{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "D7UvwwCM0wC7" }, "source": [ "# Notebook\n", "\n", "**Authors:** Colin Small (crs1031@wildcats.unh.edu), Matthew Argall (Matthew.Argall@unh.edu), Marek Petrik (Marek.Petrik@unh.edu)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "tdDrn6mBTUgT" }, "source": [ "[![MMS Mission Video](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b3/Artist_depiction_of_MMS_spacecraft_%28SVS12239%29.png/640px-Artist_depiction_of_MMS_spacecraft_%28SVS12239%29.png)](https://upload.wikimedia.org/wikipedia/commons/c/c9/NASA_Spacecraft_Finds_New_Magnetic_Process_in_Turbulent_Space.webm)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "rL5p5pJQ39Dd" }, "source": [ "## Introduction\n", "Global-scale energy flow throughout Earth’s magnetosphere is catalyzed by processes that occur at Earth’s magnetopause (MP) in the electron diffusion region (EDR) of magnetic reconnection. Until the launch of the Magnetospheric Multiscale (MMS) mission, only rare, fortuitous circumstances permitted a glimpse of the electron dynamics that break magnetic field lines and energize plasma. MMS employs automated burst triggers onboard the spacecraft and a Scientist-in-the-Loop (SITL) on the ground to select intervals likely to contain diffusion regions. Only low-resolution survey data is available to the SITL, which is insufficient to resolve electron dynamics. A strategy for the SITL, then, is to select all MP crossings. This has resulted in over 35 potential MP EDR encounters but is labor- and resource-intensive; after manual reclassification, just ∼ 0.7% of MP crossings, or 0.0001% of the mission lifetime during MMS’s first two years contained an EDR.\n", "\n", "In this notebook, we develop a Long-Short Term Memory (LSTM) neural network to detect magnetopause crossings and automate the SITL classification process. An LSTM developed with this notebook has been implemented in the MMS data stream to provide automated predictions to the SITL.\n", "\n", "\n", "This model facilitates EDR studies and helps free-up mission operation costs by consolidating manual classification processes into automated routines." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xRLV_B1CtKIP" }, "source": [ "**Authors' notes:** \n", "\n", "1. This notebook was developed after the development of the original model in use at the SDC. We have tried our best to replicate the development steps and hyperparameters of that model, but we cannot guarantee that models developed with this notebook will exactly match the performance of the original.\n", "\n", "2. This notebook was designed on, and is best run on, Google Colab. It must either be run on Colab or on a machine with an NVIDIA GPU and cuDNN installed. If your machine does not have an NVIDIA GPU, does not have cuDNN installed, or if you run into issues running this notebook yourself, please open the notebook in Google Colab, which provides you with a virtual GPU to run the notebook. (If TF Keras is unable to identify a GPU to run on, make sure the notebook is set to use one by clicking the \"Runtime\" tab in the top menu bar, selecting \"Change runtime type\", selecting \"GPU\" in the dropdown menu under \"Hardware accelerator\", and clicking save. Colab will refresh your timetime, and you will need to re-run all cells.):" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "K8513yuEtKIP" }, "source": [ "\"Open\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4Em6RZ8Z6UFl" }, "source": [ "## Import Libraries\n", "\n", "To start, we import the neccesary libraries for this notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 318 }, "colab_type": "code", "id": "3WshufyyiiLd", "outputId": "fb577231-e76a-4f6e-e81d-1c191e5e164e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: nasa-pymms in /usr/local/lib/python3.6/dist-packages (0.3.1)\n", "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.6/dist-packages (from nasa-pymms) (4.41.1)\n", "Requirement already satisfied: matplotlib>=3.1.1 in /usr/local/lib/python3.6/dist-packages (from nasa-pymms) (3.2.2)\n", "Requirement already satisfied: requests>=2.22.0 in /usr/local/lib/python3.6/dist-packages (from nasa-pymms) (2.23.0)\n", "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nasa-pymms) (1.4.1)\n", "Requirement already satisfied: cdflib in /usr/local/lib/python3.6/dist-packages (from nasa-pymms) (0.3.19)\n", "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.6/dist-packages (from nasa-pymms) (1.18.5)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.1.1->nasa-pymms) (1.2.0)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.1.1->nasa-pymms) (0.10.0)\n", "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.1.1->nasa-pymms) (2.4.7)\n", "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.1.1->nasa-pymms) (2.8.1)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.22.0->nasa-pymms) (2020.6.20)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.22.0->nasa-pymms) (3.0.4)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.22.0->nasa-pymms) (1.24.3)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.22.0->nasa-pymms) (2.10)\n", "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.6/dist-packages (from cdflib->nasa-pymms) (20.2.0)\n", "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=3.1.1->nasa-pymms) (1.15.0)\n" ] } ], "source": [ "!pip install nasa-pymms" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 87 }, "colab_type": "code", "id": "MwnLUN2cUd4S", "outputId": "784c11cf-a6ce-4e24-ad6e-2afe19051981" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow 1.x selected.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Creating root data directory /root/data/mms\n", "Creating root data directory /root/data/mms/dropbox\n" ] } ], "source": [ "from pathlib import Path\n", "from sklearn import preprocessing\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, Dropout, LSTM, CuDNNLSTM, BatchNormalization, Bidirectional, Reshape, TimeDistributed\n", "from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint\n", "from matplotlib import pyplot\n", "from sklearn.metrics import roc_curve, auc, confusion_matrix\n", "from keras import backend as K\n", "from pymms.sdc import mrmms_sdc_api as mms\n", "import keras.backend.tensorflow_backend as tfb\n", "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "plt.rcParams.update({'font.size': 18})\n", "import datetime as dt\n", "import os\n", "import time\n", "import sklearn\n", "import scipy\n", "import pickle\n", "import random\n", "import requests" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "x-LAsDIG3XD7" }, "source": [ "## Download, Preprocess, and Format MMS Data" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gWIpfE2zSCOP" }, "source": [ "After installing and importinng the neccesary libraries, we download our training and validation data. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "colab_type": "code", "id": "mcytuTHUR8U8", "outputId": "04175787-010b-43d0-9951-104ec5782768" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2020-09-16 16:36:14-- https://zenodo.org/record/3884266/files/original_training_data.csv?download=1\n", "Resolving zenodo.org (zenodo.org)... 188.184.117.155\n", "Connecting to zenodo.org (zenodo.org)|188.184.117.155|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 447842635 (427M) [text/plain]\n", "Saving to: ‘training_data.csv’\n", "\n", "training_data.csv 100%[===================>] 427.10M 7.45MB/s in 24s \n", "\n", "2020-09-16 16:36:39 (18.0 MB/s) - ‘training_data.csv’ saved [447842635/447842635]\n", "\n", "--2020-09-16 16:36:39-- https://zenodo.org/record/3884266/files/original_validation_data.csv?download=1\n", "Resolving zenodo.org (zenodo.org)... 188.184.117.155\n", "Connecting to zenodo.org (zenodo.org)|188.184.117.155|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 90314951 (86M) [text/plain]\n", "Saving to: ‘validation_data.csv’\n", "\n", "validation_data.csv 100%[===================>] 86.13M 8.10MB/s in 10s \n", "\n", "2020-09-16 16:36:49 (8.45 MB/s) - ‘validation_data.csv’ saved [90314951/90314951]\n", "\n" ] } ], "source": [ "!wget -O training_data.csv https://zenodo.org/record/3884266/files/original_training_data.csv?download=1\n", "!wget -O validation_data.csv https://zenodo.org/record/3884266/files/original_validation_data.csv?download=1" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "aDg-HJ0MAtjS" }, "source": [ "After downloading the training and validation data, we preprocess our training data in preparation for training the neural network." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "fp4mcnLfFZXw" }, "source": [ "We first load the data we downloaded above. The data is a table of measurements from the MMS spacecraft, where each row represents individual measurements taken at a given time and where each column represents a feature (variable) recorded at that time. There is an additional column representing the ground truths for each measurement (whether this measurement was selected by a SITL or not). Then, we will adjust the formatting and datatypes of several of the columns and sort the data by the time of the measurement." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", "id": "tojTr8i472HY" }, "outputs": [], "source": [ "mms_data = pd.read_csv('training_data.csv', index_col=0, infer_datetime_format=True,\n", "\t\t\t\t\t\t parse_dates=[0])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 623 }, "colab_type": "code", "id": "U2yZv1MwGqWQ", "outputId": "78dc7f6b-3f8c-43e6-b7b3-0bda18e92966" }, "outputs": [ { "data": { "text/html": [ "
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mms1_des_energyspectr_omni_fast_0mms1_des_energyspectr_omni_fast_1mms1_des_energyspectr_omni_fast_2mms1_des_energyspectr_omni_fast_3mms1_des_energyspectr_omni_fast_4mms1_des_energyspectr_omni_fast_5mms1_des_energyspectr_omni_fast_6mms1_des_energyspectr_omni_fast_7mms1_des_energyspectr_omni_fast_8mms1_des_energyspectr_omni_fast_9mms1_des_energyspectr_omni_fast_10mms1_des_energyspectr_omni_fast_11mms1_des_energyspectr_omni_fast_12mms1_des_energyspectr_omni_fast_13mms1_des_energyspectr_omni_fast_14mms1_des_energyspectr_omni_fast_15mms1_des_energyspectr_omni_fast_16mms1_des_energyspectr_omni_fast_17mms1_des_energyspectr_omni_fast_18mms1_des_energyspectr_omni_fast_19mms1_des_energyspectr_omni_fast_20mms1_des_energyspectr_omni_fast_21mms1_des_energyspectr_omni_fast_22mms1_des_energyspectr_omni_fast_23mms1_des_energyspectr_omni_fast_24mms1_des_energyspectr_omni_fast_25mms1_des_energyspectr_omni_fast_26mms1_des_energyspectr_omni_fast_27mms1_des_energyspectr_omni_fast_28mms1_des_energyspectr_omni_fast_29mms1_des_energyspectr_omni_fast_30mms1_des_numberdensity_fastmms1_des_bulkv_dbcs_fast_0mms1_des_bulkv_dbcs_fast_1mms1_des_heatq_dbcs_fast_0mms1_des_heatq_dbcs_fast_1mms1_des_temppara_fastmms1_des_tempperp_fastmms1_des_prestensor_dbcs_fast_x1_y1mms1_des_prestensor_dbcs_fast_x2_y1...mms1_dis_energyspectr_omni_fast_29mms1_dis_energyspectr_omni_fast_30mms1_dis_numberdensity_fastmms1_dis_bulkv_dbcs_fast_0mms1_dis_bulkv_dbcs_fast_1mms1_dis_heatq_dbcs_fast_0mms1_dis_heatq_dbcs_fast_1mms1_dis_temppara_fastmms1_dis_tempperp_fastmms1_dis_prestensor_dbcs_fast_x1_y1mms1_dis_prestensor_dbcs_fast_x2_y1mms1_dis_prestensor_dbcs_fast_x2_y2mms1_dis_prestensor_dbcs_fast_x3_y1mms1_dis_prestensor_dbcs_fast_x3_y2mms1_dis_prestensor_dbcs_fast_x3_y3mms1_dis_temptensor_dbcs_fast_x1_y1mms1_dis_temptensor_dbcs_fast_x2_y1mms1_dis_temptensor_dbcs_fast_x2_y2mms1_dis_temptensor_dbcs_fast_x3_y1mms1_dis_temptensor_dbcs_fast_x3_y2mms1_dis_temptensor_dbcs_fast_x3_y3mms1_dis_temp_anisotropymms1_dis_scalar_temperaturemms1_dis_N_Qmms1_dis_Vz_Qmms1_dis_nV_Qmms1_afg_srvy_dmpa_Bxmms1_afg_srvy_dmpa_Bymms1_afg_srvy_dmpa_Bzmms1_afg_srvy_dmpa_|B|mms1_afg_magnetic_pressuremms1_afg_clock_anglemms1_afg_Bz_Qmms1_edp_xmms1_edp_ymms1_edp_zmms1_edp_|E|mms1_temp_ratiomms1_plasma_betaselected
Epoch
