magnet-model-the-geomagnetic-field.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2021-02-02T22:53:14.676861Z",
     "iopub.status.busy": "2021-02-02T22:53:14.676167Z",
     "iopub.status.idle": "2021-02-02T22:53:14.689352Z",
     "shell.execute_reply": "2021-02-02T22:53:14.688757Z"
    },
    "papermill": {
     "duration": 0.042708,
     "end_time": "2021-02-02T22:53:14.689456",
     "exception": false,
     "start_time": "2021-02-02T22:53:14.646748",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/magnet-nasa/solar_wind.csv\n",
      "/kaggle/input/magnet-nasa/satellite_positions.csv\n",
      "/kaggle/input/magnet-nasa/dst_labels.csv\n",
      "/kaggle/input/magnet-nasa/sunspots.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:53:14.742574Z",
     "iopub.status.busy": "2021-02-02T22:53:14.741841Z",
     "iopub.status.idle": "2021-02-02T22:53:20.230946Z",
     "shell.execute_reply": "2021-02-02T22:53:20.232056Z"
    },
    "papermill": {
     "duration": 5.521313,
     "end_time": "2021-02-02T22:53:20.232269",
     "exception": false,
     "start_time": "2021-02-02T22:53:14.710956",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras \n",
    "from keras.layers import Dense, Conv2D, BatchNormalization, Activation \n",
    "from keras.layers import AveragePooling2D, Input, Flatten \n",
    "from keras.optimizers import Adam \n",
    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler \n",
    "from keras.callbacks import ReduceLROnPlateau \n",
    "from keras.preprocessing.image import ImageDataGenerator \n",
    "from keras.regularizers import l2 \n",
    "from keras import backend as K \n",
    "from keras.models import Model \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "execution": {
     "iopub.execute_input": "2021-02-02T22:53:20.306594Z",
     "iopub.status.busy": "2021-02-02T22:53:20.305403Z",
     "iopub.status.idle": "2021-02-02T22:54:30.917312Z",
     "shell.execute_reply": "2021-02-02T22:54:30.916127Z"
    },
    "papermill": {
     "duration": 70.649255,
     "end_time": "2021-02-02T22:54:30.917449",
     "exception": false,
     "start_time": "2021-02-02T22:53:20.268194",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "DATA_PATH = Path(\"../input/magnet-nasa\")\n",
    "\n",
    "dst = pd.read_csv(DATA_PATH / \"dst_labels.csv\")\n",
    "dst.timedelta = pd.to_timedelta(dst.timedelta)\n",
    "dst.set_index([\"period\", \"timedelta\"], inplace=True)\n",
    "\n",
    "sunspots = pd.read_csv(DATA_PATH / \"sunspots.csv\")\n",
    "sunspots.timedelta = pd.to_timedelta(sunspots.timedelta)\n",
    "sunspots.set_index([\"period\", \"timedelta\"], inplace=True)\n",
    "\n",
    "solar_wind = pd.read_csv(DATA_PATH / \"solar_wind.csv\")\n",
    "solar_wind.timedelta = pd.to_timedelta(solar_wind.timedelta)\n",
    "solar_wind.set_index([\"period\", \"timedelta\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:54:30.974797Z",
     "iopub.status.busy": "2021-02-02T22:54:30.973874Z",
     "iopub.status.idle": "2021-02-02T22:54:30.982894Z",
     "shell.execute_reply": "2021-02-02T22:54:30.983419Z"
    },
    "papermill": {
     "duration": 0.044508,
     "end_time": "2021-02-02T22:54:30.983541",
     "exception": false,
     "start_time": "2021-02-02T22:54:30.939033",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dst shape:  (139872, 1)\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>dst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">train_a</th>\n",
       "      <th>0 days 00:00:00</th>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 01:00:00</th>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 02:00:00</th>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 03:00:00</th>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 04:00:00</th>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         dst\n",
       "period  timedelta           \n",
       "train_a 0 days 00:00:00   -7\n",
       "        0 days 01:00:00  -10\n",
       "        0 days 02:00:00  -10\n",
       "        0 days 03:00:00   -6\n",
       "        0 days 04:00:00   -2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Dst shape: \", dst.shape)\n",
    "dst.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:54:31.032683Z",
     "iopub.status.busy": "2021-02-02T22:54:31.031853Z",
     "iopub.status.idle": "2021-02-02T22:54:31.078528Z",
     "shell.execute_reply": "2021-02-02T22:54:31.078998Z"
    },
    "papermill": {
     "duration": 0.073154,
     "end_time": "2021-02-02T22:54:31.079121",
     "exception": false,
     "start_time": "2021-02-02T22:54:31.005967",
     "status": "completed"
    },
    "tags": []
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">dst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</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>train_a</th>\n",
       "      <td>28824.0</td>\n",
       "      <td>-16.576707</td>\n",
       "      <td>26.083191</td>\n",
       "      <td>-387.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_b</th>\n",
       "      <td>52584.0</td>\n",
       "      <td>-9.695154</td>\n",
       "      <td>16.443049</td>\n",
       "      <td>-223.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_c</th>\n",
       "      <td>58464.0</td>\n",
       "      <td>-9.556325</td>\n",
       "      <td>16.506404</td>\n",
       "      <td>-374.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             dst                                                    \n",
       "           count       mean        std    min   25%   50%  75%   max\n",
       "period                                                              \n",
       "train_a  28824.0 -16.576707  26.083191 -387.0 -26.0 -12.0 -1.0  65.0\n",
       "train_b  52584.0  -9.695154  16.443049 -223.0 -17.0  -7.0  1.0  59.0\n",
       "train_c  58464.0  -9.556325  16.506404 -374.0 -16.0  -7.0  0.0  67.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dst.groupby(\"period\").describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:54:31.146080Z",
     "iopub.status.busy": "2021-02-02T22:54:31.145179Z",
     "iopub.status.idle": "2021-02-02T22:54:31.151276Z",
     "shell.execute_reply": "2021-02-02T22:54:31.150624Z"
    },
    "papermill": {
     "duration": 0.049445,
     "end_time": "2021-02-02T22:54:31.151403",
     "exception": false,
     "start_time": "2021-02-02T22:54:31.101958",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solar wind shape:  (8392320, 15)\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>bx_gse</th>\n",
       "      <th>by_gse</th>\n",
       "      <th>bz_gse</th>\n",
       "      <th>theta_gse</th>\n",
       "      <th>phi_gse</th>\n",
       "      <th>bx_gsm</th>\n",
       "      <th>by_gsm</th>\n",
       "      <th>bz_gsm</th>\n",
       "      <th>theta_gsm</th>\n",
       "      <th>phi_gsm</th>\n",
       "      <th>bt</th>\n",
       "      <th>density</th>\n",
       "      <th>speed</th>\n",
       "      <th>temperature</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">train_a</th>\n",
       "      <th>0 days 00:00:00</th>\n",
       "      <td>-5.55</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>11.09</td>\n",
       "      <td>153.37</td>\n",
       "      <td>-5.55</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>11.09</td>\n",
       "      <td>153.37</td>\n",
       "      <td>6.80</td>\n",
       "      <td>1.53</td>\n",
       "      <td>383.92</td>\n",
       "      <td>110237.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 00:01:00</th>\n",
       "      <td>-5.58</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>10.10</td>\n",
       "      <td>151.91</td>\n",
       "      <td>-5.58</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>10.10</td>\n",
       "      <td>151.91</td>\n",
       "      <td>6.83</td>\n",
       "      <td>1.69</td>\n",
       "      <td>381.79</td>\n",
       "      <td>123825.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 00:02:00</th>\n",
       "      <td>-5.15</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.85</td>\n",
       "      <td>7.87</td>\n",
       "      <td>146.04</td>\n",
       "      <td>-5.15</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.85</td>\n",
       "      <td>7.87</td>\n",
       "      <td>146.04</td>\n",
       "      <td>6.77</td>\n",
       "      <td>1.97</td>\n",
       "      <td>389.11</td>\n",
       "      <td>82548.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 00:03:00</th>\n",
       "      <td>-5.20</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.68</td>\n",
       "      <td>6.17</td>\n",
       "      <td>146.17</td>\n",
       "      <td>-5.20</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.68</td>\n",
       "      <td>6.17</td>\n",
       "      <td>146.17</td>\n",
       "      <td>6.74</td>\n",
       "      <td>1.97</td>\n",
       "      <td>389.11</td>\n",
       "      <td>82548.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 00:04:00</th>\n",
       "      <td>-5.12</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>4.62</td>\n",
       "      <td>145.72</td>\n",
       "      <td>-5.12</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>4.62</td>\n",
       "      <td>145.72</td>\n",
       "      <td>6.65</td>\n",
       "      <td>1.77</td>\n",
       "      <td>384.26</td>\n",
       "      <td>94269.0</td>\n",
       "      <td>ac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         bx_gse  by_gse  bz_gse  theta_gse  phi_gse  bx_gsm  \\\n",
       "period  timedelta                                                             \n",
       "train_a 0 days 00:00:00   -5.55    3.00    1.25      11.09   153.37   -5.55   \n",
       "        0 days 00:01:00   -5.58    3.16    1.17      10.10   151.91   -5.58   \n",
       "        0 days 00:02:00   -5.15    3.66    0.85       7.87   146.04   -5.15   \n",
       "        0 days 00:03:00   -5.20    3.68    0.68       6.17   146.17   -5.20   \n",
       "        0 days 00:04:00   -5.12    3.68    0.49       4.62   145.72   -5.12   \n",
       "\n",
       "                         by_gsm  bz_gsm  theta_gsm  phi_gsm    bt  density  \\\n",
       "period  timedelta                                                            \n",
