solar_flare_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3ed12f23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: libclang, flatbuffers, werkzeug, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, opt-einsum, ml-dtypes, markdown, keras, h5py, grpcio, google-pasta, gast, astunparse, absl-py, tensorboard, tensorflow-macos, tensorflow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed absl-py-2.0.0 astunparse-1.6.3 flatbuffers-23.5.26 gast-0.5.4 google-pasta-0.2.0 grpcio-1.59.0 h5py-3.10.0 keras-2.14.0 libclang-16.0.6 markdown-3.5 ml-dtypes-0.2.0 opt-einsum-3.3.0 tensorboard-2.14.1 tensorboard-data-server-0.7.1 tensorflow-2.14.0 tensorflow-estimator-2.14.0 tensorflow-io-gcs-filesystem-0.34.0 tensorflow-macos-2.14.0 termcolor-2.3.0 werkzeug-3.0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8abfe21f",
   "metadata": {},
   "source": [
    "# Import the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "3d5e9034",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0a9920",
   "metadata": {},
   "source": [
    "# Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "5e809528",
   "metadata": {},
   "outputs": [
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       "      <td>244</td>\n",
       "      <td>20</td>\n",
       "      <td>17400.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>-277</td>\n",
       "      <td>378</td>\n",
       "      <td>469</td>\n",
       "      <td>9822</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021310</td>\n",
       "      <td>2002-02-13</td>\n",
       "      <td>07:03:52</td>\n",
       "      <td>07:05:14</td>\n",
       "      <td>07:07:48</td>\n",
       "      <td>236</td>\n",
       "      <td>336</td>\n",
       "      <td>313392.0</td>\n",
       "      <td>25-50</td>\n",
       "      <td>-272</td>\n",
       "      <td>390</td>\n",
       "      <td>476</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>GS</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     flare start.date start.time      peak       end  duration.s  peak.c/s  \\\n",
       "0  2021213 2002-02-12   21:29:56  21:33:38  21:41:48         712       136   \n",
       "1  2021228 2002-02-12   21:44:08  21:45:06  21:48:56         288         7   \n",
       "2  2021332 2002-02-13   00:53:24  00:54:54  00:57:00         216        15   \n",
       "3  2021308 2002-02-13   04:22:52  04:23:50  04:26:56         244        20   \n",
       "4  2021310 2002-02-13   07:03:52  07:05:14  07:07:48         236       336   \n",
       "\n",
       "   total.counts energy.kev  x.pos.asec  y.pos.asec  radial active.region.ar  \\\n",
       "0      167304.0      12-25         592        -358     692                0   \n",
       "1        9504.0       6-12         604        -341     694             9811   \n",
       "2       11448.0       6-12        -310         375     487             9825   \n",
       "3       17400.0      12-25        -277         378     469             9822   \n",
       "4      313392.0      25-50        -272         390     476             9825   \n",
       "\n",
       "  flag.1 flag.2 flag.3 flag.4 flag.5  \n",
       "0     A1     P1    NaN    NaN    NaN  \n",
       "1     A1     P1     PE     Q1    NaN  \n",
       "2     A1     P1    NaN    NaN    NaN  \n",
       "3     A1     P1    NaN    NaN    NaN  \n",
       "4     A1     GS     P1     PE     Q2  "
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data with specified bcolumn classes\n",
    "solar_flare = pd.read_csv(\"Dataset/hessi.solar.flare.2002to2016.csv\", parse_dates=[\"start.date\"], \n",
    "                          dtype={\"energy.kev\": \"category\", \"total.counts\": \"float64\", \"active.region.ar\": \"category\"})\n",
    "solar_flare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "5dea9015",
   "metadata": {},
   "outputs": [],
   "source": [
    "solar_flare['start.date'] = solar_flare['start.date'].dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "abe09020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2002-02-12 21:33:38\n",
       "1        2002-02-12 21:45:06\n",
       "2        2002-02-13 00:54:54\n",
       "3        2002-02-13 04:23:50\n",
       "4        2002-02-13 07:05:14\n",
       "                 ...        \n",
       "113937   2016-12-26 22:17:02\n",
       "113938   2016-12-26 23:28:18\n",
       "113939   2016-12-27 08:39:06\n",
       "113940   2016-12-27 12:21:30\n",
       "113941   2016-12-27 12:37:14\n",
       "Name: start_datetime, Length: 113942, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_flare['start_datetime'] = pd.to_datetime(solar_flare['start.date'] + ' ' + solar_flare['peak'])\n",
    "solar_flare['start_datetime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "0cdf6a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set 'start_datetime' as the index of the DataFrame\n",
    "solar_flare.set_index('start_datetime', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "60ec8391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>energy.kev</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active.region.ar</th>\n",
       "      <th>flag.1</th>\n",
       "      <th>flag.2</th>\n",
       "      <th>flag.3</th>\n",
       "      <th>flag.4</th>\n",
       "      <th>flag.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-02-12 21:33:38</th>\n",
       "      <td>2021213</td>\n",
       "      <td>712</td>\n",
       "      <td>136</td>\n",
       "      <td>167304.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>592</td>\n",
       "      <td>-358</td>\n",
       "      <td>692</td>\n",
       "      <td>0</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-12 21:45:06</th>\n",
       "      <td>2021228</td>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>9504.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>604</td>\n",
       "      <td>-341</td>\n",
       "      <td>694</td>\n",
       "      <td>9811</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-13 00:54:54</th>\n",
       "      <td>2021332</td>\n",
       "      <td>216</td>\n",
       "      <td>15</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-310</td>\n",
       "      <td>375</td>\n",
       "      <td>487</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-13 04:23:50</th>\n",
       "      <td>2021308</td>\n",
       "      <td>244</td>\n",
       "      <td>20</td>\n",
       "      <td>17400.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>-277</td>\n",
       "      <td>378</td>\n",
       "      <td>469</td>\n",
       "      <td>9822</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-13 07:05:14</th>\n",
       "      <td>2021310</td>\n",
       "      <td>236</td>\n",
       "      <td>336</td>\n",
       "      <td>313392.0</td>\n",
       "      <td>25-50</td>\n",
       "      <td>-272</td>\n",
       "      <td>390</td>\n",
       "      <td>476</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>GS</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26 22:17:02</th>\n",
       "      <td>16122603</td>\n",
       "      <td>484</td>\n",
       "      <td>152</td>\n",
       "      <td>75328.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>GE</td>\n",
       "      <td>NS</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26 23:28:18</th>\n",
       "      <td>16122604</td>\n",
       "      <td>1096</td>\n",
       "      <td>304</td>\n",
       "      <td>142048.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>DR</td>\n",
       "      <td>NS</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27 08:39:06</th>\n",
       "      <td>16122701</td>\n",
       "      <td>392</td>\n",
       "      <td>80</td>\n",
       "      <td>32344.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>NS</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q3 SE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27 12:21:30</th>\n",
       "      <td>16122702</td>\n",
       "      <td>520</td>\n",
       "      <td>24</td>\n",
       "      <td>17336.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>326</td>\n",
       "      <td>326</td>\n",
       "      <td>461</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27 12:37:14</th>\n",
       "      <td>16122703</td>\n",
       "      <td>200</td>\n",
       "      <td>48</td>\n",
       "      <td>11056.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>765</td>\n",
       "      <td>198</td>\n",
       "      <td>791</td>\n",
       "      <td>2621</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113942 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        flare  duration.s  peak.c/s  total.counts energy.kev  \\\n",
       "start_datetime                                                                 \n",
       "2002-02-12 21:33:38   2021213         712       136      167304.0      12-25   \n",
       "2002-02-12 21:45:06   2021228         288         7        9504.0       6-12   \n",
       "2002-02-13 00:54:54   2021332         216        15       11448.0       6-12   \n",
       "2002-02-13 04:23:50   2021308         244        20       17400.0      12-25   \n",
       "2002-02-13 07:05:14   2021310         236       336      313392.0      25-50   \n",
       "...                       ...         ...       ...           ...        ...   \n",
       "2016-12-26 22:17:02  16122603         484       152       75328.0        3-6   \n",
       "2016-12-26 23:28:18  16122604        1096       304      142048.0        3-6   \n",
       "2016-12-27 08:39:06  16122701         392        80       32344.0        3-6   \n",
       "2016-12-27 12:21:30  16122702         520        24       17336.0       6-12   \n",
       "2016-12-27 12:37:14  16122703         200        48       11056.0       6-12   \n",
       "\n",
       "                     x.pos.asec  y.pos.asec  radial active.region.ar flag.1  \\\n",
       "start_datetime                                                                \n",
       "2002-02-12 21:33:38         592        -358     692                0     A1   \n",
       "2002-02-12 21:45:06         604        -341     694             9811     A1   \n",
       "2002-02-13 00:54:54        -310         375     487             9825     A1   \n",
       "2002-02-13 04:23:50        -277         378     469             9822     A1   \n",
       "2002-02-13 07:05:14        -272         390     476             9825     A1   \n",
       "...                         ...         ...     ...              ...    ...   \n",
       "2016-12-26 22:17:02           0           0       0                0     A0   \n",
       "2016-12-26 23:28:18           0           0       0                0     A0   \n",
       "2016-12-27 08:39:06           0           0       0                0     A0   \n",
       "2016-12-27 12:21:30         326         326     461                0     A0   \n",
       "2016-12-27 12:37:14         765         198     791             2621     A0   \n",
       "\n",
       "                    flag.2 flag.3 flag.4 flag.5  \n",
       "start_datetime                                   \n",
       "2002-02-12 21:33:38     P1    NaN    NaN    NaN  \n",
       "2002-02-12 21:45:06     P1     PE     Q1    NaN  \n",
       "2002-02-13 00:54:54     P1    NaN    NaN    NaN  \n",
       "2002-02-13 04:23:50     P1    NaN    NaN    NaN  \n",
       "2002-02-13 07:05:14     GS     P1     PE     Q2  \n",
       "...                    ...    ...    ...    ...  \n",
       "2016-12-26 22:17:02     GE     NS     PE     Q2  \n",
       "2016-12-26 23:28:18     DR     NS     PE     Q2  \n",
       "2016-12-27 08:39:06     DF     NS     PE  Q3 SE  \n",
       "2016-12-27 12:21:30     DF     DR     P1  PE Q3  \n",
       "2016-12-27 12:37:14     DF     P1     PE     Q2  \n",
       "\n",
       "[113942 rows x 14 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the specified columns from the 'solar_flare' DataFrame\n",
    "columns_to_drop = ['start.date', 'start.time', 'peak', 'end']\n",
    "solar_flare = solar_flare.drop(columns=columns_to_drop)\n",
    "\n",
    "solar_flare"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7010a5a",
   "metadata": {},
   "source": [
    "Peak Rate: peak counts/second in energy range 6-12 keV, averaged over active collimators, including background.<br>\n",
    "\n",
    "Total Counts: counts in energy range 6-12 keV integrated over duration of flare summed over all subcollimators,\n",
    "including background.<br>\n",
    "\n",
    "Energy: the highest energy band in which the flare was observed. Electron Kev (kilo electron volt)<br>\n",
    "https://en.wikipedia.org/wiki/Electronvolt<br>\n",
    "\n",
    "Radial Distance: distance from Sun center<br>\n",
    "\n",
    "Quality Codes: Qn, where n is the total number of data gap, SAA, particle, eclipse or decimation flags set for event.<br>\n",
    "n ranges from 0 to 11. Use care when analyzing the data when the quality is not zero.<br>\n",
    "<br>\n",
    "Active_Region: A number for the closest active region, if available<br>\n",
    "\n",
    "radial_offset: the offset of the flare position from the spin axis of the spacecraft in arcsec. This is used i\n",
    "spectroscopy.<br>\n",
    "\n",
    "peak_c/s: peak count rate in corrected counts.<br>\n",
    "\n",
    "Flare Flag Codes:<br>\n",
    "a0 - In attenuator state 0 (None) sometime during flare<br>\n",
    "a1 - In attenuator state 1 (Thin) sometime during flare<br>\n",
    "a2 - In attenuator state 2 (Thick) sometime during flare<br>\n",
    "a3 - In attenuator state 3 (Both) sometime during flare<br>\n",
    "An - Attenuator state (0=None, 1=Thin, 2=Thick, 3=Both) at peak of flare<br>\n",
    "DF - Front segment counts were decimated sometime during flare<br>\n",
    "DR - Rear segment counts were decimated sometime during flare<br>\n",
    "ED - Spacecraft eclipse (night) sometime during flare<br>\n",
    "EE - Flare ended in spacecraft eclipse (night)<br>\n",
    "ES - Flare started in spacecraft eclipse (night)<br>\n",
    "FE - Flare ongoing at end of file<br>\n",
    "FR - In Fast Rate Mode<br>\n",
    "FS - Flare ongoing at start of file<br>\n",
    "GD - Data gap during flare<br>\n",
    "GE - Flare ended in data gap<br>\n",
    "GS - Flare started in data gap<br>\n",
    "MR - Spacecraft in high-latitude zone during flare<br>\n",
    "NS - Non-solar event<br>\n",
    "PE - Particle event: Particles are present<br>\n",
    "PS - Possible Solar Flare; in front detectors, but no position<br>\n",
    "Pn - Position Quality: P0 = Position is NOT valid, P1 = Position is valid<br>\n",
    "Qn - Data Quality: Q0 = Highest Quality, Q11 = Lowest Quality<br>\n",
    "SD - Spacecraft was in SAA sometime during flare<br>\n",
    "SE - Flare ended when spacecraft was in SAA<br>\n",
    "SS - Flare started when spacecraft was in SAA<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "1847a451",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 113942 entries, 2002-02-12 21:33:38 to 2016-12-27 12:37:14\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count   Dtype   \n",
      "---  ------            --------------   -----   \n",
      " 0   flare             113942 non-null  int64   \n",
      " 1   duration.s        113942 non-null  int64   \n",
      " 2   peak.c/s          113942 non-null  int64   \n",
      " 3   total.counts      113942 non-null  float64 \n",
      " 4   energy.kev        113942 non-null  category\n",
      " 5   x.pos.asec        113942 non-null  int64   \n",
      " 6   y.pos.asec        113942 non-null  int64   \n",
      " 7   radial            113942 non-null  int64   \n",
      " 8   active.region.ar  113942 non-null  category\n",
      " 9   flag.1            113942 non-null  object  \n",
      " 10  flag.2            113942 non-null  object  \n",
      " 11  flag.3            94035 non-null   object  \n",
      " 12  flag.4            93740 non-null   object  \n",
      " 13  flag.5            52991 non-null   object  \n",
      "dtypes: category(2), float64(1), int64(6), object(5)\n",
      "memory usage: 11.7+ MB\n"
     ]
    }
   ],
   "source": [
    "solar_flare.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "817389ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.139420e+05</td>\n",
       "      <td>113942.000000</td>\n",
       "      <td>113942.000000</td>\n",
       "      <td>1.139420e+05</td>\n",
       "      <td>113942.000000</td>\n",
       "      <td>113942.000000</td>\n",
       "      <td>113942.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.086459e+07</td>\n",
       "      <td>493.051728</td>\n",
       "      <td>212.321532</td>\n",
       "      <td>3.772013e+05</td>\n",
       "      <td>-9.752181</td>\n",
       "      <td>-43.129812</td>\n",
       "      <td>689.528892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.832864e+06</td>\n",
       "      <td>433.389295</td>\n",
       "      <td>833.776871</td>\n",
       "      <td>3.071203e+06</td>\n",
       "      <td>757.017401</td>\n",
       "      <td>404.547666</td>\n",
       "      <td>513.203089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.021213e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>-10012.000000</td>\n",
       "      <td>-10005.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.110734e+06</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.292000e+04</td>\n",
       "      <td>-704.000000</td>\n",
       "      <td>-249.000000</td>\n",
       "      <td>469.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.112022e+07</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>5.857400e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-75.000000</td>\n",
       "      <td>759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.403013e+07</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>1.794420e+05</td>\n",
       "      <td>706.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>946.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.412161e+08</td>\n",
       "      <td>4444.000000</td>\n",
       "      <td>113156.000000</td>\n",
       "      <td>4.355501e+08</td>\n",
       "      <td>1002.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>14154.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              flare     duration.s       peak.c/s  total.counts  \\\n",
       "count  1.139420e+05  113942.000000  113942.000000  1.139420e+05   \n",
       "mean   1.086459e+07     493.051728     212.321532  3.772013e+05   \n",
       "std    9.832864e+06     433.389295     833.776871  3.071203e+06   \n",
       "min    2.021213e+06       8.000000       0.000000  8.000000e+00   \n",
       "25%    4.110734e+06     212.000000      28.000000  2.292000e+04   \n",
       "50%    1.112022e+07     364.000000      53.000000  5.857400e+04   \n",
       "75%    1.403013e+07     628.000000     144.000000  1.794420e+05   \n",
       "max    1.412161e+08    4444.000000  113156.000000  4.355501e+08   \n",
       "\n",
       "          x.pos.asec     y.pos.asec         radial  \n",
       "count  113942.000000  113942.000000  113942.000000  \n",
       "mean       -9.752181     -43.129812     689.528892  \n",
       "std       757.017401     404.547666     513.203089  \n",
       "min    -10012.000000  -10005.000000       0.000000  \n",
