: 1.7211e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4141e-04 - val_mean_squared_error: 3.4558e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 57/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 126ms/step - loss: 1.7182e-04 - mean_squared_error: 1.7182e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4116e-04 - val_mean_squared_error: 3.4534e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 58/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 124ms/step - loss: 1.7155e-04 - mean_squared_error: 1.7156e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4094e-04 - val_mean_squared_error: 3.4512e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 59/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 136ms/step - loss: 1.7131e-04 - mean_squared_error: 1.7131e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4075e-04 - val_mean_squared_error: 3.4493e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 60/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 140ms/step - loss: 1.7108e-04 - mean_squared_error: 1.7108e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4060e-04 - val_mean_squared_error: 3.4479e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 61/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 144ms/step - loss: 1.7085e-04 - mean_squared_error: 1.7085e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4049e-04 - val_mean_squared_error: 3.4468e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 62/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 140ms/step - loss: 1.7062e-04 - mean_squared_error: 1.7062e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.4042e-04 - val_mean_squared_error: 3.4462e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 63/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 140ms/step - loss: 1.7038e-04 - mean_squared_error: 1.7039e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4040e-04 - val_mean_squared_error: 3.4461e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 64/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 153ms/step - loss: 1.7014e-04 - mean_squared_error: 1.7014e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4042e-04 - val_mean_squared_error: 3.4464e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 65/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 139ms/step - loss: 1.6989e-04 - mean_squared_error: 1.6990e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4047e-04 - val_mean_squared_error: 3.4470e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 66/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 128ms/step - loss: 1.6965e-04 - mean_squared_error: 1.6965e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4056e-04 - val_mean_squared_error: 3.4480e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 67/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 131ms/step - loss: 1.6940e-04 - mean_squared_error: 1.6941e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4070e-04 - val_mean_squared_error: 3.4494e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 68/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 127ms/step - loss: 1.6916e-04 - mean_squared_error: 1.6916e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4090e-04 - val_mean_squared_error: 3.4515e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 69/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 129ms/step - loss: 1.6892e-04 - mean_squared_error: 1.6892e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4116e-04 - val_mean_squared_error: 3.4542e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 70/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 132ms/step - loss: 1.6865e-04 - mean_squared_error: 1.6865e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4149e-04 - val_mean_squared_error: 3.4576e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 71/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 140ms/step - loss: 1.6835e-04 - mean_squared_error: 1.6835e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4189e-04 - val_mean_squared_error: 3.4617e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 72/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 125ms/step - loss: 1.6803e-04 - mean_squared_error: 1.6803e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.4238e-04 - val_mean_squared_error: 3.4667e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 73/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 132ms/step - loss: 1.6767e-04 - mean_squared_error: 1.6767e-04 - root_mean_squared_error: 0.0129 - val_loss: 3.4297e-04 - val_mean_squared_error: 3.4726e-04 - val_root_mean_squared_error: 0.0186\n",
      "Epoch 74/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 144ms/step - loss: 1.6728e-04 - mean_squared_error: 1.6728e-04 - root_mean_squared_error: 0.0129 - val_loss: 3.4363e-04 - val_mean_squared_error: 3.4794e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 75/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 134ms/step - loss: 1.6688e-04 - mean_squared_error: 1.6689e-04 - root_mean_squared_error: 0.0129 - val_loss: 3.4438e-04 - val_mean_squared_error: 3.4870e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 76/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 122ms/step - loss: 1.6651e-04 - mean_squared_error: 1.6651e-04 - root_mean_squared_error: 0.0129 - val_loss: 3.4517e-04 - val_mean_squared_error: 3.4950e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 77/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 134ms/step - loss: 1.6615e-04 - mean_squared_error: 1.6615e-04 - root_mean_squared_error: 0.0129 - val_loss: 3.4597e-04 - val_mean_squared_error: 3.5031e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 78/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 135ms/step - loss: 1.6578e-04 - mean_squared_error: 1.6578e-04 - root_mean_squared_error: 0.0129 - val_loss: 3.4677e-04 - val_mean_squared_error: 3.5113e-04 - val_root_mean_squared_error: 0.0187\n",