       "train_a 0 days 00:00:00    3.00    1.25      11.09   153.37  6.80     1.53   \n",
       "        0 days 00:01:00    3.16    1.17      10.10   151.91  6.83     1.69   \n",
       "        0 days 00:02:00    3.66    0.85       7.87   146.04  6.77     1.97   \n",
       "        0 days 00:03:00    3.68    0.68       6.17   146.17  6.74     1.97   \n",
       "        0 days 00:04:00    3.68    0.49       4.62   145.72  6.65     1.77   \n",
       "\n",
       "                          speed  temperature source  \n",
       "period  timedelta                                    \n",
       "train_a 0 days 00:00:00  383.92     110237.0     ac  \n",
       "        0 days 00:01:00  381.79     123825.0     ac  \n",
       "        0 days 00:02:00  389.11      82548.0     ac  \n",
       "        0 days 00:03:00  389.11      82548.0     ac  \n",
       "        0 days 00:04:00  384.26      94269.0     ac  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Solar wind shape: \", solar_wind.shape)\n",
    "solar_wind.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:54:31.211474Z",
     "iopub.status.busy": "2021-02-02T22:54:31.210280Z",
     "iopub.status.idle": "2021-02-02T22:54:31.216563Z",
     "shell.execute_reply": "2021-02-02T22:54:31.215836Z"
    },
    "papermill": {
     "duration": 0.041238,
     "end_time": "2021-02-02T22:54:31.216740",
     "exception": false,
     "start_time": "2021-02-02T22:54:31.175502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sunspot shape:  (192, 1)\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>smoothed_ssn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">train_a</th>\n",
       "      <th>0 days</th>\n",
       "      <td>65.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13 days</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44 days</th>\n",
       "      <td>76.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74 days</th>\n",
       "      <td>80.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105 days</th>\n",
       "      <td>85.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   smoothed_ssn\n",
       "period  timedelta              \n",
       "train_a 0 days             65.4\n",
       "        13 days            72.0\n",
       "        44 days            76.9\n",
       "        74 days            80.8\n",
       "        105 days           85.4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Sunspot shape: \", sunspots.shape)\n",
    "sunspots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:54:31.272304Z",
     "iopub.status.busy": "2021-02-02T22:54:31.271133Z",
     "iopub.status.idle": "2021-02-02T22:54:36.265807Z",
     "shell.execute_reply": "2021-02-02T22:54:36.265065Z"
    },
    "papermill": {
     "duration": 5.023962,
     "end_time": "2021-02-02T22:54:36.265927",
     "exception": false,
     "start_time": "2021-02-02T22:54:31.241965",
     "status": "completed"
    },
    "tags": []
   },
   "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>period</th>\n",
       "      <th>train_a</th>\n",
       "      <th>train_b</th>\n",
       "      <th>train_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">bx_gse</th>\n",
       "      <th>count</th>\n",
       "      <td>1.575012e+06</td>\n",
       "      <td>3.084130e+06</td>\n",
       "      <td>3.407290e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.781301e+00</td>\n",
       "      <td>-3.088789e-01</td>\n",
       "      <td>-4.619076e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.339212e+00</td>\n",
       "      <td>3.627830e+00</td>\n",
       "      <td>3.245485e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.463000e+01</td>\n",
       "      <td>-2.937000e+01</td>\n",
       "      <td>-4.546000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-4.960000e+00</td>\n",
       "      <td>-3.070000e+00</td>\n",
       "      <td>-2.800000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">temperature</th>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>1.496000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.364900e+04</td>\n",
       "      <td>3.741400e+04</td>\n",
       "      <td>4.007400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.923800e+04</td>\n",
       "      <td>8.552400e+04</td>\n",
       "      <td>7.152100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.325500e+05</td>\n",
       "      <td>1.873250e+05</td>\n",
       "      <td>1.310880e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.223700e+06</td>\n",
       "      <td>4.206672e+06</td>\n",
       "      <td>5.751308e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "period                  train_a       train_b       train_c\n",
       "bx_gse      count  1.575012e+06  3.084130e+06  3.407290e+06\n",
       "            mean  -1.781301e+00 -3.088789e-01 -4.619076e-01\n",
       "            std    4.339212e+00  3.627830e+00  3.245485e+00\n",
       "            min   -5.463000e+01 -2.937000e+01 -4.546000e+01\n",
       "            25%   -4.960000e+00 -3.070000e+00 -2.800000e+00\n",
       "...                         ...           ...           ...\n",
       "temperature min    1.000000e+04  1.496000e+03  0.000000e+00\n",
       "            25%    4.364900e+04  3.741400e+04  4.007400e+04\n",
       "            50%    7.923800e+04  8.552400e+04  7.152100e+04\n",
       "            75%    1.325500e+05  1.873250e+05  1.310880e+05\n",
       "            max    6.223700e+06  4.206672e+06  5.751308e+06\n",
       "\n",
       "[112 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_wind.groupby(\"period\").describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:54:36.327091Z",
     "iopub.status.busy": "2021-02-02T22:54:36.326225Z",
     "iopub.status.idle": "2021-02-02T22:54:36.348075Z",
     "shell.execute_reply": "2021-02-02T22:54:36.347592Z"
    },
    "papermill": {
     "duration": 0.056532,
     "end_time": "2021-02-02T22:54:36.348174",
     "exception": false,
     "start_time": "2021-02-02T22:54:36.291642",
     "status": "completed"
    },
    "tags": []
   },
   "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>period</th>\n",
       "      <th>train_a</th>\n",
       "      <th>train_b</th>\n",
       "      <th>train_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">smoothed_ssn</th>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>136.902500</td>\n",
       "      <td>51.850000</td>\n",
       "      <td>24.313750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34.563168</td>\n",
       "      <td>39.200266</td>\n",
       "      <td>19.020414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>65.400000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>108.375000</td>\n",
       "      <td>15.325000</td>\n",
       "      <td>7.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>151.500000</td>\n",
       "      <td>43.150000</td>\n",
       "      <td>20.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>164.400000</td>\n",
       "      <td>91.225000</td>\n",
       "      <td>38.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>175.200000</td>\n",
       "      <td>116.400000</td>\n",
       "      <td>69.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "period                 train_a     train_b    train_c\n",
       "smoothed_ssn count   40.000000   72.000000  80.000000\n",
       "             mean   136.902500   51.850000  24.313750\n",
       "             std     34.563168   39.200266  19.020414\n",
       "             min     65.400000    3.900000   2.200000\n",
       "             25%    108.375000   15.325000   7.775000\n",
       "             50%    151.500000   43.150000  20.500000\n",
       "             75%    164.400000   91.225000  38.525000\n",
       "             max    175.200000  116.400000  69.500000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sunspots.groupby(\"period\").describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:54:36.530523Z",
     "iopub.status.busy": "2021-02-02T22:54:36.529790Z",
     "iopub.status.idle": "2021-02-02T22:54:37.557064Z",
     "shell.execute_reply": "2021-02-02T22:54:37.557609Z"
    },
    "papermill": {
     "duration": 1.183791,
     "end_time": "2021-02-02T22:54:37.557749",
     "exception": false,
     "start_time": "2021-02-02T22:54:36.373958",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_raw_visualization(data):\n",
    "    fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(15, 15), dpi=80)\n",
    "    for i, key in enumerate(data.columns):\n",
    "        t_data = data[key]\n",
    "        ax = t_data.plot(\n",
    "            ax=axes[i // 2, i % 2],\n",
    "            title=f\"{key.capitalize()}\",\n",
    "            rot=25,\n",
    "        )\n",
    "\n",
    "    fig.subplots_adjust(hspace=0.8)\n",
    "    plt.tight_layout()\n",
    "\n",
    "\n",
    "cols_to_plot = [\"bx_gse\", \"bx_gsm\", \"bt\", \"density\", \"speed\", \"temperature\"]\n",
    "show_raw_visualization(solar_wind[cols_to_plot].iloc[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:54:37.624088Z",
     "iopub.status.busy": "2021-02-02T22:54:37.623162Z",
     "iopub.status.idle": "2021-02-02T22:54:38.940262Z",
     "shell.execute_reply": "2021-02-02T22:54:38.939515Z"
    },
    "papermill": {
     "duration": 1.352174,
     "end_time": "2021-02-02T22:54:38.940393",
     "exception": false,
     "start_time": "2021-02-02T22:54:37.588219",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bx_gse         325888\n",
       "by_gse         325888\n",
       "bz_gse         325888\n",
       "theta_gse      325888\n",
       "phi_gse        326388\n",
       "bx_gsm         325888\n",
       "by_gsm         325888\n",
       "bz_gsm         325888\n",
       "theta_gsm      325888\n",
       "phi_gsm        326388\n",
       "bt             325888\n",
       "density        684890\n",
       "speed          689555\n",
       "temperature    811768\n",
       "source         316816\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_wind.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:54:39.038439Z",
     "iopub.status.busy": "2021-02-02T22:54:39.037681Z",
     "iopub.status.idle": "2021-02-02T22:56:47.298060Z",
     "shell.execute_reply": "2021-02-02T22:56:47.298581Z"
