{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "uKpOdQvxbeB4" }, "source": [ "# MagNet: Model the Geomagnetic Field Chapter 1\n", "## Develop the LSTM Model\n", "\n", "\n", "![HELIO_GRAPHIC_URL](https://ngdc.noaa.gov/geomag/img/challenge-banner.png \"HELIO\")\n", "\n", "* Creator(s): Rob.Redmon@noaa.gov (1,2), Manoj.C.Nair@noaa.gov (2,3), LiYin.Young@noaa.gov (2,3)\n", "* Affiliation(s):\n", " * 1. National Centers for Environmental Information ([NCEI](https://www.ncei.noaa.gov/)), National Oceanic and Atmospheric Administration (NOAA),\n", " * 2. NOAA Center for Artificial Intelligence ([NCAI](https://noaa.gov/ai)),\n", " * 3. Cooperative Institute for Research for Environmental Sciences [CIRES](https://cires.colorado.edu/).\n", "* History\n", " * 2023-08: Content reorganized for the [NCAI](https://noaa.gov/ai) Learning Journey library. No significant technical changes from previous version.\n", " * 2022-06: Initial notebook version developed for the [TAI4ES 2022 Summer School](https://www2.cisl.ucar.edu/events/tai4es-2022-summer-school).\n", "* Acknowledgements:\n", " * Original funding support was provided by the NCEI Innovates program.\n", " * Post-model inference and evaluation were created for the NCAR and [AI2ES](https://www.ai2es.org/) [TAI4ES 2022 Summer School](https://www2.cisl.ucar.edu/events/tai4es-2022-summer-school).\n", " * Feature exploration through model training was inspired by the benchmark [DrivenData blogpost](https://www.drivendata.co/blog/model-geomagnetic-field-benchmark/), developed for NOAA's [MagNet competition](https://ngdc.noaa.gov/geomag/mag-net-challenge.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "Chapter 1 \"Develop the LSTM Model\" of the two notebook series, provides the benchmark machine learning modeling experience for a key space weather storm indicator, the disturbance-storm-time (Dst) index, for the 2020 NOAA competition, \"MagNet: Model the Geomagnetic Field\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prerequisites\n", "\n", "* Python intermediate proficiency for data science: SciPy, Pandas, NumPy, MatplotLib,\n", "* Machine Learning intermediate experience: ML for supervised modeling of time series data using neural networks. We use the Keras framework for TensorFlow in this notebook to create a Long Short-Term Memory (LSTM) recurrent neural network,\n", "* Space Weather introductory knowledge: Basic familiarity of the Solar Wind and the Disturbance Storm Time activity index (Dst). For introductory materials on space weather and its effects on the technological systems we rely on, two resources are:\n", " * [NASA's Space Place](https://spaceplace.nasa.gov/spaceweather/),\n", " * [NOAA's Space Weather Prediction Center (SWPC)](https://www.swpc.noaa.gov/), in particular their community dashboards." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Targeted Level\n", "This notebook is targeted towards learners with beginner to intermediate experience in space weather topics, and intermediate experience in modeling time series data with neural networks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Learning Outcomes\n", "\n", "By engaging in this notebook, the learner will:\n", "1. Gain experience developing a regression model to predict the space weather disturbance-storm-time (Dst) index.\n", "2. Build the functional benchmark LSTM recurrent neural network model from the NOAA MagNet competition.\n", "2. Get familiar with using imperfect solar wind observations, as the modeling features.\n", "3. Run the trained model on classical space weather events." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Info: \n", "In this notebook, you'll notice color-coded boxes, which provide hints, exercises, and warnings. Here is the color-coding breakdown:\n", "
\n", "\n", "* Hint/Tip/Info: Helpful context and guidance, as a blue alert-info box\n", "* Exercise: Interactive activity / exercise, as a green alert-success box\n", "* Be Aware: Caution / Caveat, as a yellow alert-warn box\n", "* Danger: Conditions under which code may create an error, as a red alert-danger box" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial Material" ] }, { "cell_type": "markdown", "metadata": { "id": "kRzCSuRwbeB7" }, "source": [ "### Background on Geospace Space Weather\n", "\n", "Just like the terrestrial weather we are used to experiencing in our daily lives, weather also occurs in the space environment. If you'd like a general primer on space weather and its effects on the technological systems we rely on, check out [NASA's Space Place](https://spaceplace.nasa.gov/spaceweather/), as well as [NOAA's Space Weather Prediction Center (SWPC)](https://www.swpc.noaa.gov/), in particular their community dashboards." ] }, { "cell_type": "markdown", "metadata": { "id": "YIeZxbJXbeB7" }, "source": [ "### Background on the Geomagnetic Field\n", "\n", "The efficient transfer of energy from solar wind into the Earth’s magnetic field causes geomagnetic storms. The resulting variations in the magnetic field increase errors in magnetic navigation. The disturbance-storm-time index, or Dst, is a measure of the severity of the geomagnetic storm.\n", "\n", "As a key specification of the magnetospheric dynamics, the Dst index is used to drive geomagnetic disturbance models such as NOAA/NCEI’s High Definition Geomagnetic Model - Real-Time (HDGM-RT).\n", "![HDGMRT_GRAPHIC_URL](https://www.ngdc.noaa.gov/geomag/HDGM/images/HDGM-RT_2003_storm_720p.gif \"HDGM-RT\")\n", "\n", "In 2020-2021, NOAA and NASA conducted an international crowd sourced data science competition “MagNet: Model the Geomagnetic Field”:\n", "https://www.drivendata.org/competitions/73/noaa-magnetic-forecasting/\n", "\n", "Empirical models have been proposed as early as in 1975 to forecast Dst solely from solar-wind observations at the Lagrangian (L1) position by satellites such as NOAA’s Deep Space Climate Observatory (DSCOVR) or NASA's Advanced Composition Explorer (ACE). Over the past three decades, several models were proposed for solar wind forecasting of Dst, including empirical, physics-based, and machine learning approaches. While the ML models generally perform better than models based on the other approaches, there is still room to improve, especially when predicting extreme events. More importantly, we intentionally sought solutions that work on the raw, real-time data streams and are agnostic to sensor malfunctions and noise." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Software\n", "This notebook has been tested using the following environments:\n", "* Google Colaboratory (Python 3.10.12) with no need to install additional packages.\n", " * CPU, GPU, TPU tested\n", "* Anaconda (Python 3.9.16) with the following key package versions:\n", " * Keras TensorFlow 2.8.0\n", " * Pandas 1.5.3\n", " * Matplotlib 3.7.1\n", " * CPU, and GPU tested" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "id": "CdZDisojbeB8" }, "source": [ "### Modeling Task\n", "\n", "The MagNet competition task was to develop models for forecasting Dst that push the boundary of predictive performance, under operationally viable constraints, using the real-time solar-wind (RTSW) data feeds from NOAA’s DSCOVR and NASA’s ACE satellites. Improved models can provide more advanced warning of geomagnetic storms and reduce errors in magnetic navigation systems. Specifically, given one week of data ending at t minus 1 minute, the model must forecast Dst at time t and t plus one hour.\n", "\n", "The model described in this notebook is the benchmark model provided by the MagNet competition organizers. Long Short Term Memory networks or LSTMs are a special kind of recurrent neural network especially suited to learning nonlinear relationships between long period time series sequences. In this Chapter 1 notebook, we will show you how to implement a first-pass LSTM model for predicting Dst." ] }, { "cell_type": "markdown", "metadata": { "id": "k--TlMXrEvvi" }, "source": [ "
\n", "Exercise: Can you describe the physical process between solar wind and ground geomagnetic disturbances? \n", "What is the Dst index primarily used for?\n", "How might these infrequent solar wind events make modeling their predicted effects challenging? \n", "How might you make an accurate model with very few extreme events/samples?\n", "