      "Epoch 79/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 144ms/step - loss: 1.6540e-04 - mean_squared_error: 1.6540e-04 - root_mean_squared_error: 0.0129 - val_loss: 3.4759e-04 - val_mean_squared_error: 3.5196e-04 - val_root_mean_squared_error: 0.0188\n",
      "Epoch 80/80\n",
      "\u001b[1m301/301\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 132ms/step - loss: 1.6502e-04 - mean_squared_error: 1.6502e-04 - root_mean_squared_error: 0.0128 - val_loss: 3.4843e-04 - val_mean_squared_error: 3.5281e-04 - val_root_mean_squared_error: 0.0188\n"
     ]
    }
   ],
   "source": [
    "# train the lstm!\n",
    "history = regressor.fit(x = trainds, y = traintargetds, validation_split=0.2, epochs=80, batch_size = 2000, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0ca0fb04-1f4a-440b-a2d0-16311be86820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"mean_squared_error\"], c='k', label='training')\n",
    "plt.plot(history.history[\"val_mean_squared_error\"], c='r', label='validation')\n",
    "plt.legend(['training', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "04a2d03e-e7fa-4aea-9c32-9bbd22f71aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(750073, 1)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traintargetds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "95465197-d170-49f8-9fa4-65307abd52a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245000, 1)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testtargetds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f301d265-ed15-442f-a428-2d2b6017bd0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(750073, 4, 5)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c6d44436-9279-4ea9-bb06-432f4eef8d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245000, 4, 5)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2a413595-b0ee-4be2-8189-e0dc9b18907b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03      , 0.00723333, 0.57      , 0.88774597, 0.        ],\n",
       "       [0.03      , 0.00723333, 0.57      , 0.88774597, 0.        ],\n",
       "       [0.03      , 0.00763333, 0.58      , 0.88774597, 0.        ],\n",
       "       [0.03      , 0.00763333, 0.58      , 0.88774597, 0.        ]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4e0bb588-0649-44f8-a72b-f05475fbc3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.14      , 0.03163333, 0.97      , 0.88774597, 0.        ],\n",
       "       [0.14      , 0.03163333, 0.97      , 0.88774597, 0.        ],\n",
       "       [0.14      , 0.03163333, 0.97      , 0.88774597, 0.        ],\n",
       "       [0.14      , 0.03163333, 0.97      , 0.88774597, 0.        ]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "42aa400b-4d12-4391-8dd3-a4d0547e7779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7657/7657\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 2ms/step\n"
     ]
    }
   ],
   "source": [
    "# predict using test data\n",
    "oni_predictions = regressor.predict(testds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bb09565f-41ca-4391-b20a-df5fe3dd8822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245000, 1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oni_predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "85d10107-5873-47c9-96e2-c8d21a4913ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(245000, 1) (245000, 1)\n"
     ]
    }
   ],
   "source": [
    "# quick check that we have a match of shapes\n",
    "print(oni_predictions.shape, testtargetds.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7bb3f58c-a074-4941-b29e-0fd64cbd1786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how did we do!?\n",
    "plt.scatter(\n",
    "    testtargetds,\n",
    "    oni_predictions\n",
    "    )\n",
    "plt.xlim([0,5])\n",
    "plt.ylim([0,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "db24d34f-56c1-4f50-9fe9-6537822b81d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "samevaltest = []\n",
    "for m in range(len(testds)):\n",
    " samevaltest.append(testds[m][3][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "47ff237a-74db-4a2b-bac7-624750e04302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.58,\n",
       " 0.57,\n",
       " 0.56,\n",
       " 0.57,\n",
       " 0.56,\n",
       " 0.56,\n",
       " 0.56,\n",
       " 0.56,\n",
       " 0.56,\n",
       " 0.56,\n",
       " 0.56,\n",
       " 0.55,\n",
       " 0.56,\n",
       " 0.54,\n",
       " 0.55,\n",
       " 0.56,\n",
       " 0.55,\n",
       " 0.55,\n",
       " 0.55,\n",
       " 0.55]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samevaltest[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4ce7aa63-7b8e-4ca5-a19e-176510c5ec00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how did we do!?\n",
    "plt.scatter(\n",
    "    testtargetds,\n",
    "    samevaltest\n",
    "    )\n",
    "plt.xlim([0,5])\n",