    },
    "papermill": {
     "duration": 128.313219,
     "end_time": "2021-02-02T22:56:47.298721",
     "exception": false,
     "start_time": "2021-02-02T22:54:38.985502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = solar_wind.join(sunspots).join(dst).fillna(method=\"ffill\").corr()\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.matshow(corr, fignum=1)\n",
    "plt.xticks(range(corr.shape[1]), corr.columns, fontsize=14, rotation=90)\n",
    "plt.gca().xaxis.tick_bottom()\n",
    "plt.yticks(range(corr.shape[1]), corr.columns, fontsize=14)\n",
    "\n",
    "\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title(\"Feature Correlation Heatmap\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:56:47.375949Z",
     "iopub.status.busy": "2021-02-02T22:56:47.375102Z",
     "iopub.status.idle": "2021-02-02T22:56:47.379052Z",
     "shell.execute_reply": "2021-02-02T22:56:47.378461Z"
    },
    "papermill": {
     "duration": 0.044664,
     "end_time": "2021-02-02T22:56:47.379148",
     "exception": false,
     "start_time": "2021-02-02T22:56:47.334484",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "from tensorflow.random import set_seed\n",
    "\n",
    "seed(2020)\n",
    "set_seed(2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-02-02T22:56:47.462005Z",
     "iopub.status.busy": "2021-02-02T22:56:47.461340Z",
     "iopub.status.idle": "2021-02-02T22:56:48.055558Z",
     "shell.execute_reply": "2021-02-02T22:56:48.054042Z"
    },
    "papermill": {
     "duration": 0.642834,
     "end_time": "2021-02-02T22:56:48.055681",
     "exception": false,
     "start_time": "2021-02-02T22:56:47.412847",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# subset of solar wind features to use for modeling\n",
    "SOLAR_WIND_FEATURES = [\n",
    "    \"bt\",\n",
    "    \"temperature\",\n",
    "    \"bx_gse\",\n",
    "    \"by_gse\",\n",
    "    \"bz_gse\",\n",
    "    \"speed\",\n",
    "    \"density\",\n",
    "]\n",
    "\n",
    "# all of the features we'll use, including sunspot numbers\n",
    "XCOLS = (\n",
    "    [col + \"_mean\" for col in SOLAR_WIND_FEATURES]\n",
    "    + [col + \"_std\" for col in SOLAR_WIND_FEATURES]\n",
    "    + [\"smoothed_ssn\"]\n",
    ")\n",
    "\n",
    "\n",
    "def impute_features(feature_df):\n",
    "    \"\"\"Imputes data using the following methods:\n",
    "    - `smoothed_ssn`: forward fill\n",
    "    - `solar_wind`: interpolation\n",
    "    \"\"\"\n",
    "    # forward fill sunspot data for the rest of the month\n",
    "    feature_df.smoothed_ssn = feature_df.smoothed_ssn.fillna(method=\"ffill\")\n",
    "    # interpolate between missing solar wind values\n",
    "    feature_df = feature_df.interpolate()\n",
    "    return feature_df\n",
    "\n",
    "\n",
    "def aggregate_hourly(feature_df, aggs=[\"mean\", \"std\"]):\n",
    "    \"\"\"Aggregates features to the floor of each hour using mean and standard deviation.\n",
    "    e.g. All values from \"11:00:00\" to \"11:59:00\" will be aggregated to \"11:00:00\".\n",
    "    \"\"\"\n",
    "    # group by the floor of each hour use timedelta index\n",
    "    agged = feature_df.groupby(\n",
    "        [\"period\", feature_df.index.get_level_values(1).floor(\"H\")]\n",
    "    ).agg(aggs)\n",
    "    # flatten hierachical column index\n",
    "    agged.columns = [\"_\".join(x) for x in agged.columns]\n",
    "    return agged\n",
    "\n",
    "\n",
    "def preprocess_features(solar_wind, sunspots, scaler=None, subset=None):\n",
    "    \"\"\"\n",
    "    Preprocessing steps:\n",
    "        - Subset the data\n",
    "        - Aggregate hourly\n",
    "        - Join solar wind and sunspot data\n",
    "        - Scale using standard scaler\n",
    "        - Impute missing values\n",
    "    \"\"\"\n",
    "    # select features we want to use\n",
    "    if subset:\n",
    "        solar_wind = solar_wind[subset]\n",
    "\n",
    "    # aggregate solar wind data and join with sunspots\n",
    "    hourly_features = aggregate_hourly(solar_wind).join(sunspots)\n",
    "\n",
    "    # subtract mean and divide by standard deviation\n",
    "    if scaler is None:\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(hourly_features)\n",
    "\n",
    "    normalized = pd.DataFrame(\n",
    "        scaler.transform(hourly_features),\n",
    "        index=hourly_features.index,\n",
    "        columns=hourly_features.columns,\n",
    "    )\n",
    "\n",
    "    # impute missing values\n",
    "    imputed = impute_features(normalized)\n",
    "\n",
    "    # we want to return the scaler object as well to use later during prediction\n",
    "    return imputed, scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:56:48.271618Z",
     "iopub.status.busy": "2021-02-02T22:56:48.270265Z",
     "iopub.status.idle": "2021-02-02T22:56:53.063602Z",
     "shell.execute_reply": "2021-02-02T22:56:53.062948Z"
    },
    "papermill": {
     "duration": 4.975015,
     "end_time": "2021-02-02T22:56:53.063727",
     "exception": false,
     "start_time": "2021-02-02T22:56:48.088712",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(139872, 15)\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>bt_mean</th>\n",
       "      <th>bt_std</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>temperature_std</th>\n",
       "      <th>bx_gse_mean</th>\n",
       "      <th>bx_gse_std</th>\n",
       "      <th>by_gse_mean</th>\n",
       "      <th>by_gse_std</th>\n",
       "      <th>bz_gse_mean</th>\n",
       "      <th>bz_gse_std</th>\n",
       "      <th>speed_mean</th>\n",
       "      <th>speed_std</th>\n",
       "      <th>density_mean</th>\n",
       "      <th>density_std</th>\n",
       "      <th>smoothed_ssn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">train_a</th>\n",
       "      <th>0 days 00:00:00</th>\n",
       "      <td>0.499705</td>\n",
       "      <td>2.443614</td>\n",
       "      <td>-0.375267</td>\n",
       "      <td>0.383941</td>\n",
       "      <td>-1.599207</td>\n",
       "      <td>-0.381502</td>\n",
       "      <td>0.419516</td>\n",
       "      <td>0.031658</td>\n",
       "      <td>0.300358</td>\n",
       "      <td>-0.651645</td>\n",
       "      <td>-0.738546</td>\n",
       "      <td>0.862524</td>\n",
       "      <td>-0.775827</td>\n",
       "      <td>-0.205724</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 01:00:00</th>\n",
       "      <td>0.547177</td>\n",
       "      <td>-0.224580</td>\n",
       "      <td>-0.479430</td>\n",
       "      <td>0.953178</td>\n",
       "      <td>-1.757995</td>\n",
       "      <td>-0.867747</td>\n",
       "      <td>0.179257</td>\n",
       "      <td>-0.272971</td>\n",
       "      <td>0.446103</td>\n",
       "      <td>-0.517913</td>\n",
       "      <td>-0.986904</td>\n",
       "      <td>0.995063</td>\n",
       "      <td>-0.861692</td>\n",
       "      <td>-0.058215</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 02:00:00</th>\n",
       "      <td>0.739905</td>\n",
       "      <td>-0.770240</td>\n",
       "      <td>-0.574831</td>\n",
       "      <td>-0.192518</td>\n",
       "      <td>-1.912116</td>\n",
       "      <td>-1.114317</td>\n",
       "      <td>0.183266</td>\n",
       "      <td>-0.822786</td>\n",
       "      <td>0.770174</td>\n",
       "      <td>-0.876490</td>\n",
       "      <td>-1.013548</td>\n",
       "      <td>0.554085</td>\n",
       "      <td>-0.846222</td>\n",
       "      <td>-0.220012</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 03:00:00</th>\n",
       "      <td>0.699098</td>\n",
       "      <td>-0.278783</td>\n",
       "      <td>-0.324709</td>\n",
       "      <td>0.325491</td>\n",
       "      <td>-1.809045</td>\n",
       "      <td>-0.783042</td>\n",
       "      <td>-0.378111</td>\n",
       "      <td>0.341156</td>\n",
       "      <td>0.621194</td>\n",
       "      <td>-0.290211</td>\n",
       "      <td>-0.826469</td>\n",
       "      <td>-0.211185</td>\n",
       "      <td>-0.404306</td>\n",
       "      <td>0.218373</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 04:00:00</th>\n",
       "      <td>0.223933</td>\n",
       "      <td>-0.225168</td>\n",
       "      <td>-0.313432</td>\n",
       "      <td>0.201600</td>\n",
       "      <td>-1.338802</td>\n",
       "      <td>-0.484910</td>\n",
       "      <td>0.072745</td>\n",
       "      <td>1.023019</td>\n",
       "      <td>0.467629</td>\n",
       "      <td>-0.478080</td>\n",
       "      <td>-0.601238</td>\n",
       "      <td>1.016033</td>\n",
       "      <td>-0.371487</td>\n",
       "      <td>0.097253</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          bt_mean    bt_std  temperature_mean  \\\n",
       "period  timedelta                                               \n",
       "train_a 0 days 00:00:00  0.499705  2.443614         -0.375267   \n",
       "        0 days 01:00:00  0.547177 -0.224580         -0.479430   \n",
       "        0 days 02:00:00  0.739905 -0.770240         -0.574831   \n",
       "        0 days 03:00:00  0.699098 -0.278783         -0.324709   \n",
       "        0 days 04:00:00  0.223933 -0.225168         -0.313432   \n",
       "\n",
       "                         temperature_std  bx_gse_mean  bx_gse_std  \\\n",
       "period  timedelta                                                   \n",
       "train_a 0 days 00:00:00         0.383941    -1.599207   -0.381502   \n",
       "        0 days 01:00:00         0.953178    -1.757995   -0.867747   \n",
       "        0 days 02:00:00        -0.192518    -1.912116   -1.114317   \n",
       "        0 days 03:00:00         0.325491    -1.809045   -0.783042   \n",
       "        0 days 04:00:00         0.201600    -1.338802   -0.484910   \n",
       "\n",
       "                         by_gse_mean  by_gse_std  bz_gse_mean  bz_gse_std  \\\n",
       "period  timedelta                                                           \n",
       "train_a 0 days 00:00:00     0.419516    0.031658     0.300358   -0.651645   \n",
       "        0 days 01:00:00     0.179257   -0.272971     0.446103   -0.517913   \n",