\n", "\n", "
\n", "Tip: Roughly 85% of the time, near Earth is geomagnetically quiet.\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "bsk7hkRnEvvj" }, "source": [ "### Data Summary\n", "\n", "The target Dst values are measured by 4 ground-based observatories near the equator. These values are then averaged to provide a measurement of Dst for any given hour.\n", "To ensure similar distributions between the training and test data, the data is separated into three non-contiguous periods. All data are provided with a `period` and `timedelta` multi-index which indicates the relative timestep for each observation within a period, but not the real timestamp. The period identifiers and timedeltas are common across datasets. Converting back from our index date and time to real geophysical date and time is as simple as adding the start date/time in the table below to the relative timestep provided with the data.\n", "\n", "
Table: Dataset Period Time Ranges
\n", "\n", "| Period | Beginning | End |\n", "|---------|-------------------------|--------------------------|\n", "| train_a | 1998, 2, 16, '00:00:00' | 2001, 5, 31, '23:59:00' |\n", "| train_b | 2013, 6, 1, '00:00:00' | 2019, 5, 31, '23:59:00' |\n", "| train_c | 2004, 5, 1, '00:00:00' | 2010, 12, 31, '23:59:00' |\n", "| test_a | 2001, 6, 1, '00:00:00' | 2004, 4, 30, '23:59:00' |\n", "| test_b | 2011, 1, 1, '00:00:00' | 2013, 5, 31, '23:59:00' |\n", "| test_c | 2019, 6, 1, '00:00:00' | 2020, 10, 31, '23:59:00' |\n", "\n", "\n", "![Figure_Activity_and_Training_Splits.png](https://github.com/ai2es/tai4es-trustathon-2022/raw/space/space/notebook_figures/Figure_Activity_and_Training_Splits.png)\n", "Figure: Plot shows solar activity as the sunspot number (SSN) (red), the geomagnetic disturbance-storm-time (Dst) index (blue), and the data segments. The time range shown is January 1998 through December 2022, roughly corresponding to two solar-cycles. The data for periods “train_a”, “train_b”, and “train_c” were provided to the participants as “public” data. The data for periods “test_a”, “test_b” and “test_c” were held back for “private” validation. This figure and the table preceding it have been adapted from Nair et al (manuscript in preparation).\n", "\n", "The competitors used the training part (“train_a”,”train_b” and “train_c”) data to develop and improve their models. When they submitted a model, the competition platform used the test data sets (“test_a”,”test_b” and “test_c”) to calculate the accuracy of the model. The model evaluation was done separately for a public leaderboard and for a private leaderboard. The public leaderboard was openly accessible whereas the private leaderboard was restricted to the competition administrators. The data from all of the training sets (a, b, and c) were used on the public leaderboard and private leaderboard. We randomly sampled rows to be included in the public and private leaderboard. Based on relative performance from the public leaderboard as a clue, the teams iterated their models. The final ranking of the models was done on the private leaderboard.\n", "\n", "Input data sources (i.e. features):\n", "* Satellite measurements of the solar wind, including direction, speed, density and temperature, at 1-minute cadence.\n", "* Position of the satellite used for solar wind measurements. The ACE and DSCOVR satellites are positioned just outside Earth's exosphere approximately 1% of the distance from Earth to Sun. As noted above, this is referred to as the Sun Earth L1 position.\n", "* Number of sunspots on the Sun, measured monthly.\n", "\n", "
\n", "Info: Here is a short description of several of these inputs (features) observed by the ACE or DSCOVR satellites:\n", "\n", "* bt - Interplanetary-magnetic-field magnitude (nT)\n", "* bx_gsm - Interplanetary-magnetic-field X-component in geocentric solar magnetospheric (GSM) coordinate (nT)\n", "* by_gsm - Interplanetary-magnetic-field Y-component in GSM coordinate (nT)\n", "* bz_gsm - Interplanetary-magnetic-field Z-component in (GSM) coordinate (nT)\n", "* density - Solar wind proton density (N/cm^3)\n", "* speed - Solar wind bulk speed (km/s) flowing from Sun to Earth\n", "* temperature - Solar wind ion temperature (Kelvin)\n", "
\n", "\n", "(See [here](https://www.drivendata.org/competitions/73/noaa-magnetic-forecasting/page/279/) for the full list)\n", "\n", "To get a feeling for the GSM coordinate reference frame:\n", "\n", "The X-axis is oriented from the Earth to the Sun. The positive Z-axis is chosen to be in the same sense as the northern magnetic pole. And the Y-axis is defined to be perpendicular to the Earth's magnetic dipole so that the X-Z plane contains the dipole axis. For additional details, see [here](https://www.spenvis.oma.be/help/background/coortran/coortran.html#GSM).\n", "\n", "To see how several of these parameters look during an example space weather event see [Figure 5](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018SW001897#swe20716-fig-0005) of Redmon et al., 2018." ] }, { "cell_type": "markdown", "metadata": { "id": "She8zOM6Evvk" }, "source": [ "### Acquire Data" ] }, { "cell_type": "markdown", "metadata": { "id": "hRXm1TTrEvvm" }, "source": [ "
\n", "Info: \n", "The competition discussed in this notebook used public data for development and the public leaderboard. A private dataset was kept internal during the competition for use in scoring by the organizers. Since the competition has passed, both datasets are publicly accessible from NOAA. We will build and evaluate the model using the competition's public data and evaluate storm event case studies using the competition's private data.\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "sEnsRahUEvvm" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloaded https://ngdc.noaa.gov/geomag/data/geomag/magnet/public.zip , now unzipping.\n", "Downloaded https://ngdc.noaa.gov/geomag/data/geomag/magnet/private.zip , now unzipping.\n", "\n", "Data files for input (features) and output Dst (labels):\n", "data/public/\n", "\t satellite_positions.csv\n", "\t dst_labels.csv\n", "\t solar_wind.csv\n", "\t sunspots.csv\n", "data/private/\n", "\t satellite_positions.csv\n", "\t dst_labels.csv\n", "\t solar_wind.csv\n", "\t sunspots.csv\n" ] } ], "source": [ "# Download data we need: If directory \"data/\" already exists, we'll assume the data are already downloaded.\n", "# The files are 381 MB zipped and 1.2 GB unzipped\n", "# Retrieving these files from NOAA takes 30-60 seconds on a home internet connection.\n", "\n", "import os, urllib, zipfile\n", "\n", "dir_data = 'data/'\n", "if not os.path.isdir(dir_data):\n", " os.mkdir(dir_data)\n", "\n", " # Zenodo URLs\n", " urls = ['https://zenodo.org/record/8197443/files/public.zip?download=1',\n", " 'https://zenodo.org/record/8197443/files/private.zip?download=1']\n", " \n", " # NOAA URLs (same exact data as on Zenodo) -- uncomment to download from NOAA\n", " # urls = ['https://ngdc.noaa.gov/geomag/data/geomag/magnet/public.zip',\n", " # 'https://ngdc.noaa.gov/geomag/data/geomag/magnet/private.zip']\n", "\n", " # Download and unzip each file\n", " for url in urls:\n", " zip_path, _ = urllib.request.urlretrieve(url)\n", " with zipfile.ZipFile(zip_path, \"r\") as f:\n", " print('Downloaded ', url, ', now unzipping.')\n", " f.extractall(dir_data)\n", "\n", "# Print list of data files:\n", "print('\\nData files for input (features) and output Dst (labels):')\n", "for dir_pubpriv in ['public/', 'private/']:\n", " print(dir_data+dir_pubpriv)\n", " for path, dirs, files in os.walk(dir_data+dir_pubpriv): \n", " for f in files: print('\\t', f)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "9DmJY2oQEvvk" }, "outputs": [], "source": [ "# Modules we need to get started and Matplotlib configuration:\n", "import numpy as np, pandas as pd, pprint\n", "import matplotlib.pyplot as plt\n", "\n", "# The next two lines are nice for Jupyter, but not available for Colab:\n", "#%load_ext nb_black\n", "#%matplotlib inline\n", "\n", "# Matplotlib Configuration\n", "import matplotlib.pyplot as plt\n", "font = {'family' : 'sans-serif',\n", " 'weight' : 'normal',\n", " 'size' : 14}\n", "plt.rc('font', **font)" ] }, { "cell_type": "markdown", "metadata": { "id": "_p0ic5qPEvvm" }, "source": [ "#### Import Input (Features) and Output (Labels) as Pandas DataFrames\n", "