       "25%      -704.000000    -249.000000     469.000000  \n",
       "50%         0.000000     -75.000000     759.000000  \n",
       "75%       706.000000     198.000000     946.000000  \n",
       "max      1002.000000    1012.000000   14154.000000  "
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_flare.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "4c2693eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flare\n",
      "13111352    2\n",
      "13111351    2\n",
      "3042103     2\n",
      "Name: count, dtype: int64\n",
      "Number of duplicates in the 'flare' column: 3\n"
     ]
    }
   ],
   "source": [
    "duplicates_count = solar_flare['flare'].duplicated(keep=False).sum()\n",
    "\n",
    "value_counts = solar_flare['flare'].value_counts()\n",
    "\n",
    "duplicates = value_counts[value_counts>1]\n",
    "print(duplicates)\n",
    "print(f\"Number of duplicates in the 'flare' column: {len(duplicates)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "7a5b7f3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of dataframe before dropping duplicates:  113942\n",
      "Length of dataframe after dropping duplicates:  113939\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of dataframe before dropping duplicates: \", len(solar_flare))\n",
    "solar_flare = solar_flare.drop_duplicates(subset='flare')\n",
    "print(\"Length of dataframe after dropping duplicates: \", len(solar_flare))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "d939ab51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot the time series data for 'peak.c/s' and 'total.counts'\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(solar_flare.index, solar_flare['peak.c/s'], label='Peak Count Rate')\n",
    "plt.title('Solar Flare Time Series')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Peak.c/s')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "12a6b068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot the time series data for 'peak.c/s' and 'total.counts'\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(solar_flare.index, solar_flare['total.counts'], label='Total Counts')\n",
    "plt.title('Solar Flare Time Series')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Total counts')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "b3675470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Histogram of energy.keV\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(solar_flare['energy.kev'], bins=20, color='blue', alpha=0.7)\n",
    "plt.title('Distribution of Energy Levels (energy.keV)')\n",
    "plt.xlabel('Energy Levels (keV)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Time Series Plot of energy.keV\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(solar_flare.index, solar_flare['energy.kev'], color='green')\n",
    "plt.title('Energy Levels Over Time (energy.keV)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Energy Levels (keV)')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "ee8e25ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.violinplot(data=solar_flare, x='energy.kev', y='flare', fill='gold')\n",
    "plt.title('Solar Flares vs. Energy (KiloElectronVolt)')\n",
    "plt.xlabel('Energy (KiloElectronVolt)')\n",
    "plt.ylabel('Count of Flares (log)')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "48f593d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>energy.kev</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active.region.ar</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-02-12 21:33:38</th>\n",
       "      <td>2021213</td>\n",
       "      <td>712</td>\n",
       "      <td>136</td>\n",
       "      <td>167304.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>592</td>\n",
       "      <td>-358</td>\n",
       "      <td>692</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-12 21:45:06</th>\n",
       "      <td>2021228</td>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>9504.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>604</td>\n",
       "      <td>-341</td>\n",
       "      <td>694</td>\n",
       "      <td>9811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-13 00:54:54</th>\n",
       "      <td>2021332</td>\n",
       "      <td>216</td>\n",
       "      <td>15</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-310</td>\n",
       "      <td>375</td>\n",
       "      <td>487</td>\n",
       "      <td>9825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-13 04:23:50</th>\n",
       "      <td>2021308</td>\n",
       "      <td>244</td>\n",
       "      <td>20</td>\n",
       "      <td>17400.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>-277</td>\n",
       "      <td>378</td>\n",
       "      <td>469</td>\n",
       "      <td>9822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-13 07:05:14</th>\n",
       "      <td>2021310</td>\n",
       "      <td>236</td>\n",
       "      <td>336</td>\n",
       "      <td>313392.0</td>\n",
       "      <td>25-50</td>\n",
       "      <td>-272</td>\n",
       "      <td>390</td>\n",
       "      <td>476</td>\n",
       "      <td>9825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26 22:17:02</th>\n",
       "      <td>16122603</td>\n",
       "      <td>484</td>\n",
       "      <td>152</td>\n",
       "      <td>75328.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26 23:28:18</th>\n",
       "      <td>16122604</td>\n",
       "      <td>1096</td>\n",
       "      <td>304</td>\n",
       "      <td>142048.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27 08:39:06</th>\n",
       "      <td>16122701</td>\n",
       "      <td>392</td>\n",
       "      <td>80</td>\n",
       "      <td>32344.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27 12:21:30</th>\n",
       "      <td>16122702</td>\n",
       "      <td>520</td>\n",
       "      <td>24</td>\n",
       "      <td>17336.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>326</td>\n",
       "      <td>326</td>\n",
       "      <td>461</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27 12:37:14</th>\n",
       "      <td>16122703</td>\n",
       "      <td>200</td>\n",
       "      <td>48</td>\n",
       "      <td>11056.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>765</td>\n",
       "      <td>198</td>\n",
       "      <td>791</td>\n",
       "      <td>2621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113939 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        flare  duration.s  peak.c/s  total.counts energy.kev  \\\n",
       "start_datetime                                                                 \n",
       "2002-02-12 21:33:38   2021213         712       136      167304.0      12-25   \n",
       "2002-02-12 21:45:06   2021228         288         7        9504.0       6-12   \n",
       "2002-02-13 00:54:54   2021332         216        15       11448.0       6-12   \n",
       "2002-02-13 04:23:50   2021308         244        20       17400.0      12-25   \n",
       "2002-02-13 07:05:14   2021310         236       336      313392.0      25-50   \n",
       "...                       ...         ...       ...           ...        ...   \n",
       "2016-12-26 22:17:02  16122603         484       152       75328.0        3-6   \n",
       "2016-12-26 23:28:18  16122604        1096       304      142048.0        3-6   \n",
       "2016-12-27 08:39:06  16122701         392        80       32344.0        3-6   \n",
       "2016-12-27 12:21:30  16122702         520        24       17336.0       6-12   \n",
       "2016-12-27 12:37:14  16122703         200        48       11056.0       6-12   \n",
       "\n",
       "                     x.pos.asec  y.pos.asec  radial active.region.ar  \n",
       "start_datetime                                                        \n",
       "2002-02-12 21:33:38         592        -358     692                0  \n",
       "2002-02-12 21:45:06         604        -341     694             9811  \n",
       "2002-02-13 00:54:54        -310         375     487             9825  \n",
       "2002-02-13 04:23:50        -277         378     469             9822  \n",
       "2002-02-13 07:05:14        -272         390     476             9825  \n",
       "...                         ...         ...     ...              ...  \n",
       "2016-12-26 22:17:02           0           0       0                0  \n",
       "2016-12-26 23:28:18           0           0       0                0  \n",
       "2016-12-27 08:39:06           0           0       0                0  \n",
       "2016-12-27 12:21:30         326         326     461                0  \n",
       "2016-12-27 12:37:14         765         198     791             2621  \n",
       "\n",
       "[113939 rows x 9 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_flare = solar_flare.iloc[:, :9]\n",
    "solar_flare"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92457148",
   "metadata": {},
   "source": [
    "**Correlation Analysis:**\n",
    "Compute the correlation matrix to identify pairwise correlations between variables.\n",
    "High correlations (close to 1 or -1) between pairs of input features may indicate multicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d8a84888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming df is your DataFrame with the mentioned variables\n",
    "correlation_matrix = solar_flare[['duration.s', 'peak.c/s', 'total.counts', 'x.pos.asec', 'y.pos.asec', 'radial']].corr()\n",
    "\n",
    "# Visualize the correlation matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ada39cf",
   "metadata": {},
   "source": [
    "**VIF (Variance Inflation Factor):**\n",
    "Calculate the Variance Inflation Factor for each variable to quantify multicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "58831f73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Variable       VIF\n",
      "0    duration.s  1.086169\n",
      "1      peak.c/s  3.432127\n",
      "2  total.counts  3.404821\n",
      "3    x.pos.asec  1.158818\n",
      "4    y.pos.asec  1.721463\n",
      "5        radial  1.717288\n",
      "6     intercept  5.202871\n",
      "Variable with the highest VIF:\n",
      "Variable    intercept\n",
      "VIF          5.202871\n",
      "Name: 6, dtype: object\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# Create a DataFrame with the variables of interest\n",
    "X = solar_flare[['duration.s', 'peak.c/s', 'total.counts', 'x.pos.asec', 'y.pos.asec', 'radial']]\n",
    "\n",
    "# Add a constant for the intercept term\n",
    "X['intercept'] = 1\n",
    "\n",
    "# Calculate VIF for each variable\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"Variable\"] = X.columns\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "# Display the VIF values\n",
    "print(vif_data)\n",
    "\n",
    "# Find the variable with the highest VIF\n",
    "variable_with_highest_vif = vif_data.loc[vif_data['VIF'].idxmax()]\n",
    "\n",
    "# Display the variable with the highest VIF\n",
    "print(\"Variable with the highest VIF:\")\n",
    "print(variable_with_highest_vif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32a806f2",
   "metadata": {},
   "source": [
    "Typically, a VIF value below 5 is considered acceptable, indicating low multicollinearity. None of your features have VIF values significantly exceeding this threshold, suggesting that multicollinearity is not a severe issue.\n",
    "\n",
    "If you still want to reduce multicollinearity further, you might consider removing the feature with the highest VIF among the actual features (excluding the intercept term). In this case, the total.counts feature has the highest VIF (3.281116), but this value is not extremely high.\n",
    "\n",
    "You could choose to keep all features, or if you prefer to be conservative in addressing multicollinearity, you might consider removing the feature with the highest VIF, which is total.counts. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a85294",
   "metadata": {},
   "source": [
    "**Feature Selection:**\n",
    "Use techniques like Recursive Feature Elimination (RFE) or feature importance from tree-based models to select the most important features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "5a3de759",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features: Index(['duration.s', 'peak.c/s', 'total.counts', 'radial'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Assuming y is your target variable\n",
    "X = solar_flare[['duration.s', 'peak.c/s', 'total.counts', 'x.pos.asec', 'y.pos.asec', 'radial']]\n",
    "y = solar_flare['energy.kev']\n",
    "\n",
    "# Use RandomForestRegressor for feature selection\n",
    "model = RandomForestClassifier()\n",
    "rfe = RFE(model, n_features_to_select=4)\n",
    "fit = rfe.fit(X, y)\n",
    "\n",
    "# Display the selected features\n",
    "selected_features = X.columns[fit.support_]\n",
    "print(\"Selected Features:\", selected_features)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01310649",
   "metadata": {},
   "source": [
    "\n",
    "Recursive Feature Elimination (RFE) is a feature selection technique that works by recursively removing the least important features, based on the contribution of each feature to the model's performance. This process continues until the desired number of features is reached. RFE is particularly useful when you have a large number of features, and you want to select a subset of them that contributes the most to the model's predictive power."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e293b2",
   "metadata": {},
   "source": [
    "# Peak c/s - Target variable 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f734e459",
   "metadata": {},
   "source": [
    "## 100 Epoch training\n",
    "In the context of training machine learning models, an epoch refers to one complete pass through the entire training dataset. During an epoch, the model is exposed to all the training examples once, and the model's parameters are updated based on the loss calculated for those examples.<br>\n",
    "<br>\n",
    "Here's how the training process typically works within each epoch:<br>\n",
    "<br>\n",
    "The training dataset is divided into batches or mini-batches. Each batch contains a subset of the training examples.<br>\n",
    "<br>\n",
    "The model processes each batch in sequence, calculates the loss for that batch, and updates its parameters (e.g., weights and biases) using an optimization algorithm (e.g., gradient descent).<br>\n",
    "<br>\n",
    "This process is repeated for each batch in the training dataset.<br>\n",
    "<br>\n",
    "Once all the batches have been processed, the model has completed one epoch. The number of epochs you specify when training a machine learning model determines how many times the entire training dataset will be processed in this manner.<br>\n",
    "<br>\n",
    "The purpose of training the model through multiple epochs is to allow it to learn from the data gradually and improve its performance. The model's parameters are adjusted during each epoch to minimize the loss function, which measures the error between the model's predictions and the actual target values. As the model iteratively refines its parameters, it becomes better at making predictions on the training data. However, it's important to find the right balance in terms of the number of epochs, as training for too many epochs can lead to overfitting (where the model performs well on the training data but poorly on unseen data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "a1bae866",
   "metadata": {},
   "outputs": [],
   "source": [
    "results=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed41421",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6429e148",
   "metadata": {},
   "source": [
    "## peak.c/s - Target variable 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "77d0d236",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1425/1425 - 4s - loss: 5.9762e-05 - val_loss: 3.1399e-05 - 4s/epoch - 3ms/step\n",
      "Epoch 2/100\n",
      "1425/1425 - 3s - loss: 5.9445e-05 - val_loss: 3.1305e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 3/100\n",
      "1425/1425 - 3s - loss: 5.9324e-05 - val_loss: 3.1251e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "1425/1425 - 3s - loss: 5.9313e-05 - val_loss: 3.1178e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 5/100\n",
      "1425/1425 - 3s - loss: 5.9172e-05 - val_loss: 3.1338e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 6/100\n",
      "1425/1425 - 3s - loss: 5.9272e-05 - val_loss: 3.1166e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 7/100\n",
      "1425/1425 - 3s - loss: 5.9226e-05 - val_loss: 3.1333e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 8/100\n",
      "1425/1425 - 3s - loss: 5.9280e-05 - val_loss: 3.1303e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 9/100\n",
      "1425/1425 - 3s - loss: 5.8946e-05 - val_loss: 3.1758e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 10/100\n",
      "1425/1425 - 3s - loss: 5.9081e-05 - val_loss: 3.1459e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "1425/1425 - 3s - loss: 5.9101e-05 - val_loss: 3.1124e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "1425/1425 - 3s - loss: 5.8907e-05 - val_loss: 3.1454e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 13/100\n",
      "1425/1425 - 3s - loss: 5.9072e-05 - val_loss: 3.2157e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 14/100\n",
      "1425/1425 - 3s - loss: 5.8899e-05 - val_loss: 3.0937e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 15/100\n",
      "1425/1425 - 3s - loss: 5.8948e-05 - val_loss: 3.1225e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 16/100\n",
      "1425/1425 - 3s - loss: 5.8851e-05 - val_loss: 3.0924e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "1425/1425 - 3s - loss: 5.9033e-05 - val_loss: 3.1186e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 18/100\n",
      "1425/1425 - 3s - loss: 5.8877e-05 - val_loss: 3.1224e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 19/100\n",
      "1425/1425 - 3s - loss: 5.8917e-05 - val_loss: 3.1541e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "1425/1425 - 3s - loss: 5.9057e-05 - val_loss: 3.1495e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 21/100\n",
      "1425/1425 - 3s - loss: 5.8877e-05 - val_loss: 3.0988e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 22/100\n",
      "1425/1425 - 3s - loss: 5.8993e-05 - val_loss: 3.1391e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 23/100\n",
      "1425/1425 - 3s - loss: 5.8858e-05 - val_loss: 3.1296e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 24/100\n",
      "1425/1425 - 3s - loss: 5.8868e-05 - val_loss: 3.1242e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 25/100\n",
      "1425/1425 - 3s - loss: 5.8820e-05 - val_loss: 3.0899e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 26/100\n",
      "1425/1425 - 3s - loss: 5.8748e-05 - val_loss: 3.2889e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 27/100\n",
      "1425/1425 - 3s - loss: 5.8830e-05 - val_loss: 3.1432e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "1425/1425 - 3s - loss: 5.8857e-05 - val_loss: 3.1102e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 29/100\n",