       "        0 days 02:00:00     0.183266   -0.822786     0.770174   -0.876490   \n",
       "        0 days 03:00:00    -0.378111    0.341156     0.621194   -0.290211   \n",
       "        0 days 04:00:00     0.072745    1.023019     0.467629   -0.478080   \n",
       "\n",
       "                         speed_mean  speed_std  density_mean  density_std  \\\n",
       "period  timedelta                                                           \n",
       "train_a 0 days 00:00:00   -0.738546   0.862524     -0.775827    -0.205724   \n",
       "        0 days 01:00:00   -0.986904   0.995063     -0.861692    -0.058215   \n",
       "        0 days 02:00:00   -1.013548   0.554085     -0.846222    -0.220012   \n",
       "        0 days 03:00:00   -0.826469  -0.211185     -0.404306     0.218373   \n",
       "        0 days 04:00:00   -0.601238   1.016033     -0.371487     0.097253   \n",
       "\n",
       "                         smoothed_ssn  \n",
       "period  timedelta                      \n",
       "train_a 0 days 00:00:00      0.139444  \n",
       "        0 days 01:00:00      0.139444  \n",
       "        0 days 02:00:00      0.139444  \n",
       "        0 days 03:00:00      0.139444  \n",
       "        0 days 04:00:00      0.139444  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features, scaler = preprocess_features(solar_wind, sunspots, subset=SOLAR_WIND_FEATURES)\n",
    "print(features.shape)\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:56:53.141521Z",
     "iopub.status.busy": "2021-02-02T22:56:53.140662Z",
     "iopub.status.idle": "2021-02-02T22:56:53.160613Z",
     "shell.execute_reply": "2021-02-02T22:56:53.160113Z"
    },
    "papermill": {
     "duration": 0.060139,
     "end_time": "2021-02-02T22:56:53.160717",
     "exception": false,
     "start_time": "2021-02-02T22:56:53.100578",
     "status": "completed"
    },
    "tags": []
   },
   "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></th>\n",
       "      <th>t0</th>\n",
       "      <th>t1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">train_a</th>\n",
       "      <th>0 days 00:00:00</th>\n",
       "      <td>-7</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 01:00:00</th>\n",
       "      <td>-10</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 02:00:00</th>\n",
       "      <td>-10</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 03:00:00</th>\n",
       "      <td>-6</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 04:00:00</th>\n",
       "      <td>-2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         t0    t1\n",
       "period  timedelta                \n",
       "train_a 0 days 00:00:00  -7 -10.0\n",
       "        0 days 01:00:00 -10 -10.0\n",
       "        0 days 02:00:00 -10  -6.0\n",
       "        0 days 03:00:00  -6  -2.0\n",
       "        0 days 04:00:00  -2   3.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YCOLS = [\"t0\", \"t1\"]\n",
    "\n",
    "\n",
    "def process_labels(dst):\n",
    "    y = dst.copy()\n",
    "    y[\"t1\"] = y.groupby(\"period\").dst.shift(-1)\n",
    "    y.columns = YCOLS\n",
    "    return y\n",
    "\n",
    "\n",
    "labels = process_labels(dst)\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_kg_hide-input": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T22:56:53.237560Z",
     "iopub.status.busy": "2021-02-02T22:56:53.236485Z",
     "iopub.status.idle": "2021-02-02T22:56:53.283923Z",
     "shell.execute_reply": "2021-02-02T22:56:53.284829Z"
    },
    "papermill": {
     "duration": 0.089242,
     "end_time": "2021-02-02T22:56:53.285012",
     "exception": false,
     "start_time": "2021-02-02T22:56:53.195770",
     "status": "completed"
    },
    "tags": []
   },
   "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></th>\n",
       "      <th>t0</th>\n",
       "      <th>t1</th>\n",
       "      <th>bt_mean</th>\n",
       "      <th>bt_std</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>temperature_std</th>\n",
       "      <th>bx_gse_mean</th>\n",
       "      <th>bx_gse_std</th>\n",
       "      <th>by_gse_mean</th>\n",
       "      <th>by_gse_std</th>\n",
       "      <th>bz_gse_mean</th>\n",
       "      <th>bz_gse_std</th>\n",
       "      <th>speed_mean</th>\n",
       "      <th>speed_std</th>\n",
       "      <th>density_mean</th>\n",
       "      <th>density_std</th>\n",
       "      <th>smoothed_ssn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">train_a</th>\n",
       "      <th>0 days 00:00:00</th>\n",
       "      <td>-7</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.499705</td>\n",
       "      <td>2.443614</td>\n",
       "      <td>-0.375267</td>\n",
       "      <td>0.383941</td>\n",
       "      <td>-1.599207</td>\n",
       "      <td>-0.381502</td>\n",
       "      <td>0.419516</td>\n",
       "      <td>0.031658</td>\n",
       "      <td>0.300358</td>\n",
       "      <td>-0.651645</td>\n",
       "      <td>-0.738546</td>\n",
       "      <td>0.862524</td>\n",
       "      <td>-0.775827</td>\n",
       "      <td>-0.205724</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 01:00:00</th>\n",
       "      <td>-10</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.547177</td>\n",
       "      <td>-0.224580</td>\n",
       "      <td>-0.479430</td>\n",
       "      <td>0.953178</td>\n",
       "      <td>-1.757995</td>\n",
       "      <td>-0.867747</td>\n",
       "      <td>0.179257</td>\n",
       "      <td>-0.272971</td>\n",
       "      <td>0.446103</td>\n",
       "      <td>-0.517913</td>\n",
       "      <td>-0.986904</td>\n",
       "      <td>0.995063</td>\n",
       "      <td>-0.861692</td>\n",
       "      <td>-0.058215</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 02:00:00</th>\n",
       "      <td>-10</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.739905</td>\n",
       "      <td>-0.770240</td>\n",
       "      <td>-0.574831</td>\n",
       "      <td>-0.192518</td>\n",
       "      <td>-1.912116</td>\n",
       "      <td>-1.114317</td>\n",
       "      <td>0.183266</td>\n",
       "      <td>-0.822786</td>\n",
       "      <td>0.770174</td>\n",
       "      <td>-0.876490</td>\n",
       "      <td>-1.013548</td>\n",
       "      <td>0.554085</td>\n",
       "      <td>-0.846222</td>\n",
       "      <td>-0.220012</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 03:00:00</th>\n",
       "      <td>-6</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.699098</td>\n",
       "      <td>-0.278783</td>\n",
       "      <td>-0.324709</td>\n",
       "      <td>0.325491</td>\n",
       "      <td>-1.809045</td>\n",
       "      <td>-0.783042</td>\n",
       "      <td>-0.378111</td>\n",
       "      <td>0.341156</td>\n",
       "      <td>0.621194</td>\n",
       "      <td>-0.290211</td>\n",
       "      <td>-0.826469</td>\n",
       "      <td>-0.211185</td>\n",
       "      <td>-0.404306</td>\n",
       "      <td>0.218373</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 04:00:00</th>\n",
       "      <td>-2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.223933</td>\n",
       "      <td>-0.225168</td>\n",
       "      <td>-0.313432</td>\n",
       "      <td>0.201600</td>\n",
       "      <td>-1.338802</td>\n",
       "      <td>-0.484910</td>\n",
       "      <td>0.072745</td>\n",
       "      <td>1.023019</td>\n",
       "      <td>0.467629</td>\n",
       "      <td>-0.478080</td>\n",
       "      <td>-0.601238</td>\n",
       "      <td>1.016033</td>\n",
       "      <td>-0.371487</td>\n",
       "      <td>0.097253</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         t0    t1   bt_mean    bt_std  temperature_mean  \\\n",
       "period  timedelta                                                         \n",
       "train_a 0 days 00:00:00  -7 -10.0  0.499705  2.443614         -0.375267   \n",
       "        0 days 01:00:00 -10 -10.0  0.547177 -0.224580         -0.479430   \n",
       "        0 days 02:00:00 -10  -6.0  0.739905 -0.770240         -0.574831   \n",
       "        0 days 03:00:00  -6  -2.0  0.699098 -0.278783         -0.324709   \n",
       "        0 days 04:00:00  -2   3.0  0.223933 -0.225168         -0.313432   \n",
       "\n",
       "                         temperature_std  bx_gse_mean  bx_gse_std  \\\n",
       "period  timedelta                                                   \n",
       "train_a 0 days 00:00:00         0.383941    -1.599207   -0.381502   \n",
       "        0 days 01:00:00         0.953178    -1.757995   -0.867747   \n",
       "        0 days 02:00:00        -0.192518    -1.912116   -1.114317   \n",
       "        0 days 03:00:00         0.325491    -1.809045   -0.783042   \n",
       "        0 days 04:00:00         0.201600    -1.338802   -0.484910   \n",
       "\n",
       "                         by_gse_mean  by_gse_std  bz_gse_mean  bz_gse_std  \\\n",
       "period  timedelta                                                           \n",
       "train_a 0 days 00:00:00     0.419516    0.031658     0.300358   -0.651645   \n",
       "        0 days 01:00:00     0.179257   -0.272971     0.446103   -0.517913   \n",
       "        0 days 02:00:00     0.183266   -0.822786     0.770174   -0.876490   \n",
       "        0 days 03:00:00    -0.378111    0.341156     0.621194   -0.290211   \n",
       "        0 days 04:00:00     0.072745    1.023019     0.467629   -0.478080   \n",
       "\n",
       "                         speed_mean  speed_std  density_mean  density_std  \\\n",
       "period  timedelta                                                           \n",
       "train_a 0 days 00:00:00   -0.738546   0.862524     -0.775827    -0.205724   \n",
       "        0 days 01:00:00   -0.986904   0.995063     -0.861692    -0.058215   \n",
       "        0 days 02:00:00   -1.013548   0.554085     -0.846222    -0.220012   \n",
       "        0 days 03:00:00   -0.826469  -0.211185     -0.404306     0.218373   \n",
       "        0 days 04:00:00   -0.601238   1.016033     -0.371487     0.097253   \n",
       "\n",
       "                         smoothed_ssn  \n",
       "period  timedelta                      \n",
       "train_a 0 days 00:00:00      0.139444  \n",