\n", "Info: As described above, the input data is a time series of solar wind measurements at L1 along with sunspot number, and the output data is a time series of Dst. Recall that for the past competition, the competitors did not have the real geophysical date/time. So here, we will recreate a new column of real geophysical date/time from our timedelta and the table shown in \"Data Summary\".\n", "
" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "l6kkNSINEvvm" }, "outputs": [], "source": [ "# From our time range table in the \"Data Summary\" section:\n", "period_ranges = {\n", " 'train_a':[pd.Timestamp('1998/2/16 00:00:00'), pd.Timestamp('2001/5/31 23:59:00')],\n", " 'train_b':[pd.Timestamp('2013/6/1 00:00:00'), pd.Timestamp('2019/5/31 23:59:00')],\n", " 'train_c':[pd.Timestamp('2004/5/1 00:00:00'), pd.Timestamp('2010/12/31 23:59:00')],\n", " 'test_a' :[pd.Timestamp('2001/6/1 00:00:00'), pd.Timestamp('2004/4/30 23:59:00')],\n", " 'test_b' :[pd.Timestamp('2011/1/1 00:00:00'), pd.Timestamp('2013/5/31 23:59:00')],\n", " 'test_c' :[pd.Timestamp('2019/6/1 00:00:00'), pd.Timestamp('2020/10/31 23:59:00')]}\n", "\n", "def convert_timedelta_to_datetime( df ):\n", " \"\"\"Adds real geophysical datetimes to our DataFrame using the original \"index\" timestamps.\n", " \n", " The relative \"index\" timestamps were used in the MagNet competition datasets since all of the data\n", " were in the public domain.\n", " \n", " Parameters\n", " ----------\n", " df: pd.DataFrame\n", " Includes index time\n", " \n", " Returns\n", " -------\n", " df_datetimes: pd.DataFrame\n", " Adds datetimes to the input pd.DataFrame\n", " \"\"\"\n", " df_datetimes = pd.DataFrame(index=df.index)\n", " df_datetimes['datetime'] = pd.NaT # like Numpy NaN\n", "\n", " i = 0\n", " for period_name, timedelta in df.index:\n", " start_time = period_ranges[period_name][0]\n", " datetime = timedelta + start_time # add Pandas Timedelta to Pandas Timestamp\n", " df_datetimes['datetime'].values[i] = datetime\n", " i += 1\n", "\n", " #print('%s: %s + %s = %s' % (period_name, timedelta, start_time, df['datetime'].values[i]))\n", "\n", " return df_datetimes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "J6uYH3zGEvvm" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading in the Dst output data...\n", "Reading in the Sunspot input data...\n", "Reading in the Solarwind input data...\n", "Reading in the Satellite position input data...\n" ] } ], "source": [ "# Import as Pandas DataFrames\n", "from pathlib import Path\n", "DATA_PATH = Path(\"data/public/\")\n", "\n", "print('Reading in the Dst output data...')\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", "print('Reading in the Sunspot input data...')\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", "print('Reading in the Solarwind input data...')\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)\n", "\n", "print('Reading in the Satellite position input data...')\n", "satellite_positions = pd.read_csv(DATA_PATH / \"satellite_positions.csv\")\n", "satellite_positions.timedelta = pd.to_timedelta(satellite_positions.timedelta)\n", "satellite_positions.set_index([\"period\", \"timedelta\"], inplace=True)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "noGXnEbyEvvn", "tags": [] }, "source": [ "### Data Exploration\n", "
\n", "Info: We'll explore our input (feature) and output (label) data to better understand it's data architecture, statistical description and basic input-output relationships.
" ] }, { "cell_type": "markdown", "metadata": { "id": "WI0UhOewEvvn", "tags": [] }, "source": [ "#### Disturbance Storm-Time Index (Dst)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "-skBPQZcEvvo" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dst shape: (139872, 1)\n" ] }, { "data": { "text/html": [ "
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" ], "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" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The structure of our output (label) data, Dst time series:\n", "print(\"Dst shape: \", dst.shape)\n", "dst.head(n=3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "VVftATZCEvvo" }, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Summary statistics for Dst by training period, via the DataFrame.groupby function.\n", "dst.groupby(\"period\").describe()" ] }, { "cell_type": "markdown", "metadata": { "id": "mV3bQaI5Evvo", "tags": [] }, "source": [ "#### Visualize Dst distributions for the training periods" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "dMdcrbyZEvvo" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot Dst histogram for each of our 3 training periods\n", "fig, ax = plt.subplots()\n", "colors = [\"r\", \"b\", \"y\"]\n", "# Use Pandas DataFrame.groupby function to organize our Dst data by training period\n", "for i, period in enumerate(dst.groupby(\"period\")):\n", " period_name, df = period\n", " # FYI: For Python 3.7, (e.g. previous Google Colaboratory w/Matplotlib 3.2.2), in the call to ax.hist(), you need to transpose the DataFrame.\n", " # For Python 3.8+ (e.g. 3.9.7, with Matplotlib 3.5.1) it seems no transpose is needed, thus this version switch.\n", " # Below we differentiate on Python version, while you might want to differentiate on Matplotlib version.\n", " # Error message you might see if DataFrame isn't oriented correctly:\n", " # \"ValueError: color kwarg must have one color per data set. 28824 data sets and 1 colors were provided\"\n", " import sys\n", " from packaging import version\n", " sys_major_version = '.'.join(sys.version.split('.')[:2])\n", " if version.parse(sys_major_version) <= version.parse('3.7'):\n", " ax.hist(df.T, alpha=0.5, color=colors[i], bins=50, label=period_name)\n", " else:\n", " ax.hist(df, alpha=0.5, color=colors[i], bins=50, label=period_name)\n", " ax.set_xlim([-100,50])\n", " ax.set_xlabel('Dst')\n", " ax.set_ylabel('Count')\n", "\n", "plt.legend()\n", "plt.title(\"Distribution of Dst values across Periods\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "6M1DNPh2Evvp", "tags": [] }, "source": [ "#### Solar Wind and Sunspots\n", "This is our time series input (feature) data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "HXBzv0BhEvvp" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solar wind shape: (8392320, 15)\n" ] }, { "data": { "text/html": [ "
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bx_gseby_gsebz_gsetheta_gsephi_gsebx_gsmby_gsmbz_gsmtheta_gsmphi_gsmbtdensityspeedtemperaturesource
periodtimedelta
train_a0 days 00:00:00-5.553.001.2511.09153.37-5.553.001.2511.09153.376.801.53383.92110237.0ac
0 days 00:01:00-5.583.161.1710.10151.91-5.583.161.1710.10151.916.831.69381.79123825.0ac
0 days 00:02:00-5.153.660.857.87146.04-5.153.660.857.87146.046.771.97389.1182548.0ac
\n", "
" ], "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", "\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", "\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 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Solar wind shape: \", solar_wind.shape)\n", "solar_wind.head(n=3) # Just print the first 3 rows of our input (features) DataFrame" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "v7f3n4PPEvvp" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sunspot shape: (192, 1)\n" ] }, { "data": { "text/html": [ "
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smoothed_ssn
periodtimedelta
train_a0 days65.4
13 days72.0
44 days76.9
\n", "
" ], "text/plain": [ " smoothed_ssn\n", "period timedelta \n", "train_a 0 days 65.4\n", " 13 days 72.0\n", " 44 days 76.9" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Sunspot shape: \", sunspots.shape)\n", "sunspots.head(n=3)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "CAQmYJ7VEvvp" }, "outputs": [ { "data": { "text/html": [ "
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periodtrain_atrain_btrain_c
bx_gsecount1.575012e+063.084130e+063.407290e+06
mean-1.781301e+00-3.088789e-01-4.619076e-01
std4.339212e+003.627830e+003.245485e+00
min-5.463000e+01-2.937000e+01-4.546000e+01
25%-4.960000e+00-3.070000e+00-2.800000e+00
...............