    "plt.ylim([0,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "18ea106a-c125-46db-bc4c-890fef3989a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "190cb4d7-91c7-4714-a504-63544b668003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0002962616326530613"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(testtargetds, samevaltest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0a191ec8-0cbd-4e64-80af-aa8985412e6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00022434674759908368"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(testtargetds, oni_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b6a773e1-4af4-4475-a177-423014980502",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading datasets\n",
    "dataset1 = pd.read_csv(\n",
    "    '01302020PowerPrecipitation2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasettwo1 = pd.read_csv(\n",
    "    '01302020Discharge2010-2023.csv',\n",
    "    delimiter=',')\n",
    "datasetthree1 = pd.read_csv(\n",
    "    '01302020GageHeight2010-2023.csv',\n",
    "    delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d88c113e-bf84-42bf-a108-7445d210db62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop any nans\n",
    "dataset1 = dataset1.dropna(subset=[\"PRECTOTCORR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "332fe70f-f0d7-432b-a2ba-688112ac3dbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122395\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "n = 0\n",
    "train = []\n",
    "target = []\n",
    "for x in dataset1[\"Time2\"]:\n",
    "  check = []\n",
    "  try:\n",
    "    check.append([dataset1[\"PRECTOTCORR\"][np.where(dataset1[\"Time2\"]==x)[0][0]], datasettwo1[\"106410_00060\"][np.where(datasettwo1[\"Time2\"]==x)[0][0]], datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x)[0][0]]])\n",
    "    check.append([dataset1[\"PRECTOTCORR\"][np.where(dataset1[\"Time2\"]==x)[0][0]], datasettwo1[\"106410_00060\"][np.where(datasettwo1[\"Time2\"]==x+1)[0][0]], datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x+1)[0][0]]])\n",
    "    check.append([dataset1[\"PRECTOTCORR\"][np.where(dataset1[\"Time2\"]==x)[0][0]], datasettwo1[\"106410_00060\"][np.where(datasettwo1[\"Time2\"]==x+2)[0][0]], datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x+2)[0][0]]])\n",
    "    check.append([dataset1[\"PRECTOTCORR\"][np.where(dataset1[\"Time2\"]==x)[0][0]], datasettwo1[\"106410_00060\"][np.where(datasettwo1[\"Time2\"]==x+3)[0][0]], datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x+3)[0][0]]])\n",
    "    check.append(datasetthree1[\"106409_00065\"][np.where(datasetthree1[\"Time2\"]==x+5)[0][0]])\n",
    "    train.append(check[0:4])\n",
    "    target.append(check[4])\n",
    "    k = k+1\n",
    "  except:\n",
    "    n = n+1\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fe0453dc-7d4e-48af-aab4-e6e71ea97d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in range(len(train)):\n",
    "  for n in range(4):\n",
    "    train[m][n][1] = (train[m][n][1])/3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a4dc0e78-1652-45b9-a933-59c33e84faa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[0.14, 0.03163333333333333, 0.97],\n",
       "  [0.14, 0.03163333333333333, 0.97],\n",
       "  [0.14, 0.03163333333333333, 0.97],\n",
       "  [0.14, 0.03163333333333333, 0.97]],\n",
       " [[0.28, 0.03163333333333333, 0.97],\n",
       "  [0.28, 0.03163333333333333, 0.97],\n",
       "  [0.28, 0.03163333333333333, 0.97],\n",
       "  [0.28, 0.03163333333333333, 0.97]],\n",
       " [[0.33, 0.03163333333333333, 0.97],\n",
       "  [0.33, 0.03163333333333333, 0.97],\n",
       "  [0.33, 0.03163333333333333, 0.97],\n",
       "  [0.33, 0.0308, 0.96]],\n",
       " [[0.28, 0.0308, 0.96],\n",
       "  [0.28, 0.0308, 0.96],\n",
       "  [0.28, 0.0308, 0.96],\n",
       "  [0.28, 0.0308, 0.96]],\n",
       " [[0.22, 0.0308, 0.96],\n",
       "  [0.22, 0.0308, 0.96],\n",
       "  [0.22, 0.0308, 0.96],\n",
       "  [0.22, 0.0308, 0.96]]]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[0:5][:][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ff9ceae2-3540-469e-9452-55fa6c34be58",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainds = train[0:math.ceil(0.75*len(train))]\n",
    "traintargetds = target[0:math.ceil(0.75*len(target))]\n",
    "testds = train[math.ceil(0.75*len(train)):]\n",
    "testtargetds = target[math.ceil(0.75*len(target)):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8d336eb5-6f7e-4e5c-a981-2a2a48756bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainds = np.array(trainds)\n",
    "traintargetds = np.array(traintargetds).T\n",
    "testds = np.array(testds)\n",
    "testtargetds = np.array(testtargetds).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e3337851-97a7-44b0-a214-803c710a0f4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for help with data manipulation for time series rnn training; we can use the time series generator from keras\n",
    "\n",
    "# dummy input for understanding\n",
    "# data = np.array([[i] for i in range(50)])\n",
    "\n",
    "# here goes our training data\n",
    "data = trainds\n",
    "\n",
    "# the shape of your target is your prediction shape (e.g., here we make it 1)\n",
    "#target = data\n",
    "\n",
    "# the length of the input feature\n",
    "n_input=4 # you can change this! doesn't HAVE to be 4 months input\n",
    "n_features=data.shape[1]*data.shape[2]\n",
    "\n",
    "# this is the generator that will go into our rnn for training\n",
    "#data_gen = TimeseriesGenerator(data,\n",
    "#                               traintargetds,\n",
    "#                               length=n_input,\n",
    "#                               sampling_rate=1,\n",