       "        0 days 01:00:00      0.139444  \n",
       "        0 days 02:00:00      0.139444  \n",
       "        0 days 03:00:00      0.139444  \n",
       "        0 days 04:00:00      0.139444  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = labels.join(features)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-02-02T22:56:53.362997Z",
     "iopub.status.busy": "2021-02-02T22:56:53.362107Z",
     "iopub.status.idle": "2021-02-02T22:56:53.364935Z",
     "shell.execute_reply": "2021-02-02T22:56:53.364453Z"
    },
    "papermill": {
     "duration": 0.044763,
     "end_time": "2021-02-02T22:56:53.365035",
     "exception": false,
     "start_time": "2021-02-02T22:56:53.320272",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_train_test_val(data, test_per_period, val_per_period):\n",
    "    \"\"\"Splits data across periods into train, test, and validation\"\"\"\n",
    "    # assign the last `test_per_period` rows from each period to test\n",
    "    test = data.groupby(\"period\").tail(test_per_period)\n",
    "    interim = data[~data.index.isin(test.index)]\n",
    "    # assign the last `val_per_period` from the remaining rows to validation\n",
    "    val = interim.groupby(\"period\").tail(val_per_period)\n",
    "    # the remaining rows are assigned to train\n",
    "    train = interim[~interim.index.isin(val.index)]\n",
    "    return train, test, val\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:56:53.440347Z",
     "iopub.status.busy": "2021-02-02T22:56:53.439516Z",
     "iopub.status.idle": "2021-02-02T22:57:00.005259Z",
     "shell.execute_reply": "2021-02-02T22:57:00.004656Z"
    },
    "papermill": {
     "duration": 6.604805,
     "end_time": "2021-02-02T22:57:00.005379",
     "exception": false,
     "start_time": "2021-02-02T22:56:53.400574",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train, test, val = get_train_test_val(data, test_per_period=6_000, val_per_period=3_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:57:00.088332Z",
     "iopub.status.busy": "2021-02-02T22:57:00.087409Z",
     "iopub.status.idle": "2021-02-02T22:57:00.254310Z",
     "shell.execute_reply": "2021-02-02T22:57:00.254818Z"
    },
    "papermill": {
     "duration": 0.213157,
     "end_time": "2021-02-02T22:57:00.254950",
     "exception": false,
     "start_time": "2021-02-02T22:57:00.041793",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f51f7937210>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = [0, 1, 2]\n",
    "names = [\"train_a\", \"train_b\", \"train_c\"]\n",
    "width = 0.75\n",
    "train_cnts = [len(df) for _, df in train.groupby(\"period\")]\n",
    "val_cnts = [len(df) for _, df in val.groupby(\"period\")]\n",
    "test_cnts = [len(df) for _, df in test.groupby(\"period\")]\n",
    "\n",
    "p1 = plt.barh(ind, train_cnts, width)\n",
    "p2 = plt.barh(ind, val_cnts, width, left=train_cnts)\n",
    "p3 = plt.barh(ind, test_cnts, width, left=np.add(val_cnts, train_cnts).tolist())\n",
    "\n",
    "plt.yticks(ind, names)\n",
    "plt.ylabel(\"Period\")\n",
    "plt.xlabel(\"Hourly Timesteps\")\n",
    "plt.title(\"Train/Validation/Test Splits over Periods\", fontsize=16)\n",
    "plt.legend([\"Train\", \"Validation\", \"Test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:57:00.351816Z",
     "iopub.status.busy": "2021-02-02T22:57:00.350908Z",
     "iopub.status.idle": "2021-02-02T22:57:00.355768Z",
     "shell.execute_reply": "2021-02-02T22:57:00.356303Z"
    },
    "papermill": {
     "duration": 0.06407,
     "end_time": "2021-02-02T22:57:00.356450",
     "exception": false,
     "start_time": "2021-02-02T22:57:00.292380",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(112872, 17)\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>t0</th>\n",
       "      <th>t1</th>\n",
       "      <th>bt_mean</th>\n",
       "      <th>bt_std</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>temperature_std</th>\n",
       "      <th>bx_gse_mean</th>\n",
       "      <th>bx_gse_std</th>\n",
       "      <th>by_gse_mean</th>\n",
       "      <th>by_gse_std</th>\n",
       "      <th>bz_gse_mean</th>\n",
       "      <th>bz_gse_std</th>\n",
       "      <th>speed_mean</th>\n",
       "      <th>speed_std</th>\n",
       "      <th>density_mean</th>\n",
       "      <th>density_std</th>\n",
       "      <th>smoothed_ssn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">train_a</th>\n",
       "      <th>0 days 00:00:00</th>\n",
       "      <td>-7</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.499705</td>\n",
       "      <td>2.443614</td>\n",
       "      <td>-0.375267</td>\n",
       "      <td>0.383941</td>\n",
       "      <td>-1.599207</td>\n",
       "      <td>-0.381502</td>\n",
       "      <td>0.419516</td>\n",
       "      <td>0.031658</td>\n",
       "      <td>0.300358</td>\n",
       "      <td>-0.651645</td>\n",
       "      <td>-0.738546</td>\n",
       "      <td>0.862524</td>\n",
       "      <td>-0.775827</td>\n",
       "      <td>-0.205724</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 01:00:00</th>\n",
       "      <td>-10</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.547177</td>\n",
       "      <td>-0.224580</td>\n",
       "      <td>-0.479430</td>\n",
       "      <td>0.953178</td>\n",
       "      <td>-1.757995</td>\n",
       "      <td>-0.867747</td>\n",
       "      <td>0.179257</td>\n",
       "      <td>-0.272971</td>\n",
       "      <td>0.446103</td>\n",
       "      <td>-0.517913</td>\n",
       "      <td>-0.986904</td>\n",
       "      <td>0.995063</td>\n",
       "      <td>-0.861692</td>\n",
       "      <td>-0.058215</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 02:00:00</th>\n",
       "      <td>-10</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.739905</td>\n",
       "      <td>-0.770240</td>\n",
       "      <td>-0.574831</td>\n",
       "      <td>-0.192518</td>\n",
       "      <td>-1.912116</td>\n",
       "      <td>-1.114317</td>\n",
       "      <td>0.183266</td>\n",
       "      <td>-0.822786</td>\n",
       "      <td>0.770174</td>\n",
       "      <td>-0.876490</td>\n",
       "      <td>-1.013548</td>\n",
       "      <td>0.554085</td>\n",
       "      <td>-0.846222</td>\n",
       "      <td>-0.220012</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 03:00:00</th>\n",
       "      <td>-6</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.699098</td>\n",
       "      <td>-0.278783</td>\n",
       "      <td>-0.324709</td>\n",
       "      <td>0.325491</td>\n",
       "      <td>-1.809045</td>\n",
       "      <td>-0.783042</td>\n",
       "      <td>-0.378111</td>\n",
       "      <td>0.341156</td>\n",
       "      <td>0.621194</td>\n",
       "      <td>-0.290211</td>\n",
       "      <td>-0.826469</td>\n",
       "      <td>-0.211185</td>\n",
       "      <td>-0.404306</td>\n",
       "      <td>0.218373</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 04:00:00</th>\n",
       "      <td>-2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.223933</td>\n",
       "      <td>-0.225168</td>\n",
       "      <td>-0.313432</td>\n",
       "      <td>0.201600</td>\n",
       "      <td>-1.338802</td>\n",
       "      <td>-0.484910</td>\n",
       "      <td>0.072745</td>\n",
       "      <td>1.023019</td>\n",
       "      <td>0.467629</td>\n",
       "      <td>-0.478080</td>\n",
       "      <td>-0.601238</td>\n",
       "      <td>1.016033</td>\n",
       "      <td>-0.371487</td>\n",
       "      <td>0.097253</td>\n",
       "      <td>0.139444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         t0    t1   bt_mean    bt_std  temperature_mean  \\\n",
       "period  timedelta                                                         \n",
       "train_a 0 days 00:00:00  -7 -10.0  0.499705  2.443614         -0.375267   \n",
       "        0 days 01:00:00 -10 -10.0  0.547177 -0.224580         -0.479430   \n",
       "        0 days 02:00:00 -10  -6.0  0.739905 -0.770240         -0.574831   \n",
       "        0 days 03:00:00  -6  -2.0  0.699098 -0.278783         -0.324709   \n",
       "        0 days 04:00:00  -2   3.0  0.223933 -0.225168         -0.313432   \n",
       "\n",
       "                         temperature_std  bx_gse_mean  bx_gse_std  \\\n",
       "period  timedelta                                                   \n",
       "train_a 0 days 00:00:00         0.383941    -1.599207   -0.381502   \n",
       "        0 days 01:00:00         0.953178    -1.757995   -0.867747   \n",
       "        0 days 02:00:00        -0.192518    -1.912116   -1.114317   \n",
       "        0 days 03:00:00         0.325491    -1.809045   -0.783042   \n",
       "        0 days 04:00:00         0.201600    -1.338802   -0.484910   \n",
       "\n",
       "                         by_gse_mean  by_gse_std  bz_gse_mean  bz_gse_std  \\\n",
       "period  timedelta                                                           \n",
       "train_a 0 days 00:00:00     0.419516    0.031658     0.300358   -0.651645   \n",
       "        0 days 01:00:00     0.179257   -0.272971     0.446103   -0.517913   \n",
       "        0 days 02:00:00     0.183266   -0.822786     0.770174   -0.876490   \n",
       "        0 days 03:00:00    -0.378111    0.341156     0.621194   -0.290211   \n",
       "        0 days 04:00:00     0.072745    1.023019     0.467629   -0.478080   \n",
       "\n",
       "                         speed_mean  speed_std  density_mean  density_std  \\\n",
       "period  timedelta                                                           \n",
       "train_a 0 days 00:00:00   -0.738546   0.862524     -0.775827    -0.205724   \n",
       "        0 days 01:00:00   -0.986904   0.995063     -0.861692    -0.058215   \n",
       "        0 days 02:00:00   -1.013548   0.554085     -0.846222    -0.220012   \n",
       "        0 days 03:00:00   -0.826469  -0.211185     -0.404306     0.218373   \n",
       "        0 days 04:00:00   -0.601238   1.016033     -0.371487     0.097253   \n",
       "\n",
       "                         smoothed_ssn  \n",
       "period  timedelta                      \n",
       "train_a 0 days 00:00:00      0.139444  \n",