temperaturemin1.000000e+041.496000e+030.000000e+00
25%4.364900e+043.741400e+044.007400e+04
50%7.923800e+048.552400e+047.152100e+04
75%1.325500e+051.873250e+051.310880e+05
max6.223700e+064.206672e+065.751308e+06
\n", "

112 rows × 3 columns

\n", "
" ], "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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Summary statistics for solar wind inputs (features) by training period.\n", "# We use the transpose operator to rotate the orientation of the printed table.\n", "solar_wind.groupby(\"period\").describe().T" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "1TDpqIPFEvvp" }, "outputs": [ { "data": { "text/html": [ "
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periodtrain_atrain_btrain_c
smoothed_ssncount40.00000072.00000080.000000
mean136.90250051.85000024.313750
std34.56316839.20026619.020414
min65.4000003.9000002.200000
25%108.37500015.3250007.775000
50%151.50000043.15000020.500000
75%164.40000091.22500038.525000
max175.200000116.40000069.500000
\n", "
" ], "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": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Summary statistics for Sunspot Number inputs (features) by training period.\n", "# We use the transpose operator to rotate the orientation of the printed table.\n", "sunspots.groupby(\"period\").describe().T" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "tiRlpC2LEvvq" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def show_raw_visualization(data):\n", " \"\"\"Plots solar wind parameters on a grid.\n", " \n", " Parameters\n", " ----------\n", " data: pd.DataFrame\n", " Just the columns we want plotted.\n", " \"\"\"\n", "\n", " # Grid of 4 x 2\n", " fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(15, 20), dpi=80)\n", " for i, key in enumerate(data.columns):\n", " t_data = data[key]\n", " # Uses Pandas.DataFrame.plot method\n", " ax = t_data.plot(\n", " ax=axes[i // 2, i % 2], # Indicate subplot row and column (left to right, top to bottom)\n", " title=f\"{key.capitalize()}\",\n", " rot=25, # rotate x-axis markers by 25 degrees\n", " )\n", "\n", " fig.subplots_adjust(hspace=0.8) # reduce space between plots\n", " plt.tight_layout() # place as close together as possible\n", "\n", "cols_to_plot = [\"bx_gsm\", \"by_gsm\", \"bz_gsm\", \"bt\", \"speed\", \"density\", \"temperature\", \"theta_gsm\"]\n", "show_raw_visualization(solar_wind[cols_to_plot].iloc[:1000])" ] }, { "cell_type": "markdown", "metadata": { "id": "uB6NzWi0Evvr", "tags": [] }, "source": [ "### Feature Relationships" ] }, { "cell_type": "markdown", "metadata": { "id": "k0H4VUz2Evvq" }, "source": [ "Data gaps in the Solar Wind data are a common issue with real-time data\n", "\n", "
\n", "Be Aware: Gaps in our input (features) are something we'll need to deal carefully with, i.e. in the preprocessing steps below.\n", "
" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "v0QHlQf1Evvq" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data gaps in solar input (features) as % of data:\n" ] }, { "data": { "text/plain": [ "bx_gse 3.883169\n", "by_gse 3.883169\n", "bz_gse 3.883169\n", "theta_gse 3.883169\n", "phi_gse 3.889127\n", "bx_gsm 3.883169\n", "by_gsm 3.883169\n", "bz_gsm 3.883169\n", "theta_gsm 3.883169\n", "phi_gsm 3.889127\n", "bt 3.883169\n", "density 8.160914\n", "speed 8.216500\n", "temperature 9.672748\n", "source 3.775071\n", "dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# What percent of the time are there gaps in these data?\n", "print('Data gaps in solar input (features) as % of data:')\n", "solar_wind.isna().sum()/len(solar_wind)*100" ] }, { "cell_type": "markdown", "metadata": { "id": "WTaLLrKFEvvr" }, "source": [ "
\n", "Info: There are several challenges when working with these \"operational\" observations of the solar wind will we need to solve before modeling (e.g. missing data).\n", "
" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "fFCUj8QvEvvr" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/gn/wpms4v_901b0vzv2mvgcyrcdyqktl4/T/ipykernel_72295/1827713272.py:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", " corr = solar_wind.join(sunspots).join(satellite_positions).join(dst).fillna(method=\"ffill\").corr()\n" ] } ], "source": [ "# Correlation matrix:\n", "# Note that this is a slow command (several minutes) unless you have a GPU or TPU equivalent processor (then it's ~1 min).\n", "# Take advantage of Pandas DataFrame and merge our Input (Feature) and Output (Label) data.\n", "# I.e. merge, Solar Wind + Sunspots + Satellite Location + Dst\n", "corr = solar_wind.join(sunspots).join(satellite_positions).join(dst).fillna(method=\"ffill\").corr()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "3gwARslTEvvr" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We use the 'seismic' color map so we have a high contrast, symmetric gradient about 0. \n", "plt.figure(figsize=(10, 5))\n", "plt.matshow(corr, cmap='seismic', vmin=-1, vmax=1, fignum=1)\n", "plt.xticks(range(corr.shape[1]), corr.columns, rotation=90)\n", "plt.gca().xaxis.tick_bottom()\n", "plt.yticks(range(corr.shape[1]), corr.columns)\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": "markdown", "metadata": { "id": "7bY0Bi4eEvv2" }, "source": [ "
\n", "Info: Note which input (features) are correlated with each other and which ones are correlated with our output Dst. Note that the satellite location does not seem to be important for this modeling use case.\n", "
\n", "\n", "
\n", "Tip: You might like to consult the input parameter descriptions in the \"Data Summary\" section near the top of this notebook.\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "aZXj0zgCEvv2" }, "source": [ "### Feature Generation" ] }, { "cell_type": "markdown", "metadata": { "id": "Me6lZ5KyEvv3" }, "source": [ "#### Set seeds for reproducibility" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "KSkdq3U0Evv3" }, "outputs": [], "source": [ "from numpy.random import seed\n", "from tensorflow.random import set_seed\n", "\n", "seed(2020)\n", "set_seed(2021)" ] }, { "cell_type": "markdown", "metadata": { "id": "pSVefEirEvv3" }, "source": [ "#### Feature / Input Data we'll use to Train the Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Info: It's good to think about what features we'd recommend for use in developing our model. An additional exercise at the end of this notebook has learners try different sets of features. You can do so simply by adjusting the \"SOLAR_WIND_FEATURES\" list below. \n", "
" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "gjL2Me5oEvv3" }, "outputs": [], "source": [ "# subset of solar wind features to use for modeling\n", "SOLAR_WIND_FEATURES = [\n", " \"bt\",\n", " \"temperature\",\n", " \"bx_gsm\",\n", " \"by_gsm\",\n", " \"bz_gsm\",\n", " \"speed\",\n", " \"density\",\n", "]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "Tx6RB1ZREvv4" }, "outputs": [], "source": [ "# The model will be built on feature statistics, mean and standard deviation\n", "XCOLS = (\n", " [col + \"_mean\" for col in SOLAR_WIND_FEATURES]\n", " + [col + \"_std\" for col in SOLAR_WIND_FEATURES]\n", " + [\"smoothed_ssn\"]\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "-c1rhKvzEvv4" }, "source": [ "