      "1425/1425 - 3s - loss: 5.8814e-05 - val_loss: 3.1375e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "1425/1425 - 3s - loss: 5.8789e-05 - val_loss: 3.1487e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 31/100\n",
      "1425/1425 - 3s - loss: 5.8846e-05 - val_loss: 3.1178e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "1425/1425 - 3s - loss: 5.8805e-05 - val_loss: 3.2891e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 33/100\n",
      "1425/1425 - 3s - loss: 5.8844e-05 - val_loss: 3.0921e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "1425/1425 - 3s - loss: 5.8811e-05 - val_loss: 3.1054e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "1425/1425 - 3s - loss: 5.8916e-05 - val_loss: 3.0892e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "1425/1425 - 3s - loss: 5.8896e-05 - val_loss: 3.3784e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "1425/1425 - 3s - loss: 5.8741e-05 - val_loss: 3.0916e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 38/100\n",
      "1425/1425 - 3s - loss: 5.8822e-05 - val_loss: 3.1174e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 39/100\n",
      "1425/1425 - 3s - loss: 5.8780e-05 - val_loss: 3.2704e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 40/100\n",
      "1425/1425 - 3s - loss: 5.8833e-05 - val_loss: 3.1746e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 41/100\n",
      "1425/1425 - 3s - loss: 5.8763e-05 - val_loss: 3.1034e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 42/100\n",
      "1425/1425 - 3s - loss: 5.8880e-05 - val_loss: 3.3266e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1425/1425 - 3s - loss: 5.8822e-05 - val_loss: 3.1004e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 44/100\n",
      "1425/1425 - 3s - loss: 5.8889e-05 - val_loss: 3.0991e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 45/100\n",
      "1425/1425 - 3s - loss: 5.8784e-05 - val_loss: 3.2133e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 46/100\n",
      "1425/1425 - 3s - loss: 5.8810e-05 - val_loss: 3.4163e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "1425/1425 - 3s - loss: 5.8718e-05 - val_loss: 3.1139e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "1425/1425 - 3s - loss: 5.8717e-05 - val_loss: 3.2093e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 49/100\n",
      "1425/1425 - 3s - loss: 5.8700e-05 - val_loss: 3.1225e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "1425/1425 - 3s - loss: 5.8798e-05 - val_loss: 3.0900e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "1425/1425 - 3s - loss: 5.8711e-05 - val_loss: 3.1155e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 52/100\n",
      "1425/1425 - 3s - loss: 5.8675e-05 - val_loss: 3.1210e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1425/1425 - 3s - loss: 5.8762e-05 - val_loss: 3.0942e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 54/100\n",
      "1425/1425 - 3s - loss: 5.8745e-05 - val_loss: 3.0818e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 55/100\n",
      "1425/1425 - 3s - loss: 5.8734e-05 - val_loss: 3.1173e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "1425/1425 - 3s - loss: 5.8604e-05 - val_loss: 3.1278e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 57/100\n",
      "1425/1425 - 3s - loss: 5.8672e-05 - val_loss: 3.1021e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 58/100\n",
      "1425/1425 - 3s - loss: 5.8685e-05 - val_loss: 3.1483e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 59/100\n",
      "1425/1425 - 3s - loss: 5.8627e-05 - val_loss: 3.1107e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 60/100\n",
      "1425/1425 - 3s - loss: 5.8555e-05 - val_loss: 3.1246e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "1425/1425 - 3s - loss: 5.8675e-05 - val_loss: 3.2908e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "1425/1425 - 3s - loss: 5.8700e-05 - val_loss: 3.0901e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 63/100\n",
      "1425/1425 - 3s - loss: 5.8555e-05 - val_loss: 3.0734e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "1425/1425 - 3s - loss: 5.8631e-05 - val_loss: 3.0950e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 65/100\n",
      "1425/1425 - 3s - loss: 5.8582e-05 - val_loss: 3.1984e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 66/100\n",
      "1425/1425 - 3s - loss: 5.8684e-05 - val_loss: 3.3615e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 67/100\n",
      "1425/1425 - 3s - loss: 5.8575e-05 - val_loss: 3.4685e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "1425/1425 - 3s - loss: 5.9038e-05 - val_loss: 3.0862e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "1425/1425 - 3s - loss: 5.8603e-05 - val_loss: 3.0874e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 70/100\n",
      "1425/1425 - 3s - loss: 5.8573e-05 - val_loss: 3.0984e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "1425/1425 - 3s - loss: 5.8628e-05 - val_loss: 3.1134e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 72/100\n",
      "1425/1425 - 4s - loss: 5.8651e-05 - val_loss: 3.0972e-05 - 4s/epoch - 2ms/step\n",
      "Epoch 73/100\n",
      "1425/1425 - 3s - loss: 5.8610e-05 - val_loss: 3.1421e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "1425/1425 - 3s - loss: 5.8628e-05 - val_loss: 3.1609e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 75/100\n",
      "1425/1425 - 3s - loss: 5.8632e-05 - val_loss: 3.1077e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 76/100\n",
      "1425/1425 - 3s - loss: 5.8847e-05 - val_loss: 3.1858e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 77/100\n",
      "1425/1425 - 3s - loss: 5.8523e-05 - val_loss: 3.0819e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 78/100\n",
      "1425/1425 - 3s - loss: 5.8629e-05 - val_loss: 3.1679e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 79/100\n",
      "1425/1425 - 3s - loss: 5.8630e-05 - val_loss: 3.0943e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 80/100\n",
      "1425/1425 - 3s - loss: 5.8538e-05 - val_loss: 3.1017e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 81/100\n",
      "1425/1425 - 3s - loss: 5.8501e-05 - val_loss: 3.3169e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 82/100\n",
      "1425/1425 - 3s - loss: 5.8423e-05 - val_loss: 3.0949e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 83/100\n",
      "1425/1425 - 3s - loss: 5.8664e-05 - val_loss: 3.0876e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "1425/1425 - 3s - loss: 5.8428e-05 - val_loss: 3.0931e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 85/100\n",
      "1425/1425 - 3s - loss: 5.8701e-05 - val_loss: 3.0880e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 86/100\n",
      "1425/1425 - 3s - loss: 5.8636e-05 - val_loss: 3.0854e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "1425/1425 - 3s - loss: 5.8427e-05 - val_loss: 3.1912e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "1425/1425 - 3s - loss: 5.8444e-05 - val_loss: 3.1658e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "1425/1425 - 3s - loss: 5.8443e-05 - val_loss: 3.1089e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 90/100\n",
      "1425/1425 - 3s - loss: 5.8461e-05 - val_loss: 3.0824e-05 - 3s/epoch - 2ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 91/100\n",
      "1425/1425 - 3s - loss: 5.8790e-05 - val_loss: 3.1417e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 92/100\n",
      "1425/1425 - 3s - loss: 5.8310e-05 - val_loss: 3.1329e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 93/100\n",
      "1425/1425 - 3s - loss: 5.8292e-05 - val_loss: 3.0896e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 94/100\n",
      "1425/1425 - 3s - loss: 5.8245e-05 - val_loss: 3.1278e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 95/100\n",
      "1425/1425 - 3s - loss: 5.8561e-05 - val_loss: 3.1071e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 96/100\n",
      "1425/1425 - 3s - loss: 5.8274e-05 - val_loss: 3.1134e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 97/100\n",
      "1425/1425 - 3s - loss: 5.8380e-05 - val_loss: 3.1206e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "1425/1425 - 3s - loss: 5.8365e-05 - val_loss: 3.1176e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "1425/1425 - 3s - loss: 5.8272e-05 - val_loss: 3.0790e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 100/100\n",
      "1425/1425 - 3s - loss: 5.8205e-05 - val_loss: 3.1180e-05 - 3s/epoch - 2ms/step\n",
      "713/713 [==============================] - 0s 579us/step\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "\n",
    "# Extract the feature you want to forecast (e.g., 'peak.c/s')\n",
    "target_feature = 'peak.c/s'\n",
    "\n",
    "# Extract the target time series data\n",
    "target_data = solar_flare[target_feature].values\n",
    "\n",
    "# Normalize the data\n",
    "scaler = MinMaxScaler()\n",
    "target_data = scaler.fit_transform(target_data.reshape(-1, 1))\n",
    "\n",
    "# Create sequences and corresponding target values\n",
    "look_back = 10  # Number of previous time steps to use for prediction\n",
    "X, y = [], []\n",
    "\n",
    "for i in range(len(target_data) - look_back):\n",
    "    X.append(target_data[i:i + look_back])\n",
    "    y.append(target_data[i + look_back])\n",
    "\n",
    "X, y = np.array(X), np.array(y)\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n",
    "\n",
    "# Build the LSTM model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, activation='relu', input_shape=(look_back, 1)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test), verbose=2)\n",
    "\n",
    "# Evaluate the model\n",
    "test_predictions = model.predict(X_test)\n",
    "test_predictions = scaler.inverse_transform(test_predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b1dac771",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 155.95\n",
      "Mean Squared Error (MSE): 54690.80\n",
      "Root Mean Squared Error (RMSE): 233.86\n",
      "R-squared (R²): -1680085046.09\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# Calculate regression metrics\n",
    "mae = mean_absolute_error(y_test, test_predictions)\n",
    "mse = mean_squared_error(y_test, test_predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, test_predictions)\n",
    "\n",
    "# Print the metrics\n",
    "print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
    "print(f'Mean Squared Error (MSE): {mse:.2f}')\n",
    "print(f'Root Mean Squared Error (RMSE): {rmse:.2f}')\n",
    "print(f'R-squared (R²): {r2:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f8f6ba",
   "metadata": {},
   "source": [
    "## energy.kev - Target variable 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "c5852a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name=\"LSTM 2002-2016\"\n",
    "epochs=100\n",
    "batch_size=64\n",
    "recurrent_dropout=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "10685f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.5030246  -0.09420199 -0.06872918  0.00802093]\n",
      " [-0.47368634 -0.24788813 -0.1204874   0.01193201]\n",
      " [-0.6395429  -0.2383572  -0.11984977 -0.3928647 ]\n",
      " ...\n",
      " [-0.7961852  -0.2383572  -0.12287523 -1.1085923 ]\n",
      " [-0.59347165 -0.23716584 -0.12251837 -0.9091271 ]\n",
      " [-0.00375941 -0.20619035 -0.11841707  0.48321703]]\n",
      "Epoch 1/100\n",
      "1452/1452 [==============================] - 4s 2ms/step - loss: 0.7339 - accuracy: 0.7621 - val_loss: 0.9294 - val_accuracy: 0.7106 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "1452/1452 [==============================] - 3s 2ms/step - loss: 0.6899 - accuracy: 0.7706 - val_loss: 0.9245 - val_accuracy: 0.7074 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "1452/1452 [==============================] - 3s 2ms/step - loss: 0.6847 - accuracy: 0.7723 - val_loss: 0.9307 - val_accuracy: 0.7078 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "1452/1452 [==============================] - 3s 2ms/step - loss: 0.6814 - accuracy: 0.7731 - val_loss: 0.9399 - val_accuracy: 0.7086 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "1452/1452 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.7735 - val_loss: 0.9326 - val_accuracy: 0.7095 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "1452/1452 [==============================] - 4s 2ms/step - loss: 0.6761 - accuracy: 0.7739 - val_loss: 0.9274 - val_accuracy: 0.7093 - lr: 5.0000e-04\n",
      "Epoch 7/100\n",
      "1452/1452 [==============================] - 3s 2ms/step - loss: 0.6750 - accuracy: 0.7746 - val_loss: 0.9328 - val_accuracy: 0.7111 - lr: 5.0000e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "726/726 [==============================] - 0s 649us/step - loss: 0.9245 - accuracy: 0.7074\n",
      "Test Accuracy: 70.74%\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, GRU, Dense, Input\n",
    "from tensorflow.keras.layers import concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Assuming 'energy.keV' is a column with categorical data\n",
    "categories = solar_flare['energy.kev'].values\n",
    "\n",
    "# Encode the categories\n",
    "label_encoder = LabelEncoder()\n",
    "categories_encoded = label_encoder.fit_transform(categories)\n",
    "\n",
    "      \n",
    "data_duration = np.array(solar_flare['duration.s'])\n",
    "data_peak = np.array(solar_flare['peak.c/s'])\n",
    "data_counts = np.array(solar_flare['total.counts'])\n",
    "data_radial = np.array(solar_flare['radial'])\n",
    "\n",
    "# Define the sequence length (number of previous categories to consider)\n",
    "sequence_length = 10\n",
    "\n",
    "# Create sequences and labels\n",
    "X_sequences, y_sequences = [], []\n",
    "\n",
    "X = np.stack((data_duration, data_peak, data_counts, data_radial), axis=-1)\n",
    "X = X.astype('float32')\n",
    "\n",
    "\n",
    "# Use StandardScaler to scale your input data\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "y = categories_encoded\n",
    "\n",
    "print(X)\n",
    "\n",
    "for i in range(len(X) - sequence_length):\n",
    "    X_sequences.append(X[i:i + sequence_length])\n",
    "    y_sequences.append(y[i + sequence_length])\n",
    "\n",
    "X_sequences = np.array(X_sequences)\n",
    "y_sequences = np.array(y_sequences)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(0.8 * len(X_sequences))\n",
    "X_train, X_test = X_sequences[:train_size], X_sequences[train_size:]\n",
    "y_train, y_test = y_sequences[:train_size], y_sequences[train_size:]\n",
    "\n",
    "\n",
    "input_layer = Input(shape=(sequence_length, X_sequences.shape[2]))\n",
    "lstm_layer = LSTM(50)(input_layer)\n",
    "\n",
    "output_layer = Dense(len(np.unique(categories_encoded)), activation='softmax')(lstm_layer)\n",
    "\n",
    "model = Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Implement learning rate scheduler\n",
    "lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)\n",
    "\n",
    "# Implement early stopping to prevent overfitting\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test), callbacks=[lr_scheduler, early_stopping])\n",
    "\n",
    "# Plot training history\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
    "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "4bde3fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.append([model_name, sequence_length, recurrent_dropout, epochs, batch_size, f'{test_accuracy * 100:.2f}%'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9975681",
   "metadata": {},
   "source": [
    "# Test it on solar data 2017-2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "d116a536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>start.date</th>\n",
       "      <th>start.time</th>\n",
       "      <th>peak</th>\n",
       "      <th>end</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>energy.kev</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active.region.ar</th>\n",
       "      <th>flag.1</th>\n",
       "      <th>flag.2</th>\n",
       "      <th>flag.3</th>\n",
       "      <th>flag.4</th>\n",
       "      <th>flag.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021213</td>\n",
       "      <td>2002-02-12</td>\n",
       "      <td>21:29:56</td>\n",
       "      <td>21:33:38</td>\n",
       "      <td>21:41:48</td>\n",
       "      <td>712</td>\n",
       "      <td>136</td>\n",
       "      <td>167304.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>592</td>\n",
       "      <td>-358</td>\n",
       "      <td>692</td>\n",
       "      <td>0</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021228</td>\n",
       "      <td>2002-02-12</td>\n",
       "      <td>21:44:08</td>\n",
       "      <td>21:45:06</td>\n",
       "      <td>21:48:56</td>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>9504.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>604</td>\n",
       "      <td>-341</td>\n",
       "      <td>694</td>\n",
       "      <td>9811</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021332</td>\n",
       "      <td>2002-02-13</td>\n",
       "      <td>00:53:24</td>\n",
       "      <td>00:54:54</td>\n",
       "      <td>00:57:00</td>\n",
       "      <td>216</td>\n",
       "      <td>15</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-310</td>\n",
       "      <td>375</td>\n",
       "      <td>487</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021308</td>\n",
       "      <td>2002-02-13</td>\n",
       "      <td>04:22:52</td>\n",
       "      <td>04:23:50</td>\n",
       "      <td>04:26:56</td>\n",
       "      <td>244</td>\n",
       "      <td>20</td>\n",
       "      <td>17400.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>-277</td>\n",
       "      <td>378</td>\n",
       "      <td>469</td>\n",
       "      <td>9822</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021310</td>\n",
       "      <td>2002-02-13</td>\n",
       "      <td>07:03:52</td>\n",
       "      <td>07:05:14</td>\n",
       "      <td>07:07:48</td>\n",
       "      <td>236</td>\n",
       "      <td>336</td>\n",
       "      <td>313392.0</td>\n",
       "      <td>25-50</td>\n",
       "      <td>-272</td>\n",
       "      <td>390</td>\n",
       "      <td>476</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>GS</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     flare start.date start.time      peak       end  duration.s  peak.c/s  \\\n",
       "0  2021213 2002-02-12   21:29:56  21:33:38  21:41:48         712       136   \n",
       "1  2021228 2002-02-12   21:44:08  21:45:06  21:48:56         288         7   \n",
       "2  2021332 2002-02-13   00:53:24  00:54:54  00:57:00         216        15   \n",
       "3  2021308 2002-02-13   04:22:52  04:23:50  04:26:56         244        20   \n",
       "4  2021310 2002-02-13   07:03:52  07:05:14  07:07:48         236       336   \n",
       "\n",
       "   total.counts energy.kev  x.pos.asec  y.pos.asec  radial active.region.ar  \\\n",
       "0      167304.0      12-25         592        -358     692                0   \n",
       "1        9504.0       6-12         604        -341     694             9811   \n",
       "2       11448.0       6-12        -310         375     487             9825   \n",