       "        0 days 01:00:00      0.139444  \n",
       "        0 days 02:00:00      0.139444  \n",
       "        0 days 03:00:00      0.139444  \n",
       "        0 days 04:00:00      0.139444  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:57:00.452549Z",
     "iopub.status.busy": "2021-02-02T22:57:00.451531Z",
     "iopub.status.idle": "2021-02-02T22:57:00.456349Z",
     "shell.execute_reply": "2021-02-02T22:57:00.456852Z"
    },
    "papermill": {
     "duration": 0.062822,
     "end_time": "2021-02-02T22:57:00.456984",
     "exception": false,
     "start_time": "2021-02-02T22:57:00.394162",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18000, 17)\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>t0</th>\n",
       "      <th>t1</th>\n",
       "      <th>bt_mean</th>\n",
       "      <th>bt_std</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>temperature_std</th>\n",
       "      <th>bx_gse_mean</th>\n",
       "      <th>bx_gse_std</th>\n",
       "      <th>by_gse_mean</th>\n",
       "      <th>by_gse_std</th>\n",
       "      <th>bz_gse_mean</th>\n",
       "      <th>bz_gse_std</th>\n",
       "      <th>speed_mean</th>\n",
       "      <th>speed_std</th>\n",
       "      <th>density_mean</th>\n",
       "      <th>density_std</th>\n",
       "      <th>smoothed_ssn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">train_a</th>\n",
       "      <th>951 days 00:00:00</th>\n",
       "      <td>-9</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-0.750759</td>\n",
       "      <td>0.234958</td>\n",
       "      <td>-0.321374</td>\n",
       "      <td>-0.263304</td>\n",
       "      <td>0.437479</td>\n",
       "      <td>-0.443221</td>\n",
       "      <td>-0.202748</td>\n",
       "      <td>0.318096</td>\n",
       "      <td>0.849393</td>\n",
       "      <td>-0.020478</td>\n",
       "      <td>-0.145924</td>\n",
       "      <td>-0.503193</td>\n",
       "      <td>-0.407614</td>\n",
       "      <td>-0.315154</td>\n",
       "      <td>2.113449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951 days 01:00:00</th>\n",
       "      <td>-9</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-0.791318</td>\n",
       "      <td>0.284752</td>\n",
       "      <td>-0.319784</td>\n",
       "      <td>-0.301259</td>\n",
       "      <td>0.321370</td>\n",
       "      <td>0.422124</td>\n",
       "      <td>-0.240443</td>\n",
       "      <td>0.166232</td>\n",
       "      <td>0.776471</td>\n",
       "      <td>-0.220270</td>\n",
       "      <td>-0.127991</td>\n",
       "      <td>-0.084971</td>\n",
       "      <td>-0.427620</td>\n",
       "      <td>-0.198320</td>\n",
       "      <td>2.113449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951 days 02:00:00</th>\n",
       "      <td>-9</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-0.980331</td>\n",
       "      <td>0.573975</td>\n",
       "      <td>-0.257948</td>\n",
       "      <td>-0.344924</td>\n",
       "      <td>0.530721</td>\n",
       "      <td>0.818555</td>\n",
       "      <td>-0.137579</td>\n",
       "      <td>0.157993</td>\n",
       "      <td>0.147218</td>\n",
       "      <td>-0.276363</td>\n",
       "      <td>-0.120626</td>\n",
       "      <td>-0.310757</td>\n",
       "      <td>-0.435274</td>\n",
       "      <td>-0.374498</td>\n",
       "      <td>2.113449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951 days 03:00:00</th>\n",
       "      <td>-6</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-1.043536</td>\n",
       "      <td>-0.412378</td>\n",
       "      <td>-0.209809</td>\n",
       "      <td>-0.397676</td>\n",
       "      <td>0.753977</td>\n",
       "      <td>-0.432534</td>\n",
       "      <td>-0.157578</td>\n",
       "      <td>-0.430909</td>\n",
       "      <td>0.121454</td>\n",
       "      <td>-0.282263</td>\n",
       "      <td>-0.180313</td>\n",
       "      <td>-0.531222</td>\n",
       "      <td>-0.414209</td>\n",
       "      <td>-0.365694</td>\n",
       "      <td>2.113449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951 days 04:00:00</th>\n",
       "      <td>-7</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-0.993770</td>\n",
       "      <td>-0.603035</td>\n",
       "      <td>-0.189010</td>\n",
       "      <td>-0.402766</td>\n",
       "      <td>0.789420</td>\n",
       "      <td>-0.391195</td>\n",
       "      <td>-0.252788</td>\n",
       "      <td>-0.372368</td>\n",
       "      <td>-0.149991</td>\n",
       "      <td>-0.510691</td>\n",
       "      <td>-0.219717</td>\n",
       "      <td>-0.649039</td>\n",
       "      <td>-0.437909</td>\n",
       "      <td>-0.267683</td>\n",
       "      <td>2.113449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           t0   t1   bt_mean    bt_std  temperature_mean  \\\n",
       "period  timedelta                                                          \n",
       "train_a 951 days 00:00:00  -9 -9.0 -0.750759  0.234958         -0.321374   \n",
       "        951 days 01:00:00  -9 -9.0 -0.791318  0.284752         -0.319784   \n",
       "        951 days 02:00:00  -9 -6.0 -0.980331  0.573975         -0.257948   \n",
       "        951 days 03:00:00  -6 -7.0 -1.043536 -0.412378         -0.209809   \n",
       "        951 days 04:00:00  -7 -7.0 -0.993770 -0.603035         -0.189010   \n",
       "\n",
       "                           temperature_std  bx_gse_mean  bx_gse_std  \\\n",
       "period  timedelta                                                     \n",
       "train_a 951 days 00:00:00        -0.263304     0.437479   -0.443221   \n",
       "        951 days 01:00:00        -0.301259     0.321370    0.422124   \n",
       "        951 days 02:00:00        -0.344924     0.530721    0.818555   \n",
       "        951 days 03:00:00        -0.397676     0.753977   -0.432534   \n",
       "        951 days 04:00:00        -0.402766     0.789420   -0.391195   \n",
       "\n",
       "                           by_gse_mean  by_gse_std  bz_gse_mean  bz_gse_std  \\\n",
       "period  timedelta                                                             \n",
       "train_a 951 days 00:00:00    -0.202748    0.318096     0.849393   -0.020478   \n",
       "        951 days 01:00:00    -0.240443    0.166232     0.776471   -0.220270   \n",
       "        951 days 02:00:00    -0.137579    0.157993     0.147218   -0.276363   \n",
       "        951 days 03:00:00    -0.157578   -0.430909     0.121454   -0.282263   \n",
       "        951 days 04:00:00    -0.252788   -0.372368    -0.149991   -0.510691   \n",
       "\n",
       "                           speed_mean  speed_std  density_mean  density_std  \\\n",
       "period  timedelta                                                             \n",
       "train_a 951 days 00:00:00   -0.145924  -0.503193     -0.407614    -0.315154   \n",
       "        951 days 01:00:00   -0.127991  -0.084971     -0.427620    -0.198320   \n",
       "        951 days 02:00:00   -0.120626  -0.310757     -0.435274    -0.374498   \n",
       "        951 days 03:00:00   -0.180313  -0.531222     -0.414209    -0.365694   \n",
       "        951 days 04:00:00   -0.219717  -0.649039     -0.437909    -0.267683   \n",
       "\n",
       "                           smoothed_ssn  \n",
       "period  timedelta                        \n",
       "train_a 951 days 00:00:00      2.113449  \n",
       "        951 days 01:00:00      2.113449  \n",
       "        951 days 02:00:00      2.113449  \n",
       "        951 days 03:00:00      2.113449  \n",
       "        951 days 04:00:00      2.113449  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test.shape)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:57:00.556363Z",
     "iopub.status.busy": "2021-02-02T22:57:00.555513Z",
     "iopub.status.idle": "2021-02-02T22:57:00.561381Z",
     "shell.execute_reply": "2021-02-02T22:57:00.561980Z"
    },
    "papermill": {
     "duration": 0.06626,
     "end_time": "2021-02-02T22:57:00.562146",
     "exception": false,
     "start_time": "2021-02-02T22:57:00.495886",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9000, 17)\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>t0</th>\n",
       "      <th>t1</th>\n",
       "      <th>bt_mean</th>\n",
       "      <th>bt_std</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>temperature_std</th>\n",
       "      <th>bx_gse_mean</th>\n",
       "      <th>bx_gse_std</th>\n",
       "      <th>by_gse_mean</th>\n",
       "      <th>by_gse_std</th>\n",
       "      <th>bz_gse_mean</th>\n",
       "      <th>bz_gse_std</th>\n",
       "      <th>speed_mean</th>\n",
       "      <th>speed_std</th>\n",
       "      <th>density_mean</th>\n",
       "      <th>density_std</th>\n",
       "      <th>smoothed_ssn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>timedelta</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">train_a</th>\n",
       "      <th>826 days 00:00:00</th>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.947600</td>\n",
       "      <td>-0.229038</td>\n",
       "      <td>-0.473006</td>\n",
       "      <td>-0.372466</td>\n",
       "      <td>-1.575773</td>\n",
       "      <td>-0.882804</td>\n",
       "      <td>0.097349</td>\n",
       "      <td>1.132243</td>\n",
       "      <td>1.867509</td>\n",
       "      <td>-0.274071</td>\n",
       "      <td>-0.323757</td>\n",
       "      <td>-0.527592</td>\n",
       "      <td>0.392352</td>\n",
       "      <td>-0.296261</td>\n",
       "      <td>2.191722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826 days 01:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.464361</td>\n",
       "      <td>0.276422</td>\n",
       "      <td>0.316358</td>\n",
       "      <td>0.379133</td>\n",
       "      <td>-1.614668</td>\n",
       "      <td>-0.280527</td>\n",
       "      <td>0.397057</td>\n",
       "      <td>0.655653</td>\n",
       "      <td>0.530847</td>\n",
       "      <td>0.238450</td>\n",
       "      <td>-0.155733</td>\n",
       "      <td>0.142157</td>\n",
       "      <td>0.022523</td>\n",
       "      <td>-0.150077</td>\n",