\n", "Info: As discussed above, we'll need to fill in gaps and create statistical summaries (hourly means and standard deviations) of our features before modeling. The following routines provide this \"preprocessing\" functionality of gap filling, and scaling by features' statistics.\n", "
" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "BbhKxEukEvv4" }, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "\n", "def impute_features(feature_df):\n", " \"\"\"Imputes (inplace) missing input (feature) data.\n", " \n", " Imputes using the following methods:\n", " `smoothed_ssn` - forward fill\n", " `solar_wind` - interpolation\n", " \n", " Parameters\n", " ----------\n", " feature_df : pd.DataFrame\n", " Our original input (feature) data which has gaps.\n", " \n", " Returns\n", " -------\n", " feature_df : pd.DataFrame\n", " Updated input (feature) data with gaps filled, inplace using the input DataFrame.\n", " \"\"\"\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 input (features) to the floor of each hour using mean and standard deviation.\n", " \n", " e.g. All values from \"11:00:00\" to \"11:59:00\" will be aggregated to \"11:00:00\".\n", " \n", " feature_df : pd.DataFrame\n", " Our original input (feature) data to be aggregated.\n", " \n", " aggs : [\"mean\", \"std\"] \n", " Specifies the desired method, either \"mean\" or \"std\".\n", " \n", " Returns\n", " -------\n", " agged : pd.DataFrame\n", " New input (feature) data aggregated per chosen method.\n", " \"\"\"\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", " \"\"\"Preprocesses the input (feature) data.\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", " Parameters\n", " ----------\n", " solar_wind : pd.DataFrame\n", " Will be imputed (gap filled), aggregated (hourly), joined to sunspots, and scaled.\n", " \n", " sunspots : pd.DataFrame\n", " Will be scaled and joined to the imputed, aggregated, scaled solar_wind.\n", " \n", " scaler : sklearn.preprocessing.StandardScaler, None, optional\n", " If not provided, a StandardScaler() instance is created.\n", " \n", " subset: None, iterable, optional\n", " Subset of the \"solar_wind\" features we'd like processed.\n", "\n", " Returns\n", " -------\n", " imputed : pd.DataFrame\n", " This is the solar_wind hourly aggregated joined with \"sunspots\", and scaled.\n", "\n", " scaler : sklearn.preprocessing.StandardScaler\n", " The scaler that was used to normalize the solar_wind and sunspots.\n", " \n", " \"\"\"\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": 20, "metadata": { "id": "qrs16z0gEvv5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(139872, 15)\n" ] }, { "data": { "text/html": [ "
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bt_meanbt_stdtemperature_meantemperature_stdbx_gsm_meanbx_gsm_stdby_gsm_meanby_gsm_stdbz_gsm_meanbz_gsm_stdspeed_meanspeed_stddensity_meandensity_stdsmoothed_ssn
periodtimedelta
train_a0 days 00:00:000.4997052.443614-0.3752670.383941-1.600307-0.3817270.4344240.0211560.292754-0.645095-0.7385460.862524-0.775827-0.2057240.139444
0 days 01:00:000.547177-0.224580-0.4794300.953178-1.759200-0.8680440.189021-0.2828450.433737-0.511040-0.9869040.995063-0.861692-0.0582150.139444
0 days 02:00:000.739905-0.770240-0.574831-0.192518-1.913422-1.1146490.193116-0.8315260.747220-0.870482-1.0135480.554085-0.846222-0.2200120.139444
\n", "
" ], "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", "\n", " temperature_std bx_gsm_mean bx_gsm_std \\\n", "period timedelta \n", "train_a 0 days 00:00:00 0.383941 -1.600307 -0.381727 \n", " 0 days 01:00:00 0.953178 -1.759200 -0.868044 \n", " 0 days 02:00:00 -0.192518 -1.913422 -1.114649 \n", "\n", " by_gsm_mean by_gsm_std bz_gsm_mean bz_gsm_std \\\n", "period timedelta \n", "train_a 0 days 00:00:00 0.434424 0.021156 0.292754 -0.645095 \n", " 0 days 01:00:00 0.189021 -0.282845 0.433737 -0.511040 \n", " 0 days 02:00:00 0.193116 -0.831526 0.747220 -0.870482 \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", "\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 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features, scaler = preprocess_features(solar_wind, sunspots, subset=SOLAR_WIND_FEATURES)\n", "print(features.shape)\n", "features.head(n=3)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "4Os6-0W7Evv5" }, "outputs": [], "source": [ "# check to make sure missing values are filled\n", "assert (features.isna().sum() == 0).all()" ] }, { "cell_type": "markdown", "metadata": { "id": "2zLrR9YWEvv5" }, "source": [ "
\n", "Info: We also need to prepare our output (labels), i.e. our space weather storm index Dst, which is already a time series with an hourly cadence. The modeling task is to predict Dst at hour t0 and the next hour t1.\n", "
" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "q_HKa3mqEvv5" }, "outputs": [ { "data": { "text/html": [ "
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t0t1
periodtimedelta
train_a0 days 00:00:00-7-10.0
0 days 01:00:00-10-10.0
0 days 02:00:00-10-6.0
0 days 03:00:00-6-2.0
0 days 04:00:00-23.0
\n", "
" ], "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": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "YCOLS = [\"t0\", \"t1\"]\n", "\n", "\n", "def process_labels(dst):\n", " \"\"\"Create dst[t0] (current time) and dst[t1] (next hour) labels and group by training periods.\n", " \n", " This is needed because we wish to train the model on predicting Dst at the current time (t0)\n", " and for the next hour (t1). The method is a simple Pandas DataFrame array timeshift from dst[0:] to get dst[1:].\n", " \n", " Parameters\n", " ----------\n", " dst : pd.DataFrame\n", " \n", " Returns\n", " -------\n", " y : pd.DataFrame\n", " New copy of dst pd.DataFrame now including shifted Dst, and is grouped by training period.\n", " This is what we will train the model on.\n", " \"\"\"\n", "\n", " y = dst.copy()\n", " y[\"t0\"] = y.groupby(\"period\").dst.shift( 0)\n", " y[\"t1\"] = y.groupby(\"period\").dst.shift(-1)\n", " return y[YCOLS]\n", "\n", "\n", "labels = process_labels(dst)\n", "labels.head(n=5)" ] }, { "cell_type": "markdown", "metadata": { "id": "yi-13zy1Evv6" }, "source": [ "
\n", "Tip: For convenience, join our processed solar wind hourly inputs (features) and our Dst (labels) into one Pandas DataFrame.\n", "
" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "3WrOK4diEvv6" }, "outputs": [ { "data": { "text/html": [ "
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t0t1bt_meanbt_stdtemperature_meantemperature_stdbx_gsm_meanbx_gsm_stdby_gsm_meanby_gsm_stdbz_gsm_meanbz_gsm_stdspeed_meanspeed_stddensity_meandensity_stdsmoothed_ssn
periodtimedelta
train_a0 days 00:00:00-7-10.00.4997052.443614-0.3752670.383941-1.600307-0.3817270.4344240.0211560.292754-0.645095-0.7385460.862524-0.775827-0.2057240.139444
0 days 01:00:00-10-10.00.547177-0.224580-0.4794300.953178-1.759200-0.8680440.189021-0.2828450.433737-0.511040-0.9869040.995063-0.861692-0.0582150.139444
0 days 02:00:00-10-6.00.739905-0.770240-0.574831-0.192518-1.913422-1.1146490.193116-0.8315260.747220-0.870482-1.0135480.554085-0.846222-0.2200120.139444
\n", "