2017-01-01 01:49:08.736524172560370.0141811650.0115564310.0103489660.0109156240.0138017710.0199794740.0313011970.0495821400.07.675890e+081.102645e+091.355750e+091.283706e+09876457860.0437018100.0178497440.061324772.017706034.05950672.01453186.01230702.81290793.01361365.001443553.01538581.101600447.11693706.61860683.81821310.11785482.62143655.047.289574-64.066220-59.8503840.067417-0.03308768.66341469.6437200.5306810.000513...1847614.41368769.6045.166210-65.301070-75.960210-0.017214-0.054081496.30145558.620104.1053840.0393053.9793790.0775440.3727123.5914204.1053840.0393053.9793790.0775440.3727123.591420-0.111558537.847200.0000000.0000005217.443037-0.02619733.162117-39.65694051.6952602.126628e+091.5715861.467709-0.8622751.6650130.8270682.0493481.0524.89600False
2017-01-01 01:49:13.236552160474430.0134115120.0113082936.0106597736.0118977260.0157128700.0234196500.0368190240.0588617700.09.143196e+081.280595e+091.386385e+091.020292e+09546922600.0238565300.095543030.034221956.010795790.03346869.81132098.61230606.61290689.81370548.601217164.61303508.001396131.61496823.21930027.91878429.5675747.12085773.547.806107-34.529873-128.3425100.091984-0.00041259.05984062.0346300.4731320.000187...1385553.01053661.5046.629120-60.415287-118.971080-0.0037610.163968415.75192523.831503.8837460.0740543.9430840.0495510.0032023.1059793.8837460.0740543.9430840.0495510.0032023.105979-0.206325487.804961.09718239.2561626031.1446683.003209-17.920328-18.67769226.0578905.403419e+08-1.4047532.396316-0.5728420.670425-0.5702901.0501651.0929.00616False
2017-01-01 01:49:17.736573143446660.0120096200.0103888320.099487384.0114008050.0152906400.0228558100.0359750530.0572418500.08.799912e+081.192516e+091.208780e+098.369016e+08437589570.0191214960.078865330.028987052.09752265.03037489.21132141.21230653.41290740.11361307.601453176.61303684.901417762.91178981.41254457.61834328.21900604.51562550.443.848970-17.841991-80.636430-0.0175040.00735355.00536061.1002430.4326110.002111...1216220.01003711.9042.998802-37.589535-101.1163250.0022840.062132379.23505442.852943.202901-0.0232772.8988290.074439-0.1315502.6125943.202901-0.0232772.8988290.074439-0.1315502.612594-0.143655421.646971.62555647.4372225204.2414550.250873-3.805478-25.77231026.0529585.401373e+08-1.5049671.586905-0.3574240.633575-0.1603080.7448941.01132.09920False
2017-01-01 01:49:22.236602143115380.0120483730.0103335910.099983890.0115342700.0156445900.0236307790.0374869920.0598786900.09.304425e+081.259985e+091.203378e+097.605617e+08361202240.0147414610.058348600.021312232.07609467.03276093.51132188.61230705.21290795.81361368.001443556.01548742.101391640.81787557.81927728.01754844.51890987.52347546.844.116190-24.291311-51.504208-0.010656-0.06507856.31973657.4965970.4051840.001348...1146827.8996494.7542.281338-15.571530-88.482050-0.0395050.037287350.79077375.923342.6082620.0162382.484871-0.012547-0.0626052.3763142.6082620.0162382.484871-0.012547-0.0626052.376314-0.066856367.545802.43795063.6045324158.1290410.571565-26.909445-33.61694343.0644151.475799e+09-1.5495590.4142311.9629651.813941-1.0013142.8541631.0257.55072False
2017-01-01 01:49:26.736624154996270.0129635576.0109879810.0104667430.0118939120.0159722750.0240743730.0384687520.0622868740.09.923178e+081.407102e+091.379264e+098.729052e+08412877600.0165255970.064202252.022482496.07437224.53127818.01132103.51230611.91290695.21361258.801443435.91492447.101570121.21725425.41605647.42037316.21869736.62320483.047.787876-43.337650-68.804990-0.138768-0.23358156.95401458.3217540.449897-0.001409...1509011.91205417.5044.735374-29.033060-83.403510-0.047671-0.056385396.54170400.258332.9473060.0070262.790271-0.076785-0.3114232.8421502.9473060.0070262.790271-0.076785-0.3114232.842150-0.009286399.019440.08326257.7804164519.709417-1.338079-25.596290-35.26012843.5917101.512161e+09-1.6230250.0096671.6742210.755573-0.4228701.8848671.0423.39264False
......................................................................................................................................................................................................................................................
2017-01-31 01:59:37.612761195522660.0169047760.0151144740.0155255970.0187461070.0260878460.0391024700.0601519170.0933239230.01.431671e+092.001644e+091.911962e+099.213409e+08266009100.070228190.023561248.08219571.03132344.01069388.61156007.01205684.81285194.1776630.401443075.81507529.501614608.11747582.81609458.82050434.22227281.82083288.266.366800-114.178600-66.3549350.118533-0.16759054.73800049.9987600.5425380.017298...1173978.5946332.5046.211296-99.599365-137.597990-0.087668-0.111775269.56824291.431582.067940-0.0591062.0421810.0329450.0233442.2011182.067940-0.0591062.0421810.0329450.0233442.201118-0.075020284.143802.02677510.799598722.083772-12.985076-21.6497173.80409325.5302495.186808e+08-2.1110541.8258850.569317-1.014906-0.4782031.2581071.0451.70038False
2017-01-31 01:59:42.112805188999890.0167058830.0153385100.0160276850.0194781820.0265345520.0388861400.0597449200.0947348600.01.491336e+091.999952e+091.605463e+096.863398e+08206139580.060198680.021560316.08027363.53163052.51069362.91155979.51205655.91227593.41093269.801310439.91257120.901626893.61687317.91547724.21889413.21736235.62222297.064.247070-114.180830-57.8735240.079878-0.02705149.83791748.9502800.507997-0.001615...1173978.5946332.5046.211296-99.599365-137.597990-0.087668-0.111775269.56824291.431582.067940-0.0591062.0421810.0329450.0233442.2011182.067940-0.0591062.0421810.0329450.0233442.201118-0.075020284.143802.02677510.799598722.083772-15.43644711.2149944.80984519.6772613.081197e+082.5132842.0992051.721005-1.0337571.9008642.7647421.0205.54817False
2017-01-31 01:59:46.612839156668940.0136574260.0123834980.0129313670.0158046480.0222452110.0343151600.0552882500.0898671700.01.377260e+091.645098e+091.142623e+094.697292e+08148775630.047467200.018703922.07264802.52508120.01085820.61155923.61167832.61042989.01352492.101176428.91383997.801534980.21675194.21783336.11992431.11171974.12034246.553.937880-147.811950-66.3324360.0318490.02117248.72533447.5220900.415525-0.001351...1173978.5946332.5046.211296-99.599365-137.597990-0.087668-0.111775269.56824291.431582.067940-0.0591062.0421810.0329450.0233442.2011182.067940-0.0591062.0421810.0329450.0233442.201118-0.075020284.143802.02677510.799598722.0837729.732903-5.53475721.61504624.3428254.715547e+08-0.5170602.3475742.787715-2.293623-2.0824774.1675861.0136.35893False
2017-01-31 01:59:51.112882148339340.0131617280.0123383950.0133689650.0169121890.0242394990.0371708160.0592586500.0940750800.01.356828e+091.457272e+099.310631e+083.592990e+08107206430.034867150.014601406.05957955.01804840.91102969.01155993.61165338.01181596.9630540.941371131.61339956.001473016.61658352.91457381.91135691.62071354.12416306.052.308780-141.634200-85.162560-0.0317700.03575745.69808044.9619940.374102-0.003214...1173978.5946332.5046.211296-99.599365-137.597990-0.087668-0.111775269.56824291.431582.067940-0.0591062.0421810.0329450.0233442.2011182.067940-0.0591062.0421810.0329450.0233442.201118-0.075020284.143802.02677510.799598722.08377213.513677-6.46138629.84856433.3961908.875320e+08-0.4460053.3690513.261839-4.248680-2.1281065.7636541.098.59849False
2017-01-31 01:59:55.612917135440930.0119457550.0111787400.0121276030.0155176540.0227165100.0355259520.0570692000.0891547700.01.239421e+091.285931e+098.412920e+083.517979e+08116526360.041865636.018613406.08321585.53825070.01326246.51155995.01205672.41224776.41275854.501217636.2838192.251057988.51786348.41358997.82065777.12144108.02296880.848.376137-109.131290-104.492080-0.023458-0.05328546.80138045.0597900.350553-0.002084...1173978.5946332.5046.211296-99.599365-137.597990-0.087668-0.111775269.56824291.431582.067940-0.0591062.0421810.0329450.0233442.2011182.067940-0.0591062.0421810.0329450.0233442.201118-0.075020284.143802.02677510.799598722.083772-9.63113329.820732-9.85626532.8508878.587848e+081.8831893.069619-0.2514881.1932910.4947891.3160571.0431.81090False
\n", "

302188 rows × 124 columns

\n", "
" ], "text/plain": [ " mms1_des_energyspectr_omni_fast_0 ... selected\n", "Epoch ... \n", "2017-01-01 01:49:08.736524 172560370.0 ... False\n", "2017-01-01 01:49:13.236552 160474430.0 ... False\n", "2017-01-01 01:49:17.736573 143446660.0 ... False\n", "2017-01-01 01:49:22.236602 143115380.0 ... False\n", "2017-01-01 01:49:26.736624 154996270.0 ... False\n", "... ... ... ...\n", "2017-01-31 01:59:37.612761 195522660.0 ... False\n", "2017-01-31 01:59:42.112805 188999890.0 ... False\n", "2017-01-31 01:59:46.612839 156668940.0 ... False\n", "2017-01-31 01:59:51.112882 148339340.0 ... False\n", "2017-01-31 01:59:55.612917 135440930.0 ... False\n", "\n", "[302188 rows x 124 columns]" ] }, "execution_count": 5, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "mms_data[mms_data['selected'] == False]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Fe4HZy7CHvFF" }, "source": [ "We save references to data's index and column names for later use and additionally pop off the ground truths column. We will reattach the ground truths column after standardizing and interpolating the data." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", "id": "d2bm6URzIeFT" }, "outputs": [], "source": [ "index = mms_data.index\n", "selections = mms_data.pop(\"selected\")\n", "column_names = mms_data.columns" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9gBjgkNsJvPV" }, "source": [ "Since there exists a possibility that the training contains missing data or data misreported by the MMS spacecraft (reported as either infinity or negative infinity), we need to fill in (interpolate) any missing data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": {}, "colab_type": "code", "id": "IBg8Fds8KIhW" }, "outputs": [], "source": [ "mms_data = mms_data.replace([np.inf, -np.inf], np.nan)\n", "mms_data = mms_data.interpolate(method='time', limit_area='inside')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "rdMDvmcYLQ-1" }, "source": [ "We normalize all features with standardization:\n", "\n", "![z = (x - u) / s ](https://wikimedia.org/api/rest_v1/media/math/render/svg/b0aa2e7d203db1526c577192f2d9102b718eafd5)\n", "\n", "Where x̄ is the mean of the data, and σ is the standard deviation of the data.\n", "\n", "Normalization ensures that the numerical values of all features of the data fall within a range from one to negative one and are centered around their mean (zero-mean and unit variance). Normalization improves the speed and performance of training neural networks as it unifies the scale by which differences in the data are represented without altering the data themselves." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", "id": "pXlE0dj_PbqI" }, "outputs": [], "source": [ "scaler = preprocessing.StandardScaler()\n", "mms_data = scaler.fit_transform(mms_data)\n", "mms_data = pd.DataFrame(mms_data, index, column_names)\n", "mms_data = mms_data.join(selections)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "HytkkKWLR6KS" }, "source": [ "Next, we calculate class weights for our data classes (selected data points and non-selected data points). Since the distribution of our data is heavily skewed towards non-selected data points (just 1.9% of all data points in our training data were selected), it's important to give the class of selected data points a higher weight when training. In fact, without establishing these class weights our model would quickly acheive 98% accuracy by naively leaving all data points unselected." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", "id": "03pNFL5zTINT" }, "outputs": [], "source": [ "false_weight = len(mms_data)/(2*np.bincount(mms_data['selected'].values)[0])\n", "true_weight = len(mms_data)/(2*np.bincount(mms_data['selected'].values)[1])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ckN-_FOuTlI4" }, "source": [ "Our entire dataset is not contigous, and it contains time intervals with no observations. Therefore, we break it up into contigous chunks. We can do so by breaking up the data into the windows that the SITLs used to review the data." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": {}, "colab_type": "code", "id": "vuoPyoU5UV3Q" }, "outputs": [], "source": [ "sitl_windows = mms.mission_events('sroi', mms_data.index[0].to_pydatetime(), mms_data.index[-1].to_pydatetime(), sc='mms1')\n", "windows = []\n", "for start, end in zip(sitl_windows['tstart'], sitl_windows['tend']):\n", " window = mms_data[start:end]\n", " if not window.empty and len(window[window['selected']==True])>1:\n", " windows.append(window)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "colab_type": "code", "id": "cyCpZWhRfIOc", "outputId": "eeab4449-7153-41b4-cfec-8c4dab623228" }, "outputs": [ { "data": { "text/plain": [ "[ mms1_des_energyspectr_omni_fast_0 ... selected\n", " Epoch ... \n", " 2017-01-01 01:49:31.236651 1.883959 ... 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False\n", " 2017-01-21 23:39:36.897277 0.125554 ... False\n", " 2017-01-21 23:39:41.397315 0.644362 ... False\n", " 2017-01-21 23:39:45.897345 0.794314 ... False\n", " 2017-01-21 23:39:50.397383 0.748639 ... False\n", " ... ... ... ...\n", " 2017-01-22 13:27:32.804047 -1.281105 ... False\n", " 2017-01-22 13:27:37.304090 -1.316070 ... False\n", " 2017-01-22 13:27:41.804122 -1.296095 ... False\n", " 2017-01-22 13:27:46.304166 -1.274427 ... False\n", " 2017-01-22 13:27:50.804198 -1.259403 ... False\n", " \n", " [11045 rows x 124 columns],\n", " mms1_des_energyspectr_omni_fast_0 ... selected\n", " Epoch ... \n", " 2017-01-22 23:34:32.497506 0.371171 ... False\n", " 2017-01-22 23:34:36.997536 0.306008 ... False\n", " 2017-01-22 23:34:41.497574 0.222378 ... False\n", " 2017-01-22 23:34:45.997588 0.303669 ... False\n", " 2017-01-22 23:34:50.497642 0.300867 ... False\n", " ... ... ... ...\n", " 2017-01-23 13:22:37.407060 -1.084231 ... False\n", " 2017-01-23 13:22:41.907095 -1.132926 ... False\n", " 2017-01-23 13:22:46.407137 -1.138529 ... False\n", " 2017-01-23 13:22:50.907171 -1.112983 ... False\n", " 2017-01-23 13:22:55.407213 -1.114196 ... False\n", " \n", " [11046 rows x 124 columns],\n", " mms1_des_energyspectr_omni_fast_0 ... selected\n", " Epoch ... \n", " 2017-01-23 23:30:17.579249 0.625622 ... False\n", " 2017-01-23 23:30:22.079286 0.446295 ... False\n", " 2017-01-23 23:30:26.579317 0.340665 ... False\n", " 2017-01-23 23:30:31.079355 0.494095 ... False\n", " 2017-01-23 23:30:35.579386 0.285995 ... False\n", " ... ... ... ...\n", " 2017-01-24 08:16:56.833894 0.304322 ... False\n", " 2017-01-24 08:17:01.333935 0.400155 ... False\n", " 2017-01-24 08:17:05.833968 0.147983 ... False\n", " 2017-01-24 08:17:10.334009 0.348916 ... False\n", " 2017-01-24 08:17:14.834041 0.285743 ... False\n", " \n", " [7026 rows x 124 columns],\n", " mms1_des_energyspectr_omni_fast_0 ... selected\n", " Epoch ... \n", " 2017-01-24 23:25:19.951727 0.397294 ... False\n", " 2017-01-24 23:25:24.451768 0.536392 ... False\n", " 2017-01-24 23:25:28.951798 0.343806 ... False\n", " 2017-01-24 23:25:33.451838 0.284095 ... False\n", " 2017-01-24 23:25:37.951870 0.483337 ... False\n", " ... ... ... ...\n", " 2017-01-25 13:06:44.364210 -1.271923 ... False\n", " 2017-01-25 13:06:48.864244 -1.243242 ... False\n", " 2017-01-25 13:06:53.364289 -1.241685 ... False\n", " 2017-01-25 13:06:57.864325 -1.216165 ... False\n", " 2017-01-25 13:07:02.364367 -1.243583 ... False\n", " \n", " [10957 rows x 124 columns],\n", " mms1_des_energyspectr_omni_fast_0 ... selected\n", " Epoch ... \n", " 2017-01-25 23:20:21.915845 0.629981 ... False\n", " 2017-01-25 23:20:26.415884 0.712099 ... False\n", " 2017-01-25 23:20:30.915915 0.690743 ... False\n", " 2017-01-25 23:20:35.415953 0.771954 ... False\n", " 2017-01-25 23:20:39.915985 0.598590 ... False\n", " ... ... ... ...\n", " 2017-01-26 13:01:41.834981 1.751733 ... False\n", " 2017-01-26 13:01:46.335025 1.385139 ... False\n", " 2017-01-26 13:01:50.835061 1.505051 ... False\n", " 2017-01-26 13:01:55.335105 2.003533 ... False\n", " 2017-01-26 13:01:59.835140 2.122904 ... False\n", " \n", " [10956 rows x 124 columns],\n", " mms1_des_energyspectr_omni_fast_0 ... selected\n", " Epoch ... \n", " 2017-01-26 23:15:17.804388 1.620798 ... False\n", " 2017-01-26 23:15:22.304427 1.499084 ... False\n", " 2017-01-26 23:15:26.804459 1.292690 ... False\n", " 2017-01-26 23:15:31.304499 1.313568 ... False\n", " 2017-01-26 23:15:35.804530 1.579847 ... False\n", " ... ... ... ...\n", " 2017-01-27 12:56:46.723774 -1.481024 ... False\n", " 2017-01-27 12:56:51.223817 -1.474493 ... False\n", " 2017-01-27 12:56:55.723852 -1.458199 ... False\n", " 2017-01-27 12:57:00.223895 -1.479174 ... False\n", " 2017-01-27 12:57:04.723930 -1.497171 ... False\n", " \n", " [10957 rows x 124 columns],\n", " mms1_des_energyspectr_omni_fast_0 ... selected\n", " Epoch ... \n", " 2017-01-27 23:05:17.911570 0.274722 ... False\n", " 2017-01-27 23:05:22.411609 0.108195 ... False\n", " 2017-01-27 23:05:26.911641 0.005769 ... False\n", " 2017-01-27 23:05:31.411680 -0.030007 ... False\n", " 2017-01-27 23:05:35.911711 -0.060532 ... False\n", " ... ... ... ...\n", " 2017-01-28 12:46:42.333037 -1.247610 ... False\n", " 2017-01-28 12:46:46.833072 -1.237887 ... False\n", " 2017-01-28 12:46:51.333117 -1.219926 ... False\n", " 2017-01-28 12:46:55.833153 -1.233068 ... False\n", " 2017-01-28 12:47:00.333198 -1.255480 ... False\n", " \n", " [10957 rows x 124 columns],\n", " mms1_des_energyspectr_omni_fast_0 ... selected\n", " Epoch ... \n", " 2017-01-28 23:00:18.906511 0.274405 ... False\n", " 2017-01-28 23:00:23.406551 0.333638 ... False\n", " 2017-01-28 23:00:27.906583 0.324077 ... False\n", " 2017-01-28 23:00:32.406623 0.413629 ... False\n", " 2017-01-28 23:00:36.906655 0.240758 ... False\n", " ... ... ... ...\n", " 2017-01-29 12:41:43.330800 -1.292784 ... False\n", " 2017-01-29 12:41:47.830835 -1.283827 ... False\n", " 2017-01-29 12:41:52.330878 -1.331312 ... False\n", " 2017-01-29 12:41:56.830914 -1.298229 ... False\n", " 2017-01-29 12:42:01.330958 -1.317063 ... False\n", " \n", " [10957 rows x 124 columns],\n", " mms1_des_energyspectr_omni_fast_0 ... selected\n", " Epoch ... \n", " 2017-01-29 23:00:22.371527 0.205416 ... False\n", " 2017-01-29 23:00:26.871558 0.070299 ... False\n", " 2017-01-29 23:00:31.371597 0.251162 ... False\n", " 2017-01-29 23:00:35.871629 0.143420 ... False\n", " 2017-01-29 23:00:40.371669 0.299162 ... False\n", " ... ... ... ...\n", " 2017-01-30 12:41:37.792835 3.766927 ... False\n", " 2017-01-30 12:41:42.292879 3.941938 ... False\n", " 2017-01-30 12:41:46.792914 4.024080 ... False\n", " 2017-01-30 12:41:51.292959 4.471779 ... False\n", " 2017-01-30 12:41:55.792994 4.125512 ... False\n", " \n", " [10954 rows x 124 columns]]" ] }, "execution_count": 11, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "windows" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "btKBuEPkUrXW" }, "source": [ "Finally, we break up our data into individual sequences that will be fed to our neural network." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pY_SGxXFU88m" }, "source": [ "We define a SEQ_LEN variable that will determine the length of our sequences. This variable will also be passed to our network so that it knows how long of a data sequence to expect while training. The choice of sequence length is largely arbitrary." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": {}, "colab_type": "code", "id": "YQzSPJXkU7J8" }, "outputs": [], "source": [ "SEQ_LEN = 250" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gtXhi2H2j7x6" }, "source": [ "For each window, we assemble two sequences: an X_sequence containing individual data points from our training data and a y_sequence containing the truth values for those data points (whether or not those data points were selected by a SITL). \n", "\n", "We add those sequences to four collections: X_train and y_train containing X_sequences and y_sequences for our training data and X_test and y_test containing X_sequences and y_sequences for our testing data. We allocate 80% of the sequences to trainining and the remaining 20% to testing. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": {}, "colab_type": "code", "id": "w9ajG41MVJWc" }, "outputs": [], "source": [ " while True:\n", " X_train, X_test, y_train, y_test = [], [], [], []\n", "\n", " sequences = []\n", " for i in range(len(windows)):\n", " X_sequence = []\n", " y_sequence = []\n", "\n", " if random.random() < 0.6:\n", " for value in windows[i].values:\n", " X_sequence.append(value[:-1])\n", " y_sequence.append(value[-1])\n", " if len(X_sequence) == SEQ_LEN:\n", " X_train.append(X_sequence.copy())\n", " \n", " y_train.append(y_sequence.copy())\n", "\n", " X_sequence = []\n", " y_sequence = []\n", "\n", " else:\n", " for value in windows[i].values:\n", " X_sequence.append(value[:-1])\n", " y_sequence.append(value[-1])\n", " if len(X_sequence) == SEQ_LEN:\n", " X_test.append(X_sequence.copy())\n", " \n", " y_test.append(y_sequence.copy())\n", "\n", " X_sequence = []\n", " y_sequence = []\n", "\n", " X_train = np.array(X_train)\n", " X_test = np.array(X_test)\n", " y_train = np.expand_dims(np.array(y_train), axis=2)\n", " y_test = np.expand_dims(np.array(y_test), axis=2)\n", "\n", " if len(X_train) > len(X_test):\n", " break" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "NcGTxO4tYIEa" }, "source": [ "We can see how many sequences of data we have for training and testing, respectively:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "colab_type": "code", "id": "w6Xc30NSYIgK", "outputId": "dc987b91-eaab-46f3-ddd0-bb3ee20bb321" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of sequences in training data: 753\n", "Number of sequences in test data: 519\n" ] } ], "source": [ "print(f\"Number of sequences in training data: {len(X_train)}\")\n", "print(f\"Number of sequences in test data: {len(X_test)}\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "CRsSmLuDs029" }, "source": [ "## Define and Train LSTM\n", "\n", "Now that we have processed our data into our training and test sets, we can begin to build and train and our LSTM." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "r8Hqo34gs4cx" }, "source": [ "First, we need to define a custom F1 score and weighted binary crossentropy functions." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "MPl9Nicf4K_d" }, "source": [ "An F1 score is a measure of a model's accuracy, calculated as a balance of the model's precision (the number of true positives predicted by the model divided by the total number of positives predicted by the model) and recall (the number of true positives predicted by the model divided by the number of actual positives in the data):\n", "\n", "![F1 = 2 * (precision * recall) / (precision + recall)](https://wikimedia.org/api/rest_v1/media/math/render/svg/1bf179c30b00db201ce1895d88fe2915d58e6bfd)\n", "\n", "We will evaluate our model using the F1 score since we want to strike a balance between the model's precision and recall. Remember, we cannot use true accuracy (the number of true positives and true negatives divided by the number of data points in the data) because of the imbalance between our classes.\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": {}, "colab_type": "code", "id": "EKhoUVlFmfUx" }, "outputs": [], "source": [ "# (Credit: Paddy and Kev1n91 from https://stackoverflow.com/a/45305384/3988976)\n", "def f1(y_true, y_pred):\n", " def recall(y_true, y_pred):\n", " \"\"\"Recall metric.\n", "\n", " Only computes a batch-wise average of recall.\n", "\n", " Computes the recall, a metric for multi-label classification of\n", " how many relevant items are selected.\n", " \"\"\"\n", " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n", " possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n", " recall = true_positives / (possible_positives + K.epsilon())\n", " return recall\n", "\n", " def precision(y_true, y_pred):\n", " \"\"\"Precision metric.\n", "\n", " Only computes a batch-wise average of precision.