examples/text/toxic_comments-bigru.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\";\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import ktrain\n",
    "from ktrain import text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Label Text Classification:  Identifying Toxic Online Comments\n",
    "\n",
    "Here, we will classify Wikipedia comments into one or more categories of so-called *toxic comments*.   Categories of toxic online behavior include toxic, severe_toxic, obscene, threat, insult, and identity_hate.  The dataset can be downloaded from the [Kaggle Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) as a CSV file (i.e., download the file ```train.csv```).  We will load the data using the ```texts_from_csv``` method, which assumes the label_columns are already one-hot-encoded in the spreadsheet. Since *val_filepath* is None, 10% of the data will automatically be used as a validation set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word Counts: 197516\n",
      "Nrows: 143613\n",
      "143613 train sequences\n",
      "Average train sequence length: 66\n",
      "x_train shape: (143613,150)\n",
      "y_train shape: (143613,6)\n",
      "15958 test sequences\n",
      "Average test sequence length: 66\n",
      "x_test shape: (15958,150)\n",
      "y_test shape: (15958,6)\n"
     ]
    }
   ],
   "source": [
    "DATA_PATH = 'data/toxic-comments/train.csv'\n",
    "NUM_WORDS = 50000\n",
    "MAXLEN = 150\n",
    "(x_train, y_train), (x_test, y_test), preproc = text.texts_from_csv(DATA_PATH,\n",
    "                      'comment_text',\n",
    "                      label_columns = [\"toxic\", \"severe_toxic\", \"obscene\", \"threat\", \"insult\", \"identity_hate\"],\n",
    "                      val_filepath=None, # if None, 10% of data will be used for validation\n",
    "                      max_features=NUM_WORDS, maxlen=MAXLEN,\n",
    "                      ngram_range=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fasttext: a fastText-like model (http://arxiv.org/pdf/1607.01759.pdf)\n",