" ], "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", "\n", " temperature_std bx_gsm_mean bx_gsm_std \\\n", "period timedelta \n", "train_a 0 days 00:00:00 0.383941 -1.600307 -0.381727 \n", " 0 days 01:00:00 0.953178 -1.759200 -0.868044 \n", " 0 days 02:00:00 -0.192518 -1.913422 -1.114649 \n", "\n", " by_gsm_mean by_gsm_std bz_gsm_mean bz_gsm_std \\\n", "period timedelta \n", "train_a 0 days 00:00:00 0.434424 0.021156 0.292754 -0.645095 \n", " 0 days 01:00:00 0.189021 -0.282845 0.433737 -0.511040 \n", " 0 days 02:00:00 0.193116 -0.831526 0.747220 -0.870482 \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", "\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 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now we are ready to join our input (solar wind & sunspot features) and our output (Dst labels)\n", "data = labels.join(features)\n", "data.head(n=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "XoQxxeShEvv6" }, "source": [ "### Splitting the Data\n", "\n", "
\n", "Info: We'll split our features and labels into Training, Testing and Validation sets for each of the 3 training periods, named train_a, train_b, train_c (see Data Summary for additional details).\n", "
" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "uSdIiKQLOOEQ" }, "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", " \n", " Parameters\n", " ----------\n", " data : pd.DataFrame\n", " This is our input (features) and output (labels) DataFrame.\n", " \n", " test_per_period : int\n", " The number of timestamps to use in test period.\n", " \n", " val_per_period : int\n", " The number of timestamps to use in validation period.\n", "\n", " Returns\n", " -------\n", " test : pd.DataFrame\n", " Test data grouped by the desired period size\n", "\n", " val : pd.DataFrame\n", " Validation data grouped by the desired period size\n", "\n", " train : pd.DataFrame\n", " Remaining data as Training data\n", "\n", " \"\"\"\n", " \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", "train, test, val = get_train_test_val(data, test_per_period=6_000, val_per_period=3_000)" ] }, { "cell_type": "markdown", "metadata": { "id": "HqKPO3qXOWDb" }, "source": [ "### Visualize splits - Counts across periods" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "8mapzk9eOYMt" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "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": "markdown", "metadata": { "id": "_islsePpOgRI" }, "source": [ "### Final look at the shapes of our \"Training\", \"Test\", and \"Validation\" datasets" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "0zdssN9POjE4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(112872, 17)\n" ] } ], "source": [ "print(train.shape)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "id": "DdVye7Q6OuW5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(18000, 17)\n" ] } ], "source": [ "print(test.shape)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "XDWiaLh0OwQb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(9000, 17)\n" ] } ], "source": [ "print(val.shape)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "id": "YpYjboipHS0w" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Here is a list of pre-trained models:\n", "\n", " 0: trained_models_lstm/model_lstm_nepochs-04_nneurons-0016/\n", " 1: trained_models_lstm/model_lstm_nepochs-20_nneurons-0512/\n" ] } ], "source": [ "import tensorflow.keras as keras\n", "\n", "import glob\n", "# List existing LSTM models:\n", "dir_list = glob.glob('trained_models_lstm/model_lstm_*/')\n", "print('Here is a list of pre-trained models:\\n')\n", "for i in range(len(dir_list)):\n", " print(' %d: %s' % (i, dir_list[i]))" ] }, { "cell_type": "markdown", "metadata": { "id": "Y07bR5JTPwra" }, "source": [ "### Define and Build our LSTM Model" ] }, { "cell_type": "markdown", "metadata": { "id": "DHXdMwzzTHaF" }, "source": [ "#### Define LSTM Model" ] }, { "cell_type": "markdown", "metadata": { "id": "1aMIfKprbeCI" }, "source": [ "
\n", "Exercise (highly recommended): The least complex LSTM hyper parameters are set for notebook execution speed (i.e. few neurons and training epochs) and thus training does not fully converge. You you can select a predefined model_config by commenting out the appropriate line below or set the model_config yourself and evaluate consequences on model convergence in this notebook, as well as storm event use case performance in the Chapter 2 notebook.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Tip: The MagNet challenge benchmark LSTM used 512 neurons. Several options are listed below including an estimate of the training time required using Google Colab.\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "YTt6vnSWEvwA" }, "source": [ "
\n", "Be Aware: The least performant 'model_config' below is for notebook execution speed and training will not fully converge. Set 'model_config' to the MagNet benchmark case for convergence and benchmark performance.\n", "
" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "C966_JivTtcJ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data_config = \n", "{'batch_size': 32, 'timesteps': 32}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2023-08-14 21:35:58.435779: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " lstm (LSTM) (None, 512) 1081344 \n", " \n", " dense (Dense) (None, 2) 1026 \n", " \n", "=================================================================\n", "Total params: 1,082,370\n", "Trainable params: 1,082,370\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "# Define and Train a New Model\n", "from keras.layers import Dense, LSTM\n", "from keras.models import Sequential\n", "\n", "# Define our model\n", "data_config = {\n", " \"timesteps\": 32,\n", " \"batch_size\": 32,\n", "}\n", "print('data_config = ')\n", "pprint.pprint(data_config)\n", "\n", "# Hyper Parameter Tuning\n", "#\n", "# Going Big (takes hours):\n", "# model_config = {\"n_epochs\": 30, \"n_neurons\": 2048, \"dropout\": 0.4, \"stateful\": False}\n", "#\n", "# Original from MagNet blogpost benchmark, takes about 1.5 hours:\n", "model_config = {\"n_epochs\": 20, \"n_neurons\": 512, \"dropout\": 0.4, \"stateful\": False}\n", "#\n", "# Takes 10-15 minutes (moderate performance):\n", "# model_config = {\"n_epochs\": 8, \"n_neurons\": 64, \"dropout\": 0.4, \"stateful\": False}\n", "#\n", "# Takes 20 seconds (anticipate bad performance):\n", "# model_config = {\"n_epochs\": 4, \"n_neurons\": 16, \"dropout\": 0.4, \"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", "model.compile(\n", " loss=\"mean_squared_error\",\n", " optimizer=\"adam\",\n", " run_eagerly=None, # set to True for debugging (very slow), None or False\n", ")\n", "\n", "model.summary()\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Qr1uqro9Evv-" }, "source": [ "### Train Model" ] }, { "cell_type": "markdown", "metadata": { "id": "xg8cMNbnEvv-" }, "source": [ "#### BatchDataset: Training, Validation and Test Data\n", "In order to train and/or test our New or Pre-trained model, we'll create [tensorflow.python.data.ops.dataset_ops.BatchDataset](https://www.tensorflow.org/guide/data#batching_dataset_elements) structures for our Training, Validation and Test DataFrames." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "XD7CJDs0PzQ4" }, "outputs": [], "source": [ "import tensorflow.keras as keras\n", "from keras import preprocessing\n", "\n", "def timeseries_dataset_from_df(df, batch_size):\n", " \"\"\"Provides a batched dataset as pd.DataFrame\n", " \n", " Parameters\n", " ----------\n", " df : pd.DataFrame\n", " batch_size : int\n", "\n", " Returns\n", " -------\n", " dataset : pd.DataFrame\n", " Batched data.