    "#                               batch_size=1000, # you can try different batch sizes\n",
    "#                               shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "18c3b221-f346-41f1-a68b-2cdcc03f0997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">67,584</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">520</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m67,584\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_3 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │           \u001b[38;5;34m520\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_4 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m9\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_5 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m0\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">76,369</span> (298.32 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m76,369\u001b[0m (298.32 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">76,369</span> (298.32 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m76,369\u001b[0m (298.32 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# many-to-one example\n",
    "# lets make our rnn!\n",
    "regressor = Sequential()\n",
    "\n",
    "# LSTM; input shape is 1 (for one time step at a time) and n_features which is also 1 for our toy example\n",
    "regressor.add(keras.layers.Input(shape=(1, 3)))\n",
    "\n",
    "# don't return sequence; just the last output of the input sequence\n",
    "regressor.add(LSTM(units=128, return_sequences=False))\n",
    "\n",
    "regressor.add(Dense(64))\n",
    "regressor.add(Activation('linear'))\n",
    "\n",
    "regressor.add(Dense(8))\n",
    "regressor.add(Activation('linear'))\n",
    "\n",
    "# one neuron for output prediction\n",
    "regressor.add(Dense(1))\n",
    "regressor.add(Activation('linear'))\n",
    "\n",
    "METRICS = [\n",
    "    keras.metrics.MeanSquaredError(name='mean_squared_error'),\n",
    "    keras.metrics.RootMeanSquaredError(name='root_mean_squared_error'),\n",
    "]\n",
    "\n",
    "# compile model\n",
    "regressor.compile(keras.optimizers.Adam(learning_rate=0.002), loss='mean_squared_error', metrics=METRICS)\n",
    "\n",
    "keras.utils.set_random_seed(1010)\n",
    "\n",
    "# what does graph look like?\n",
    "regressor.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c2a26630-f1a0-4bf9-b4d8-f9b6595ec956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 114ms/step - loss: 0.1457 - mean_squared_error: 0.1457 - root_mean_squared_error: 0.3564 - val_loss: 0.0050 - val_mean_squared_error: 0.0054 - val_root_mean_squared_error: 0.0735\n",
      "Epoch 2/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 120ms/step - loss: 0.0029 - mean_squared_error: 0.0029 - root_mean_squared_error: 0.0532 - val_loss: 0.0017 - val_mean_squared_error: 0.0018 - val_root_mean_squared_error: 0.0429\n",
      "Epoch 3/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 139ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0016 - val_mean_squared_error: 0.0017 - val_root_mean_squared_error: 0.0417\n",
      "Epoch 4/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 131ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0016 - val_mean_squared_error: 0.0017 - val_root_mean_squared_error: 0.0413\n",
      "Epoch 5/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 126ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0016 - val_mean_squared_error: 0.0017 - val_root_mean_squared_error: 0.0412\n",
      "Epoch 6/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 122ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0015 - val_mean_squared_error: 0.0017 - val_root_mean_squared_error: 0.0410\n",
      "Epoch 7/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 142ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0015 - val_mean_squared_error: 0.0016 - val_root_mean_squared_error: 0.0405\n",
      "Epoch 8/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 131ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0015 - val_mean_squared_error: 0.0016 - val_root_mean_squared_error: 0.0401\n",
      "Epoch 9/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 125ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0015 - val_mean_squared_error: 0.0016 - val_root_mean_squared_error: 0.0398\n",
      "Epoch 10/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 131ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0014 - val_mean_squared_error: 0.0016 - val_root_mean_squared_error: 0.0395\n",
      "Epoch 11/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 134ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0014 - val_mean_squared_error: 0.0015 - val_root_mean_squared_error: 0.0390\n",
      "Epoch 12/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 137ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0014 - val_mean_squared_error: 0.0015 - val_root_mean_squared_error: 0.0383\n",
      "Epoch 13/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 145ms/step - loss: 0.0010 - mean_squared_error: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0013 - val_mean_squared_error: 0.0014 - val_root_mean_squared_error: 0.0378\n",
      "Epoch 14/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 143ms/step - loss: 0.0010 - mean_squared_error: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0013 - val_mean_squared_error: 0.0014 - val_root_mean_squared_error: 0.0374\n",