      "logreg: logistic regression using a trainable Embedding layer\n",
      "nbsvm: NBSVM model (http://www.aclweb.org/anthology/P12-2018)\n",
      "bigru: Bidirectional GRU with pretrained word vectors\n",
      "bert: Bidirectional Encoder Representations from Transformers (https://arxiv.org/abs/1810.04805)\n"
     ]
    }
   ],
   "source": [
    "text.print_text_classifiers()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We weill employ a Bidirectional GRU with pretrained word vectors.  The following code cell loads a BIGRU model and defines a ```Learner``` object based on that model. The file ```crawl-300d-2M.vec ``` contains 2 million word vectors trained by Facebook and will be automatically downloaded for use with this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is Multi-Label? True\n",
      "compiling word ID features...\n",
      "max_features is 49325\n",
      "processing pretrained word vectors...\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "model = text.text_classifier('bigru', (x_train, y_train), preproc=preproc)\n",
    "\n",
    "learner = ktrain.get_learner(model, train_data=(x_train, y_train), val_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we use our learning rate finder to find a good learning rate. In this case, a learning rate of 0.0007 appears to be good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating training for different learning rates... this may take a few moments...\n",
      "Epoch 1/1\n",
      "100352/143613 [===================>..........] - ETA: 10:26 - loss: 0.3649 - acc: 0.7886\n",
      "\n",
      "done.\n",
      "Please invoke the Learner.lr_plot() method to visually inspect the loss plot to help identify the maximal learning rate associated with falling loss.\n"
     ]
    },
    {
     "data": {