\n", " \"\"\"\n", " \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 = keras.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" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "G65lroOLEvv-" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of training batches: 3523\n", "Number of validation batches: 276\n", "Number of test batches: 558\n" ] } ], "source": [ "train_ds = timeseries_dataset_from_df(train, data_config[\"batch_size\"])\n", "val_ds = timeseries_dataset_from_df(val, data_config[\"batch_size\"])\n", "test_ds = timeseries_dataset_from_df(test, data_config[\"batch_size\"])\n", "\n", "print(f\"Number of training batches: {len(train_ds)}\")\n", "print(f\"Number of validation batches: {len(val_ds)}\")\n", "print(f\"Number of test batches: {len(test_ds)}\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "id": "MWiq8-BDTyz5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "3523/3523 [==============================] - 501s 142ms/step - loss: 322.2149 - val_loss: 528.2767\n", "Epoch 2/20\n", "3523/3523 [==============================] - 352s 100ms/step - loss: 270.9564 - val_loss: 428.2550\n", "Epoch 3/20\n", "3523/3523 [==============================] - 338s 96ms/step - loss: 211.8180 - val_loss: 365.3229\n", "Epoch 4/20\n", "3523/3523 [==============================] - 305s 87ms/step - loss: 176.5962 - val_loss: 296.9467\n", "Epoch 5/20\n", "3523/3523 [==============================] - 301s 85ms/step - loss: 154.6030 - val_loss: 269.1597\n", "Epoch 6/20\n", "3523/3523 [==============================] - 289s 82ms/step - loss: 140.2445 - val_loss: 257.4117\n", "Epoch 7/20\n", "3523/3523 [==============================] - 283s 80ms/step - loss: 131.9551 - val_loss: 245.8935\n", "Epoch 8/20\n", "3523/3523 [==============================] - 289s 82ms/step - loss: 128.2601 - val_loss: 224.3057\n", "Epoch 9/20\n", "3523/3523 [==============================] - 274s 78ms/step - loss: 123.8335 - val_loss: 217.1344\n", "Epoch 10/20\n", "3523/3523 [==============================] - 254s 72ms/step - loss: 119.8076 - val_loss: 207.3412\n", "Epoch 11/20\n", "3523/3523 [==============================] - 267s 76ms/step - loss: 119.0391 - val_loss: 200.9446\n", "Epoch 12/20\n", "3523/3523 [==============================] - 244s 69ms/step - loss: 116.1431 - val_loss: 192.1378\n", "Epoch 13/20\n", "3523/3523 [==============================] - 275s 78ms/step - loss: 111.8916 - val_loss: 180.0028\n", "Epoch 14/20\n", "3523/3523 [==============================] - 256s 73ms/step - loss: 109.8615 - val_loss: 176.0776\n", "Epoch 15/20\n", "3523/3523 [==============================] - 267s 76ms/step - loss: 114.1101 - val_loss: 197.2197\n", "Epoch 16/20\n", "3523/3523 [==============================] - 247s 70ms/step - loss: 112.2123 - val_loss: 191.0351\n", "Epoch 17/20\n", "3523/3523 [==============================] - 251s 71ms/step - loss: 109.8667 - val_loss: 183.9971\n", "Epoch 18/20\n", "3523/3523 [==============================] - 242s 69ms/step - loss: 104.1729 - val_loss: 168.1111\n", "Epoch 19/20\n", "3523/3523 [==============================] - 253s 72ms/step - loss: 103.0035 - val_loss: 169.4247\n", "Epoch 20/20\n", "3523/3523 [==============================] - 250s 71ms/step - loss: 100.3220 - val_loss: 173.2876\n" ] } ], "source": [ "# Train Model\n", "history_keras = model.fit(\n", " train_ds,\n", " batch_size=data_config[\"batch_size\"],\n", " epochs=model_config[\"n_epochs\"],\n", " verbose=True,\n", " shuffle=False,\n", " validation_data=val_ds,\n", ")\n", "history = history_keras.history # Convert Keras 'history' callback reference to a simple dictionary.\n", " # This will make saving and loading as a pickle easier, i.e. while\n", " # the TAI4ES Jupyter \"GPU\" environment did not have trouble saving/pickling\n", " # as a weak reference to the keras object, the \"CPU\" environment gave\n", " # error \"TypeError: cannot pickle 'weakref' object\"." ] }, { "cell_type": "markdown", "metadata": { "id": "wkwxYtOqT8WT" }, "source": [ "### Evaluate Trained Model" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "87GD27_tT-JY" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for name, values in history.items():\n", " plt.plot(values, 's-', label=name)\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "id": "rvj69RaTUoBw" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "558/558 [==============================] - 14s 26ms/step - loss: 163.6485\n", "Test RMSE: 12.79 nano-Tesla\n" ] } ], "source": [ "rmse = model.evaluate(test_ds)**0.5\n", "print(f\"Test RMSE: {rmse:.2f} nano-Tesla\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Jsey4js4HMK4" }, "source": [ "### Save Model and Scaler" ] }, { "cell_type": "markdown", "metadata": { "id": "pT_Ox_gJEvwA" }, "source": [ "
\n", "Tip: We need to save both the Model and the Scaler. Recall that we used a scaler (mean and standard deviation) to scale the features. We'll need to save both the trained model and scaler if we'd like to use it again in a future Jupyter session without having to retrain.\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "nl-bgU9WHXQO" }, "source": [ "#### Save: New Model, Scaler, History and Configuration" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "id": "FRJVvgXDHPJh" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-08-14 23:11:53.091652: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n", "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: trained_models_lstm/model_lstm_nepochs-20_nneurons-0512/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: trained_models_lstm/model_lstm_nepochs-20_nneurons-0512/assets\n", "WARNING:absl: has the same name 'LSTMCell' as a built-in Keras object. Consider renaming to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved model to trained_models_lstm/model_lstm_nepochs-20_nneurons-0512/\n" ] } ], "source": [ "# Pickle the Scaler and History, and JSON the Config\n", "import json\n", "import pickle\n", "\n", "# Keep our models in their own subdirectories\n", "dir_model = 'trained_models_lstm/model_lstm_nepochs-%02d_nneurons-%04d/' % \\\n", " (model_config['n_epochs'], model_config['n_neurons'])\n", "\n", "# Save Model\n", "model.save(dir_model)\n", "\n", "# Save Scaler (pickle)\n", "with open(dir_model+\"/scaler.pck\", \"wb\") as f:\n", " pickle.dump(scaler, f)\n", "\n", "# Save History (pickle)\n", "with open(dir_model+\"/history.pck\", \"wb\") as f:\n", " pickle.dump(history, f)\n", "\n", "# Save Configuration (as JSON)\n", "data_config[\"solar_wind_subset\"] = SOLAR_WIND_FEATURES\n", "with open(dir_model+\"/config.json\", \"w\") as f:\n", " json.dump(data_config, f)\n", "\n", "print('Saved model to %s' % dir_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "### Additional Excercises - Using the current notebook\n", "\n", "The following exercises are designed to help expand your intuition, by extending concepts in earlier sections. You should find these straight forward to engage in, using the materials in this notebook plus a couple of supplementary resources as indicated below." ] }, { "cell_type": "markdown", "metadata": { "id": "de9Wow0cEvv3" }, "source": [ "