\n", "\n", " Computes the precision, a metric for multi-label classification of\n", " how many selected items are relevant.\n", " \"\"\"\n", " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n", " predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n", " precision = true_positives / (predicted_positives + K.epsilon())\n", " return precision\n", " precision = precision(y_true, y_pred)\n", " recall = recall(y_true, y_pred)\n", " return 2*((precision*recall)/(precision+recall+K.epsilon()))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "emVoV71O4MAD" }, "source": [ "Cross-entropy is a function used to determine the loss between a set of predictions and their truth values. The larger the difference between a prediction and its true value, the larger the loss will be. In general, many machine learning architectures (including our LSTM) are designed to minimize their given loss function. A perfect model will have a loss of 0.\n", "\n", "Binary cross-entropy is used when we only have two classes (in our case, selected or not selected) and weighted binary cross-entropy allows us to assign a weight to one of the classes. This weight can effectively increase or decrease the loss of that class. In our case, we have previously defined a variable *true_weight* to be the class weight for positive (selected) datapoints. We will pass that weight into the function.\n", "\n", "This cross-entropy function will be passed in to our model as our loss function.\n", "\n", "(Because the loss function of a model needs to be differentiable to perform gradient descent, we cannot use our F1 score as our loss function.)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": {}, "colab_type": "code", "id": "yBxIZXbMpJG9" }, "outputs": [], "source": [ "# (Credit: tobigue from https://stackoverflow.com/questions/42158866/neural-network-for-multi-label-classification-with-large-number-of-classes-outpu)\n", "def weighted_binary_crossentropy(target, output):\n", " \"\"\"\n", " Weighted binary crossentropy between an output tensor \n", " and a target tensor. POS_WEIGHT is used as a multiplier \n", " for the positive targets.\n", "\n", " Combination of the following functions:\n", " * keras.losses.binary_crossentropy\n", " * keras.backend.tensorflow_backend.binary_crossentropy\n", " * tf.nn.weighted_cross_entropy_with_logits\n", " \"\"\"\n", " # transform back to logits\n", " _epsilon = tfb._to_tensor(tfb.epsilon(), output.dtype.base_dtype)\n", " output = tf.clip_by_value(output, _epsilon, 1 - _epsilon)\n", " output = tf.log(output / (1 - output))\n", " # compute weighted loss\n", " loss = tf.nn.weighted_cross_entropy_with_logits(targets=target,\n", " logits=output,\n", " pos_weight=true_weight)\n", " return tf.reduce_mean(loss, axis=-1)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "V7ScqhL295sl" }, "source": [ "Before building our LSTM, we define several hyperparameters that will define how the model is trained:\n", "\n", "EPOCHS: The number of times the model trains through our entire dataset\n", "\n", "BATCH_SIZE: The number of sequences that our model trains using at any given point\n", "\n", "LAYER_SIZE: The number of LSTM internal to each layer of the model.\n", "\n", "Choices for these hyperparameters are largely arbitrary and can be altered to tune our LSTM." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": {}, "colab_type": "code", "id": "U7HBAE5wwTDU" }, "outputs": [], "source": [ "EPOCHS = 100\n", "BATCH_SIZE = 128\n", "LAYER_SIZE = 300" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WMHwIiE9Aec9" }, "source": [ "We now define our LSTM.\n", "\n", "For this version of the model, we two bidirectional LSTM layers, two dropout layers, and one time distributed dense layer.\n", "\n", "Internally, an LSTM layer uses a for loop to iterate over the timesteps of a sequence, while maintaining states that encode information from those timesteps. Using these internal states, the LSTM learns the characteristics of our data (the X_sequences we defined earlier) and how those data relate to our expected output (the y_sequences we defined earlier). Normal (unidirectional) LSTMs only encode information from prior-seen timesteps. Bidirectional LSTMs can can encode information prior to and after a given timestep.\n", "\n", "With the addition of a dense layer, the LSTM will output a value between 0 and 1 that corresponds to the model's certainty about whether or not a timestep was selected by the SITL.\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 303 }, "colab_type": "code", "id": "3jU7BfnJBj0V", "outputId": "9605a3ef-47b3-4477-faf8-a60049a7719d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Call initializer instance with the dtype argument instead of passing it to the constructor\n", "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/init_ops.py:97: calling Orthogonal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Call initializer instance with the dtype argument instead of passing it to the constructor\n", "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Call initializer instance with the dtype argument instead of passing it to the constructor\n", "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "If using Keras pass *_constraint arguments to layers.\n", "WARNING:tensorflow:From :20: calling weighted_cross_entropy_with_logits (from tensorflow.python.ops.nn_impl) with targets is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "targets is deprecated, use labels instead\n" ] } ], "source": [ "model_name = f\"{SEQ_LEN}-SEQ_LEN-{BATCH_SIZE}-BATCH_SIZE-{LAYER_SIZE}-LAYER_SIZE-{int(time.time())}\"\n", "\n", "model = Sequential()\n", "\n", "model.add(Bidirectional(LSTM(LAYER_SIZE, return_sequences=True), input_shape=(None, X_train.shape[2])))\n", "\n", "model.add(Dropout(0.4))\n", "\n", "model.add(Bidirectional(LSTM(LAYER_SIZE, return_sequences=True), input_shape=(None, X_train.shape[2])))\n", "\n", "model.add(Dropout(0.4))\n", "\n", "model.add(TimeDistributed(Dense(1, activation='sigmoid')))\n", "\n", "opt = tf.keras.optimizers.Adam()\n", "\n", "model.compile(loss=weighted_binary_crossentropy,\n", " optimizer=opt,\n", " metrics=['accuracy', f1, tf.keras.metrics.Precision()])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 336 }, "colab_type": "code", "id": "asBMdOujBrbk", "outputId": "8cecf30c-864d-485b-ce81-05d1fb1b31ed" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "bidirectional (Bidirectional (None, None, 600) 1017600 \n", "_________________________________________________________________\n", "dropout (Dropout) (None, None, 600) 0 \n", "_________________________________________________________________\n", "bidirectional_1 (Bidirection (None, None, 600) 2162400 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, None, 600) 0 \n", "_________________________________________________________________\n", "time_distributed (TimeDistri (None, None, 1) 601 \n", "=================================================================\n", "Total params: 3,180,601\n", "Trainable params: 3,180,601\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vIaRlNZJBkNE" }, "source": [ "We set our training process to save the best versions of our model according to the previously defined F1 score. Each epoch, if a version of the model is trained with a higher F1 score than the previous best, the model saved on disk will be overwritten with the current best model." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": {}, "colab_type": "code", "id": "rFaaHkcwCP7X" }, "outputs": [], "source": [ "filepath = \"mp-dl-unh\" \n", "checkpoint = ModelCheckpoint(filepath, monitor='val_f1', verbose=1, save_best_only=True, mode='max')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "l8TWoDyKCRrE" }, "source": [ "The following will train the model and save the training history for later visualization." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "colab_type": "code", "id": "UCHQs29JCZMU", "outputId": "ab439715-f690-438e-a0d1-d0745dc11466" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n", "Train on 753 samples, validate on 519 samples\n", "Epoch 1/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.8397 - acc: 0.6974 - f1: 0.3451 - precision: 0.1849\n", "Epoch 00001: val_f1 improved from -inf to 0.42757, saving model to mp-dl-unh\n", "753/753 [==============================] - 15s 19ms/sample - loss: 0.7737 - acc: 0.7232 - f1: 0.3541 - precision: 0.1909 - val_loss: 0.6396 - val_acc: 0.8299 - val_f1: 0.4276 - val_precision: 0.2014\n", "Epoch 2/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.6136 - acc: 0.8747 - f1: 0.5461 - precision: 0.3894\n", "Epoch 00002: val_f1 did not improve from 0.42757\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.5620 - acc: 0.8849 - f1: 0.5549 - precision: 0.3958 - val_loss: 0.5994 - val_acc: 0.8189 - val_f1: 0.2793 - val_precision: 0.1862\n", "Epoch 3/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.5949 - acc: 0.8367 - f1: 0.4756 - precision: 0.3271\n", "Epoch 00003: val_f1 did not improve from 0.42757\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.5476 - acc: 0.8467 - f1: 0.4810 - precision: 0.3289 - val_loss: 0.4256 - val_acc: 0.8731 - val_f1: 0.3077 - val_precision: 0.2567\n", "Epoch 4/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.4788 - acc: 0.8978 - f1: 0.5785 - precision: 0.4482\n", "Epoch 00004: val_f1 improved from 0.42757 to 0.50001, saving model to mp-dl-unh\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.4369 - acc: 0.9070 - f1: 0.5971 - precision: 0.4580 - val_loss: 0.3668 - val_acc: 0.9128 - val_f1: 0.5000 - val_precision: 0.3469\n", "Epoch 5/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.4156 - acc: 0.8689 - f1: 0.5636 - precision: 0.3923\n", "Epoch 00005: val_f1 did not improve from 0.50001\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.3772 - acc: 0.8815 - f1: 0.5819 - precision: 0.4019 - val_loss: 0.3872 - val_acc: 0.9183 - val_f1: 0.4034 - val_precision: 0.3632\n", "Epoch 6/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.3745 - acc: 0.9192 - f1: 0.6637 - precision: 0.5168\n", "Epoch 00006: val_f1 did not improve from 0.50001\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.3420 - acc: 0.9244 - f1: 0.6662 - precision: 0.5181 - val_loss: 0.4222 - val_acc: 0.8848 - val_f1: 0.3684 - val_precision: 0.2857\n", "Epoch 7/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.3198 - acc: 0.9050 - f1: 0.6347 - precision: 0.4735\n", "Epoch 00007: val_f1 improved from 0.50001 to 0.51366, saving model to mp-dl-unh\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.2950 - acc: 0.9119 - f1: 0.6405 - precision: 0.4771 - val_loss: 0.4070 - val_acc: 0.8908 - val_f1: 0.5137 - val_precision: 0.2971\n", "Epoch 8/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2917 - acc: 0.9302 - f1: 0.6997 - precision: 0.5554\n", "Epoch 00008: val_f1 did not improve from 0.51366\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.2700 - acc: 0.9330 - f1: 0.6947 - precision: 0.5498 - val_loss: 0.4036 - val_acc: 0.9009 - val_f1: 0.4554 - val_precision: 0.3199\n", "Epoch 9/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2607 - acc: 0.9331 - f1: 0.7108 - precision: 0.5656\n", "Epoch 00009: val_f1 did not improve from 0.51366\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.2403 - acc: 0.9365 - f1: 0.7086 - precision: 0.5627 - val_loss: 0.4023 - val_acc: 0.9023 - val_f1: 0.4948 - val_precision: 0.3243\n", "Epoch 10/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2405 - acc: 0.9361 - f1: 0.7232 - precision: 0.5768\n", "Epoch 00010: val_f1 did not improve from 0.51366\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.2214 - acc: 0.9394 - f1: 0.7211 - precision: 0.5741 - val_loss: 0.4330 - val_acc: 0.9020 - val_f1: 0.4683 - val_precision: 0.3236\n", "Epoch 11/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2338 - acc: 0.9385 - f1: 0.7312 - precision: 0.5870\n", "Epoch 00011: val_f1 did not improve from 