      "Epoch 15/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 147ms/step - loss: 9.8793e-04 - mean_squared_error: 9.8791e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0013 - val_mean_squared_error: 0.0014 - val_root_mean_squared_error: 0.0370\n",
      "Epoch 16/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 147ms/step - loss: 9.6050e-04 - mean_squared_error: 9.6048e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0012 - val_mean_squared_error: 0.0013 - val_root_mean_squared_error: 0.0366\n",
      "Epoch 17/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 150ms/step - loss: 9.2254e-04 - mean_squared_error: 9.2253e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0012 - val_mean_squared_error: 0.0013 - val_root_mean_squared_error: 0.0357\n",
      "Epoch 18/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 145ms/step - loss: 8.7674e-04 - mean_squared_error: 8.7674e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0011 - val_mean_squared_error: 0.0012 - val_root_mean_squared_error: 0.0349\n",
      "Epoch 19/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 152ms/step - loss: 8.3430e-04 - mean_squared_error: 8.3430e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0011 - val_mean_squared_error: 0.0012 - val_root_mean_squared_error: 0.0344\n",
      "Epoch 20/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 152ms/step - loss: 7.9427e-04 - mean_squared_error: 7.9428e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0010 - val_mean_squared_error: 0.0011 - val_root_mean_squared_error: 0.0337\n",
      "Epoch 21/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 156ms/step - loss: 7.5202e-04 - mean_squared_error: 7.5204e-04 - root_mean_squared_error: 0.0274 - val_loss: 9.5839e-04 - val_mean_squared_error: 0.0010 - val_root_mean_squared_error: 0.0323\n",
      "Epoch 22/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 158ms/step - loss: 6.9325e-04 - mean_squared_error: 6.9326e-04 - root_mean_squared_error: 0.0263 - val_loss: 8.9867e-04 - val_mean_squared_error: 9.7444e-04 - val_root_mean_squared_error: 0.0312\n",
      "Epoch 23/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 158ms/step - loss: 6.3124e-04 - mean_squared_error: 6.3126e-04 - root_mean_squared_error: 0.0251 - val_loss: 8.1999e-04 - val_mean_squared_error: 8.8969e-04 - val_root_mean_squared_error: 0.0298\n",
      "Epoch 24/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 155ms/step - loss: 5.9717e-04 - mean_squared_error: 5.9722e-04 - root_mean_squared_error: 0.0244 - val_loss: 7.9998e-04 - val_mean_squared_error: 8.6796e-04 - val_root_mean_squared_error: 0.0295\n",
      "Epoch 25/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 153ms/step - loss: 5.6333e-04 - mean_squared_error: 5.6338e-04 - root_mean_squared_error: 0.0237 - val_loss: 7.1332e-04 - val_mean_squared_error: 7.7242e-04 - val_root_mean_squared_error: 0.0278\n",
      "Epoch 26/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 154ms/step - loss: 4.8014e-04 - mean_squared_error: 4.8018e-04 - root_mean_squared_error: 0.0219 - val_loss: 6.2296e-04 - val_mean_squared_error: 6.7440e-04 - val_root_mean_squared_error: 0.0260\n",
      "Epoch 27/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 157ms/step - loss: 4.2426e-04 - mean_squared_error: 4.2431e-04 - root_mean_squared_error: 0.0206 - val_loss: 6.3242e-04 - val_mean_squared_error: 6.8263e-04 - val_root_mean_squared_error: 0.0261\n",
      "Epoch 28/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 156ms/step - loss: 4.1252e-04 - mean_squared_error: 4.1252e-04 - root_mean_squared_error: 0.0203 - val_loss: 6.4593e-04 - val_mean_squared_error: 6.9868e-04 - val_root_mean_squared_error: 0.0264\n",
      "Epoch 29/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 156ms/step - loss: 4.0670e-04 - mean_squared_error: 4.0660e-04 - root_mean_squared_error: 0.0202 - val_loss: 7.5505e-04 - val_mean_squared_error: 8.1524e-04 - val_root_mean_squared_error: 0.0286\n",
      "Epoch 30/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 156ms/step - loss: 4.1697e-04 - mean_squared_error: 4.1701e-04 - root_mean_squared_error: 0.0204 - val_loss: 5.7423e-04 - val_mean_squared_error: 6.1946e-04 - val_root_mean_squared_error: 0.0249\n",
      "Epoch 31/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 153ms/step - loss: 3.6181e-04 - mean_squared_error: 3.6172e-04 - root_mean_squared_error: 0.0190 - val_loss: 7.0852e-04 - val_mean_squared_error: 7.6404e-04 - val_root_mean_squared_error: 0.0276\n",
      "Epoch 32/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 150ms/step - loss: 3.8131e-04 - mean_squared_error: 3.8134e-04 - root_mean_squared_error: 0.0195 - val_loss: 5.3826e-04 - val_mean_squared_error: 5.8044e-04 - val_root_mean_squared_error: 0.0241\n",
      "Epoch 33/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 156ms/step - loss: 3.3322e-04 - mean_squared_error: 3.3318e-04 - root_mean_squared_error: 0.0182 - val_loss: 6.6407e-04 - val_mean_squared_error: 7.1551e-04 - val_root_mean_squared_error: 0.0267\n",
      "Epoch 34/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 153ms/step - loss: 3.8135e-04 - mean_squared_error: 3.8136e-04 - root_mean_squared_error: 0.0195 - val_loss: 4.9271e-04 - val_mean_squared_error: 5.3186e-04 - val_root_mean_squared_error: 0.0231\n",
      "Epoch 35/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 150ms/step - loss: 3.1948e-04 - mean_squared_error: 3.1946e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.4212e-04 - val_mean_squared_error: 5.8474e-04 - val_root_mean_squared_error: 0.0242\n",