       "3       17400.0      12-25        -277         378     469             9822   \n",
       "4      313392.0      25-50        -272         390     476             9825   \n",
       "\n",
       "  flag.1 flag.2 flag.3 flag.4 flag.5  \n",
       "0     A1     P1    NaN    NaN    NaN  \n",
       "1     A1     P1     PE     Q1    NaN  \n",
       "2     A1     P1    NaN    NaN    NaN  \n",
       "3     A1     P1    NaN    NaN    NaN  \n",
       "4     A1     GS     P1     PE     Q2  "
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data with specified bcolumn classes\n",
    "solar_flare = pd.read_csv(\"Dataset/hessi.solar.flare.UP_To_2018.csv\", parse_dates=[\"start.date\"], \n",
    "                          dtype={\"energy.kev\": \"category\", \"total.counts\": \"float64\", \"active.region.ar\": \"category\"})\n",
    "solar_flare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "a3df7fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "solar_flare['start.date'] = solar_flare['start.date'].dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "f80f0d92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2002-02-12 21:33:38\n",
       "1        2002-02-12 21:45:06\n",
       "2        2002-02-13 00:54:54\n",
       "3        2002-02-13 04:23:50\n",
       "4        2002-02-13 07:05:14\n",
       "                 ...        \n",
       "116138   2018-02-09 16:42:54\n",
       "116139   2018-02-09 18:17:26\n",
       "116140   2018-02-10 13:06:46\n",
       "116141   2018-02-26 15:51:18\n",
       "116142   2018-03-03 04:06:22\n",
       "Name: start_datetime, Length: 116143, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_flare['start_datetime'] = pd.to_datetime(solar_flare['start.date'] + ' ' + solar_flare['peak'])\n",
    "solar_flare['start_datetime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "619c01f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set 'start_datetime' as the index of the DataFrame\n",
    "solar_flare.set_index('start_datetime', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "51a615ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
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       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active.region.ar</th>\n",
       "      <th>flag.1</th>\n",
       "      <th>flag.2</th>\n",
       "      <th>flag.3</th>\n",
       "      <th>flag.4</th>\n",
       "      <th>flag.5</th>\n",
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       "    <tr>\n",
       "      <th>start_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-02-12 21:33:38</th>\n",
       "      <td>2021213</td>\n",
       "      <td>712</td>\n",
       "      <td>136</td>\n",
       "      <td>167304.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>592</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-12 21:45:06</th>\n",
       "      <td>2021228</td>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>9504.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>604</td>\n",
       "      <td>-341</td>\n",
       "      <td>694</td>\n",
       "      <td>9811</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-13 00:54:54</th>\n",
       "      <td>2021332</td>\n",
       "      <td>216</td>\n",
       "      <td>15</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-310</td>\n",
       "      <td>375</td>\n",
       "      <td>487</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-13 04:23:50</th>\n",
       "      <td>2021308</td>\n",
       "      <td>244</td>\n",
       "      <td>20</td>\n",
       "      <td>17400.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>-277</td>\n",
       "      <td>378</td>\n",
       "      <td>469</td>\n",
       "      <td>9822</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-13 07:05:14</th>\n",
       "      <td>2021310</td>\n",
       "      <td>236</td>\n",
       "      <td>336</td>\n",
       "      <td>313392.0</td>\n",
       "      <td>25-50</td>\n",
       "      <td>-272</td>\n",
       "      <td>390</td>\n",
       "      <td>476</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>GS</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09 16:42:54</th>\n",
       "      <td>18020903</td>\n",
       "      <td>124</td>\n",
       "      <td>18</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-345</td>\n",
       "      <td>-38</td>\n",
       "      <td>347</td>\n",
       "      <td>2699</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09 18:17:26</th>\n",
       "      <td>18020904</td>\n",
       "      <td>104</td>\n",
       "      <td>16</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-268</td>\n",
       "      <td>-38</td>\n",
       "      <td>271</td>\n",
       "      <td>2699</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-10 13:06:46</th>\n",
       "      <td>18021001</td>\n",
       "      <td>148</td>\n",
       "      <td>15</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-115</td>\n",
       "      <td>-38</td>\n",
       "      <td>121</td>\n",
       "      <td>2699</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-26 15:51:18</th>\n",
       "      <td>18022601</td>\n",
       "      <td>236</td>\n",
       "      <td>16</td>\n",
       "      <td>3312.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>115</td>\n",
       "      <td>192</td>\n",
       "      <td>223</td>\n",
       "      <td>2700</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-03 04:06:22</th>\n",
       "      <td>18030301</td>\n",
       "      <td>492</td>\n",
       "      <td>42</td>\n",
       "      <td>15816.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>926</td>\n",
       "      <td>128</td>\n",
       "      <td>935</td>\n",
       "      <td>2700</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116143 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        flare  duration.s  peak.c/s  total.counts energy.kev  \\\n",
       "start_datetime                                                                 \n",
       "2002-02-12 21:33:38   2021213         712       136      167304.0      12-25   \n",
       "2002-02-12 21:45:06   2021228         288         7        9504.0       6-12   \n",
       "2002-02-13 00:54:54   2021332         216        15       11448.0       6-12   \n",
       "2002-02-13 04:23:50   2021308         244        20       17400.0      12-25   \n",
       "2002-02-13 07:05:14   2021310         236       336      313392.0      25-50   \n",
       "...                       ...         ...       ...           ...        ...   \n",
       "2018-02-09 16:42:54  18020903         124        18        2888.0       6-12   \n",
       "2018-02-09 18:17:26  18020904         104        16        1656.0       6-12   \n",
       "2018-02-10 13:06:46  18021001         148        15        2224.0       6-12   \n",
       "2018-02-26 15:51:18  18022601         236        16        3312.0       6-12   \n",
       "2018-03-03 04:06:22  18030301         492        42       15816.0       6-12   \n",
       "\n",
       "                     x.pos.asec  y.pos.asec  radial active.region.ar flag.1  \\\n",
       "start_datetime                                                                \n",
       "2002-02-12 21:33:38         592        -358     692                0     A1   \n",
       "2002-02-12 21:45:06         604        -341     694             9811     A1   \n",
       "2002-02-13 00:54:54        -310         375     487             9825     A1   \n",
       "2002-02-13 04:23:50        -277         378     469             9822     A1   \n",
       "2002-02-13 07:05:14        -272         390     476             9825     A1   \n",
       "...                         ...         ...     ...              ...    ...   \n",
       "2018-02-09 16:42:54        -345         -38     347             2699     A0   \n",
       "2018-02-09 18:17:26        -268         -38     271             2699     A0   \n",
       "2018-02-10 13:06:46        -115         -38     121             2699     A0   \n",
       "2018-02-26 15:51:18         115         192     223             2700     A0   \n",
       "2018-03-03 04:06:22         926         128     935             2700     A0   \n",
       "\n",
       "                    flag.2 flag.3 flag.4 flag.5  \n",
       "start_datetime                                   \n",
       "2002-02-12 21:33:38     P1    NaN    NaN    NaN  \n",
       "2002-02-12 21:45:06     P1     PE     Q1    NaN  \n",
       "2002-02-13 00:54:54     P1    NaN    NaN    NaN  \n",
       "2002-02-13 04:23:50     P1    NaN    NaN    NaN  \n",
       "2002-02-13 07:05:14     GS     P1     PE     Q2  \n",
       "...                    ...    ...    ...    ...  \n",
       "2018-02-09 16:42:54     DF     P1     PE     Q2  \n",
       "2018-02-09 18:17:26     DF     P1     PE     Q2  \n",
       "2018-02-10 13:06:46     DF     P1     PE     Q2  \n",
       "2018-02-26 15:51:18     DF     P1     PE     Q2  \n",
       "2018-03-03 04:06:22     P1     PE     Q1    NaN  \n",
       "\n",
       "[116143 rows x 14 columns]"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the specified columns from the 'solar_flare' DataFrame\n",
    "columns_to_drop = ['start.date', 'start.time', 'peak', 'end']\n",
    "solar_flare = solar_flare.drop(columns=columns_to_drop)\n",
    "\n",
    "solar_flare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "cba55cd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 116143 entries, 2002-02-12 21:33:38 to 2018-03-03 04:06:22\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count   Dtype   \n",
      "---  ------            --------------   -----   \n",
      " 0   flare             116143 non-null  int64   \n",
      " 1   duration.s        116143 non-null  int64   \n",
      " 2   peak.c/s          116143 non-null  int64   \n",
      " 3   total.counts      116143 non-null  float64 \n",
      " 4   energy.kev        116143 non-null  category\n",
      " 5   x.pos.asec        116143 non-null  int64   \n",
      " 6   y.pos.asec        116143 non-null  int64   \n",
      " 7   radial            116143 non-null  int64   \n",
      " 8   active.region.ar  116143 non-null  category\n",
      " 9   flag.1            116143 non-null  object  \n",
      " 10  flag.2            116143 non-null  object  \n",
      " 11  flag.3            96236 non-null   object  \n",
      " 12  flag.4            95941 non-null   object  \n",
      " 13  flag.5            54963 non-null   object  \n",
      "dtypes: category(2), float64(1), int64(6), object(5)\n",
      "memory usage: 11.9+ MB\n"
     ]
    }
   ],
   "source": [
    "solar_flare.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "49a5447b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.161430e+05</td>\n",
       "      <td>116143.000000</td>\n",
       "      <td>116143.000000</td>\n",
       "      <td>1.161430e+05</td>\n",
       "      <td>116143.000000</td>\n",
       "      <td>116143.000000</td>\n",
       "      <td>116143.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.099563e+07</td>\n",
       "      <td>493.643009</td>\n",
       "      <td>215.086617</td>\n",
       "      <td>3.768843e+05</td>\n",
       "      <td>-7.681625</td>\n",
       "      <td>-42.185495</td>\n",
       "      <td>687.896989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.888161e+06</td>\n",
       "      <td>434.131763</td>\n",
       "      <td>839.382841</td>\n",
       "      <td>3.048797e+06</td>\n",
       "      <td>755.773503</td>\n",
       "      <td>401.904509</td>\n",
       "      <td>511.364382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.021213e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>-10012.000000</td>\n",
       "      <td>-10005.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.112632e+06</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.284000e+04</td>\n",
       "      <td>-701.000000</td>\n",
       "      <td>-247.000000</td>\n",
       "      <td>467.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.112297e+07</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>5.856000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-71.000000</td>\n",
       "      <td>759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.404222e+07</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>1.798080e+05</td>\n",
       "      <td>708.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>946.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.711151e+08</td>\n",
       "      <td>4444.000000</td>\n",
       "      <td>113156.000000</td>\n",
       "      <td>4.355501e+08</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>1223.000000</td>\n",
       "      <td>14154.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              flare     duration.s       peak.c/s  total.counts  \\\n",
       "count  1.161430e+05  116143.000000  116143.000000  1.161430e+05   \n",
       "mean   1.099563e+07     493.643009     215.086617  3.768843e+05   \n",
       "std    9.888161e+06     434.131763     839.382841  3.048797e+06   \n",
       "min    2.021213e+06       8.000000       0.000000  8.000000e+00   \n",
       "25%    4.112632e+06     212.000000      28.000000  2.284000e+04   \n",
       "50%    1.112297e+07     364.000000      56.000000  5.856000e+04   \n",
       "75%    1.404222e+07     628.000000     144.000000  1.798080e+05   \n",
       "max    1.711151e+08    4444.000000  113156.000000  4.355501e+08   \n",
       "\n",
       "          x.pos.asec     y.pos.asec         radial  \n",
       "count  116143.000000  116143.000000  116143.000000  \n",
       "mean       -7.681625     -42.185495     687.896989  \n",
       "std       755.773503     401.904509     511.364382  \n",
       "min    -10012.000000  -10005.000000       0.000000  \n",
       "25%      -701.000000    -247.000000     467.000000  \n",
       "50%         0.000000     -71.000000     759.000000  \n",
       "75%       708.000000     198.000000     946.000000  \n",
       "max      1190.000000    1223.000000   14154.000000  "
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_flare.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "711a3c4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flare\n",
      "13111352    2\n",
      "13111351    2\n",
      "3042103     2\n",
      "Name: count, dtype: int64\n",
      "Number of duplicates in the 'flare' column: 3\n"
     ]
    }
   ],
   "source": [
    "duplicates_count = solar_flare['flare'].duplicated(keep=False).sum()\n",
    "\n",
    "value_counts = solar_flare['flare'].value_counts()\n",
    "\n",
    "duplicates = value_counts[value_counts>1]\n",
    "print(duplicates)\n",
    "print(f\"Number of duplicates in the 'flare' column: {len(duplicates)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "b810f0bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of dataframe before dropping duplicates:  116143\n",
      "Length of dataframe after dropping duplicates:  116140\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of dataframe before dropping duplicates: \", len(solar_flare))\n",
    "solar_flare = solar_flare.drop_duplicates(subset='flare')\n",
    "print(\"Length of dataframe after dropping duplicates: \", len(solar_flare))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "f7a0ea1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter data for the years 2017 and 2018\n",
    "solar_flare[solar_flare.index.year == year_to_extract]\n",
    "new_solar_flare = solar_flare[(solar_flare.index.year == 2017) | (solar_flare.index.year == 2018)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "6d9877b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>energy.kev</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active.region.ar</th>\n",
       "      <th>flag.1</th>\n",
       "      <th>flag.2</th>\n",
       "      <th>flag.3</th>\n",
       "      <th>flag.4</th>\n",
       "      <th>flag.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-10 13:21:34</th>\n",
       "      <td>17011014</td>\n",
       "      <td>296</td>\n",
       "      <td>26</td>\n",
       "      <td>11561.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>883</td>\n",
       "      <td>268</td>\n",
       "      <td>923</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10 15:13:34</th>\n",
       "      <td>17011001</td>\n",
       "      <td>268</td>\n",
       "      <td>232</td>\n",
       "      <td>67456.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>928</td>\n",
       "      <td>262</td>\n",
       "      <td>965</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "      <td>SE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10 16:45:46</th>\n",
       "      <td>17011004</td>\n",
       "      <td>724</td>\n",
       "      <td>720</td>\n",
       "      <td>340656.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>283</td>\n",
       "      <td>65</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10 17:09:06</th>\n",
       "      <td>17011011</td>\n",
       "      <td>136</td>\n",
       "      <td>64</td>\n",
       "      <td>7104.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>918</td>\n",
       "      <td>270</td>\n",
       "      <td>957</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10 18:07:14</th>\n",
       "      <td>17011005</td>\n",
       "      <td>604</td>\n",
       "      <td>720</td>\n",
       "      <td>239760.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>923</td>\n",
       "      <td>285</td>\n",
       "      <td>966</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09 16:42:54</th>\n",
       "      <td>18020903</td>\n",
       "      <td>124</td>\n",
       "      <td>18</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-345</td>\n",
       "      <td>-38</td>\n",
       "      <td>347</td>\n",
       "      <td>2699</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09 18:17:26</th>\n",
       "      <td>18020904</td>\n",
       "      <td>104</td>\n",
       "      <td>16</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-268</td>\n",
       "      <td>-38</td>\n",
       "      <td>271</td>\n",
       "      <td>2699</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-10 13:06:46</th>\n",
       "      <td>18021001</td>\n",
       "      <td>148</td>\n",
       "      <td>15</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-115</td>\n",
       "      <td>-38</td>\n",
       "      <td>121</td>\n",
       "      <td>2699</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-26 15:51:18</th>\n",
       "      <td>18022601</td>\n",
       "      <td>236</td>\n",
       "      <td>16</td>\n",
       "      <td>3312.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>115</td>\n",
       "      <td>192</td>\n",
       "      <td>223</td>\n",
       "      <td>2700</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-03 04:06:22</th>\n",
       "      <td>18030301</td>\n",
       "      <td>492</td>\n",
       "      <td>42</td>\n",
       "      <td>15816.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>926</td>\n",
       "      <td>128</td>\n",
       "      <td>935</td>\n",
       "      <td>2700</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2201 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        flare  duration.s  peak.c/s  total.counts energy.kev  \\\n",