0.51366\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.2133 - acc: 0.9426 - f1: 0.7347 - precision: 0.5887 - val_loss: 0.4408 - val_acc: 0.9032 - val_f1: 0.3857 - val_precision: 0.3265\n", "Epoch 12/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2178 - acc: 0.9344 - f1: 0.7238 - precision: 0.5687\n", "Epoch 00012: val_f1 did not improve from 0.51366\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1993 - acc: 0.9396 - f1: 0.7310 - precision: 0.5738 - val_loss: 0.4235 - val_acc: 0.9129 - val_f1: 0.4649 - val_precision: 0.3482\n", "Epoch 13/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2071 - acc: 0.9458 - f1: 0.7570 - precision: 0.6187\n", "Epoch 00013: val_f1 did not improve from 0.51366\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1917 - acc: 0.9483 - f1: 0.7531 - precision: 0.6145 - val_loss: 0.4799 - val_acc: 0.9062 - val_f1: 0.4858 - val_precision: 0.3319\n", "Epoch 14/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2104 - acc: 0.9504 - f1: 0.7719 - precision: 0.6420\n", "Epoch 00014: val_f1 improved from 0.51366 to 0.52297, saving model to mp-dl-unh\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1932 - acc: 0.9527 - f1: 0.7684 - precision: 0.6379 - val_loss: 0.5000 - val_acc: 0.8869 - val_f1: 0.5230 - val_precision: 0.2922\n", "Epoch 15/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2647 - acc: 0.9105 - f1: 0.6608 - precision: 0.4893\n", "Epoch 00015: val_f1 did not improve from 0.52297\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.2380 - acc: 0.9205 - f1: 0.6866 - precision: 0.5039 - val_loss: 0.4565 - val_acc: 0.9263 - val_f1: 0.4257 - val_precision: 0.3876\n", "Epoch 16/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2233 - acc: 0.9420 - f1: 0.7482 - precision: 0.6011\n", "Epoch 00016: val_f1 did not improve from 0.52297\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.2056 - acc: 0.9446 - f1: 0.7432 - precision: 0.5968 - val_loss: 0.4489 - val_acc: 0.8952 - val_f1: 0.3653 - val_precision: 0.3088\n", "Epoch 17/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2032 - acc: 0.9488 - f1: 0.7627 - precision: 0.6338\n", "Epoch 00017: val_f1 did not improve from 0.52297\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1852 - acc: 0.9525 - f1: 0.7681 - precision: 0.6367 - val_loss: 0.4300 - val_acc: 0.9151 - val_f1: 0.4169 - val_precision: 0.3557\n", "Epoch 18/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1870 - acc: 0.9412 - f1: 0.7454 - precision: 0.5952\n", "Epoch 00018: val_f1 improved from 0.52297 to 0.54961, saving model to mp-dl-unh\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1700 - acc: 0.9465 - f1: 0.7572 - precision: 0.6034 - val_loss: 0.4437 - val_acc: 0.9228 - val_f1: 0.5496 - val_precision: 0.3775\n", "Epoch 19/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1757 - acc: 0.9597 - f1: 0.8024 - precision: 0.6911\n", "Epoch 00019: val_f1 did not improve from 0.54961\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1617 - acc: 0.9615 - f1: 0.7995 - precision: 0.6864 - val_loss: 0.4601 - val_acc: 0.9069 - val_f1: 0.4623 - val_precision: 0.3355\n", "Epoch 20/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1771 - acc: 0.9395 - f1: 0.7388 - precision: 0.5875\n", "Epoch 00020: val_f1 improved from 0.54961 to 0.57562, saving model to mp-dl-unh\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1675 - acc: 0.9453 - f1: 0.7526 - precision: 0.5974 - val_loss: 0.4548 - val_acc: 0.9226 - val_f1: 0.5756 - val_precision: 0.3766\n", "Epoch 21/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1746 - acc: 0.9539 - f1: 0.7852 - precision: 0.6552\n", "Epoch 00021: val_f1 did not improve from 0.57562\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1626 - acc: 0.9557 - f1: 0.7794 - precision: 0.6496 - val_loss: 0.4955 - val_acc: 0.9261 - val_f1: 0.4233 - val_precision: 0.3881\n", "Epoch 22/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1686 - acc: 0.9592 - f1: 0.8006 - precision: 0.6878\n", "Epoch 00022: val_f1 did not improve from 0.57562\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1551 - acc: 0.9614 - f1: 0.8001 - precision: 0.6854 - val_loss: 0.5072 - val_acc: 0.9091 - val_f1: 0.4278 - val_precision: 0.3412\n", "Epoch 23/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1871 - acc: 0.9423 - f1: 0.7492 - precision: 0.6002\n", "Epoch 00023: val_f1 did not improve from 0.57562\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1701 - acc: 0.9480 - f1: 0.7638 - precision: 0.6105 - val_loss: 0.4906 - val_acc: 0.9208 - val_f1: 0.4136 - val_precision: 0.3704\n", "Epoch 24/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1611 - acc: 0.9591 - f1: 0.8048 - precision: 0.6841\n", "Epoch 00024: val_f1 improved from 0.57562 to 0.58272, saving model to mp-dl-unh\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1511 - acc: 0.9617 - f1: 0.8058 - precision: 0.6854 - val_loss: 0.4396 - val_acc: 0.9231 - val_f1: 0.5827 - val_precision: 0.3796\n", "Epoch 25/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1908 - acc: 0.9429 - f1: 0.7511 - precision: 0.6034\n", "Epoch 00025: val_f1 did not improve from 0.58272\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1759 - acc: 0.9468 - f1: 0.7537 - precision: 0.6052 - val_loss: 0.4258 - val_acc: 0.9310 - val_f1: 0.5641 - val_precision: 0.4088\n", "Epoch 26/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1779 - acc: 0.9626 - f1: 0.8137 - precision: 0.7120\n", "Epoch 00026: val_f1 did not improve from 0.58272\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.1625 - acc: 0.9649 - f1: 0.8147 - precision: 0.7107 - val_loss: 0.4802 - val_acc: 0.9150 - val_f1: 0.5226 - val_precision: 0.3566\n", "Epoch 27/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2158 - acc: 0.9254 - f1: 0.7089 - precision: 0.5350\n", "Epoch 00027: val_f1 did not improve from 0.58272\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1950 - acc: 0.9332 - f1: 0.7270 - precision: 0.5473 - val_loss: 0.4961 - val_acc: 0.9092 - val_f1: 0.3794 - val_precision: 0.3318\n", "Epoch 28/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1749 - acc: 0.9619 - f1: 0.8177 - precision: 0.7044\n", "Epoch 00028: val_f1 did not improve from 0.58272\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1619 - acc: 0.9623 - f1: 0.8055 - precision: 0.6914 - val_loss: 0.4533 - val_acc: 0.9053 - val_f1: 0.4927 - val_precision: 0.3323\n", "Epoch 29/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1875 - acc: 0.9393 - f1: 0.7402 - precision: 0.5881\n", "Epoch 00029: val_f1 did not improve from 0.58272\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1698 - acc: 0.9455 - f1: 0.7568 - precision: 0.5993 - val_loss: 0.4593 - val_acc: 0.9332 - val_f1: 0.5228 - val_precision: 0.4165\n", "Epoch 30/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1464 - acc: 0.9578 - f1: 0.8005 - precision: 0.6740\n", "Epoch 00030: val_f1 improved from 0.58272 to 0.58565, saving model to mp-dl-unh\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1355 - acc: 0.9602 - f1: 0.8003 - precision: 0.6732 - val_loss: 0.4969 - val_acc: 0.9232 - val_f1: 0.5856 - val_precision: 0.3801\n", "Epoch 31/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1404 - acc: 0.9606 - f1: 0.8147 - precision: 0.6887\n", "Epoch 00031: val_f1 did not improve from 0.58565\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.1298 - acc: 0.9633 - f1: 0.8167 - precision: 0.6905 - val_loss: 0.5545 - val_acc: 0.9055 - val_f1: 0.4497 - val_precision: 0.3303\n", "Epoch 32/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1644 - acc: 0.9551 - f1: 0.7862 - precision: 0.6627\n", "Epoch 00032: val_f1 did not improve from 0.58565\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.1520 - acc: 0.9591 - f1: 0.7960 - precision: 0.6699 - val_loss: 0.5339 - val_acc: 0.9174 - val_f1: 0.3990 - val_precision: 0.3617\n", "Epoch 33/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1696 - acc: 0.9425 - f1: 0.7557 - precision: 0.6000\n", "Epoch 00033: val_f1 did not improve from 0.58565\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1558 - acc: 0.9483 - f1: 0.7703 - precision: 0.6109 - val_loss: 0.5203 - val_acc: 0.9297 - val_f1: 0.4219 - val_precision: 0.3965\n", "Epoch 34/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2050 - acc: 0.9600 - f1: 0.8113 - precision: 0.6996\n", "Epoch 00034: val_f1 did not improve from 0.58565\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1913 - acc: 0.9600 - f1: 0.7960 - precision: 0.6826 - val_loss: 0.5136 - val_acc: 0.8894 - val_f1: 0.4170 - val_precision: 0.2989\n", "Epoch 35/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.2066 - acc: 0.9286 - f1: 0.7089 - precision: 0.5464\n", "Epoch 00035: val_f1 did not improve from 0.58565\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.1891 - acc: 0.9360 - f1: 0.7271 - precision: 0.5584 - val_loss: 0.4396 - val_acc: 0.9269 - val_f1: 0.4287 - val_precision: 0.3925\n", "Epoch 36/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1601 - acc: 0.9583 - f1: 0.7980 - precision: 0.6796\n", "Epoch 00036: val_f1 did not improve from 0.58565\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.1476 - acc: 0.9607 - f1: 0.7982 - precision: 0.6783 - val_loss: 0.4820 - val_acc: 0.9197 - val_f1: 0.4863 - val_precision: 0.3710\n", "Epoch 37/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1486 - acc: 0.9507 - f1: 0.7752 - precision: 0.6365\n", "Epoch 00037: val_f1 did not improve from 0.58565\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1382 - acc: 0.9545 - f1: 0.7814 - precision: 0.6408 - val_loss: 0.4972 - val_acc: 0.9274 - val_f1: 0.4364 - val_precision: 0.3943\n", "Epoch 38/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1395 - acc: 0.9684 - f1: 0.8379 - precision: 0.7417\n", "Epoch 00038: val_f1 did not improve from 0.58565\n", "753/753 [==============================] - 11s 15ms/sample - loss: 0.1305 - acc: 0.9692 - f1: 0.8310 - precision: 0.7329 - val_loss: 0.5471 - val_acc: 0.9167 - val_f1: 0.5373 - val_precision: 0.3608\n", "Epoch 39/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1557 - acc: 0.9579 - f1: 0.8039 - precision: 0.6752\n", "Epoch 00039: val_f1 did not improve from 0.58565\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.1427 - acc: 0.9614 - f1: 0.8108 - precision: 0.6808 - val_loss: 0.5140 - val_acc: 0.9252 - val_f1: 0.4214 - val_precision: 0.3871\n", "Epoch 40/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1403 - acc: 0.9549 - f1: 0.7917 - precision: 0.6567\n", "Epoch 00040: val_f1 improved from 0.58565 to 0.61811, saving model to mp-dl-unh\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1295 - acc: 0.9590 - f1: 0.8019 - precision: 0.6646 - val_loss: 0.5232 - val_acc: 0.9301 - val_f1: 0.6181 - val_precision: 0.4024\n", "Epoch 41/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1241 - acc: 0.9669 - f1: 0.8349 - precision: 0.7268\n", "Epoch 00041: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1149 - acc: 0.9692 - f1: 0.8372 - precision: 0.7278 - val_loss: 0.5646 - val_acc: 0.9202 - val_f1: 0.5297 - val_precision: 0.3688\n", "Epoch 42/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1113 - acc: 0.9679 - f1: 0.8384 - precision: 0.7304\n", "Epoch 00042: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1038 - acc: 0.9704 - f1: 0.8435 - precision: 0.7343 - val_loss: 0.5828 - val_acc: 0.9263 - val_f1: 0.5464 - val_precision: 0.3863\n", "Epoch 43/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0992 - acc: 0.9745 - f1: 0.8704 - precision: 0.7740\n", "Epoch 00043: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0928 - acc: 0.9761 - f1: 0.8715 - precision: 0.7748 - val_loss: 0.5917 - val_acc: 0.9313 - val_f1: 0.5702 - val_precision: 0.4053\n", "Epoch 44/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0955 - acc: 0.9770 - f1: 0.8823 - precision: 0.7930\n", "Epoch 00044: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0893 - acc: 0.9782 - f1: 0.8808 - precision: 0.7913 - val_loss: 0.6317 - val_acc: 0.9255 - val_f1: 0.4839 - val_precision: 0.3868\n", "Epoch 45/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1074 - acc: 0.9722 - f1: 0.8606 - precision: 0.7602\n", "Epoch 00045: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0990 - acc: 0.9746 - f1: 0.8660 - precision: 0.7649 - val_loss: 0.6379 - val_acc: 0.9302 - val_f1: 0.5052 - val_precision: 0.4005\n", "Epoch 46/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1209 - acc: 0.9640 - f1: 0.8311 - precision: 0.7056\n", "Epoch 00046: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.1117 - acc: 0.9664 - f1: 0.8319 - precision: 0.7069 - val_loss: 0.6452 - val_acc: 0.9338 - val_f1: 0.4438 - val_precision: 0.4144\n", "Epoch 47/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1087 - acc: 0.9763 - f1: 0.8758 - precision: 0.7941\n", "Epoch 00047: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1004 - acc: 0.9774 - f1: 0.8739 - precision: 0.7907 - val_loss: 0.6740 - val_acc: 0.9174 - val_f1: 0.5592 - val_precision: 0.3589\n", "Epoch 48/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1176 - acc: 0.9667 - f1: 0.8328 - precision: 0.7232\n", "Epoch 00048: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1074 - acc: 0.9698 - f1: 0.8419 - precision: 0.7300 - val_loss: 0.7245 - val_acc: 0.9103 - val_f1: 0.4316 - val_precision: 0.3335\n", "Epoch 49/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0990 - acc: 0.9761 - f1: 0.8772 - precision: 0.7874\n", "Epoch 00049: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0921 - acc: 0.9774 - f1: 0.8755 - precision: 0.7854 - val_loss: 0.6579 - val_acc: 0.9159 - val_f1: 0.4782 - val_precision: 0.3560\n", "Epoch 50/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1273 - acc: 0.9606 - f1: 0.8043 - precision: 0.6868\n", "Epoch 00050: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.1173 - acc: 0.9646 - f1: 0.8183 - precision: 0.6969 - val_loss: 0.6570 - val_acc: 0.9305 - val_f1: 0.4314 - val_precision: 0.3995\n", "Epoch 51/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1187 - acc: 0.9750 - f1: 0.8755 - precision: 0.7838\n", "Epoch 00051: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1125 - acc: 0.9751 - f1: 0.8645 - precision: 0.7723 - val_loss: 0.5983 - val_acc: 0.9080 - val_f1: 0.5348 - val_precision: 0.3320\n", "Epoch 52/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1176 - acc: 0.9691 - f1: 0.8425 - precision: 0.7401\n", "Epoch 00052: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.1075 - acc: 0.9715 - f1: 0.8479 - precision: 0.7436 - val_loss: 0.5963 - val_acc: 0.9281 - val_f1: 0.5786 - val_precision: 0.3919\n", "Epoch 53/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0958 - acc: 0.9731 - f1: 0.8657 - precision: 0.7643\n", "Epoch 00053: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0890 - acc: 0.9754 - f1: 0.8711 - precision: 0.7693 - val_loss: 0.5994 - val_acc: 0.9329 - val_f1: 0.4421 - val_precision: 0.4104\n", "Epoch 54/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0904 - acc: 0.9796 - f1: 0.8945 - precision: 0.8134\n", "Epoch 00054: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0837 - acc: 0.9807 - f1: 0.8931 - precision: 0.8118 - val_loss: 0.6191 - val_acc: 0.9318 - val_f1: 0.5887 - val_precision: 0.4080\n", "Epoch 55/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0798 - acc: 0.9788 - f1: 0.8889 - precision: 0.8035\n", "Epoch 00055: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0743 - acc: 0.9801 - f1: 0.8897 - precision: 0.8038 - val_loss: 0.6557 - val_acc: 0.9358 - val_f1: 0.5383 - val_precision: 0.4236\n", "Epoch 56/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0775 - acc: 0.9822 - f1: 0.9072 - precision: 0.8325\n", "Epoch 00056: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0720 - acc: 0.9832 - f1: 0.9067 - precision: 0.8317 - val_loss: 0.6965 - val_acc: 0.9321 - val_f1: 0.5790 - val_precision: 0.4076\n", "Epoch 57/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0784 - acc: 0.9786 - f1: 0.8890 - precision: 0.8014\n", "Epoch 00057: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0743 - acc: 0.9804 - f1: 0.8933 - precision: 0.8066 - val_loss: 0.7530 - val_acc: 0.9334 - val_f1: 0.4333 - val_precision: 0.4096\n", "Epoch 58/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0754 - acc: 0.9807 - f1: 0.8987 - precision: 0.8184\n", "Epoch 00058: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0704 - acc: 0.9816 - f1: 0.8968 - precision: 0.8163 - val_loss: 0.7244 - val_acc: 0.9340 - val_f1: 0.4407 - val_precision: 0.4142\n", "Epoch 59/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0727 - acc: 0.9844 - f1: 0.9139 - precision: 0.8513\n", "Epoch 00059: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0678 - acc: 0.9848 - f1: 0.9102 - precision: 0.8465 - val_loss: 0.7185 - val_acc: 0.9304 - val_f1: 0.4268 - val_precision: 0.4012\n", "Epoch 60/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0800 - acc: 0.9764 - f1: 0.8778 - precision: 0.7848\n", "Epoch 00060: val_f1 did not improve from 0.61811\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0737 - acc: 0.9788 - f1: 0.8865 - precision: 0.7926 - val_loss: 0.8552 - val_acc: 0.9380 - val_f1: 0.5040 - val_precision: 0.4282\n", "Epoch 61/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0829 - acc: 0.9833 - f1: 0.9133 - precision: 0.8460\n", "Epoch 00061: val_f1 improved from 0.61811 to 0.62276, saving model to mp-dl-unh\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0767 - acc: 0.9838 - f1: 0.9090 - precision: 0.8411 - val_loss: 0.6703 - val_acc: 0.9328 - val_f1: 0.6228 - val_precision: 0.4134\n", "Epoch 62/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1030 - acc: 0.9752 - f1: 0.8691 - precision: 0.7839\n", "Epoch 00062: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0927 - acc: 0.9775 - f1: 0.8774 - precision: 0.7899 - val_loss: 0.6816 - val_acc: 0.9329 - val_f1: 0.4430 - val_precision: 0.4115\n", "Epoch 63/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1066 - acc: 0.9658 - f1: 0.8387 - precision: 0.7154\n", "Epoch 00063: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0994 - acc: 0.9680 - f1: 0.8388 - precision: 0.7161 - val_loss: 0.7887 - val_acc: 0.9395 - val_f1: 0.5539 - val_precision: 0.4356\n", "Epoch 64/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1056 - acc: 0.9796 - f1: 0.8957 - precision: 0.8205\n", "Epoch 00064: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0962 - acc: 0.9807 - f1: 0.8945 - precision: 0.8184 - val_loss: 0.6480 - val_acc: 0.9326 - val_f1: 0.5222 - val_precision: 0.4126\n", "Epoch 65/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0979 - acc: 0.9704 - f1: 0.8489 - precision: 0.7450\n", "Epoch 00065: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0896 - acc: 0.9736 - f1: 0.8617 - precision: 0.7551 - val_loss: 0.6877 - val_acc: 0.9373 - val_f1: 0.5169 - val_precision: 0.4292\n", "Epoch 66/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0807 - acc: 0.9820 - f1: 0.9010 - precision: 0.8328\n", "Epoch 00066: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0743 - acc: 0.9834 - f1: 0.9041 - precision: 0.8348 - val_loss: 0.7158 - val_acc: 0.9290 - val_f1: 0.5774 - val_precision: 0.3962\n", "Epoch 67/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0812 - acc: 0.9760 - f1: 0.8776 - precision: 0.7820\n", "Epoch 00067: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0742 - acc: 0.9783 - f1: 0.8851 - precision: 0.7887 - val_loss: 0.7923 - val_acc: 0.9326 - val_f1: 0.5752 - val_precision: 0.4064\n", "Epoch 68/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0761 - acc: 0.9855 - f1: 0.9219 - precision: 0.8645\n", "Epoch 00068: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0700 - acc: 0.9862 - f1: 0.9206 - precision: 0.8623 - val_loss: 0.7038 - val_acc: 0.9285 - val_f1: 0.5257 - val_precision: 0.3945\n", "Epoch 69/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0764 - acc: 0.9787 - f1: 0.8876 - precision: 0.8028\n", "Epoch 00069: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0700 - acc: 0.9805 - f1: 0.8923 - precision: 0.8067 - val_loss: 0.7089 - val_acc: 0.9354 - val_f1: 0.5950 - val_precision: 0.4220\n", "Epoch 70/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0776 - acc: 0.9852 - f1: 0.9193 - precision: 0.8628\n", "Epoch 00070: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0709 - acc: 0.9862 - f1: 0.9208 - precision: 0.8634 - val_loss: 0.7348 - val_acc: 0.9339 - val_f1: 0.5063 - val_precision: 0.4143\n", "Epoch 71/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0956 - acc: 0.9716 - f1: 0.8585 - precision: 0.7520\n", "Epoch 00071: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0872 - acc: 0.9743 - f1: 0.8664 - precision: 0.7587 - val_loss: 0.7796 - val_acc: 0.9365 - val_f1: 0.4359 - val_precision: 0.4240\n", "Epoch 72/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0798 - acc: 0.9852 - f1: 0.9184 - precision: 0.8621\n", "Epoch 00072: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0739 - acc: 0.9860 - f1: 0.9177 - precision: 0.8603 - val_loss: 0.6781 - val_acc: 0.9315 - val_f1: 0.5643 - val_precision: 0.4053\n", "Epoch 73/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0780 - acc: 0.9763 - f1: 0.8782 - precision: 0.7841\n", "Epoch 00073: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0716 - acc: 0.9783 - f1: 0.8835 - precision: 0.7886 - val_loss: 0.7094 - val_acc: 0.9371 - val_f1: 0.4491 - val_precision: 0.4293\n", "Epoch 74/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0702 - acc: 0.9856 - f1: 0.9217 - precision: 0.8623\n", "Epoch 00074: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0648 - acc: 0.9867 - f1: 0.9233 - precision: 0.8637 - val_loss: 0.7048 - val_acc: 0.9354 - val_f1: 0.5295 - val_precision: 0.4215\n", "Epoch 75/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0825 - acc: 0.9770 - f1: 0.8841 - precision: 0.7894\n", "Epoch 00075: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0766 - acc: 0.9787 - f1: 0.8862 - precision: 0.7919 - val_loss: 0.7872 - val_acc: 0.9431 - val_f1: 0.4503 - val_precision: 0.4560\n", "Epoch 76/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0949 - acc: 0.9846 - f1: 0.9168 - precision: 0.8601\n", "Epoch 00076: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0857 - acc: 0.9857 - f1: 0.9188 - precision: 0.8612 - val_loss: 0.7124 - val_acc: 0.9257 - val_f1: 0.5371 - val_precision: 0.3839\n", "Epoch 77/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1075 - acc: 0.9680 - f1: 0.8385 - precision: 0.7302\n", "Epoch 00077: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0967 - acc: 0.9715 - f1: 0.8521 - precision: 0.7404 - val_loss: 0.6554 - val_acc: 0.9293 - val_f1: 0.5662 - val_precision: 0.3928\n", "Epoch 78/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1066 - acc: 0.9816 - f1: 0.9014 - precision: 0.8366\n", "Epoch 00078: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0962 - acc: 0.9827 - f1: 0.9015 - precision: 0.8352 - val_loss: 0.5857 - val_acc: 0.9272 - val_f1: 0.5520 - val_precision: 0.3899\n", "Epoch 79/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.1019 - acc: 0.9699 - f1: 0.8456 - precision: 0.7424\n", "Epoch 00079: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0923 - acc: 0.9731 - f1: 0.8578 - precision: 0.7519 - val_loss: 0.6039 - val_acc: 0.9381 - val_f1: 0.5612 - val_precision: 0.4322\n", "Epoch 80/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0848 - acc: 0.9832 - f1: 0.9094 - precision: 0.8446\n", "Epoch 00080: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0779 - acc: 0.9842 - f1: 0.9101 - precision: 0.8446 - val_loss: 0.6343 - val_acc: 0.9332 - val_f1: 0.5017 - val_precision: 0.4113\n", "Epoch 81/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0787 - acc: 0.9779 - f1: 0.8874 - precision: 0.7957\n", "Epoch 00081: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0718 - acc: 0.9799 - f1: 0.8936 - precision: 0.8015 - val_loss: 0.7117 - val_acc: 0.9346 - val_f1: 0.4091 - val_precision: 0.4157\n", "Epoch 