      "Epoch 36/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 161ms/step - loss: 3.2976e-04 - mean_squared_error: 3.2974e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.2532e-04 - val_mean_squared_error: 5.6649e-04 - val_root_mean_squared_error: 0.0238\n",
      "Epoch 37/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 161ms/step - loss: 3.2363e-04 - mean_squared_error: 3.2361e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.0704e-04 - val_mean_squared_error: 5.4680e-04 - val_root_mean_squared_error: 0.0234\n",
      "Epoch 38/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 156ms/step - loss: 3.1695e-04 - mean_squared_error: 3.1693e-04 - root_mean_squared_error: 0.0178 - val_loss: 4.9976e-04 - val_mean_squared_error: 5.3891e-04 - val_root_mean_squared_error: 0.0232\n",
      "Epoch 39/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 156ms/step - loss: 3.1407e-04 - mean_squared_error: 3.1405e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.9248e-04 - val_mean_squared_error: 5.3101e-04 - val_root_mean_squared_error: 0.0230\n",
      "Epoch 40/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 159ms/step - loss: 3.1140e-04 - mean_squared_error: 3.1137e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.8404e-04 - val_mean_squared_error: 5.2188e-04 - val_root_mean_squared_error: 0.0228\n",
      "Epoch 41/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 158ms/step - loss: 3.0824e-04 - mean_squared_error: 3.0822e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.7657e-04 - val_mean_squared_error: 5.1380e-04 - val_root_mean_squared_error: 0.0227\n",
      "Epoch 42/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 147ms/step - loss: 3.0562e-04 - mean_squared_error: 3.0559e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.7025e-04 - val_mean_squared_error: 5.0695e-04 - val_root_mean_squared_error: 0.0225\n",
      "Epoch 43/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 148ms/step - loss: 3.0352e-04 - mean_squared_error: 3.0349e-04 - root_mean_squared_error: 0.0174 - val_loss: 4.6435e-04 - val_mean_squared_error: 5.0055e-04 - val_root_mean_squared_error: 0.0224\n",
      "Epoch 44/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 147ms/step - loss: 3.0151e-04 - mean_squared_error: 3.0148e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.5880e-04 - val_mean_squared_error: 4.9452e-04 - val_root_mean_squared_error: 0.0222\n",
      "Epoch 45/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 147ms/step - loss: 2.9959e-04 - mean_squared_error: 2.9955e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.5381e-04 - val_mean_squared_error: 4.8908e-04 - val_root_mean_squared_error: 0.0221\n",
      "Epoch 46/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 145ms/step - loss: 2.9783e-04 - mean_squared_error: 2.9780e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.4942e-04 - val_mean_squared_error: 4.8429e-04 - val_root_mean_squared_error: 0.0220\n",
      "Epoch 47/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 155ms/step - loss: 2.9623e-04 - mean_squared_error: 2.9619e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.4560e-04 - val_mean_squared_error: 4.8010e-04 - val_root_mean_squared_error: 0.0219\n",
      "Epoch 48/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 150ms/step - loss: 2.9474e-04 - mean_squared_error: 2.9470e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4241e-04 - val_mean_squared_error: 4.7658e-04 - val_root_mean_squared_error: 0.0218\n",
      "Epoch 49/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 154ms/step - loss: 2.9336e-04 - mean_squared_error: 2.9332e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.3991e-04 - val_mean_squared_error: 4.7382e-04 - val_root_mean_squared_error: 0.0218\n",
      "Epoch 50/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 159ms/step - loss: 2.9208e-04 - mean_squared_error: 2.9203e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.3817e-04 - val_mean_squared_error: 4.7185e-04 - val_root_mean_squared_error: 0.0217\n",
      "Epoch 51/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 156ms/step - loss: 2.9081e-04 - mean_squared_error: 2.9076e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.3713e-04 - val_mean_squared_error: 4.7065e-04 - val_root_mean_squared_error: 0.0217\n",
      "Epoch 52/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 153ms/step - loss: 2.8941e-04 - mean_squared_error: 2.8935e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.3651e-04 - val_mean_squared_error: 4.6992e-04 - val_root_mean_squared_error: 0.0217\n",
      "Epoch 53/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 156ms/step - loss: 2.8764e-04 - mean_squared_error: 2.8758e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.3576e-04 - val_mean_squared_error: 4.6906e-04 - val_root_mean_squared_error: 0.0217\n",
      "Epoch 54/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 155ms/step - loss: 2.8529e-04 - mean_squared_error: 2.8523e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.3435e-04 - val_mean_squared_error: 4.6752e-04 - val_root_mean_squared_error: 0.0216\n",
      "Epoch 55/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 153ms/step - loss: 2.8243e-04 - mean_squared_error: 2.8237e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3239e-04 - val_mean_squared_error: 4.6536e-04 - val_root_mean_squared_error: 0.0216\n",
      "Epoch 56/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 139ms/step - loss: 2.7949e-04 - mean_squared_error: 2.7943e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.3046e-04 - val_mean_squared_error: 4.6323e-04 - val_root_mean_squared_error: 0.0215\n",