       "start_datetime                                                                 \n",
       "2017-01-10 13:21:34  17011014         296        26       11561.0       6-12   \n",
       "2017-01-10 15:13:34  17011001         268       232       67456.0       6-12   \n",
       "2017-01-10 16:45:46  17011004         724       720      340656.0       6-12   \n",
       "2017-01-10 17:09:06  17011011         136        64        7104.0       6-12   \n",
       "2017-01-10 18:07:14  17011005         604       720      239760.0      12-25   \n",
       "...                       ...         ...       ...           ...        ...   \n",
       "2018-02-09 16:42:54  18020903         124        18        2888.0       6-12   \n",
       "2018-02-09 18:17:26  18020904         104        16        1656.0       6-12   \n",
       "2018-02-10 13:06:46  18021001         148        15        2224.0       6-12   \n",
       "2018-02-26 15:51:18  18022601         236        16        3312.0       6-12   \n",
       "2018-03-03 04:06:22  18030301         492        42       15816.0       6-12   \n",
       "\n",
       "                     x.pos.asec  y.pos.asec  radial active.region.ar flag.1  \\\n",
       "start_datetime                                                                \n",
       "2017-01-10 13:21:34         883         268     923                0     A0   \n",
       "2017-01-10 15:13:34         928         262     965                0     A0   \n",
       "2017-01-10 16:45:46         283          65     291                0     A0   \n",
       "2017-01-10 17:09:06         918         270     957                0     A0   \n",
       "2017-01-10 18:07:14         923         285     966                0     A0   \n",
       "...                         ...         ...     ...              ...    ...   \n",
       "2018-02-09 16:42:54        -345         -38     347             2699     A0   \n",
       "2018-02-09 18:17:26        -268         -38     271             2699     A0   \n",
       "2018-02-10 13:06:46        -115         -38     121             2699     A0   \n",
       "2018-02-26 15:51:18         115         192     223             2700     A0   \n",
       "2018-03-03 04:06:22         926         128     935             2700     A0   \n",
       "\n",
       "                    flag.2 flag.3 flag.4 flag.5  \n",
       "start_datetime                                   \n",
       "2017-01-10 13:21:34     P1     PE     Q1    NaN  \n",
       "2017-01-10 15:13:34     P1     PE     Q2     SE  \n",
       "2017-01-10 16:45:46     P1     PE     Q1    NaN  \n",
       "2017-01-10 17:09:06     P1     PE     Q1    NaN  \n",
       "2017-01-10 18:07:14     DR     P1     PE     Q2  \n",
       "...                    ...    ...    ...    ...  \n",
       "2018-02-09 16:42:54     DF     P1     PE     Q2  \n",
       "2018-02-09 18:17:26     DF     P1     PE     Q2  \n",
       "2018-02-10 13:06:46     DF     P1     PE     Q2  \n",
       "2018-02-26 15:51:18     DF     P1     PE     Q2  \n",
       "2018-03-03 04:06:22     P1     PE     Q1    NaN  \n",
       "\n",
       "[2201 rows x 14 columns]"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_solar_flare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "3ed342ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 741us/step - loss: 1.0465 - accuracy: 0.7070\n",
      "New Test Accuracy: 70.70%\n"
     ]
    }
   ],
   "source": [
    "# Assuming 'energy.keV' is a column with categorical data in the new data (2017-2018)\n",
    "new_data_categories = new_solar_flare['energy.kev'].values\n",
    "\n",
    "# Encode the categories for the new data using the same label encoder\n",
    "new_categories_encoded = label_encoder.transform(new_data_categories)\n",
    "\n",
    "# Assuming the other features in the new data have the same column names\n",
    "new_data_duration = np.array(new_solar_flare['duration.s'])\n",
    "new_data_peak = np.array(new_solar_flare['peak.c/s'])\n",
    "new_data_counts = np.array(new_solar_flare['total.counts'])\n",
    "new_data_radial = np.array(new_solar_flare['radial'])\n",
    "\n",
    "# Create sequences and labels for the new data\n",
    "new_X_sequences, new_y_sequences = [], []\n",
    "\n",
    "new_X = np.stack((new_data_duration, new_data_peak, new_data_counts, new_data_radial), axis=-1)\n",
    "new_X = new_X.astype('float32')\n",
    "\n",
    "# Use the same StandardScaler to scale your input data\n",
    "new_X = scaler.transform(new_X)\n",
    "\n",
    "new_y = new_categories_encoded\n",
    "\n",
    "for i in range(len(new_X) - sequence_length):\n",
    "    new_X_sequences.append(new_X[i:i + sequence_length])\n",
    "    new_y_sequences.append(new_y[i + sequence_length])\n",
    "\n",
    "new_X_sequences = np.array(new_X_sequences)\n",
    "new_y_sequences = np.array(new_y_sequences)\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_loss, new_test_accuracy = model.evaluate(new_X_sequences, new_y_sequences)\n",
    "print(f'New Test Accuracy: {new_test_accuracy * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "776df46f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 684us/step\n",
      "Precision: 59.70%\n",
      "Recall: 70.70%\n",
      "F1 Score: 62.24%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_predictions = model.predict(new_X_sequences)\n",
    "new_test_predictions_classes = np.argmax(new_test_predictions, axis=1)\n",
    "\n",
    "precision = precision_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "recall = recall_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "f1 = f1_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "\n",
    "print(f'Precision: {precision * 100:.2f}%')\n",
    "print(f'Recall: {recall * 100:.2f}%')\n",
    "print(f'F1 Score: {f1 * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532e3a08",
   "metadata": {},
   "source": [
    "# LSTM + GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "835ed73a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name=\"LSTM + GRU 2002-2016\"\n",
    "epochs=100\n",
    "batch_size=64\n",
    "recurrent_dropout=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "5702833f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.5030246  -0.09420199 -0.06872918  0.00802093]\n",
      " [-0.47368634 -0.24788813 -0.1204874   0.01193201]\n",
      " [-0.6395429  -0.2383572  -0.11984977 -0.3928647 ]\n",
      " ...\n",
      " [-0.7961852  -0.2383572  -0.12287523 -1.1085923 ]\n",
      " [-0.59347165 -0.23716584 -0.12251837 -0.9091271 ]\n",
      " [-0.00375941 -0.20619035 -0.11841707  0.48321703]]\n",
      "Epoch 1/100\n",
      "1452/1452 [==============================] - 5s 3ms/step - loss: 0.7231 - accuracy: 0.7650 - val_loss: 0.9150 - val_accuracy: 0.7128 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6846 - accuracy: 0.7720 - val_loss: 0.9153 - val_accuracy: 0.7142 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6805 - accuracy: 0.7730 - val_loss: 0.8924 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6782 - accuracy: 0.7737 - val_loss: 0.8904 - val_accuracy: 0.7164 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6760 - accuracy: 0.7745 - val_loss: 0.8970 - val_accuracy: 0.7166 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6744 - accuracy: 0.7746 - val_loss: 0.8924 - val_accuracy: 0.7178 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "1452/1452 [==============================] - 4s 2ms/step - loss: 0.6725 - accuracy: 0.7746 - val_loss: 0.8958 - val_accuracy: 0.7173 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6693 - accuracy: 0.7755 - val_loss: 0.8884 - val_accuracy: 0.7179 - lr: 5.0000e-04\n",
      "Epoch 9/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6682 - accuracy: 0.7759 - val_loss: 0.8832 - val_accuracy: 0.7185 - lr: 5.0000e-04\n",
      "Epoch 10/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6671 - accuracy: 0.7759 - val_loss: 0.8898 - val_accuracy: 0.7185 - lr: 5.0000e-04\n",
      "Epoch 11/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6659 - accuracy: 0.7763 - val_loss: 0.8942 - val_accuracy: 0.7176 - lr: 5.0000e-04\n",
      "Epoch 12/100\n",
      "1452/1452 [==============================] - 4s 2ms/step - loss: 0.6650 - accuracy: 0.7763 - val_loss: 0.8849 - val_accuracy: 0.7176 - lr: 5.0000e-04\n",
      "Epoch 13/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6629 - accuracy: 0.7768 - val_loss: 0.8813 - val_accuracy: 0.7180 - lr: 2.5000e-04\n",
      "Epoch 14/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6622 - accuracy: 0.7772 - val_loss: 0.8932 - val_accuracy: 0.7182 - lr: 2.5000e-04\n",
      "Epoch 15/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6617 - accuracy: 0.7769 - val_loss: 0.8871 - val_accuracy: 0.7183 - lr: 2.5000e-04\n",
      "Epoch 16/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6608 - accuracy: 0.7773 - val_loss: 0.8824 - val_accuracy: 0.7187 - lr: 2.5000e-04\n",
      "Epoch 17/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6597 - accuracy: 0.7773 - val_loss: 0.8875 - val_accuracy: 0.7176 - lr: 1.2500e-04\n",
      "Epoch 18/100\n",
      "1452/1452 [==============================] - 4s 3ms/step - loss: 0.6594 - accuracy: 0.7774 - val_loss: 0.8913 - val_accuracy: 0.7178 - lr: 1.2500e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "726/726 [==============================] - 1s 687us/step - loss: 0.8813 - accuracy: 0.7180\n",
      "Test Accuracy: 71.80%\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, GRU, Dense, Input\n",
    "from tensorflow.keras.layers import concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Assuming 'energy.keV' is a column with categorical data\n",
    "categories = solar_flare['energy.kev'].values\n",
    "\n",
    "# Encode the categories\n",
    "label_encoder = LabelEncoder()\n",
    "categories_encoded = label_encoder.fit_transform(categories)\n",
    "\n",
    "      \n",
    "data_duration = np.array(solar_flare['duration.s'])\n",
    "data_peak = np.array(solar_flare['peak.c/s'])\n",
    "data_counts = np.array(solar_flare['total.counts'])\n",
    "data_radial = np.array(solar_flare['radial'])\n",
    "\n",
    "# Define the sequence length (number of previous categories to consider)\n",
    "sequence_length = 10\n",
    "\n",
    "# Create sequences and labels\n",
    "X_sequences, y_sequences = [], []\n",
    "\n",
    "X = np.stack((data_duration, data_peak, data_counts, data_radial), axis=-1)\n",
    "X = X.astype('float32')\n",
    "\n",
    "\n",
    "# Use StandardScaler to scale your input data\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "y = categories_encoded\n",
    "\n",
    "print(X)\n",
    "\n",
    "for i in range(len(X) - sequence_length):\n",
    "    X_sequences.append(X[i:i + sequence_length])\n",
    "    y_sequences.append(y[i + sequence_length])\n",
    "\n",
    "X_sequences = np.array(X_sequences)\n",
    "y_sequences = np.array(y_sequences)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(0.8 * len(X_sequences))\n",
    "X_train, X_test = X_sequences[:train_size], X_sequences[train_size:]\n",
    "y_train, y_test = y_sequences[:train_size], y_sequences[train_size:]\n",
    "\n",
    "\n",
    "# Create an LSTM and a GRU model in parallel\n",
    "input_layer = Input(shape=(sequence_length, X_sequences.shape[2]))\n",
    "lstm_layer = LSTM(50)(input_layer)\n",
    "gru_layer = GRU(50)(input_layer)\n",
    "\n",
    "# Combine the LSTM and GRU outputs\n",
    "combined = concatenate([lstm_layer, gru_layer], axis=-1)\n",
    "\n",
    "output_layer = Dense(len(np.unique(categories_encoded)), activation='softmax')(combined)\n",
    "\n",
    "model = Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Implement learning rate scheduler\n",
    "lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)\n",
    "\n",
    "# Implement early stopping to prevent overfitting\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test), callbacks=[lr_scheduler, early_stopping])\n",
    "\n",
    "# Plot training history\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
    "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "4ae8323b",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.append([model_name, sequence_length, recurrent_dropout, epochs, batch_size, f'{test_accuracy * 100:.2f}%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "64af184d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['LSTM 2002-2016', 10, 0.0, 100, 64, '70.74%'],\n",
       " ['LSTM + GRU 2002-2016', 10, 0.0, 100, 64, '71.80%']]"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce91ecb",
   "metadata": {},
   "source": [
    "# Test it on solar data 2017-2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "2f4d064d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 903us/step - loss: 1.0410 - accuracy: 0.7052\n",
      "New Test Accuracy: 70.52%\n"
     ]
    }
   ],
   "source": [
    "# Assuming 'energy.keV' is a column with categorical data in the new data (2017-2018)\n",
    "new_data_categories = new_solar_flare['energy.kev'].values\n",
    "\n",
    "# Encode the categories for the new data using the same label encoder\n",
    "new_categories_encoded = label_encoder.transform(new_data_categories)\n",
    "\n",
    "# Assuming the other features in the new data have the same column names\n",
    "new_data_duration = np.array(new_solar_flare['duration.s'])\n",
    "new_data_peak = np.array(new_solar_flare['peak.c/s'])\n",
    "new_data_counts = np.array(new_solar_flare['total.counts'])\n",
    "new_data_radial = np.array(new_solar_flare['radial'])\n",
    "\n",
    "# Create sequences and labels for the new data\n",
    "new_X_sequences, new_y_sequences = [], []\n",
    "\n",
    "new_X = np.stack((new_data_duration, new_data_peak, new_data_counts, new_data_radial), axis=-1)\n",
    "new_X = new_X.astype('float32')\n",
    "\n",
    "# Use the same StandardScaler to scale your input data\n",
    "new_X = scaler.transform(new_X)\n",
    "\n",
    "new_y = new_categories_encoded\n",
    "\n",
    "for i in range(len(new_X) - sequence_length):\n",
    "    new_X_sequences.append(new_X[i:i + sequence_length])\n",
    "    new_y_sequences.append(new_y[i + sequence_length])\n",
    "\n",
    "new_X_sequences = np.array(new_X_sequences)\n",
    "new_y_sequences = np.array(new_y_sequences)\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_loss, new_test_accuracy = model.evaluate(new_X_sequences, new_y_sequences)\n",
    "print(f'New Test Accuracy: {new_test_accuracy * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "3cb6c3b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 723us/step\n",
      "Precision: 58.83%\n",
      "Recall: 70.52%\n",
      "F1 Score: 62.30%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_predictions = model.predict(new_X_sequences)\n",
    "new_test_predictions_classes = np.argmax(new_test_predictions, axis=1)\n",
    "\n",
    "precision = precision_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "recall = recall_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "f1 = f1_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "\n",
    "print(f'Precision: {precision * 100:.2f}%')\n",
    "print(f'Recall: {recall * 100:.2f}%')\n",
    "print(f'F1 Score: {f1 * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79e1fc46",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "8cd10359",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1425/1425 [==============================] - 5s 3ms/step - loss: 0.7278 - accuracy: 0.7637 - val_loss: 0.8715 - val_accuracy: 0.7215 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6855 - accuracy: 0.7712 - val_loss: 0.8639 - val_accuracy: 0.7226 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6812 - accuracy: 0.7724 - val_loss: 0.8511 - val_accuracy: 0.7261 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "1425/1425 [==============================] - 4s 2ms/step - loss: 0.6786 - accuracy: 0.7732 - val_loss: 0.8498 - val_accuracy: 0.7272 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6766 - accuracy: 0.7736 - val_loss: 0.8525 - val_accuracy: 0.7285 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6754 - accuracy: 0.7738 - val_loss: 0.8460 - val_accuracy: 0.7287 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6740 - accuracy: 0.7742 - val_loss: 0.8462 - val_accuracy: 0.7275 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6730 - accuracy: 0.7742 - val_loss: 0.8422 - val_accuracy: 0.7256 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6718 - accuracy: 0.7743 - val_loss: 0.8485 - val_accuracy: 0.7284 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6711 - accuracy: 0.7745 - val_loss: 0.8464 - val_accuracy: 0.7283 - lr: 0.0010\n",
      "Epoch 11/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6693 - accuracy: 0.7747 - val_loss: 0.8486 - val_accuracy: 0.7272 - lr: 0.0010\n",
      "Epoch 12/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6667 - accuracy: 0.7754 - val_loss: 0.8404 - val_accuracy: 0.7264 - lr: 5.0000e-04\n",
      "Epoch 13/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6656 - accuracy: 0.7754 - val_loss: 0.8484 - val_accuracy: 0.7276 - lr: 5.0000e-04\n",
      "Epoch 14/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6645 - accuracy: 0.7754 - val_loss: 0.8480 - val_accuracy: 0.7276 - lr: 5.0000e-04\n",
      "Epoch 15/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6636 - accuracy: 0.7757 - val_loss: 0.8503 - val_accuracy: 0.7265 - lr: 5.0000e-04\n",
      "Epoch 16/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6614 - accuracy: 0.7760 - val_loss: 0.8496 - val_accuracy: 0.7256 - lr: 2.5000e-04\n",
      "Epoch 17/100\n",
      "1425/1425 [==============================] - 4s 3ms/step - loss: 0.6606 - accuracy: 0.7760 - val_loss: 0.8478 - val_accuracy: 0.7252 - lr: 2.5000e-04\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "713/713 [==============================] - 0s 684us/step - loss: 0.8404 - accuracy: 0.7264\n",
      "Recurrent Dropout: 0.0, Test Accuracy: 72.64%\n",
      "Epoch 1/100\n",
      "1425/1425 [==============================] - 13s 8ms/step - loss: 0.7283 - accuracy: 0.7638 - val_loss: 0.8893 - val_accuracy: 0.7169 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6882 - accuracy: 0.7701 - val_loss: 0.8920 - val_accuracy: 0.7210 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6823 - accuracy: 0.7719 - val_loss: 0.8798 - val_accuracy: 0.7226 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6795 - accuracy: 0.7728 - val_loss: 0.8729 - val_accuracy: 0.7244 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6780 - accuracy: 0.7731 - val_loss: 0.8613 - val_accuracy: 0.7255 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6766 - accuracy: 0.7737 - val_loss: 0.8561 - val_accuracy: 0.7265 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6750 - accuracy: 0.7736 - val_loss: 0.8617 - val_accuracy: 0.7261 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6741 - accuracy: 0.7738 - val_loss: 0.8492 - val_accuracy: 0.7287 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6728 - accuracy: 0.7742 - val_loss: 0.8483 - val_accuracy: 0.7263 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6716 - accuracy: 0.7740 - val_loss: 0.8434 - val_accuracy: 0.7262 - lr: 0.0010\n",