\n", "Exercise: Adjust the list of inputs (features) used to train the model and compare the traing, validation and test losses. Using the Chapter 2 notebook you can also compare the performance differences in predicting specific storms.\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "mR73zQn-Evv3" }, "source": [ "
\n", "Exercise: Are there any additional inputs (features) we should consider adding to improve our prediction of Dst?\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "-b9wR3IOEvwJ" }, "source": [ "
\n", "Exercise: Improving Performance: \n", " \n", "What hyper parameter changes to the LSTM architecture might you explore to increase its performance?\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "6NOmthg4EvwG" }, "source": [ "
\n", "Exercise: Instrument Availability:\n", "\n", "If one or more solar wind instruments were to degrade on orbit how might this impact model performance?\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Hint: Review the model sensitivities to the input parameters via the permutation importance section.\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "qZx6hF-EEvv4" }, "source": [ "
\n", "Exercise: In this LSTM notebook, we use means and standard deviations. Optionally try different statistical tools such as medians and interquartile ranges, such as used in our sibling CNN notebook.\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "9kEgSgWZbeCN" }, "source": [ "
\n", "Exercise: Degraded Observations\n", "\n", "Degrade the instrument measurements and run the model to see how the performance is impacted. Start simple by adding Gaussian noise (mean 0), to the least important and the most important input parameters (aka features) and evaluating a specific event.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next steps\n", "\n", "Congratulations on engaging with the learning objectives of this MagNet Chapter 1 LSTM focused notebook--the benchmark from the NOAA MagNet competition. There is one additional Chapter 2 notebook in the MagNet LSTM series, on explainable AI (XAI) and includes using the model you've built to explore Dst prediction for user chosen storms.\n", "\n", "There is an additional NCAI notebook in preparation for this MagNet series:\n", "A higher performing ensemble Convolutional Neural Netowork (CNN) from the NOAA Geomagnetism team based on the 2nd place entry from the MagNet competition. \n", "As mentioned in an earlier section, this notebook's precursor is the [TAI4ES Space Weather CNN Notebook](https://github.com/ai2es/tai4es-trustathon-2022/tree/main/space)\n", "\n", "Additionally, a web search will provide other Dst modeling notebooks and publications using ML techniques." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples in the community\n", "\n", "For a comprehensive treatment of the need to build robust predictions of the Dst space weather storm indicator (e.g. for magnetic navigation applications), see Nair et al., 2023 and references therein:\n", "* Nair et al., 2023 (in press) (TODO: Update with public URL as soon as available),\n", "\n", "For a summary, see:\n", "* https://www.drivendata.org/competitions/73/noaa-magnetic-forecasting/\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data statement\n", "The competition discussed above used public data for development and the public leaderboard. A private dataset was kept internal during the competition for use in scoring by the organizers. Since the competition has passed, both datasets are publicly accessible from NOAA.\n", "\n", "All data used in this notebook are publicly available here:\n", "* https://ngdc.noaa.gov/geomag/data/geomag/magnet/public.zip\n", "* https://ngdc.noaa.gov/geomag/data/geomag/magnet/private.zip" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "* Nair, M., Redmon, R.J., Young, L.Y., Chulliat, A., Trotta, B., Chung, C., Lipstein, G., Slavitt, I. (2023),\"MagNet - a data-science competition to predict Disturbance Storm-time index (Dst) from solar wind data\", Space Weather, In Press. (TODO: update with URL once available.)\n", "* [CIRES GeoMag MagNet repository](https://github.com/liyo6397/MagNet/), TODO: update URL to new CIRES repo.\n", "* [Trustworthy Artificial Intelligence for Environmental Science 2022 Summer School](https://www2.cisl.ucar.edu/events/tai4es-2022-summer-school), TAI4ES, accessed July 2022.\n", "* [TAI4ES Space Weather Notebooks (LSTM, CNN)](https://github.com/ai2es/tai4es-trustathon-2022/tree/main/space), GitHub, accessed July 2022.\n", "* [MagNet: Model the Geomagnetic Field](https://ngdc.noaa.gov/geomag/mag-net-challenge.html), NOAA, accessed March 2022.\n", "* Chung, C. (2020), \"HOW TO PREDICT DISTURBANCES IN THE GEOMAGENTIC FIELD WITH LSTMS - BENCHMARK\", Blogpost, Accessed March 2022, Available Online: https://drivendata.co/blog/model-geomagnetic-field-benchmark/.\n", "* DrivenData (2020), \"MagNet: Model the Geomagnetic Field\", Web Resource, Accessed March 2022, Available Online: https://www.drivendata.org/competitions/73/noaa-magnetic-forecasting/.\n", "* [Interpretable Machine Learning by Christop Molnar](https://christophm.github.io/interpretable-ml-book/shap.html)\n", "* Redmon, R. J., Seaton, D. B., Steenburgh, R., He, J., & Rodriguez, J. V. (2018). September 2017's geoeffective space weather and impacts to Caribbean radio communications during hurricane response. Space Weather, 16, 1190–1201. https://doi.org/10.1029/2018SW001897" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Metadata\n", " * Language / package(s):\n", " * Language: Python, \n", " * Packages: Keras Tensor Flow, Matplotlib, Numpy, Pandas, Scikit-learn\n", " * Scientific domain:\n", " * Space Weather, Geomagnetic modeling\n", " * Application keywords\n", " * Magnetic Navigation\n", " * Geophysical keywords\n", " * Disturbance Storm Index (Dst), Solar Wind\n", " * AI keywords\n", " * Long Short-Term Memory (LSTM)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## License\n", "\n", "### Software and Content Description License\n", "Software code created by U.S. Government employees is not subject to copyright in the United States (17 U.S.C. §105). The United States/Department of Commerce reserve all rights to seek and obtain copyright protection in countries other than the United States for Software authored in its entirety by the Department of Commerce. To this end, the Department of Commerce hereby grants to Recipient a royalty-free, nonexclusive license to use, copy, and create derivative works of the Software outside of the United States." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Disclaimer\n", "\n", "> This Jupyter notebook is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA Jupyter notebooks are provided on an 'as is' basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this Jupyter notebook will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "v003LoxwbeB6", "kRzCSuRwbeB7", "YIeZxbJXbeB7", "CdZDisojbeB8", "kJQsAZGlbeB8", "GCrRyAmibeB9", "5WEhRi6RMX0o", "AVRI8V_AI3NS", "ZDtowUzbMXNx", "Vk0h7YPGMxUw", "Is66dqN0M3VP", "5HoW8LxqM8eS", "J261z_FvOK_p", "HqKPO3qXOWDb", "_islsePpOgRI", "DHXdMwzzTHaF", "wkwxYtOqT8WT", "nl-bgU9WHXQO", "yLbZKYfgEjoT", "dWs2MeMe0ZFm", "acm3GxIUbeCL", "jn5uRW1kDq2p", "9kEgSgWZbeCN" ], "gpuType": "V100", "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 4 }