      "Epoch 57/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 137ms/step - loss: 2.7694e-04 - mean_squared_error: 2.7689e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.2888e-04 - val_mean_squared_error: 4.6144e-04 - val_root_mean_squared_error: 0.0215\n",
      "Epoch 58/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 139ms/step - loss: 2.7486e-04 - mean_squared_error: 2.7480e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.2742e-04 - val_mean_squared_error: 4.5979e-04 - val_root_mean_squared_error: 0.0214\n",
      "Epoch 59/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 141ms/step - loss: 2.7301e-04 - mean_squared_error: 2.7295e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.2587e-04 - val_mean_squared_error: 4.5806e-04 - val_root_mean_squared_error: 0.0214\n",
      "Epoch 60/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 142ms/step - loss: 2.7124e-04 - mean_squared_error: 2.7118e-04 - root_mean_squared_error: 0.0164 - val_loss: 4.2426e-04 - val_mean_squared_error: 4.5628e-04 - val_root_mean_squared_error: 0.0214\n",
      "Epoch 61/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 136ms/step - loss: 2.6956e-04 - mean_squared_error: 2.6950e-04 - root_mean_squared_error: 0.0164 - val_loss: 4.2267e-04 - val_mean_squared_error: 4.5453e-04 - val_root_mean_squared_error: 0.0213\n",
      "Epoch 62/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 141ms/step - loss: 2.6799e-04 - mean_squared_error: 2.6793e-04 - root_mean_squared_error: 0.0163 - val_loss: 4.2112e-04 - val_mean_squared_error: 4.5282e-04 - val_root_mean_squared_error: 0.0213\n",
      "Epoch 63/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 145ms/step - loss: 2.6650e-04 - mean_squared_error: 2.6645e-04 - root_mean_squared_error: 0.0163 - val_loss: 4.1956e-04 - val_mean_squared_error: 4.5111e-04 - val_root_mean_squared_error: 0.0212\n",
      "Epoch 64/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 147ms/step - loss: 2.6508e-04 - mean_squared_error: 2.6503e-04 - root_mean_squared_error: 0.0163 - val_loss: 4.1798e-04 - val_mean_squared_error: 4.4937e-04 - val_root_mean_squared_error: 0.0212\n",
      "Epoch 65/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 144ms/step - loss: 2.6371e-04 - mean_squared_error: 2.6365e-04 - root_mean_squared_error: 0.0162 - val_loss: 4.1637e-04 - val_mean_squared_error: 4.4762e-04 - val_root_mean_squared_error: 0.0212\n",
      "Epoch 66/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 155ms/step - loss: 2.6238e-04 - mean_squared_error: 2.6233e-04 - root_mean_squared_error: 0.0162 - val_loss: 4.1474e-04 - val_mean_squared_error: 4.4584e-04 - val_root_mean_squared_error: 0.0211\n",
      "Epoch 67/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 145ms/step - loss: 2.6109e-04 - mean_squared_error: 2.6104e-04 - root_mean_squared_error: 0.0161 - val_loss: 4.1306e-04 - val_mean_squared_error: 4.4401e-04 - val_root_mean_squared_error: 0.0211\n",
      "Epoch 68/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 136ms/step - loss: 2.5984e-04 - mean_squared_error: 2.5978e-04 - root_mean_squared_error: 0.0161 - val_loss: 4.1129e-04 - val_mean_squared_error: 4.4210e-04 - val_root_mean_squared_error: 0.0210\n",
      "Epoch 69/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 137ms/step - loss: 2.5859e-04 - mean_squared_error: 2.5854e-04 - root_mean_squared_error: 0.0161 - val_loss: 4.0940e-04 - val_mean_squared_error: 4.4006e-04 - val_root_mean_squared_error: 0.0210\n",
      "Epoch 70/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 136ms/step - loss: 2.5736e-04 - mean_squared_error: 2.5731e-04 - root_mean_squared_error: 0.0160 - val_loss: 4.0732e-04 - val_mean_squared_error: 4.3782e-04 - val_root_mean_squared_error: 0.0209\n",
      "Epoch 71/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 137ms/step - loss: 2.5611e-04 - mean_squared_error: 2.5606e-04 - root_mean_squared_error: 0.0160 - val_loss: 4.0494e-04 - val_mean_squared_error: 4.3528e-04 - val_root_mean_squared_error: 0.0209\n",
      "Epoch 72/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 159ms/step - loss: 2.5481e-04 - mean_squared_error: 2.5476e-04 - root_mean_squared_error: 0.0159 - val_loss: 4.0208e-04 - val_mean_squared_error: 4.3224e-04 - val_root_mean_squared_error: 0.0208\n",
      "Epoch 73/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 150ms/step - loss: 2.5340e-04 - mean_squared_error: 2.5335e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.9845e-04 - val_mean_squared_error: 4.2841e-04 - val_root_mean_squared_error: 0.0207\n",
      "Epoch 74/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 148ms/step - loss: 2.5177e-04 - mean_squared_error: 2.5173e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.9353e-04 - val_mean_squared_error: 4.2324e-04 - val_root_mean_squared_error: 0.0206\n",
      "Epoch 75/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 147ms/step - loss: 2.4971e-04 - mean_squared_error: 2.4966e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.8668e-04 - val_mean_squared_error: 4.1606e-04 - val_root_mean_squared_error: 0.0204\n",
      "Epoch 76/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 134ms/step - loss: 2.4682e-04 - mean_squared_error: 2.4678e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.7785e-04 - val_mean_squared_error: 4.0682e-04 - val_root_mean_squared_error: 0.0202\n",