      "Epoch 11/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6703 - accuracy: 0.7742 - val_loss: 0.8557 - val_accuracy: 0.7280 - lr: 0.0010\n",
      "Epoch 12/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6687 - accuracy: 0.7741 - val_loss: 0.8458 - val_accuracy: 0.7285 - lr: 0.0010\n",
      "Epoch 13/100\n",
      "1425/1425 [==============================] - 12s 8ms/step - loss: 0.6674 - accuracy: 0.7744 - val_loss: 0.8402 - val_accuracy: 0.7265 - lr: 0.0010\n",
      "Epoch 14/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6658 - accuracy: 0.7747 - val_loss: 0.8489 - val_accuracy: 0.7282 - lr: 0.0010\n",
      "Epoch 15/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6643 - accuracy: 0.7747 - val_loss: 0.8474 - val_accuracy: 0.7283 - lr: 0.0010\n",
      "Epoch 16/100\n",
      "1425/1425 [==============================] - 12s 8ms/step - loss: 0.6629 - accuracy: 0.7747 - val_loss: 0.8506 - val_accuracy: 0.7269 - lr: 0.0010\n",
      "Epoch 17/100\n",
      "1425/1425 [==============================] - 12s 8ms/step - loss: 0.6605 - accuracy: 0.7749 - val_loss: 0.8410 - val_accuracy: 0.7252 - lr: 5.0000e-04\n",
      "Epoch 18/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6594 - accuracy: 0.7752 - val_loss: 0.8457 - val_accuracy: 0.7278 - lr: 5.0000e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "713/713 [==============================] - 1s 1ms/step - loss: 0.8402 - accuracy: 0.7265\n",
      "Recurrent Dropout: 0.2, Test Accuracy: 72.65%\n",
      "Epoch 1/100\n",
      "1425/1425 [==============================] - 12s 8ms/step - loss: 0.7268 - accuracy: 0.7637 - val_loss: 0.8710 - val_accuracy: 0.7208 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6875 - accuracy: 0.7708 - val_loss: 0.8746 - val_accuracy: 0.7241 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6831 - accuracy: 0.7716 - val_loss: 0.8628 - val_accuracy: 0.7255 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6809 - accuracy: 0.7725 - val_loss: 0.8643 - val_accuracy: 0.7261 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6791 - accuracy: 0.7728 - val_loss: 0.8633 - val_accuracy: 0.7259 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6780 - accuracy: 0.7733 - val_loss: 0.8450 - val_accuracy: 0.7263 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6767 - accuracy: 0.7734 - val_loss: 0.8519 - val_accuracy: 0.7259 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6755 - accuracy: 0.7734 - val_loss: 0.8525 - val_accuracy: 0.7276 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6749 - accuracy: 0.7737 - val_loss: 0.8474 - val_accuracy: 0.7276 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6726 - accuracy: 0.7740 - val_loss: 0.8479 - val_accuracy: 0.7272 - lr: 5.0000e-04\n",
      "Epoch 11/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6718 - accuracy: 0.7741 - val_loss: 0.8479 - val_accuracy: 0.7272 - lr: 5.0000e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "713/713 [==============================] - 1s 1ms/step - loss: 0.8450 - accuracy: 0.7263\n",
      "Recurrent Dropout: 0.4, Test Accuracy: 72.63%\n",
      "Epoch 1/100\n",
      "1425/1425 [==============================] - 12s 8ms/step - loss: 0.7326 - accuracy: 0.7636 - val_loss: 0.8745 - val_accuracy: 0.7204 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6920 - accuracy: 0.7692 - val_loss: 0.8901 - val_accuracy: 0.7194 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6861 - accuracy: 0.7706 - val_loss: 0.8808 - val_accuracy: 0.7215 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6828 - accuracy: 0.7715 - val_loss: 0.8804 - val_accuracy: 0.7208 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6797 - accuracy: 0.7723 - val_loss: 0.8721 - val_accuracy: 0.7236 - lr: 5.0000e-04\n",
      "Epoch 6/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6790 - accuracy: 0.7727 - val_loss: 0.8635 - val_accuracy: 0.7244 - lr: 5.0000e-04\n",
      "Epoch 7/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6782 - accuracy: 0.7727 - val_loss: 0.8672 - val_accuracy: 0.7240 - lr: 5.0000e-04\n",
      "Epoch 8/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6774 - accuracy: 0.7729 - val_loss: 0.8673 - val_accuracy: 0.7242 - lr: 5.0000e-04\n",
      "Epoch 9/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6770 - accuracy: 0.7731 - val_loss: 0.8673 - val_accuracy: 0.7256 - lr: 5.0000e-04\n",
      "Epoch 10/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6761 - accuracy: 0.7735 - val_loss: 0.8624 - val_accuracy: 0.7259 - lr: 2.5000e-04\n",
      "Epoch 11/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6752 - accuracy: 0.7734 - val_loss: 0.8631 - val_accuracy: 0.7255 - lr: 2.5000e-04\n",
      "Epoch 12/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6752 - accuracy: 0.7735 - val_loss: 0.8613 - val_accuracy: 0.7257 - lr: 2.5000e-04\n",
      "Epoch 13/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6748 - accuracy: 0.7734 - val_loss: 0.8620 - val_accuracy: 0.7256 - lr: 2.5000e-04\n",
      "Epoch 14/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6747 - accuracy: 0.7734 - val_loss: 0.8626 - val_accuracy: 0.7253 - lr: 2.5000e-04\n",
      "Epoch 15/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6746 - accuracy: 0.7738 - val_loss: 0.8614 - val_accuracy: 0.7254 - lr: 2.5000e-04\n",
      "Epoch 16/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6741 - accuracy: 0.7735 - val_loss: 0.8622 - val_accuracy: 0.7256 - lr: 1.2500e-04\n",
      "Epoch 17/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6740 - accuracy: 0.7738 - val_loss: 0.8613 - val_accuracy: 0.7257 - lr: 1.2500e-04\n",
      "Epoch 18/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6736 - accuracy: 0.7739 - val_loss: 0.8647 - val_accuracy: 0.7253 - lr: 1.2500e-04\n",
      "Epoch 19/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6735 - accuracy: 0.7738 - val_loss: 0.8618 - val_accuracy: 0.7251 - lr: 6.2500e-05\n",
      "Epoch 20/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6732 - accuracy: 0.7737 - val_loss: 0.8623 - val_accuracy: 0.7256 - lr: 6.2500e-05\n",
      "Epoch 21/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6732 - accuracy: 0.7737 - val_loss: 0.8610 - val_accuracy: 0.7257 - lr: 6.2500e-05\n",
      "Epoch 22/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6732 - accuracy: 0.7737 - val_loss: 0.8608 - val_accuracy: 0.7256 - lr: 6.2500e-05\n",
      "Epoch 23/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6728 - accuracy: 0.7738 - val_loss: 0.8602 - val_accuracy: 0.7257 - lr: 6.2500e-05\n",
      "Epoch 24/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6730 - accuracy: 0.7741 - val_loss: 0.8623 - val_accuracy: 0.7256 - lr: 6.2500e-05\n",
      "Epoch 25/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6731 - accuracy: 0.7738 - val_loss: 0.8611 - val_accuracy: 0.7258 - lr: 6.2500e-05\n",
      "Epoch 26/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6728 - accuracy: 0.7739 - val_loss: 0.8603 - val_accuracy: 0.7258 - lr: 6.2500e-05\n",
      "Epoch 27/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6727 - accuracy: 0.7737 - val_loss: 0.8596 - val_accuracy: 0.7258 - lr: 3.1250e-05\n",
      "Epoch 28/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6727 - accuracy: 0.7736 - val_loss: 0.8594 - val_accuracy: 0.7259 - lr: 3.1250e-05\n",
      "Epoch 29/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6728 - accuracy: 0.7741 - val_loss: 0.8587 - val_accuracy: 0.7259 - lr: 3.1250e-05\n",
      "Epoch 30/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6728 - accuracy: 0.7738 - val_loss: 0.8589 - val_accuracy: 0.7260 - lr: 3.1250e-05\n",
      "Epoch 31/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6727 - accuracy: 0.7738 - val_loss: 0.8595 - val_accuracy: 0.7257 - lr: 3.1250e-05\n",
      "Epoch 32/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6725 - accuracy: 0.7737 - val_loss: 0.8595 - val_accuracy: 0.7259 - lr: 3.1250e-05\n",
      "Epoch 33/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6722 - accuracy: 0.7739 - val_loss: 0.8596 - val_accuracy: 0.7258 - lr: 1.5625e-05\n",
      "Epoch 34/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6725 - accuracy: 0.7741 - val_loss: 0.8591 - val_accuracy: 0.7259 - lr: 1.5625e-05\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "713/713 [==============================] - 1s 1ms/step - loss: 0.8587 - accuracy: 0.7259\n",
      "Recurrent Dropout: 0.6, Test Accuracy: 72.59%\n",
      "Epoch 1/100\n",
      "1425/1425 [==============================] - 12s 8ms/step - loss: 0.7388 - accuracy: 0.7614 - val_loss: 0.8774 - val_accuracy: 0.7225 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6933 - accuracy: 0.7688 - val_loss: 0.8673 - val_accuracy: 0.7241 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6879 - accuracy: 0.7701 - val_loss: 0.8607 - val_accuracy: 0.7230 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6846 - accuracy: 0.7710 - val_loss: 0.8648 - val_accuracy: 0.7249 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6823 - accuracy: 0.7721 - val_loss: 0.8573 - val_accuracy: 0.7258 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6808 - accuracy: 0.7724 - val_loss: 0.8583 - val_accuracy: 0.7255 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6797 - accuracy: 0.7728 - val_loss: 0.8532 - val_accuracy: 0.7242 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6785 - accuracy: 0.7727 - val_loss: 0.8523 - val_accuracy: 0.7246 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6780 - accuracy: 0.7727 - val_loss: 0.8497 - val_accuracy: 0.7265 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6773 - accuracy: 0.7730 - val_loss: 0.8474 - val_accuracy: 0.7265 - lr: 0.0010\n",
      "Epoch 11/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6768 - accuracy: 0.7733 - val_loss: 0.8497 - val_accuracy: 0.7273 - lr: 0.0010\n",
      "Epoch 12/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6759 - accuracy: 0.7733 - val_loss: 0.8484 - val_accuracy: 0.7268 - lr: 0.0010\n",
      "Epoch 13/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6753 - accuracy: 0.7736 - val_loss: 0.8471 - val_accuracy: 0.7265 - lr: 0.0010\n",
      "Epoch 14/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6745 - accuracy: 0.7737 - val_loss: 0.8496 - val_accuracy: 0.7273 - lr: 0.0010\n",
      "Epoch 15/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6743 - accuracy: 0.7733 - val_loss: 0.8470 - val_accuracy: 0.7273 - lr: 0.0010\n",
      "Epoch 16/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6740 - accuracy: 0.7733 - val_loss: 0.8421 - val_accuracy: 0.7279 - lr: 0.0010\n",
      "Epoch 17/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6733 - accuracy: 0.7729 - val_loss: 0.8451 - val_accuracy: 0.7280 - lr: 0.0010\n",
      "Epoch 18/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6724 - accuracy: 0.7739 - val_loss: 0.8420 - val_accuracy: 0.7287 - lr: 0.0010\n",
      "Epoch 19/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6723 - accuracy: 0.7739 - val_loss: 0.8501 - val_accuracy: 0.7285 - lr: 0.0010\n",
      "Epoch 20/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6716 - accuracy: 0.7739 - val_loss: 0.8468 - val_accuracy: 0.7281 - lr: 0.0010\n",
      "Epoch 21/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6711 - accuracy: 0.7739 - val_loss: 0.8476 - val_accuracy: 0.7269 - lr: 0.0010\n",
      "Epoch 22/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6696 - accuracy: 0.7740 - val_loss: 0.8457 - val_accuracy: 0.7273 - lr: 5.0000e-04\n",
      "Epoch 23/100\n",
      "1425/1425 [==============================] - 11s 8ms/step - loss: 0.6692 - accuracy: 0.7738 - val_loss: 0.8474 - val_accuracy: 0.7280 - lr: 5.0000e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "713/713 [==============================] - 1s 1ms/step - loss: 0.8420 - accuracy: 0.7287\n",
      "Recurrent Dropout: 0.8, Test Accuracy: 72.87%\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import LSTM, GRU, Dense, Input, concatenate\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
    "\n",
    "# Assuming 'energy.keV' is a column with categorical data\n",
    "categories = solar_flare['energy.kev'].values\n",
    "\n",
    "# Encode the categories\n",
    "label_encoder = LabelEncoder()\n",
    "categories_encoded = label_encoder.fit_transform(categories)\n",
    "\n",
    "data_duration = np.array(solar_flare['duration.s'])\n",
    "data_peak = np.array(solar_flare['peak.c/s'])\n",
    "data_counts = np.array(solar_flare['total.counts'])\n",
    "data_radial = np.array(solar_flare['radial'])\n",
    "\n",
    "# Define the sequence length (number of previous categories to consider)\n",
    "sequence_length = 10\n",
    "\n",
    "# Create sequences and labels\n",
    "X_sequences, y_sequences = [], []\n",
    "\n",
    "X = np.stack((data_duration, data_peak, data_counts, data_radial), axis=-1)\n",
    "X = X.astype('float32')\n",
    "\n",
    "\n",
    "# Use StandardScaler to scale your input data\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "y = categories_encoded\n",
    "\n",
    "for i in range(len(X) - sequence_length):\n",
    "    X_sequences.append(X[i:i + sequence_length])\n",
    "    y_sequences.append(y[i + sequence_length])\n",
    "\n",
    "X_sequences = np.array(X_sequences)\n",
    "y_sequences = np.array(y_sequences)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(0.8 * len(X_sequences))\n",
    "X_train, X_test = X_sequences[:train_size], X_sequences[train_size:]\n",
    "y_train, y_test = y_sequences[:train_size], y_sequences[train_size:]\n",
    "\n",
    "# Define a list of recurrent dropout values to try\n",
    "recurrent_dropout_values = [0.0, 0.2, 0.4, 0.6, 0.8]\n",
    "\n",
    "for dropout_value in recurrent_dropout_values:\n",
    "    # Create an LSTM and a GRU model in parallel\n",
    "    input_layer = Input(shape=(sequence_length, X_sequences.shape[2]))\n",
    "    lstm_layer = LSTM(50, recurrent_dropout=dropout_value)(input_layer)\n",
    "    gru_layer = GRU(50, recurrent_dropout=dropout_value)(input_layer)\n",
    "\n",
    "    # Combine the LSTM and GRU outputs\n",
    "    combined = concatenate([lstm_layer, gru_layer], axis=-1)\n",
    "\n",
    "    output_layer = Dense(len(np.unique(categories_encoded)), activation='softmax')(combined)\n",
    "\n",
    "    model = Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "    # Implement learning rate scheduler\n",
    "    lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)\n",
    "\n",
    "    # Implement early stopping to prevent overfitting\n",
    "    early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "    # Train the model\n",
    "    history = model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test), callbacks=[lr_scheduler, early_stopping])\n",
    "\n",
    "    # Plot training history\n",
    "    plt.figure(figsize=(12, 6))\n",
    "\n",
    "    # Plot training & validation loss values\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('Model loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Evaluate the model\n",
    "    test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
    "    print(f'Recurrent Dropout: {dropout_value}, Test Accuracy: {test_accuracy * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed144c5",
   "metadata": {},
   "source": [
    "Recurrent Dropout: 0.9, Test Accuracy: 72.87% has the highest accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "83f2ad5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['LSTM 2002-2016', 10, 0.0, 100, 64, '72.16%'],\n",
       " ['LSTM + GRU 2002-2016', 10, 0.0, 100, 64, '73.01%']]"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c76246b",
   "metadata": {},
   "source": [
    "## LSTM + GRU with recurrent_dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "14301f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name=\"LSTM + GRU 2002-2016 with recurrent_dropout\"\n",
    "epochs=100\n",
    "batch_size=64\n",
    "recurrent_dropout=0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "cfe21528",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.5030246  -0.09420199 -0.06872918  0.00802093]\n",
      " [-0.47368634 -0.24788813 -0.1204874   0.01193201]\n",
      " [-0.6395429  -0.2383572  -0.11984977 -0.3928647 ]\n",
      " ...\n",
      " [-0.7961852  -0.2383572  -0.12287523 -1.1085923 ]\n",
      " [-0.59347165 -0.23716584 -0.12251837 -0.9091271 ]\n",
      " [-0.00375941 -0.20619035 -0.11841707  0.48321703]]\n",
      "Epoch 1/100\n",
      "1452/1452 [==============================] - 7s 4ms/step - loss: 0.7305 - accuracy: 0.7627 - val_loss: 0.9015 - val_accuracy: 0.7112 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "1452/1452 [==============================] - 6s 4ms/step - loss: 0.6894 - accuracy: 0.7712 - val_loss: 0.9228 - val_accuracy: 0.7100 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "1452/1452 [==============================] - 6s 4ms/step - loss: 0.6835 - accuracy: 0.7727 - val_loss: 0.9243 - val_accuracy: 0.7078 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "1452/1452 [==============================] - 6s 4ms/step - loss: 0.6805 - accuracy: 0.7731 - val_loss: 0.9116 - val_accuracy: 0.7093 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "1452/1452 [==============================] - 6s 4ms/step - loss: 0.6771 - accuracy: 0.7739 - val_loss: 0.9061 - val_accuracy: 0.7103 - lr: 5.0000e-04\n",
      "Epoch 6/100\n",
      "1452/1452 [==============================] - 6s 4ms/step - loss: 0.6762 - accuracy: 0.7739 - val_loss: 0.9086 - val_accuracy: 0.7120 - lr: 5.0000e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "726/726 [==============================] - 1s 741us/step - loss: 0.9015 - accuracy: 0.7112\n",
      "Test Accuracy: 71.12%\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, GRU, Dense, Input\n",
    "from tensorflow.keras.layers import concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Assuming 'energy.keV' is a column with categorical data\n",
    "categories = solar_flare['energy.kev'].values\n",
    "\n",
    "# Encode the categories\n",
    "label_encoder = LabelEncoder()\n",
    "categories_encoded = label_encoder.fit_transform(categories)\n",
    "\n",
    "      \n",
    "data_duration = np.array(solar_flare['duration.s'])\n",
    "data_peak = np.array(solar_flare['peak.c/s'])\n",
    "data_counts = np.array(solar_flare['total.counts'])\n",
    "data_radial = np.array(solar_flare['radial'])\n",
    "\n",
    "# Define the sequence length (number of previous categories to consider)\n",
    "sequence_length = 10\n",
    "\n",
    "# Create sequences and labels\n",
    "X_sequences, y_sequences = [], []\n",
    "\n",
    "X = np.stack((data_duration, data_peak, data_counts, data_radial), axis=-1)\n",
    "X = X.astype('float32')\n",
    "\n",
    "# Use StandardScaler to scale your input data\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "y = categories_encoded\n",
    "\n",
    "print(X)\n",
    "\n",
    "for i in range(len(X) - sequence_length):\n",
    "    X_sequences.append(X[i:i + sequence_length])\n",
    "    y_sequences.append(y[i + sequence_length])\n",
    "\n",
    "X_sequences = np.array(X_sequences)\n",
    "y_sequences = np.array(y_sequences)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(0.8 * len(X_sequences))\n",