       "      <td>2.191722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826 days 02:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.273709</td>\n",
       "      <td>0.197794</td>\n",
       "      <td>0.073052</td>\n",
       "      <td>0.302776</td>\n",
       "      <td>-1.130185</td>\n",
       "      <td>0.809654</td>\n",
       "      <td>0.320615</td>\n",
       "      <td>0.122597</td>\n",
       "      <td>-1.297872</td>\n",
       "      <td>-0.367564</td>\n",
       "      <td>-0.072557</td>\n",
       "      <td>0.563556</td>\n",
       "      <td>-0.200011</td>\n",
       "      <td>-0.089490</td>\n",
       "      <td>2.191722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826 days 03:00:00</th>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.078250</td>\n",
       "      <td>0.445950</td>\n",
       "      <td>0.290167</td>\n",
       "      <td>0.460514</td>\n",
       "      <td>-0.943295</td>\n",
       "      <td>1.891776</td>\n",
       "      <td>-0.062777</td>\n",
       "      <td>-0.024515</td>\n",
       "      <td>-0.681155</td>\n",
       "      <td>1.253984</td>\n",
       "      <td>-0.001116</td>\n",
       "      <td>0.127350</td>\n",
       "      <td>0.135548</td>\n",
       "      <td>0.048349</td>\n",
       "      <td>2.191722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826 days 04:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-0.172001</td>\n",
       "      <td>0.071668</td>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.437184</td>\n",
       "      <td>-0.771915</td>\n",
       "      <td>0.408708</td>\n",
       "      <td>0.332551</td>\n",
       "      <td>-0.049795</td>\n",
       "      <td>-0.704262</td>\n",
       "      <td>1.021870</td>\n",
       "      <td>0.063829</td>\n",
       "      <td>0.143625</td>\n",
       "      <td>-0.027986</td>\n",
       "      <td>-0.066235</td>\n",
       "      <td>2.191722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           t0   t1   bt_mean    bt_std  temperature_mean  \\\n",
       "period  timedelta                                                          \n",
       "train_a 826 days 00:00:00   9  8.0  0.947600 -0.229038         -0.473006   \n",
       "        826 days 01:00:00   8  8.0  0.464361  0.276422          0.316358   \n",
       "        826 days 02:00:00   8  9.0  0.273709  0.197794          0.073052   \n",
       "        826 days 03:00:00   9  8.0  0.078250  0.445950          0.290167   \n",
       "        826 days 04:00:00   8  8.0 -0.172001  0.071668          0.336941   \n",
       "\n",
       "                           temperature_std  bx_gse_mean  bx_gse_std  \\\n",
       "period  timedelta                                                     \n",
       "train_a 826 days 00:00:00        -0.372466    -1.575773   -0.882804   \n",
       "        826 days 01:00:00         0.379133    -1.614668   -0.280527   \n",
       "        826 days 02:00:00         0.302776    -1.130185    0.809654   \n",
       "        826 days 03:00:00         0.460514    -0.943295    1.891776   \n",
       "        826 days 04:00:00         0.437184    -0.771915    0.408708   \n",
       "\n",
       "                           by_gse_mean  by_gse_std  bz_gse_mean  bz_gse_std  \\\n",
       "period  timedelta                                                             \n",
       "train_a 826 days 00:00:00     0.097349    1.132243     1.867509   -0.274071   \n",
       "        826 days 01:00:00     0.397057    0.655653     0.530847    0.238450   \n",
       "        826 days 02:00:00     0.320615    0.122597    -1.297872   -0.367564   \n",
       "        826 days 03:00:00    -0.062777   -0.024515    -0.681155    1.253984   \n",
       "        826 days 04:00:00     0.332551   -0.049795    -0.704262    1.021870   \n",
       "\n",
       "                           speed_mean  speed_std  density_mean  density_std  \\\n",
       "period  timedelta                                                             \n",
       "train_a 826 days 00:00:00   -0.323757  -0.527592      0.392352    -0.296261   \n",
       "        826 days 01:00:00   -0.155733   0.142157      0.022523    -0.150077   \n",
       "        826 days 02:00:00   -0.072557   0.563556     -0.200011    -0.089490   \n",
       "        826 days 03:00:00   -0.001116   0.127350      0.135548     0.048349   \n",
       "        826 days 04:00:00    0.063829   0.143625     -0.027986    -0.066235   \n",
       "\n",
       "                           smoothed_ssn  \n",
       "period  timedelta                        \n",
       "train_a 826 days 00:00:00      2.191722  \n",
       "        826 days 01:00:00      2.191722  \n",
       "        826 days 02:00:00      2.191722  \n",
       "        826 days 03:00:00      2.191722  \n",
       "        826 days 04:00:00      2.191722  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(val.shape)\n",
    "val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-02-02T22:57:00.652972Z",
     "iopub.status.busy": "2021-02-02T22:57:00.652047Z",
     "iopub.status.idle": "2021-02-02T22:57:03.547837Z",
     "shell.execute_reply": "2021-02-02T22:57:03.548901Z"
    },
    "papermill": {
     "duration": 2.94664,
     "end_time": "2021-02-02T22:57:03.549082",
     "exception": false,
     "start_time": "2021-02-02T22:57:00.602442",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of train batches: 3315\n",
      "Number of val batches: 261\n"
     ]
    }
   ],
   "source": [
    "from keras import preprocessing\n",
    "\n",
    "\n",
    "data_config = {\n",
    "    \"timesteps\": 34,\n",
    "    \"batch_size\": 34,\n",
    "}\n",
    "\n",
    "\n",
    "def timeseries_dataset_from_df(df, batch_size):\n",
    "    dataset = None\n",
    "    timesteps = data_config[\"timesteps\"]\n",
    "\n",
    "    # iterate through periods\n",
    "    for _, period_df in df.groupby(\"period\"):\n",
    "        # realign features and labels so that first sequence of 32 is aligned with the 33rd target\n",
    "        inputs = period_df[XCOLS][:-timesteps]\n",
    "        outputs = period_df[YCOLS][timesteps:]\n",
    "\n",
    "        period_ds = preprocessing.timeseries_dataset_from_array(\n",
    "            inputs,\n",
    "            outputs,\n",
    "            timesteps,\n",
    "            batch_size=batch_size,\n",
    "        )\n",
    "\n",
    "        if dataset is None:\n",
    "            dataset = period_ds\n",
    "        else:\n",
    "            dataset = dataset.concatenate(period_ds)\n",
    "\n",
    "    return dataset\n",
    "\n",
    "\n",
    "train_ds = timeseries_dataset_from_df(train, data_config[\"batch_size\"])\n",
    "val_ds = timeseries_dataset_from_df(val, data_config[\"batch_size\"])\n",
    "\n",
    "print(f\"Number of train batches: {len(train_ds)}\")\n",
    "print(f\"Number of val batches: {len(val_ds)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-02-02T22:57:03.647139Z",
     "iopub.status.busy": "2021-02-02T22:57:03.646534Z",
     "iopub.status.idle": "2021-02-02T22:57:05.970289Z",
     "shell.execute_reply": "2021-02-02T22:57:05.971842Z"
    },
    "papermill": {
     "duration": 2.380788,
     "end_time": "2021-02-02T22:57:05.972078",
     "exception": false,
     "start_time": "2021-02-02T22:57:03.591290",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bidirectional (Bidirectional (None, 34, 512)           557056    \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 34, 250)           638000    \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, 34, 250)           376000    \n",
      "_________________________________________________________________\n",
      "bidirectional_3 (Bidirection (None, 250)               376000    \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 8)                 2008      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 18        \n",
      "=================================================================\n",
      "Total params: 1,949,082\n",
      "Trainable params: 1,949,082\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dense, LSTM,Bidirectional\n",
    "from keras.models import Sequential\n",
    "\n",
    "# define our model\n",
    "model_config = {\"n_epochs\": 15, \"n_neurons\": 256, \"dropout\": 0.1, \"stateful\": False}\n",
    "\n",
    "# model = Sequential()\n",
    "# model.add(\n",
    "#     LSTM(\n",
    "#         model_config[\"n_neurons\"],\n",
    "#         # usually set to (`batch_size`, `sequence_length`, `n_features`)\n",
    "#         # setting the batch size to None allows for variable length batches\n",
    "#         batch_input_shape=(None, data_config[\"timesteps\"], len(XCOLS)),\n",
    "#         stateful=model_config[\"stateful\"],\n",
    "#         dropout=model_config[\"dropout\"],\n",
    "#     )\n",
    "# )\n",
    "# model.add(Dense(len(YCOLS)))\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Input(shape=(data_config[\"timesteps\"],\n",
    "                             len(XCOLS))))\n",
    "model.add(Bidirectional(LSTM(model_config[\"n_neurons\"],\n",
    "                             batch_input_shape=(None, data_config[\"timesteps\"],\n",
    "                             len(XCOLS)),stateful=model_config[\"stateful\"],\n",
    "                             dropout=model_config[\"dropout\"],return_sequences=True)))\n",
    "\n",
    "model.add(Bidirectional(LSTM(125,\n",
    "                             batch_input_shape=(None, data_config[\"timesteps\"],\n",
    "                             len(XCOLS)),stateful=model_config[\"stateful\"],\n",
    "                             dropout=model_config[\"dropout\"],return_sequences=True)))\n",
    "model.add(Bidirectional(LSTM(125, return_sequences=True, dropout=0.1)))\n",
    "# model.add(Dense(512))\n",
    "# model.add(Dense(256))\n",
    "# model.add(Dense(125))\n",
    "# model.add(Dense(50))\n",
    "# model.add(Dense(len(YCOLS)))\n",
    "# model.add(Dense(50))\n",
    "# model.add(Dense(125))\n",
    "# model.add(Dense(256))\n",
    "# model.add(Dense(512))\n",
    "# model.add(Dense(256))\n",
    "# model.add(Dense(125))\n",
    "# model.add(Dense(50))\n",
    "model.add(Bidirectional(LSTM(125, return_sequences=False, dropout=0.1)))\n",
    "model.add(Dense(8))\n",