      "Epoch 77/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 131ms/step - loss: 2.4278e-04 - mean_squared_error: 2.4274e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.7040e-04 - val_mean_squared_error: 3.9899e-04 - val_root_mean_squared_error: 0.0200\n",
      "Epoch 78/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 139ms/step - loss: 2.3897e-04 - mean_squared_error: 2.3893e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.7227e-04 - val_mean_squared_error: 4.0079e-04 - val_root_mean_squared_error: 0.0200\n",
      "Epoch 79/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 141ms/step - loss: 2.4043e-04 - mean_squared_error: 2.4039e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.8350e-04 - val_mean_squared_error: 4.1238e-04 - val_root_mean_squared_error: 0.0203\n",
      "Epoch 80/80\n",
      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 169ms/step - loss: 2.4553e-04 - mean_squared_error: 2.4549e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.8363e-04 - val_mean_squared_error: 4.1256e-04 - val_root_mean_squared_error: 0.0203\n"
     ]
    }
   ],
   "source": [
    "# train the lstm!\n",
    "history = regressor.fit(x = trainds, y = traintargetds, validation_split=0.2, epochs=80, batch_size = 2000, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "de0d2dcd-53c5-4932-8567-9e91f11667dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"mean_squared_error\"], c='k', label='training')\n",
    "plt.plot(history.history[\"val_mean_squared_error\"], c='r', label='validation')\n",
    "plt.legend(['training', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7f258d82-fafc-4e3f-8c09-be30b3dfb4ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m957/957\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step\n"
     ]
    }
   ],
   "source": [
    "# predict using test data\n",
    "oni_predictions = regressor.predict(testds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "eaa51ee1-9566-4cb5-84f9-1b507be59764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2869/2869\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step\n"
     ]
    }
   ],
   "source": [
    "train_predictions = regressor.predict(trainds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b0c1d7f7-002a-4504-9be1-177966a14532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how did we do!?\n",
    "plt.scatter(\n",
    "    testtargetds,\n",
    "    oni_predictions\n",
    "    )\n",
    "plt.xlim([0,5])\n",
    "plt.ylim([0,5])\n",
    "plt.xlabel(\"Gage Height (ft), Actual\")\n",
    "plt.ylabel(\"Gage Height (ft), Predicted\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "de38813a-afe0-42e5-b8e3-a332ef3926f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how did we train!?\n",
    "plt.scatter(\n",
    "    traintargetds,\n",
    "    train_predictions\n",
    "    )\n",
    "plt.xlim([0,5])\n",
    "plt.ylim([0,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8782649d-18df-46f6-b47b-3dbb02fdfeb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "samevaltest = []\n",
    "for m in range(len(testds)):\n",
    " samevaltest.append(testds[m][3][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5a87aa6e-3385-490f-9f4d-9ca2b7cadc05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how did we do!?\n",
    "plt.scatter(\n",
    "    testtargetds,\n",
    "    samevaltest\n",
    "    )\n",
    "plt.xlim([0,5])\n",
    "plt.ylim([0,5])\n",
    "plt.xlabel(\"Gage Height (ft), Actual\")\n",
    "plt.ylabel(\"Gage Height (ft), Last Value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4b3754ac-4156-4bac-8d78-3e6fddc1008a",
   "metadata": {},
   "outputs": [],
   "source": [
    "samevaltest2 = []\n",
    "for m in range(len(trainds)):\n",
    " samevaltest2.append(trainds[m][3][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7efbab42-0343-4a7f-b7f6-ccc7f9406a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how did we do!?\n",
    "plt.scatter(\n",
    "    traintargetds,\n",
    "    samevaltest2\n",
    "    )\n",
    "plt.xlim([0,5])\n",
    "plt.ylim([0,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "67c1a2d4-4b0b-4479-9f2a-5dbd26588d93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0006606314138178967"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(testtargetds, samevaltest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "796ec23d-1f44-4c54-bda3-4b04e63807d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00032060053713719495"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(testtargetds, oni_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3acce4eb-5d95-4402-af25-a147ccded3d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0005478065731995598"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(traintargetds, samevaltest2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f9033fc2-58b4-4e76-9888-51224b60d420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0002935652043371892"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(traintargetds, train_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "42c71545-1b87-45c0-ae4a-43b8eb19a2c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05      , 0.00683333, 0.56      ],\n",
       "       [0.05      , 0.00683333, 0.56      ],\n",
       "       [0.05      , 0.00683333, 0.56      ],\n",
       "       [0.05      , 0.00723333, 0.57      ]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testds[1:100][:][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a362a576-072b-439f-a32c-09bef549961e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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