    "X_train, X_test = X_sequences[:train_size], X_sequences[train_size:]\n",
    "y_train, y_test = y_sequences[:train_size], y_sequences[train_size:]\n",
    "\n",
    "\n",
    "# Create an LSTM and a GRU model in parallel\n",
    "input_layer = Input(shape=(sequence_length, X_sequences.shape[2]))\n",
    "lstm_layer = LSTM(50)(input_layer)\n",
    "gru_layer = GRU(50, recurrent_dropout=0.9)(input_layer)\n",
    "\n",
    "# Combine the LSTM and GRU outputs\n",
    "combined = concatenate([lstm_layer, gru_layer], axis=-1)\n",
    "\n",
    "output_layer = Dense(len(np.unique(categories_encoded)), activation='softmax')(combined)\n",
    "\n",
    "bestmodel = Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "bestmodel.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Implement learning rate scheduler\n",
    "lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)\n",
    "\n",
    "# Implement early stopping to prevent overfitting\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Train the model\n",
    "history = bestmodel.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test), callbacks=[lr_scheduler, early_stopping])\n",
    "\n",
    "# Plot training history\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss, test_accuracy = bestmodel.evaluate(X_test, y_test)\n",
    "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "a578429e",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.append([model_name, sequence_length, recurrent_dropout, epochs, batch_size, f'{test_accuracy * 100:.2f}%'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c021bc7e",
   "metadata": {},
   "source": [
    "# Test it on solar data 2017-2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "cc745953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 886us/step - loss: 1.0384 - accuracy: 0.7006\n",
      "New Test Accuracy: 70.06%\n"
     ]
    }
   ],
   "source": [
    "# Assuming 'energy.keV' is a column with categorical data in the new data (2017-2018)\n",
    "new_data_categories = new_solar_flare['energy.kev'].values\n",
    "\n",
    "# Encode the categories for the new data using the same label encoder\n",
    "new_categories_encoded = label_encoder.transform(new_data_categories)\n",
    "\n",
    "# Assuming the other features in the new data have the same column names\n",
    "new_data_duration = np.array(new_solar_flare['duration.s'])\n",
    "new_data_peak = np.array(new_solar_flare['peak.c/s'])\n",
    "new_data_counts = np.array(new_solar_flare['total.counts'])\n",
    "new_data_radial = np.array(new_solar_flare['radial'])\n",
    "\n",
    "# Create sequences and labels for the new data\n",
    "new_X_sequences, new_y_sequences = [], []\n",
    "\n",
    "new_X = np.stack((new_data_duration, new_data_peak, new_data_counts, new_data_radial), axis=-1)\n",
    "new_X = new_X.astype('float32')\n",
    "\n",
    "# Use the same StandardScaler to scale your input data\n",
    "new_X = scaler.transform(new_X)\n",
    "\n",
    "new_y = new_categories_encoded\n",
    "\n",
    "for i in range(len(new_X) - sequence_length):\n",
    "    new_X_sequences.append(new_X[i:i + sequence_length])\n",
    "    new_y_sequences.append(new_y[i + sequence_length])\n",
    "\n",
    "new_X_sequences = np.array(new_X_sequences)\n",
    "new_y_sequences = np.array(new_y_sequences)\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_loss, new_test_accuracy = bestmodel.evaluate(new_X_sequences, new_y_sequences)\n",
    "print(f'New Test Accuracy: {new_test_accuracy * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "c1e62f3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 755us/step\n",
      "Precision: 60.07%\n",
      "Recall: 70.06%\n",
      "F1 Score: 63.37%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_predictions = bestmodel.predict(new_X_sequences)\n",
    "new_test_predictions_classes = np.argmax(new_test_predictions, axis=1)\n",
    "\n",
    "precision = precision_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "recall = recall_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "f1 = f1_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "\n",
    "print(f'Precision: {precision * 100:.2f}%')\n",
    "print(f'Recall: {recall * 100:.2f}%')\n",
    "print(f'F1 Score: {f1 * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e761514",
   "metadata": {},
   "source": [
    "## LSTM + GRU 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "e1a0b43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>energy.kev</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active.region.ar</th>\n",
       "      <th>flag.1</th>\n",
       "      <th>flag.2</th>\n",
       "      <th>flag.3</th>\n",
       "      <th>flag.4</th>\n",
       "      <th>flag.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:57:42</th>\n",
       "      <td>12010101</td>\n",
       "      <td>204</td>\n",
       "      <td>100</td>\n",
       "      <td>47480.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>PE</td>\n",
       "      <td>PS</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:24:54</th>\n",
       "      <td>12010102</td>\n",
       "      <td>260</td>\n",
       "      <td>42</td>\n",
       "      <td>34120.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>948</td>\n",
       "      <td>182</td>\n",
       "      <td>965</td>\n",
       "      <td>1384</td>\n",
       "      <td>A0</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:52:30</th>\n",
       "      <td>12010104</td>\n",
       "      <td>2104</td>\n",
       "      <td>336</td>\n",
       "      <td>782640.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-540</td>\n",
       "      <td>-395</td>\n",
       "      <td>669</td>\n",
       "      <td>1389</td>\n",
       "      <td>A0</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:23:26</th>\n",
       "      <td>12010107</td>\n",
       "      <td>176</td>\n",
       "      <td>44</td>\n",
       "      <td>23160.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>966</td>\n",
       "      <td>167</td>\n",
       "      <td>981</td>\n",
       "      <td>1384</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:39:06</th>\n",
       "      <td>12010108</td>\n",
       "      <td>248</td>\n",
       "      <td>26</td>\n",
       "      <td>25680.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-521</td>\n",
       "      <td>-397</td>\n",
       "      <td>655</td>\n",
       "      <td>1389</td>\n",
       "      <td>A0</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:39:46</th>\n",
       "      <td>12123122</td>\n",
       "      <td>472</td>\n",
       "      <td>64</td>\n",
       "      <td>79152.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-930</td>\n",
       "      <td>322</td>\n",
       "      <td>985</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:44:30</th>\n",
       "      <td>12123128</td>\n",
       "      <td>204</td>\n",
       "      <td>24</td>\n",
       "      <td>23328.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-101</td>\n",
       "      <td>476</td>\n",
       "      <td>486</td>\n",
       "      <td>1640</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:49:02</th>\n",
       "      <td>12123126</td>\n",
       "      <td>400</td>\n",
       "      <td>24</td>\n",
       "      <td>39672.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-99</td>\n",
       "      <td>465</td>\n",
       "      <td>475</td>\n",
       "      <td>1640</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:16:54</th>\n",
       "      <td>12123125</td>\n",
       "      <td>496</td>\n",
       "      <td>784</td>\n",
       "      <td>760848.0</td>\n",
       "      <td>12-25</td>\n",
       "      <td>-102</td>\n",
       "      <td>482</td>\n",
       "      <td>493</td>\n",
       "      <td>1640</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:46:34</th>\n",
       "      <td>12123127</td>\n",
       "      <td>992</td>\n",
       "      <td>46</td>\n",
       "      <td>130248.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-977</td>\n",
       "      <td>190</td>\n",
       "      <td>995</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11454 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        flare  duration.s  peak.c/s  total.counts energy.kev  \\\n",
       "start_datetime                                                                 \n",
       "2012-01-01 01:57:42  12010101         204       100       47480.0        3-6   \n",
       "2012-01-01 02:24:54  12010102         260        42       34120.0       6-12   \n",
       "2012-01-01 03:52:30  12010104        2104       336      782640.0       6-12   \n",
       "2012-01-01 05:23:26  12010107         176        44       23160.0       6-12   \n",
       "2012-01-01 05:39:06  12010108         248        26       25680.0       6-12   \n",
       "...                       ...         ...       ...           ...        ...   \n",
       "2012-12-31 21:39:46  12123122         472        64       79152.0       6-12   \n",
       "2012-12-31 21:44:30  12123128         204        24       23328.0       6-12   \n",
       "2012-12-31 21:49:02  12123126         400        24       39672.0       6-12   \n",
       "2012-12-31 22:16:54  12123125         496       784      760848.0      12-25   \n",
       "2012-12-31 23:46:34  12123127         992        46      130248.0       6-12   \n",
       "\n",
       "                     x.pos.asec  y.pos.asec  radial active.region.ar flag.1  \\\n",
       "start_datetime                                                                \n",
       "2012-01-01 01:57:42           0           0       0                0     A0   \n",
       "2012-01-01 02:24:54         948         182     965             1384     A0   \n",
       "2012-01-01 03:52:30        -540        -395     669             1389     A0   \n",
       "2012-01-01 05:23:26         966         167     981             1384     A0   \n",
       "2012-01-01 05:39:06        -521        -397     655             1389     A0   \n",
       "...                         ...         ...     ...              ...    ...   \n",
       "2012-12-31 21:39:46        -930         322     985                0     A0   \n",
       "2012-12-31 21:44:30        -101         476     486             1640     A0   \n",
       "2012-12-31 21:49:02         -99         465     475             1640     A0   \n",
       "2012-12-31 22:16:54        -102         482     493             1640     A0   \n",
       "2012-12-31 23:46:34        -977         190     995                0     A0   \n",
       "\n",
       "                    flag.2 flag.3 flag.4 flag.5  \n",
       "start_datetime                                   \n",
       "2012-01-01 01:57:42     PE     PS     Q1    NaN  \n",
       "2012-01-01 02:24:54     DR     P1     PE     Q2  \n",
       "2012-01-01 03:52:30     DR     P1     PE     Q2  \n",
       "2012-01-01 05:23:26     P1     PE     Q1    NaN  \n",
       "2012-01-01 05:39:06     DR     P1     PE     Q2  \n",
       "...                    ...    ...    ...    ...  \n",
       "2012-12-31 21:39:46     P1     PE     Q1    NaN  \n",
       "2012-12-31 21:44:30     P1     PE     Q1    NaN  \n",
       "2012-12-31 21:49:02     P1     PE     Q1    NaN  \n",
       "2012-12-31 22:16:54     P1     PE     Q1    NaN  \n",
       "2012-12-31 23:46:34     P1     PE     Q1    NaN  \n",
       "\n",
       "[11454 rows x 14 columns]"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract data for the year 2012\n",
    "year_to_extract = 2012\n",
    "solar_flare_for_2012 =  solar_flare[solar_flare.index.year == year_to_extract]\n",
    "\n",
    "solar_flare_for_2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "c7a62652",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name=\"LSTM + GRU 2012 with recurrent_dropout\"\n",
    "epochs=100\n",
    "batch_size=64\n",
    "recurrent_dropout=0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "0783a14f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.6707729  -0.13847892 -0.1619157  -2.444481  ]\n",
      " [-0.5342047  -0.23506333 -0.17023934  0.96252745]\n",
      " [ 3.96279     0.25451973  0.29610974 -0.08252381]\n",
      " ...\n",
      " [-0.1927843  -0.2650378  -0.1667803  -0.7674561 ]\n",
      " [ 0.04133258  1.0005511   0.2825327  -0.7039057 ]\n",
      " [ 1.2509364  -0.22840233 -0.1103489   1.0684448 ]]\n",
      "Epoch 1/100\n",
      "144/144 [==============================] - 2s 4ms/step - loss: 0.9096 - accuracy: 0.7361 - val_loss: 0.5656 - val_accuracy: 0.8345 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "144/144 [==============================] - 0s 2ms/step - loss: 0.6553 - accuracy: 0.7877 - val_loss: 0.5676 - val_accuracy: 0.8349 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "144/144 [==============================] - 0s 2ms/step - loss: 0.6463 - accuracy: 0.7883 - val_loss: 0.5652 - val_accuracy: 0.8354 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "144/144 [==============================] - 0s 2ms/step - loss: 0.6440 - accuracy: 0.7886 - val_loss: 0.5679 - val_accuracy: 0.8328 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "144/144 [==============================] - 0s 2ms/step - loss: 0.6371 - accuracy: 0.7900 - val_loss: 0.5528 - val_accuracy: 0.8345 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "144/144 [==============================] - 0s 2ms/step - loss: 0.6367 - accuracy: 0.7895 - val_loss: 0.5502 - val_accuracy: 0.8380 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "144/144 [==============================] - 0s 2ms/step - loss: 0.6353 - accuracy: 0.7904 - val_loss: 0.5608 - val_accuracy: 0.8332 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "144/144 [==============================] - 0s 2ms/step - loss: 0.6335 - accuracy: 0.7904 - val_loss: 0.5864 - val_accuracy: 0.8275 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "144/144 [==============================] - 0s 2ms/step - loss: 0.6325 - accuracy: 0.7905 - val_loss: 0.5575 - val_accuracy: 0.8380 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "144/144 [==============================] - 0s 3ms/step - loss: 0.6280 - accuracy: 0.7917 - val_loss: 0.5647 - val_accuracy: 0.8301 - lr: 5.0000e-04\n",
      "Epoch 11/100\n",
      "144/144 [==============================] - 0s 2ms/step - loss: 0.6284 - accuracy: 0.7916 - val_loss: 0.5679 - val_accuracy: 0.8301 - lr: 5.0000e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72/72 [==============================] - 0s 669us/step - loss: 0.5502 - accuracy: 0.8380\n",
      "Test Accuracy: 83.80%\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, GRU, Dense, Input\n",
    "from tensorflow.keras.layers import concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Assuming 'energy.keV' is a column with categorical data\n",
    "categories = solar_flare_for_2012['energy.kev'].values\n",
    "\n",
    "# Encode the categories\n",
    "label_encoder = LabelEncoder()\n",
    "categories_encoded = label_encoder.fit_transform(categories)\n",
    "\n",
    "      \n",
    "data_duration = np.array(solar_flare_for_2012['duration.s'])\n",
    "data_peak = np.array(solar_flare_for_2012['peak.c/s'])\n",
    "data_counts = np.array(solar_flare_for_2012['total.counts'])\n",
    "data_radial = np.array(solar_flare_for_2012['radial'])\n",
    "\n",
    "# Define the sequence length (number of previous categories to consider)\n",
    "sequence_length = 5\n",
    "\n",
    "# Create sequences and labels\n",
    "X_sequences, y_sequences = [], []\n",
    "\n",
    "X = np.stack((data_duration, data_peak, data_counts, data_radial), axis=-1)\n",
    "X = X.astype('float32')\n",
    "\n",
    "# Use StandardScaler to scale your input data\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "y = categories_encoded\n",
    "\n",
    "print(X)\n",
    "\n",
    "for i in range(len(X) - sequence_length):\n",
    "    X_sequences.append(X[i:i + sequence_length])\n",
    "    y_sequences.append(y[i + sequence_length])\n",
    "\n",
    "X_sequences = np.array(X_sequences)\n",
    "y_sequences = np.array(y_sequences)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(0.8 * len(X_sequences))\n",
    "X_train, X_test = X_sequences[:train_size], X_sequences[train_size:]\n",
    "y_train, y_test = y_sequences[:train_size], y_sequences[train_size:]\n",
    "\n",
    "\n",
    "# Create an LSTM and a GRU model in parallel\n",
    "input_layer = Input(shape=(sequence_length, X_sequences.shape[2]))\n",
    "lstm_layer = LSTM(50)(input_layer)\n",
    "gru_layer = GRU(50, recurrent_dropout=0.6)(input_layer)\n",
    "\n",
    "# Combine the LSTM and GRU outputs\n",
    "combined = concatenate([lstm_layer, gru_layer], axis=-1)\n",
    "\n",
    "output_layer = Dense(len(np.unique(categories_encoded)), activation='softmax')(combined)\n",
    "\n",
    "model = Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Implement learning rate scheduler\n",
    "lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)\n",
    "\n",
    "# Implement early stopping to prevent overfitting\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test), callbacks=[lr_scheduler, early_stopping])\n",
    "\n",
    "# Plot training history\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
    "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "47292597",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.append([model_name, sequence_length, recurrent_dropout, epochs, batch_size, f'{test_accuracy * 100:.2f}%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "10889cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['LSTM 2002-2016', 10, 0.0, 100, 64, '70.74%'],\n",
       " ['LSTM + GRU 2002-2016', 10, 0.0, 100, 64, '71.80%'],\n",
       " ['LSTM + GRU 2002-2016 with recurrent_dropout', 10, 0.9, 100, 64, '71.12%'],\n",
       " ['LSTM + GRU 2012 with recurrent_dropout', 5, 0.9, 100, 64, '83.80%']]"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee876cd3",
   "metadata": {},
   "source": [
    "# Test it on solar data 2017-2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "2a869a4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 656us/step - loss: 1.1813 - accuracy: 0.6995\n",
      "New Test Accuracy: 69.95%\n"
     ]
    }
   ],
   "source": [
    "# Assuming 'energy.keV' is a column with categorical data in the new data (2017-2018)\n",
    "new_data_categories = new_solar_flare['energy.kev'].values\n",
    "\n",
    "# Encode the categories for the new data using the same label encoder\n",
    "new_categories_encoded = label_encoder.transform(new_data_categories)\n",
    "\n",
    "# Assuming the other features in the new data have the same column names\n",
    "new_data_duration = np.array(new_solar_flare['duration.s'])\n",
    "new_data_peak = np.array(new_solar_flare['peak.c/s'])\n",
    "new_data_counts = np.array(new_solar_flare['total.counts'])\n",
    "new_data_radial = np.array(new_solar_flare['radial'])\n",
    "\n",
    "# Create sequences and labels for the new data\n",
    "new_X_sequences, new_y_sequences = [], []\n",
    "\n",
    "new_X = np.stack((new_data_duration, new_data_peak, new_data_counts, new_data_radial), axis=-1)\n",
    "new_X = new_X.astype('float32')\n",
    "\n",