    "model.add(Dense(len(YCOLS)))\n",
    "\n",
    "\n",
    "model.compile(\n",
    "    loss=\"mean_squared_error\",\n",
    "    optimizer=\"adam\",\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T22:57:06.065528Z",
     "iopub.status.busy": "2021-02-02T22:57:06.064779Z",
     "iopub.status.idle": "2021-02-02T23:21:48.717892Z",
     "shell.execute_reply": "2021-02-02T23:21:48.717320Z"
    },
    "papermill": {
     "duration": 1482.703042,
     "end_time": "2021-02-02T23:21:48.718016",
     "exception": false,
     "start_time": "2021-02-02T22:57:06.014974",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "Epoch 2/15\n",
      "Epoch 3/15\n",
      "Epoch 4/15\n",
      "Epoch 5/15\n",
      "Epoch 6/15\n",
      "Epoch 7/15\n",
      "Epoch 8/15\n",
      "Epoch 9/15\n",
      "Epoch 10/15\n",
      "Epoch 11/15\n",
      "Epoch 12/15\n",
      "Epoch 13/15\n",
      "Epoch 14/15\n",
      "Epoch 15/15\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_ds,\n",
    "    batch_size=data_config[\"batch_size\"],\n",
    "    epochs=model_config[\"n_epochs\"],\n",
    "    verbose=-1,\n",
    "    shuffle=False,\n",
    "    validation_data=val_ds,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T23:21:48.818395Z",
     "iopub.status.busy": "2021-02-02T23:21:48.817437Z",
     "iopub.status.idle": "2021-02-02T23:21:48.975607Z",
     "shell.execute_reply": "2021-02-02T23:21:48.976301Z"
    },
    "papermill": {
     "duration": 0.210333,
     "end_time": "2021-02-02T23:21:48.976480",
     "exception": false,
     "start_time": "2021-02-02T23:21:48.766147",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, values in history.history.items():\n",
    "    plt.plot(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T23:21:49.084944Z",
     "iopub.status.busy": "2021-02-02T23:21:49.084057Z",
     "iopub.status.idle": "2021-02-02T23:21:49.244287Z",
     "shell.execute_reply": "2021-02-02T23:21:49.243713Z"
    },
    "papermill": {
     "duration": 0.214842,
     "end_time": "2021-02-02T23:21:49.244396",
     "exception": false,
     "start_time": "2021-02-02T23:21:49.029554",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_ds = timeseries_dataset_from_df(test, data_config[\"batch_size\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T23:21:49.348339Z",
     "iopub.status.busy": "2021-02-02T23:21:49.347412Z",
     "iopub.status.idle": "2021-02-02T23:21:57.213048Z",
     "shell.execute_reply": "2021-02-02T23:21:57.213861Z"
    },
    "papermill": {
     "duration": 7.920617,
     "end_time": "2021-02-02T23:21:57.214046",
     "exception": false,
     "start_time": "2021-02-02T23:21:49.293429",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "525/525 [==============================] - 8s 15ms/step - loss: 163.6439\n",
      "Test RMSE: 12.79\n"
     ]
    }
   ],
   "source": [
    "mse = model.evaluate(test_ds)\n",
    "print(f\"Test RMSE: {mse**.5:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T23:21:57.499595Z",
     "iopub.status.busy": "2021-02-02T23:21:57.498699Z",
     "iopub.status.idle": "2021-02-02T23:22:43.046488Z",
     "shell.execute_reply": "2021-02-02T23:22:43.047017Z"
    },
    "papermill": {
     "duration": 45.695096,
     "end_time": "2021-02-02T23:22:43.047146",
     "exception": false,
     "start_time": "2021-02-02T23:21:57.352050",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'timesteps': 34, 'batch_size': 34, 'solar_wind_subset': ['bt', 'temperature', 'bx_gse', 'by_gse', 'bz_gse', 'speed', 'density']}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import pickle\n",
    "\n",
    "model.save(\"model\")\n",
    "\n",
    "with open(\"scaler.pck\", \"wb\") as f:\n",
    "    pickle.dump(scaler, f)\n",
    "\n",
    "data_config[\"solar_wind_subset\"] = SOLAR_WIND_FEATURES\n",
    "print(data_config)\n",
    "with open(\"config.json\", \"w\") as f:\n",
    "    json.dump(data_config, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-02-02T23:22:43.253043Z",
     "iopub.status.busy": "2021-02-02T23:22:43.251948Z",
     "iopub.status.idle": "2021-02-02T23:22:43.256373Z",
     "shell.execute_reply": "2021-02-02T23:22:43.255661Z"
    },
    "papermill": {
     "duration": 0.112948,
     "end_time": "2021-02-02T23:22:43.256514",
     "exception": false,
     "start_time": "2021-02-02T23:22:43.143566",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing predict.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile predict.py\n",
    "\n",
    "import json\n",
    "import pickle\n",
    "from typing import Tuple\n",
    "\n",
    "import keras\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Load in serialized model, config, and scaler\n",
    "model = keras.models.load_model(\"model\")\n",
    "\n",
    "with open(\"config.json\", \"r\") as f:\n",
    "    CONFIG = json.load(f)\n",
    "\n",
    "with open(\"scaler.pck\", \"rb\") as f:\n",
    "    scaler = pickle.load(f)\n",
    "\n",
    "# Set global variables    \n",
    "TIMESTEPS = CONFIG[\"timesteps\"]\n",
    "SOLAR_WIND_FEATURES = [\n",
    "    \"bt\",\n",
    "    \"temperature\",\n",
    "    \"bx_gse\",\n",
    "    \"by_gse\",\n",
    "    \"bz_gse\",\n",
    "    \"speed\",\n",
    "    \"density\",\n",
    "]\n",
    "XCOLS = (\n",
    "    [col + \"_mean\" for col in SOLAR_WIND_FEATURES]\n",
    "    + [col + \"_std\" for col in SOLAR_WIND_FEATURES]\n",
    "    + [\"smoothed_ssn\"]\n",
    ")\n",
    "\n",
    "\n",
    "# Define functions for preprocessing\n",
    "def impute_features(feature_df):\n",
    "    \"\"\"Imputes data using the following methods:\n",
    "    - `smoothed_ssn`: forward fill\n",
    "    - `solar_wind`: interpolation\n",
    "    \"\"\"\n",
    "    # forward fill sunspot data for the rest of the month\n",
    "    feature_df.smoothed_ssn = feature_df.smoothed_ssn.fillna(method=\"ffill\")\n",
    "    # interpolate between missing solar wind values\n",
    "    feature_df = feature_df.interpolate()\n",
    "    return feature_df\n",
    "\n",
    "\n",
    "def aggregate_hourly(feature_df, aggs=[\"mean\", \"std\"]):\n",
    "    \"\"\"Aggregates features to the floor of each hour using mean and standard deviation.\n",
    "    e.g. All values from \"11:00:00\" to \"11:59:00\" will be aggregated to \"11:00:00\".\n",
    "    \"\"\"\n",
    "    # group by the floor of each hour use timedelta index\n",
    "    agged = feature_df.groupby([feature_df.index.floor(\"H\")]).agg(aggs)\n",
    "\n",
    "    # flatten hierachical column index\n",
    "    agged.columns = [\"_\".join(x) for x in agged.columns]\n",
    "    return agged\n",
    "\n",
    "\n",
    "def preprocess_features(solar_wind, sunspots, scaler=None, subset=None):\n",
    "    \"\"\"\n",
    "    Preprocessing steps:\n",
    "        - Subset the data\n",
    "        - Aggregate hourly\n",
    "        - Join solar wind and sunspot data\n",
    "        - Scale using standard scaler\n",
    "        - Impute missing values\n",
    "    \"\"\"\n",
    "    # select features we want to use\n",
    "    if subset:\n",
    "        solar_wind = solar_wind[subset]\n",
    "\n",
    "    # aggregate solar wind data and join with sunspots\n",
    "    hourly_features = aggregate_hourly(solar_wind).join(sunspots)\n",
    "\n",
    "    # subtract mean and divide by standard deviation\n",
    "    if scaler is None:\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(hourly_features)\n",
    "\n",
    "    normalized = pd.DataFrame(\n",
    "        scaler.transform(hourly_features),\n",
    "        index=hourly_features.index,\n",
    "        columns=hourly_features.columns,\n",
    "    )\n",
    "\n",
    "    # impute missing values\n",
    "    imputed = impute_features(normalized)\n",
    "\n",
    "    # we want to return the scaler object as well to use later during prediction\n",
    "    return imputed, scaler\n",
    "\n",
    "\n",
    "# THIS MUST BE DEFINED FOR YOUR SUBMISSION TO RUN\n",
    "def predict_dst(\n",
    "    solar_wind_7d: pd.DataFrame,\n",
    "    satellite_positions_7d: pd.DataFrame,\n",
    "    latest_sunspot_number: float,\n",
    ") -> Tuple[float, float]:\n",
    "    \"\"\"\n",
    "    Take all of the data up until time t-1, and then make predictions for\n",
    "    times t and t+1.\n",
    "    Parameters\n",
    "    ----------\n",
    "    solar_wind_7d: pd.DataFrame\n",
    "        The last 7 days of satellite data up until (t - 1) minutes [exclusive of t]\n",
    "    satellite_positions_7d: pd.DataFrame\n",
    "        The last 7 days of satellite position data up until the present time [inclusive of t]\n",
    "    latest_sunspot_number: float\n",
    "        The latest monthly sunspot number (SSN) to be available\n",
    "    Returns\n",
    "    -------\n",
    "    predictions : Tuple[float, float]\n",
    "        A tuple of two predictions, for (t and t + 1 hour) respectively; these should\n",
    "        be between -2,000 and 500.\n",
    "    \"\"\"\n",
    "    # Re-format data to fit into our pipeline\n",
    "    sunspots = pd.DataFrame(index=solar_wind_7d.index, columns=[\"smoothed_ssn\"])\n",
    "    sunspots[\"smoothed_ssn\"] = latest_sunspot_number\n",
    "    \n",
    "    # Process our features and grab last 32 (timesteps) hours\n",
    "    features, s = preprocess_features(\n",
    "        solar_wind_7d, sunspots, scaler=scaler, subset=SOLAR_WIND_FEATURES\n",
    "    )\n",
    "    model_input = features[-TIMESTEPS:][XCOLS].values.reshape(\n",
    "        (1, TIMESTEPS, features.shape[1])\n",
    "    )\n",
    "    \n",
    "    # Make a prediction\n",
    "    prediction_at_t0, prediction_at_t1 = model.predict(model_input)[0]\n",
    "\n",
    "    # Optional check for unexpected values\n",
    "    if not np.isfinite(prediction_at_t0):\n",
    "        prediction_at_t0 = -12\n",
    "    if not np.isfinite(prediction_at_t1):\n",
    "        prediction_at_t1 = -12\n",
    "\n",
    "    return prediction_at_t0, prediction_at_t1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "papermill": {
   "duration": 1774.508122,
   "end_time": "2021-02-02T23:22:44.645720",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-02-02T22:53:10.137598",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}