82/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0771 - acc: 0.9854 - f1: 0.9218 - precision: 0.8642\n", "Epoch 00082: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0702 - acc: 0.9862 - f1: 0.9215 - precision: 0.8631 - val_loss: 0.7005 - val_acc: 0.9317 - val_f1: 0.4330 - val_precision: 0.4045\n", "Epoch 83/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0696 - acc: 0.9811 - f1: 0.9012 - precision: 0.8209\n", "Epoch 00083: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0638 - acc: 0.9831 - f1: 0.9082 - precision: 0.8276 - val_loss: 0.7277 - val_acc: 0.9353 - val_f1: 0.4932 - val_precision: 0.4181\n", "Epoch 84/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0619 - acc: 0.9858 - f1: 0.9236 - precision: 0.8598\n", "Epoch 00084: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0568 - acc: 0.9868 - f1: 0.9251 - precision: 0.8612 - val_loss: 0.7442 - val_acc: 0.9352 - val_f1: 0.5954 - val_precision: 0.4190\n", "Epoch 85/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0591 - acc: 0.9845 - f1: 0.9155 - precision: 0.8484\n", "Epoch 00085: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0544 - acc: 0.9858 - f1: 0.9188 - precision: 0.8514 - val_loss: 0.8300 - val_acc: 0.9384 - val_f1: 0.5778 - val_precision: 0.4317\n", "Epoch 86/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0568 - acc: 0.9879 - f1: 0.9340 - precision: 0.8792\n", "Epoch 00086: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0524 - acc: 0.9886 - f1: 0.9341 - precision: 0.8790 - val_loss: 0.8088 - val_acc: 0.9378 - val_f1: 0.5847 - val_precision: 0.4296\n", "Epoch 87/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0531 - acc: 0.9865 - f1: 0.9272 - precision: 0.8653\n", "Epoch 00087: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0491 - acc: 0.9878 - f1: 0.9312 - precision: 0.8693 - val_loss: 0.8416 - val_acc: 0.9402 - val_f1: 0.5885 - val_precision: 0.4411\n", "Epoch 88/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0506 - acc: 0.9886 - f1: 0.9374 - precision: 0.8845\n", "Epoch 00088: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0467 - acc: 0.9893 - f1: 0.9378 - precision: 0.8847 - val_loss: 0.8522 - val_acc: 0.9400 - val_f1: 0.4461 - val_precision: 0.4407\n", "Epoch 89/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0477 - acc: 0.9893 - f1: 0.9402 - precision: 0.8904\n", "Epoch 00089: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0441 - acc: 0.9900 - f1: 0.9415 - precision: 0.8914 - val_loss: 0.8778 - val_acc: 0.9400 - val_f1: 0.5881 - val_precision: 0.4405\n", "Epoch 90/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0468 - acc: 0.9888 - f1: 0.9382 - precision: 0.8857\n", "Epoch 00090: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0432 - acc: 0.9898 - f1: 0.9409 - precision: 0.8883 - val_loss: 0.9254 - val_acc: 0.9417 - val_f1: 0.5656 - val_precision: 0.4487\n", "Epoch 91/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0440 - acc: 0.9908 - f1: 0.9482 - precision: 0.9043\n", "Epoch 00091: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0409 - acc: 0.9913 - f1: 0.9474 - precision: 0.9032 - val_loss: 0.9377 - val_acc: 0.9405 - val_f1: 0.5700 - val_precision: 0.4427\n", "Epoch 92/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0436 - acc: 0.9906 - f1: 0.9463 - precision: 0.9025\n", "Epoch 00092: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0403 - acc: 0.9912 - f1: 0.9471 - precision: 0.9030 - val_loss: 0.9483 - val_acc: 0.9404 - val_f1: 0.4586 - val_precision: 0.4416\n", "Epoch 93/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0461 - acc: 0.9889 - f1: 0.9404 - precision: 0.8862\n", "Epoch 00093: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0426 - acc: 0.9898 - f1: 0.9423 - precision: 0.8885 - val_loss: 0.9636 - val_acc: 0.9421 - val_f1: 0.4537 - val_precision: 0.4496\n", "Epoch 94/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0441 - acc: 0.9914 - f1: 0.9523 - precision: 0.9107\n", "Epoch 00094: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0413 - acc: 0.9916 - f1: 0.9497 - precision: 0.9080 - val_loss: 0.8937 - val_acc: 0.9405 - val_f1: 0.5775 - val_precision: 0.4432\n", "Epoch 95/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0471 - acc: 0.9886 - f1: 0.9361 - precision: 0.8842\n", "Epoch 00095: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0435 - acc: 0.9896 - f1: 0.9392 - precision: 0.8874 - val_loss: 0.9624 - val_acc: 0.9423 - val_f1: 0.4566 - val_precision: 0.4509\n", "Epoch 96/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0444 - acc: 0.9905 - f1: 0.9482 - precision: 0.9015\n", "Epoch 00096: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0414 - acc: 0.9909 - f1: 0.9465 - precision: 0.8997 - val_loss: 0.9201 - val_acc: 0.9415 - val_f1: 0.4490 - val_precision: 0.4480\n", "Epoch 97/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0445 - acc: 0.9914 - f1: 0.9511 - precision: 0.9121\n", "Epoch 00097: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 11s 14ms/sample - loss: 0.0410 - acc: 0.9919 - f1: 0.9505 - precision: 0.9109 - val_loss: 0.9170 - val_acc: 0.9384 - val_f1: 0.4426 - val_precision: 0.4335\n", "Epoch 98/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0584 - acc: 0.9850 - f1: 0.9210 - precision: 0.8519\n", "Epoch 00098: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0533 - acc: 0.9865 - f1: 0.9264 - precision: 0.8576 - val_loss: 1.0123 - val_acc: 0.9432 - val_f1: 0.5754 - val_precision: 0.4548\n", "Epoch 99/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0556 - acc: 0.9910 - f1: 0.9505 - precision: 0.9138\n", "Epoch 00099: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0513 - acc: 0.9912 - f1: 0.9472 - precision: 0.9093 - val_loss: 0.8208 - val_acc: 0.9389 - val_f1: 0.4494 - val_precision: 0.4374\n", "Epoch 100/100\n", "640/753 [========================>.....] - ETA: 1s - loss: 0.0715 - acc: 0.9814 - f1: 0.9007 - precision: 0.8240\n", "Epoch 00100: val_f1 did not improve from 0.62276\n", "753/753 [==============================] - 10s 14ms/sample - loss: 0.0645 - acc: 0.9832 - f1: 0.9072 - precision: 0.8296 - val_loss: 0.7622 - val_acc: 0.9396 - val_f1: 0.4736 - val_precision: 0.4397\n" ] } ], "source": [ "history = model.fit(\n", " x=X_train, y=y_train,\n", " batch_size=BATCH_SIZE,\n", " epochs=EPOCHS,\n", " validation_data=(X_test, y_test),\n", " callbacks=[checkpoint],\n", " verbose=1,\n", " shuffle=False\n", ")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VzBPJKF0Q5sW" }, "source": [ "## Performance Visualization" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "mWCIq-enCh3U" }, "source": [ "To evaluate the training of our model over time, we visualize the model's loss on its training and testing data." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 316 }, "colab_type": "code", "id": "ZhneNTcYChdt", "outputId": "84529fcd-1875-493f-f03d-fb36e69487a3" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('Model Training Loss vs. Testing Loss by Epoch')\n", "plt.ylabel('Loss')\n", "plt.xlabel('Epoch')\n", "plt.legend(['train', 'testing'], loc='upper right')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 316 }, "colab_type": "code", "id": "DlTr3Cs4VNOB", "outputId": "c4f34a1a-1372-40f8-85e9-14c40913d08c" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "plt.plot(history.history['f1'])\n", "plt.plot(history.history['val_f1'])\n", "plt.title('Model Training F1 vs. Testing F1 by Epoch')\n", "plt.ylabel('F1')\n", "plt.xlabel('Epoch')\n", "plt.legend(['train', 'testing'], loc='upper right')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 316 }, "colab_type": "code", "id": "NQnqV-Kaw6fm", "outputId": "9840871d-7ab2-4892-e126-458ec21106e1" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "plt.plot(history.history['precision'])\n", "plt.plot(history.history['val_precision'])\n", "plt.title('Model Training Precision vs. Testing Precision by Epoch')\n", "plt.ylabel('Precision')\n", "plt.xlabel('Epoch')\n", "plt.legend(['train', 'testing'], loc='upper right')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "D8cXg6gpsZbN" }, "source": [ "(We can see that the model performs much better on its training data. This is expected, as the model learns to recreate the selections of the training data. We can also see that the performance of the model on the testing data decreases over time. This is evidence of the model overfitting. At some point, the model begins to naively recreate the selections of the training data rather than truly learning how to make selections. In practice, we effectively ignore this as we have already saved the version of the model with the best performance on the testing data - mitigating any overfitting.)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": {}, "colab_type": "code", "id": "EVdN-Urh-mdX" }, "outputs": [], "source": [ "model = tf.keras.models.load_model('/content/mp-dl-unh', {'weighted_binary_crossentropy':weighted_binary_crossentropy, 'f1':f1})" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "K2Hd8FlY2Xty" }, "source": [ "## Model Performance Visualization\n", "\n", "Now that we have trained the model, we will visualize its selection-making ability compared to the SITLs." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "jwbEhnbi2Xt0" }, "source": [ "Since we've already preprocessed the testing/training data into a format suitable for model training, we reload that data to preprocess it into a format suitable for evaluation." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": {}, "colab_type": "code", "id": "loaf_Rpz2Xt0" }, "outputs": [], "source": [ "validation_data = pd.read_csv('training_data.csv', index_col=0, infer_datetime_format=True,\n", "\t\t\t\t\t\t parse_dates=[0])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "oujploNp2Xt4" }, "source": [ "We apply the same preprocessing steps to this data as we did for the original training and testing data." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": {}, "colab_type": "code", "id": "UarjtN552Xt4" }, "outputs": [], "source": [ "index = validation_data.index\n", "selections = validation_data.pop(\"selected\")\n", "column_names = validation_data.columns" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": {}, "colab_type": "code", "id": "PB666aem2Xt7" }, "outputs": [], "source": [ "validation_data = validation_data.replace([np.inf, -np.inf], np.nan)\n", "validation_data = validation_data.interpolate(method='time', limit_area='inside')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": {}, "colab_type": "code", "id": "DtkJHVze2Xt-" }, "outputs": [], "source": [ "validation_data = scaler.transform(validation_data)\n", "validation_data = pd.DataFrame(validation_data, index, column_names)\n", "validation_data = validation_data.join(selections)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": {}, "colab_type": "code", "id": "k84Veyug2XuC" }, "outputs": [], "source": [ "validation_X = validation_data.values[:,:-1]\n", "validation_y = validation_data.values[:,-1]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "zux-H0AMLRF0" }, "source": [ "Using the model we trainend earlier, we make test predctions on our validation data." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": {}, "colab_type": "code", "id": "QWHQ9x8x2XuH" }, "outputs": [], "source": [ "test_predictions = model.predict(np.expand_dims(validation_X, axis=0))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "jTUqi-6C2XuJ" }, "source": [ "We visualize the true SITL selections made over the validation data by plotting the ground truth values for each datapoint in the data (where a 1 denotes that an individual datapoint was selected and a 0 denotes that it wasn't).\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 372 }, "colab_type": "code", "id": "izyEw0C32XuJ", "outputId": "091dd4bf-6170-4765-b927-c438e4f4b742" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(28, 5))\n", "plt.plot(validation_y.astype(int))\n", "plt.title(\"Ground Truth (SITL) Selections by Datapoint\")\n", "plt.ylabel('Selected (1) or not (0)')\n", "plt.xlabel('Datapoint')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "C_f_rD7T2XuM" }, "source": [ "...and we do the same for the model's predictions." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 372 }, "colab_type": "code", "id": "QRFwAeCF2XuN", "outputId": "f91a11ea-9ed9-4644-b9f8-bef79b7a92a5" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(28, 5))\n", "plt.plot(test_predictions.squeeze())\n", "plt.title(\"Model Predicted Selections by Datapoint\")\n", "plt.ylabel('Selection confidence (continous)')\n", "plt.xlabel('Datapoint')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "nzomETjp3tUa" }, "source": [ "From this plot, we can see the continuous nature of the model's predictions. As mentioned earlier, the model outputs a continuous value between 0 and 1 for each datapoint that(very roughly) corresponds to its confidence in the selection of a point (i.e. an outputted value of 0.95 for a datapoint roughly means that the model is 95% certain that that point should be selected)." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "KRuZkV0t2XuP" }, "source": [ "With this in mind, we filter the model's predictions so that only those predictions with a >= 50% probability of being a magnetopause crossing are kept. This choice of probability/certainty is known as the threshold. \n", "\n", "This choice of threshold is chosen to optimize between over-selecting datapoints (resulting in more false-positives) and under-selecting them (resulting in more false-negatives).\n", "\n", "As an example, consider an email server's spam-detection system. Such a system might have a fairly high threshold (>99%), as you don't want to accidentally send a user's non-spam email to their spam inbox. At the same time, it's okay if a handful of spam emails make it through their regular inbox.\n", "\n", "In our case, we can afford to over-select datapoints as we do not want to miss out on any potential magnetopause crossings." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 372 }, "colab_type": "code", "id": "Qq6_n8gP2XuP", "outputId": "ecab9483-0719-4a43-8b17-5a636d3b7914" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "t_output = [0 if x < 0.5 else 1 for x in test_predictions.squeeze()]\n", "plt.figure(figsize=(28, 5))\n", "plt.plot(t_output)\n", "plt.title(\"Filtered Model Predictions by Datapoint\")\n", "plt.ylabel('Selected (1) or not (0)')\n", "plt.xlabel('Datapoint')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "--HNA2LF-AE4" }, "source": [ "## Model Validation\n", "\n", "Although we have already validated our model on data it has not seen (the testing set), we need to make sure that its ability to select magnetopause crossings is transferable to another range of data." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "tfv571-4MdaP" }, "source": [ "We load a third set of data, the validation set, which serves as an independent check on the model. " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": {}, "colab_type": "code", "id": "1YUj-vy80I2l" }, "outputs": [], "source": [ "validation_data = pd.read_csv('validation_data.csv', index_col=0, infer_datetime_format=True,\n", "\t\t\t\t\t\t parse_dates=[0])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "tsg-zH1Y0I1p" }, "source": [ "We apply the same preprocessing steps to the validation data as we did for the training and testing data." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "colab": {}, "colab_type": "code", "id": "Z3AsLjh-xsYd" }, "outputs": [], "source": [ "index = validation_data.index\n", "selections = validation_data.pop(\"selected\")\n", "column_names = validation_data.columns" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "colab": {}, "colab_type": "code", "id": "FMXckJVX0QMs" }, "outputs": [], "source": [ "validation_data = validation_data.replace([np.inf, -np.inf], np.nan)\n", "validation_data = validation_data.interpolate(method='time', limit_area='inside')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dcgfN3Y20bs8" }, "source": [ "However, we standardize the validation data to the scale of the training/testing data." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "colab": {}, "colab_type": "code", "id": "E2pTbLeH0Ryk" }, "outputs": [], "source": [ "validation_data = scaler.transform(validation_data)\n", "validation_data = pd.DataFrame(validation_data, index, column_names)\n", "validation_data = validation_data.join(selections)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "colab": {}, "colab_type": "code", "id": "HmvIfkOQ0S1N" }, "outputs": [], "source": [ "validation_X = validation_data.values[:,:-1]\n", "validation_y = validation_data.values[:,-1]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "8Iuoi7WQ53gk" }, "source": [ "Using the model we trained earlier, we make test predctions on our validation data." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "colab": {}, "colab_type": "code", "id": "AlOFmA_R0Wm1" }, "outputs": [], "source": [ "test_predictions = model.predict(np.expand_dims(validation_X, axis=0))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "YZZPb5EjM9EB" }, "source": [ "We visualize the true SITL selections made over the validation data in the same way we did above.\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 372 }, "colab_type": "code", "id": "42AfNry2FO7V", "outputId": "2f281410-11c5-40f5-9740-3a071b488e3e" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(28, 5))\n", "plt.plot(validation_y.astype(int))\n", "plt.title(\"Ground Truth (SITL) Selections by Datapoint\")\n", "plt.ylabel('Selected (1) or not (0)')\n", "plt.xlabel('Datapoints')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "oSdniM9HND_c" }, "source": [ "...and we do the same for the model's predictions." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 372 }, "colab_type": "code", "id": "IQvn147CFVgq", "outputId": "7ffd419d-50f4-457a-8ba5-785d96c152bb" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(28, 5))\n", "plt.plot(test_predictions.squeeze())\n", "plt.title(\"Model Predicted Selections by Datapoint\")\n", "plt.ylabel('Selection confidence (continous)')\n", "plt.xlabel('Datapoints')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Fbr_DnYhNJOJ" }, "source": [ "Once again, we filter the model's predictions so that only those predictions with a >= 50% probability of being a magnetopause crossing are kept." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 372 }, "colab_type": "code", "id": "kKbbKKOAFZ7y", "outputId": "e1a0b94c-2dce-4281-ed91-e755af68917f" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "t_output = [0 if x < 0.5 else 1 for x in test_predictions.squeeze()]\n", "plt.figure(figsize=(28, 5))\n", "plt.plot(t_output)\n", "plt.title(\"Filtered Model Predictions by Datapoint\")\n", "plt.ylabel('Selected (1) or not (0)')\n", "plt.xlabel('Datapoints')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "7Z42aU4b4ieu" }, "source": [ "We now plot a receiver operating characteristic (ROC) curve based on the model's performance over the evaluation data. \n", "\n", "An ROC curve will plot a model's true-positive vs. false positive rates of predictions for varying choices of thresholds. As the threshold approaches 1, the false positive rate and the true positive rates approach 0, as every prediction made is over the threshold and is thus considered a selection. As the threshold approaches 1, the false positive rate and the true positive rates approach 0, as no prediction made surpasses the threshold of 1.\n", "\n", "While we can use the plot to determine where we want to set our threshold (considering the importance of under-selecting or over-selecting points), it is more often used to get a sense of the performance of our model.\n", "\n", "To do so, we calculate the total area under the ROC curve. This area is equal to the probability that the model will output a higher prediction value for a randomly chosen datapoint whose ground truth was \"selected\" than for a randomly chosen datapoint whose ground truth value was \"not selected\"." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 316 }, "colab_type": "code", "id": "62howqH4NP2h", "outputId": "0846195d-924d-47e5-8a84-d92e34ceb729" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "fpr, tpr, thresholds = roc_curve(validation_y.astype(int), test_predictions.squeeze())\n", "lw = 2\n", "plt.plot(fpr, tpr, color='darkorange',\n", " lw=lw, label='ROC curve')\n", "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.0])\n", "plt.xlabel('False Positive Rate')\n", "plt.ylabel('True Positive Rate')\n", "plt.title('ROC curve - AUC = {:.2f}'.format(auc(fpr, tpr)))\n", "plt.legend(loc=\"lower right\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "J2X1ovuuNyE6" }, "source": [ "Finally, we generate a list of predicted selection windows. The following code groups contiguous selected datapoints into windows and list the start and dates of those windows.\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "colab": {}, "colab_type": "code", "id": "XC-65ktvIKUc" }, "outputs": [], "source": [ "predicts_df = pd.DataFrame()\n", "predicts_df.insert(0, \"time\", validation_data.index)\n", "predicts_df.insert(1, \"prediction\", t_output)\n", "predicts_df['group'] = (predicts_df.prediction != predicts_df.prediction.shift()).cumsum()\n", "predicts_df = predicts_df.loc[predicts_df['prediction'] == True]\n", "selections = pd.DataFrame({'BeginDate' : predicts_df.groupby('group').time.first(), \n", " 'EndDate' : predicts_df.groupby('group').time.last()})\n", "selections = selections.set_index('BeginDate')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 436 }, "colab_type": "code", "id": "db_RET1gfmLf", "outputId": "6be953ad-02e1-4886-d1a9-20a1269b66a2" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " EndDate\n", "BeginDate \n", "2017-02-03 22:27:28.784841 2017-02-03 22:29:03.285435\n", "2017-02-03 22:31:13.786249 2017-02-03 22:33:55.787267\n", "2017-02-03 23:35:52.811378 2017-02-03 23:56:48.319683\n", "2017-02-04 00:09:19.824671 2017-02-04 00:16:04.827367\n", "2017-02-04 00:38:52.836503 2017-02-04 00:41:03.337379\n", "... ...\n", "2017-02-09 08:00:15.435380 2017-02-09 08:00:24.435452\n", "2017-02-09 08:00:51.435666 2017-02-09 08:01:27.435952\n", "2017-02-09 08:02:52.936624 2017-02-09 08:07:00.438590\n", "2017-02-09 08:15:01.942399 2017-02-09 08:46:04.957144\n", "2017-02-09 09:43:13.984273 2017-02-09 09:46:58.986053\n", "\n", "[97 rows x 1 columns]" ] }, "execution_count": 46, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "selections" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "O5H3KGodtKKR" }, "source": [ "## Conclusion\n", "\n", "The above steps have walked you through the development of the GLS-MP model currently deployed at NASA to assist SITLs with data selection. \n", "\n", "Since being implemented into the near real-time data stream, the GLS-MP model has selected 78% of SITL-identified MP crossings in the outbound leg of its orbit, 44% more than the existing MP-crossing selection algorithm onboard MMS spacecraft (ABS). \n", "\n", "The model and its associated paper represent the first attempt to introduce machine learning into critical mission operations. \n", "\n", "Additionally, the nature of the model and its training make it easily adoptable for use in other phenomena-detection tasks, such as identifying reconnection jets or Kelvin-Helmholtz waves in the magntopause. By expanding GLS-MP into a hierarchy of machine learning models, MMS progresses toward full autonomy in its burst management system, thereby reducing operations costs and transferring information and resources back to answering fundamental science questions." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "machine_shape": "hm", "name": "notebook.ipynb", "provenance": [], "toc_visible": true }, "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": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 4 }