    "# Use the same StandardScaler to scale your input data\n",
    "new_X = scaler.transform(new_X)\n",
    "\n",
    "new_y = new_categories_encoded\n",
    "\n",
    "for i in range(len(new_X) - sequence_length):\n",
    "    new_X_sequences.append(new_X[i:i + sequence_length])\n",
    "    new_y_sequences.append(new_y[i + sequence_length])\n",
    "\n",
    "new_X_sequences = np.array(new_X_sequences)\n",
    "new_y_sequences = np.array(new_y_sequences)\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_loss, new_test_accuracy = model.evaluate(new_X_sequences, new_y_sequences)\n",
    "print(f'New Test Accuracy: {new_test_accuracy * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "1fe283c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 590us/step\n",
      "Precision: 58.69%\n",
      "Recall: 69.95%\n",
      "F1 Score: 61.40%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_predictions = model.predict(new_X_sequences)\n",
    "new_test_predictions_classes = np.argmax(new_test_predictions, axis=1)\n",
    "\n",
    "precision = precision_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "recall = recall_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "f1 = f1_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "\n",
    "print(f'Precision: {precision * 100:.2f}%')\n",
    "print(f'Recall: {recall * 100:.2f}%')\n",
    "print(f'F1 Score: {f1 * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9678a7f",
   "metadata": {},
   "source": [
    "## LSTM + GRU 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "86fb2047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>energy.kev</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active.region.ar</th>\n",
       "      <th>flag.1</th>\n",
       "      <th>flag.2</th>\n",
       "      <th>flag.3</th>\n",
       "      <th>flag.4</th>\n",
       "      <th>flag.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 03:57:46</th>\n",
       "      <td>16010101</td>\n",
       "      <td>268</td>\n",
       "      <td>28</td>\n",
       "      <td>14964.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>854</td>\n",
       "      <td>-292</td>\n",
       "      <td>903</td>\n",
       "      <td>2473</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:01:46</th>\n",
       "      <td>16010102</td>\n",
       "      <td>156</td>\n",
       "      <td>26</td>\n",
       "      <td>7980.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>855</td>\n",
       "      <td>-285</td>\n",
       "      <td>902</td>\n",
       "      <td>2473</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 05:16:10</th>\n",
       "      <td>16010103</td>\n",
       "      <td>244</td>\n",
       "      <td>38</td>\n",
       "      <td>10200.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>828</td>\n",
       "      <td>-312</td>\n",
       "      <td>885</td>\n",
       "      <td>2473</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 05:24:10</th>\n",
       "      <td>16010104</td>\n",
       "      <td>624</td>\n",
       "      <td>52</td>\n",
       "      <td>47100.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>794</td>\n",
       "      <td>-310</td>\n",
       "      <td>853</td>\n",
       "      <td>2473</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 05:36:18</th>\n",
       "      <td>16010105</td>\n",
       "      <td>904</td>\n",
       "      <td>44</td>\n",
       "      <td>79212.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>899</td>\n",
       "      <td>-299</td>\n",
       "      <td>948</td>\n",
       "      <td>2473</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26 22:17:02</th>\n",
       "      <td>16122603</td>\n",
       "      <td>484</td>\n",
       "      <td>152</td>\n",
       "      <td>75328.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>GE</td>\n",
       "      <td>NS</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26 23:28:18</th>\n",
       "      <td>16122604</td>\n",
       "      <td>1096</td>\n",
       "      <td>304</td>\n",
       "      <td>142048.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>DR</td>\n",
       "      <td>NS</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27 08:39:06</th>\n",
       "      <td>16122701</td>\n",
       "      <td>392</td>\n",
       "      <td>80</td>\n",
       "      <td>32344.0</td>\n",
       "      <td>3-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>NS</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q3 SE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27 12:21:30</th>\n",
       "      <td>16122702</td>\n",
       "      <td>520</td>\n",
       "      <td>24</td>\n",
       "      <td>17336.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>326</td>\n",
       "      <td>326</td>\n",
       "      <td>461</td>\n",
       "      <td>0</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>DR</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27 12:37:14</th>\n",
       "      <td>16122703</td>\n",
       "      <td>200</td>\n",
       "      <td>48</td>\n",
       "      <td>11056.0</td>\n",
       "      <td>6-12</td>\n",
       "      <td>765</td>\n",
       "      <td>198</td>\n",
       "      <td>791</td>\n",
       "      <td>2621</td>\n",
       "      <td>A0</td>\n",
       "      <td>DF</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2591 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        flare  duration.s  peak.c/s  total.counts energy.kev  \\\n",
       "start_datetime                                                                 \n",
       "2016-01-01 03:57:46  16010101         268        28       14964.0       6-12   \n",
       "2016-01-01 04:01:46  16010102         156        26        7980.0       6-12   \n",
       "2016-01-01 05:16:10  16010103         244        38       10200.0       6-12   \n",
       "2016-01-01 05:24:10  16010104         624        52       47100.0       6-12   \n",
       "2016-01-01 05:36:18  16010105         904        44       79212.0       6-12   \n",
       "...                       ...         ...       ...           ...        ...   \n",
       "2016-12-26 22:17:02  16122603         484       152       75328.0        3-6   \n",
       "2016-12-26 23:28:18  16122604        1096       304      142048.0        3-6   \n",
       "2016-12-27 08:39:06  16122701         392        80       32344.0        3-6   \n",
       "2016-12-27 12:21:30  16122702         520        24       17336.0       6-12   \n",
       "2016-12-27 12:37:14  16122703         200        48       11056.0       6-12   \n",
       "\n",
       "                     x.pos.asec  y.pos.asec  radial active.region.ar flag.1  \\\n",
       "start_datetime                                                                \n",
       "2016-01-01 03:57:46         854        -292     903             2473     A0   \n",
       "2016-01-01 04:01:46         855        -285     902             2473     A0   \n",
       "2016-01-01 05:16:10         828        -312     885             2473     A0   \n",
       "2016-01-01 05:24:10         794        -310     853             2473     A0   \n",
       "2016-01-01 05:36:18         899        -299     948             2473     A0   \n",
       "...                         ...         ...     ...              ...    ...   \n",
       "2016-12-26 22:17:02           0           0       0                0     A0   \n",
       "2016-12-26 23:28:18           0           0       0                0     A0   \n",
       "2016-12-27 08:39:06           0           0       0                0     A0   \n",
       "2016-12-27 12:21:30         326         326     461                0     A0   \n",
       "2016-12-27 12:37:14         765         198     791             2621     A0   \n",
       "\n",
       "                    flag.2 flag.3 flag.4 flag.5  \n",
       "start_datetime                                   \n",
       "2016-01-01 03:57:46     DF     DR     P1  PE Q3  \n",
       "2016-01-01 04:01:46     DF     DR     P1  PE Q3  \n",
       "2016-01-01 05:16:10     DF     DR     P1  PE Q3  \n",
       "2016-01-01 05:24:10     DF     DR     P1  PE Q3  \n",
       "2016-01-01 05:36:18     DF     DR     P1  PE Q3  \n",
       "...                    ...    ...    ...    ...  \n",
       "2016-12-26 22:17:02     GE     NS     PE     Q2  \n",
       "2016-12-26 23:28:18     DR     NS     PE     Q2  \n",
       "2016-12-27 08:39:06     DF     NS     PE  Q3 SE  \n",
       "2016-12-27 12:21:30     DF     DR     P1  PE Q3  \n",
       "2016-12-27 12:37:14     DF     P1     PE     Q2  \n",
       "\n",
       "[2591 rows x 14 columns]"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract data for the year 2016\n",
    "year_to_extract = 2016\n",
    "solar_flare_for_2016 =  solar_flare[solar_flare.index.year == year_to_extract]\n",
    "\n",
    "solar_flare_for_2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "03abd847",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name=\"LSTM + GRU 2016 with recurrent_dropout\"\n",
    "epochs=100\n",
    "batch_size=64\n",
    "recurrent_dropout=0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "63c5d086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.51585805 -0.34540248 -0.2881604  -0.08666818]\n",
      " [-0.7845769  -0.34871662 -0.29964396 -0.08704862]\n",
      " [-0.5734407  -0.32883173 -0.2959937  -0.09351614]\n",
      " ...\n",
      " [-0.21834794 -0.2592346  -0.25958306 -0.4302076 ]\n",
      " [ 0.08875929 -0.35203078 -0.2842602  -0.25482368]\n",
      " [-0.6790088  -0.312261   -0.2945862  -0.12927772]]\n",
      "Epoch 1/100\n",
      "33/33 [==============================] - 1s 9ms/step - loss: 1.5765 - accuracy: 0.5648 - val_loss: 1.1101 - val_accuracy: 0.7857 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 1.0640 - accuracy: 0.6494 - val_loss: 0.8077 - val_accuracy: 0.7973 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 0.9540 - accuracy: 0.6760 - val_loss: 0.8520 - val_accuracy: 0.7876 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 0.9318 - accuracy: 0.6900 - val_loss: 0.8460 - val_accuracy: 0.7915 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 0.9218 - accuracy: 0.6944 - val_loss: 0.8475 - val_accuracy: 0.7896 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 0.9140 - accuracy: 0.6929 - val_loss: 0.8474 - val_accuracy: 0.7896 - lr: 5.0000e-04\n",
      "Epoch 7/100\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 0.9090 - accuracy: 0.7016 - val_loss: 0.8424 - val_accuracy: 0.7934 - lr: 5.0000e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 0s 895us/step - loss: 0.8077 - accuracy: 0.7973\n",
      "Test Accuracy: 79.73%\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, GRU, Dense, Input\n",
    "from tensorflow.keras.layers import concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Assuming 'energy.keV' is a column with categorical data\n",
    "categories = solar_flare_for_2016['energy.kev'].values\n",
    "\n",
    "# Encode the categories\n",
    "label_encoder = LabelEncoder()\n",
    "categories_encoded = label_encoder.fit_transform(categories)\n",
    "\n",
    "      \n",
    "data_duration = np.array(solar_flare_for_2016['duration.s'])\n",
    "data_peak = np.array(solar_flare_for_2016['peak.c/s'])\n",
    "data_counts = np.array(solar_flare_for_2016['total.counts'])\n",
    "data_radial = np.array(solar_flare_for_2016['radial'])\n",
    "\n",
    "# Define the sequence length (number of previous categories to consider)\n",
    "sequence_length = 5\n",
    "\n",
    "# Create sequences and labels\n",
    "X_sequences, y_sequences = [], []\n",
    "\n",
    "X = np.stack((data_duration, data_peak, data_counts, data_radial), axis=-1)\n",
    "X = X.astype('float32')\n",
    "\n",
    "# Use StandardScaler to scale your input data\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "y = categories_encoded\n",
    "\n",
    "print(X)\n",
    "\n",
    "for i in range(len(X) - sequence_length):\n",
    "    X_sequences.append(X[i:i + sequence_length])\n",
    "    y_sequences.append(y[i + sequence_length])\n",
    "\n",
    "X_sequences = np.array(X_sequences)\n",
    "y_sequences = np.array(y_sequences)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(0.8 * len(X_sequences))\n",
    "X_train, X_test = X_sequences[:train_size], X_sequences[train_size:]\n",
    "y_train, y_test = y_sequences[:train_size], y_sequences[train_size:]\n",
    "\n",
    "\n",
    "# Create an LSTM and a GRU model in parallel\n",
    "input_layer = Input(shape=(sequence_length, X_sequences.shape[2]))\n",
    "lstm_layer = LSTM(50)(input_layer)\n",
    "gru_layer = GRU(50, recurrent_dropout=0.6)(input_layer)\n",
    "\n",
    "# Combine the LSTM and GRU outputs\n",
    "combined = concatenate([lstm_layer, gru_layer], axis=-1)\n",
    "\n",
    "output_layer = Dense(len(np.unique(categories_encoded)), activation='softmax')(combined)\n",
    "\n",
    "model = Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Implement learning rate scheduler\n",
    "lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)\n",
    "\n",
    "# Implement early stopping to prevent overfitting\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test), callbacks=[lr_scheduler, early_stopping])\n",
    "\n",
    "# Plot training history\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
    "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "9fffe423",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.append([model_name, sequence_length, recurrent_dropout, epochs, batch_size, f'{test_accuracy * 100:.2f}%'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11463edb",
   "metadata": {},
   "source": [
    "# Test it on solar data 2017-2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "1526ad63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 711us/step - loss: 1.2744 - accuracy: 0.5496\n",
      "New Test Accuracy: 54.96%\n"
     ]
    }
   ],
   "source": [
    "# Assuming 'energy.keV' is a column with categorical data in the new data (2017-2018)\n",
    "new_data_categories = new_solar_flare['energy.kev'].values\n",
    "\n",
    "# Encode the categories for the new data using the same label encoder\n",
    "new_categories_encoded = label_encoder.transform(new_data_categories)\n",
    "\n",
    "# Assuming the other features in the new data have the same column names\n",
    "new_data_duration = np.array(new_solar_flare['duration.s'])\n",
    "new_data_peak = np.array(new_solar_flare['peak.c/s'])\n",
    "new_data_counts = np.array(new_solar_flare['total.counts'])\n",
    "new_data_radial = np.array(new_solar_flare['radial'])\n",
    "\n",
    "# Create sequences and labels for the new data\n",
    "new_X_sequences, new_y_sequences = [], []\n",
    "\n",
    "new_X = np.stack((new_data_duration, new_data_peak, new_data_counts, new_data_radial), axis=-1)\n",
    "new_X = new_X.astype('float32')\n",
    "\n",
    "# Use the same StandardScaler to scale your input data\n",
    "new_X = scaler.transform(new_X)\n",
    "\n",
    "new_y = new_categories_encoded\n",
    "\n",
    "for i in range(len(new_X) - sequence_length):\n",
    "    new_X_sequences.append(new_X[i:i + sequence_length])\n",
    "    new_y_sequences.append(new_y[i + sequence_length])\n",
    "\n",
    "new_X_sequences = np.array(new_X_sequences)\n",
    "new_y_sequences = np.array(new_y_sequences)\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_loss, new_test_accuracy = model.evaluate(new_X_sequences, new_y_sequences)\n",
    "print(f'New Test Accuracy: {new_test_accuracy * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "16fdff4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 592us/step\n",
      "Precision: 36.79%\n",
      "Recall: 54.96%\n",
      "F1 Score: 43.34%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "\n",
    "# Evaluate the model on the new data\n",
    "new_test_predictions = model.predict(new_X_sequences)\n",
    "new_test_predictions_classes = np.argmax(new_test_predictions, axis=1)\n",
    "\n",
    "precision = precision_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "recall = recall_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "f1 = f1_score(new_y_sequences, new_test_predictions_classes, average='weighted')\n",
    "\n",
    "print(f'Precision: {precision * 100:.2f}%')\n",
    "print(f'Recall: {recall * 100:.2f}%')\n",
    "print(f'F1 Score: {f1 * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed892ef7",
   "metadata": {},
   "source": [
    "# Display results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "a03247d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['LSTM 2002-2016', 10, 0.0, 100, 64, '70.74%'],\n",
       " ['LSTM + GRU 2002-2016', 10, 0.0, 100, 64, '71.80%'],\n",
       " ['LSTM + GRU 2002-2016 with recurrent_dropout', 10, 0.9, 100, 64, '71.12%'],\n",
       " ['LSTM + GRU 2012 with recurrent_dropout', 5, 0.9, 100, 64, '83.80%'],\n",
       " ['LSTM + GRU 2016 with recurrent_dropout', 5, 0.9, 100, 64, '79.73%']]"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "d6beb1d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Sequence length</th>\n",
       "      <th>recurrent_dropout</th>\n",
       "      <th>epochs</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>Accuracy%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LSTM 2002-2016</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>70.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LSTM + GRU 2002-2016</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>71.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LSTM + GRU 2002-2016 with recurrent_dropout</td>\n",
       "      <td>10</td>\n",
       "      <td>0.9</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>71.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LSTM + GRU 2012 with recurrent_dropout</td>\n",
       "      <td>5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>83.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSTM + GRU 2016 with recurrent_dropout</td>\n",
       "      <td>5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>79.73%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Model Name  Sequence length  \\\n",
       "0                               LSTM 2002-2016               10   \n",
       "1                         LSTM + GRU 2002-2016               10   \n",
       "2  LSTM + GRU 2002-2016 with recurrent_dropout               10   \n",
       "3       LSTM + GRU 2012 with recurrent_dropout                5   \n",
       "4       LSTM + GRU 2016 with recurrent_dropout                5   \n",
       "\n",
       "   recurrent_dropout  epochs  batch_size Accuracy%  \n",
       "0                0.0     100          64    70.74%  \n",
       "1                0.0     100          64    71.80%  \n",
       "2                0.9     100          64    71.12%  \n",
       "3                0.9     100          64    83.80%  \n",
       "4                0.9     100          64    79.73%  "
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "headers=['Model Name', 'Sequence length', 'recurrent_dropout', 'epochs', 'batch_size', 'Accuracy%']\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame(results, columns=headers)\n",
    "\n",
    "# Display the DataFrame\n",
    "df"
   ]
  }
 ],
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