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TKIjEHDeumNeqd7G3oTnsUkRCo1AQidnfr6BTSDKQKRREYmaNLSYvM8KTq7eGXYpIaBQKIjFZ6RFmji3mefUryACmUBBpZ/a4Eqq27mFrbX3YpYiEQqEg0s7x40oAqFyv+xVkYOpVKJjZl8xskEX91sxeMrPTgy5OJNEOH15AJM1YsUmD48nA1NsjhcvcfTdwOjAYuBi4IbCqREKSnRHhyJGDeHG9+hVkYOptKFjs3zOBO919ebt1Iinl+HElvPzWDuoaNUWnDDy9DYUlZvYI0VBYaGYFQGtPLzKzuWa22syqzOzqbtqdZ2ZuZhW9rEckMMePL6GpxVmicZBkAOptKFwOXA3MdPd9QAbw6e5eYGYR4BbgDGAKcJGZTemkXQHwJeCFPtQtEpiZ5cWkpxnPrtkWdikiCdfbUDgeWO3uO83sU8A3gV09vGYWUOXua929EbgbOKeTdt8FfgjoGkDpF/Ky0jl6dBHP6c5mGYB6Gwq/BPaZ2dHAV4A1wB96eE0ZsKHd8sbYujZmNh0Y7e4P9rIOkYQ4flwJr27cxR6NgyQDTG9Dodmjs5qfA9zs7rcABYeyYTNLA35KNGR6ajvPzCrNrLKmRrNjSfBOGB8dB2nxOl2FJANLb0Oh1sy+TvRS1AdjH+gZPbymGhjdbnlUbN1+BcBRwFNmth6YDczvrLPZ3W919wp3rygtLe1lySIHb3psfoXFujRVBpjehsLHgQai9ytsJvoBf2MPr1kMTDSzsWaWCVwIzN//pLvvcvch7l7u7uXA88DZ7l7Z150QibfsjAjlJXm8sXVP2KWIJFSvQiEWBH8CCs3sw0C9u3fbp+DuzcCVwEJgJXCPuy83s+vM7OxDrFskcBOG5rNGoSADTHpvGpnZBUSPDJ4ietPaTWb2VXe/t7vXufsCYEGHdd/qou2pvalFJFEmDM3nkRVbaGxuJTNdw4TJwNCrUAC+QfQeha0AZlYKPAZ0GwoiyWzC0HxaWp312/cyadghXVchkjR6++dP2v5AiNneh9eKJKXxpfkAVOkUkgwgvT1SeNjMFgJ/ji1/nA6nhURSzfjSfMwUCjKw9CoU3P2rZnYeMCe26lZ3/1twZYmELyczQllRjkJBBpTeHing7vcB9wVYi0i/M2FovkJBBpRuQ8HMagHv7CnA3X1QIFWJ9BMTSvN5fu12WludtDSNFi+pr9tQcHddciED2oSh+dQ3tVK9s47RxblhlyMSOF1BJNKNCUOjVyC9sbU25EpEEkOhINKN/aGgfgUZKBQKIt0oys1kSH6WQkEGDIWCSA8mDs3XwHgyYCgURHowcVg+VVv2EJ1SRCS1KRREejBxaD61Dc1srW0IuxSRwCkURHowPtbZ/PoWXYEkqU+hINKDI0cUAvDqxl0hVyISPIWCSA8KczMYNiiLtTV7wy5FJHAKBZFeGDskj7XbdAWSpD6FgkgvjCvNZ902HSlI6lMoiPTCuCF57NzXxDt7G8MuRSRQgYaCmc01s9VmVmVmV3fy/OfM7DUze8XMnjGzKUHWI3KwNAubDBSBhYKZRYBbgDOAKcBFnXzo3+XuU939GOBHwE+DqkfkUEweER0weNXm3SFXIhKsII8UZgFV7r7W3RuBu4Fz2jdw9/b/w/LofO4GkdANH5RNcV4mKzYpFCS1BRkKZcCGdssbY+sOYGb/bmZriB4pfDHAekQOmplRVpTD3Ys39NxYJImF3tHs7re4+3jga8A3O2tjZvPMrNLMKmtqahJboEhMeiQ681ptfVPIlYgEJ8hQqAZGt1seFVvXlbuBj3b2hLvf6u4V7l5RWloaxxJFeu/yE8cCsPJtDXchqSvIUFgMTDSzsWaWCVwIzG/fwMwmtls8C3gjwHpEDknFYcWAOpsltXU7R/OhcPdmM7sSWAhEgN+5+3Izuw6odPf5wJVmdhrQBOwALgmqHpFDNWxQFlnpaWx4Z1/YpYgEJrBQAHD3BcCCDuu+1e7xl4Lcvkg8mRmji3NZt02hIKkr9I5mkWQyaVg+a2t0A5ukLoWCSB+UFeWwcWcdra26pUZSk0JBpA9GDc6lsbmVbXs1C5ukJoWCSB+MKckFYL36FSRFKRRE+uCI4YMAWPm2LkuV1KRQEOmDYYOyGJyboTGQJGUpFET6wMzYsa+Jv1RuYLeGu5AUpFAQOUgLl20OuwSRuFMoiPTRsms/BMBburNZUpBCQaSP8rOiAwHc9ERVyJWIxJ9CQeQgjCmOXpq6dMPOkCsRiS+FgshB+MUnpwPw++fWh1qHSLwpFEQOwlFlhaQZpJmFXYpIXCkURA7SzPJillXvCrsMkbhSKIgcpKNHF7F2215aNDiepBCFgshBGpybSWNzK4uqtoVdikjcKBREDtJx46LTc67QOEiSQhQKIgfpmFFFZKWnsa1Ww2hL6lAoiByktDSjvCSP9dv3hl2KSNwoFEQOwaThBbxWvQt3dTZLagg0FMxsrpmtNrMqM7u6k+e/bGYrzOxVM3vczA4Lsh6ReJs9rpgtuxtYt01HC5IaAgsFM4sAtwBnAFOAi8xsSodmLwMV7j4NuBf4UVD1iAThhPFDAHhu7faQKxGJjyCPFGYBVe6+1t0bgbuBc9o3cPcn3X3/UJPPA6MCrEck7spLchlRmM2zVQoFSQ1BhkIZsKHd8sbYuq5cDjwUYD0icWdmnDhhCE+/UaOb2CQl9IuOZjP7FFAB3NjF8/PMrNLMKmtqahJbnEgPZo8rYXd9M+u27Qm7FJFDFmQoVAOj2y2Piq07gJmdBnwDONvdO73g291vdfcKd68oLS0NpFiRgzVl5CAAXt2ocZAk+QUZCouBiWY21swygQuB+e0bmNmxwK+JBsLWAGsRCcykYQUU5mTw3Br1K0jyCywU3L0ZuBJYCKwE7nH35WZ2nZmdHWt2I5AP/NXMXjGz+V28nUi/FUkzTpw4hKder9H9ChKIllbnqdVbE9JvlR7km7v7AmBBh3Xfavf4tCC3L5Iop0wq5cFX32ZNzR4mDC0IuxxJMY+v3MK8O5fwjTOP4DMnjwt0W/2io1kk2U0tKwRgxdu1IVciqWjV5ujv1f5BGIOkUBCJg/Gl+WREjOWb1Nks8ffTR18H3v3jI0gKBZE4yExP49gxg/n1P9Zy35KNbHhnX88vEumlzEgaeZkRLAHTvwbapyAykORlRgD4yl+XArDmB2cSSdMcznJomlpacZxLThibkO3pSEEkTv5z7uQDlu9dsoFr/76c8qsfpKG5JaSqJNnd8NAqmlqccaX5CdmeQkEkTo4YMYj3Tx7atvy1+17j9kXrAbjsjsUhVSXJzN357TPrAA743QqSQkEkjn536UzW33DWe04bLarazjNvaC5n6Zt9je8eYRbnZSZkm+pTEAnAS9/8IIvWbOPlt3YwtCCb7y9YyRV/WMxJE0v52tzDdS+D9Mo7exsB+NF50xK2TYWCSAAKczM4c+oIzpw6AoA3ttZyT+VGHl2xhaqte3jsy6cA0OpORkQH7NK5nfuaACjKzUjYNhUKIgnwLxWjuadyIwDrtu1l/DULuKBiFPdUbmR8aR5raqIzty26+v2UFeWEWar0I5t21QEwOEGnjkB9CiIJMbO8mAe+cCILvnhS27r9IbE/EADm3PAE6zW1p8S8Fht5d1hBdsK2qVAQSZCjygqZMnIQa39wZtu6ScPee5nhf8TucxBJi12wMGpw4o4edfpIJMHS0owHvnAiP35kNb/85AyWvLmDGYcNJi0NDv/mwyzduJPmllbS1dcw4O1taCYvM9IWDomg3zqREBxVVsgdn55FTmaEEycOISczQlZ6hFnlxTS1OBO+8dB7hkl2d3751Bo27axj6Yad1DXqhrhUt6e+mfzsxP7triMFkX7k+vOm8oGf/AOA7z6wgrlHDeelt3Zw+6L11NRGJyb84cOr2trPPXI4N5w3lcKcjISMiyOJtaehmfysxH5MW7JNClJRUeGVlZVhlyESmOfWbOei3zzf59ddc+Zk5p08PoCKJCzlVz9IXmaE5dfNPeT3MrMl7l7RUzudPhLpZ44fX8K8TiZSmTOhhF99ajpnTR3B1zqMswTwgwWrKL/6QZ5ctVUzwKWA/eNlDSnISuh2daQg0k9d/NsXALjlk9Op3lHHESMGvadNS6tz/8vV3PHsel6rfncuh/Omj+InFxydsFolvlpanfHXRCet/PG/HM35M0Yd8nv29khBoSCSIrbsrufcXzxL9c66tnVLv3U6jlOUm7ibn+TQ/fP1Gv71dy8CMP/KOUwbVXTI79kvTh+Z2VwzW21mVWZ2dSfPn2xmL5lZs5mdH2QtIqlu2KBsFl39/gP+qjz6ukc45rpHeXXjTlpbXaeVksSdz78JQGlBVkJmW2svsFAwswhwC3AGMAW4yMymdGj2FnApcFdQdYgMNDeeP43Hv3LKAevOvnkR465ZwKwfPK5gSAKLqqIj6j7/9Q8k/KqyIK91mgVUuftaADO7GzgHWLG/gbuvjz3XGmAdIgOKmTG+NJ/1N5zFc2u2s2lnXdtscDW1DYz9evRc9c8+fjQfO/bQz1VLfG3dXc++xhauOm1SKDP3BRkKZcCGdssbgeMC3J6IdHD8+BIg2nG5q66J7y9Y2fbcVX9ZypjiXNZv20fNngYumzOWzHRdkBi2d/ZFh8ue2MkQKImQFDevmdk8YB7AmDFjQq5GJPlcMHM0AJ85eRy765uY9p1HADjvl8+1tbnhoVVMHJrPoJwMfntJBXVNLSyq2s72PQ1c/9AqjhtbzK8+NSOhI3YORFf8PnohTaJvWtsvyK1WA6PbLY+Kreszd78VuBWiVx8demkiA9eg7AzW33AWdz63nv/6v+UHPPfG1j0AHHPdo+953Qvr3uHY7767/vhxJfzxiuOIpBl7G5o575fPsmpzLSV5mbxwzQc0dtNB2rgjevXYsWMO/YqjgxFkKCwGJprZWKJhcCHwiQC3JyJ98KnZhxFJS+Ojx44kJyPCb59Zx/ceXNlp26/NnXzA8BoAz63d3nYtfXvb9zYy4RsPse76MzX0Rh+trYmG8rnHllGQnbiJddoLLBTcvdnMrgQWAhHgd+6+3MyuAyrdfb6ZzQT+BgwGPmJm17r7kUHVJCLvMjM+cdy7p2OvOGkcx48vYeyQPKZ8ayFD8rN44ZoPtHV2fvbkcZz586fJTE/jex89irNvXvSe93zgCyfy4ZueAeC+l6rZXdfEp2Yfpr6KHtQ3tTD5vx5uW07kUNkd6eY1ETkoexuaOfLbCzllUim/vngG2RkRAGrrm5ga67MAuPSEcr5ztv7W686zVdv4xG3RO9hL8jJ58IsnMbwwvhPr9PbmtaToaBaR/icvK531N5z1nvUF2RncevEM5t25BIA7nl3PsWOKOOeYskSXmBRaWp3fLVpHdkYaL//X6eRkRkKtR0cKIhKYt7bv4+Qbn2xbfuSqk5k4NH/A9jXsbWjmgVc3MWVEIZ+9s5JNu+rbnjtmdBH3//ucwLatIwURCd2Yklz+c+7h/Ojh1QCc/rN/AnD0qEJGFOZw/blTB8wlrm/vquP465/o8vnrzukfp9h0pCAigWtqaWXiNx56z/rDhxXwm3+tYExJbghVHZrP/2kJjyzfwhUnjeOSEw5jRGG0c3j/h39ZUQ7/UjGK/37sjU5fP2XEIMoG5/DhaSM4a+qIwC/h1SipItIvLavexYdveobyklzWb993wHPLr/0QeVnpvL6llkHZGd12ttY3tZCdEQl8Pmt3f8/prk076zjhhgP/6n/syyczYWgBs3/wOJt319OZ0cU5PPGVU8kI4R4OhYKI9HuX37GYx1dt7bbNFSeO5ZsfPnAszd8/u55vz4/eeGcGf/v8HKp31LHgtbe55qwjKCs6tEs6n1y9lX+srmHzrnoeW7mFz586nqs+OIntextpaG5lzg1dnwbab1B2Orvrm/nsKeM4YfwQVr69m3OnlzG0IL5XFfWWQkFEksKehmZufHgV971UzZ6G5i7bvXDNB/jvx97gzy++1ev3fvEbH6AkL4va+qZO55RoamklI5LGvsZm/rG6hn+8XsPfXq6mobnnMTpPO2IYv/nXGbjDeb96lpff2tn23DfPOoIrTnrv7HlhUiiISFKpa2zhV/9Yw6mHl3LM6CIp3WWSAAAJ6klEQVTe2dvIgmWb+a/7l3Xa/vsfO4qyohyWb9rNjQujHdn5WendBgvAoqvfzxtbarn09sU91lScl8ktn5je6ZzZHe/Yrt5Zx+V3LOarHzqckyaW9rsb9hQKIpISmltamX3942zb00hRbgY/Om8aH5wy7IAP5P2fY/vXtbY635q/jD8+3/ujCoDhg7KZflgRZ00dyVnTRhzwXF1jC46Tm5mcF20qFERkQHN3nly9lRljilm/fS+vbNjJ8MJsPhu7qe6Mo4bz/Y9N5caFqynNz+TLpx8ecsXBUiiIiEibfjFHs4iIJBeFgoiItFEoiIhIG4WCiIi0USiIiEgbhYKIiLRRKIiISBuFgoiItEm6m9fMrAZ4EygEdrV7qv1yZ4+HANsOcfMdt9nXNl09192+dFxOpn3raV1vHh/qvvVmv3pq15t9668/s+7a6fex631pv5yMv48d1xUCRe5e2uOW3T0pv4Bbu1ru7DFQGe9t9rVNV891ty/JvG89revl40Pat97sVzz2rb/+zLprp9/Hrvelw/4k3e9jT/vW3Vcynz76ezfLXT2O9zb72qar57rbl47LybRvPa3rL/vVU7ve7Ft//Zl1106/j93X//cu1h+KRP0+dlzX631IutNHB8vMKr0X434kI+1b8knV/QLtW7JL5iOFvro17AICpH1LPqm6X6B9S2oD5khBRER6NpCOFEREpAcKBRERaaNQEBGRNgoFwMxOMrNfmdltZvZs2PXEk5mlmdn3zewmM7sk7HriycxONbOnYz+7U8OuJ57MLM/MKs3sw2HXEk9mdkTs53Wvmf1b2PXEk5l91Mx+Y2Z/MbPTw67nYCV9KJjZ78xsq5kt67B+rpmtNrMqM7u6u/dw96fd/XPAA8Dvg6y3L+Kxb8A5wCigCdgYVK19Fad9c2APkE0/2bc47RfA14B7gqny4MTp/9rK2P+1C4A5QdbbF3Hat/vd/TPA54CPB1lvkJL+6iMzO5noB8Mf3P2o2LoI8DrwQaIfFouBi4AIcH2Ht7jM3bfGXncPcLm71yao/G7FY99iXzvc/ddmdq+7n5+o+rsTp33b5u6tZjYM+Km7fzJR9XclTvt1NFBCNOy2ufsDiam+e/H6v2ZmZwP/Btzp7nclqv7uxPlz5CfAn9z9pQSVH1fpYRdwqNz9n2ZW3mH1LKDK3dcCmNndwDnufj3Q6eG4mY0BdvWXQID47JuZbQQaY4stwVXbN/H6ucXsALKCqLOv4vQzOxXIA6YAdWa2wN1bg6y7N+L1M3P3+cB8M3sQ6BehEKefmwE3AA8layBACoRCF8qADe2WNwLH9fCay4HbA6sofvq6b/8L3GRmJwH/DLKwOOjTvpnZucCHgCLg5mBLOyR92i93/waAmV1K7Ggo0OoOTV9/ZqcC5xIN8QWBVnbo+vp/7QvAaUChmU1w918FWVxQUjUU+szdvx12DUFw931EAy/luPv/Eg29lOTud4RdQ7y5+1PAUyGXEQh3/znw87DrOFRJ39HchWpgdLvlUbF1qUD7lnxSdb9A+5ZyUjUUFgMTzWysmWUCFwLzQ64pXrRvySdV9wu0bykn6UPBzP4MPAccbmYbzexyd28GrgQWAiuBe9x9eZh1HgztW/LtW6ruF2jfSNJ966ukvyRVRETiJ+mPFEREJH4UCiIi0kahICIibRQKIiLSRqEgIiJtFAoiItJGoSCBM7M9CdjG2b0ckjqe2zzVzE44iNcda2a/jT2+1Mz6xbhNZlbecejoTtqUmtnDiapJEk+hIEkjNpRxp9x9vrvfEMA2uxsf7FSgz6EAXEOSjpHj7jXA22bWb+ZCkPhSKEhCmdlXzWyxmb1qZte2W3+/mS0xs+VmNq/d+j1m9hMzWwocb2brzexaM3vJzF4zs8mxdm1/cZvZHWb2czN71szWmtn5sfVpZvYLM1tlZo+a2YL9z3Wo8Skz+28zqwS+ZGYfMbMXzOxlM3vMzIbFhln+HHCVmb1i0dn7Ss3svtj+Le7sg9PMCoBp7r60k+fKzeyJ2Pfm8dhw7pjZeDN7Pra/3+vsyMuiM7U9aGZLzWyZmX08tn5m7Puw1MxeNLOC2Haejn0PX+rsaMfMImZ2Y7uf1WfbPX0/EPrcFRIQd9eXvgL9AvbE/j0duBUwon+QPACcHHuuOPZvDrAMKIktO3BBu/daD3wh9vjzwG2xx5cCN8ce3wH8NbaNKUTHxAc4n+hwzWnAcKLzMJzfSb1PAb9otzyYd+/+vwL4Sezxd4D/aNfuLuDE2OMxwMpO3vt9wH3tltvX/Xfgktjjy4D7Y48fAC6KPf7c/u9nh/c9D/hNu+VCIBNYC8yMrRtEdGTkXCA7tm4iUBl7XA4siz2eB3wz9jgLqATGxpbLgNfC/r3SVzBfGjpbEun02NfLseV8oh9K/wS+aGYfi60fHVu/nejEQPd1eJ/9w2UvITo2f2fu9+g8BCssOjMbwInAX2PrN5vZk93U+pd2j0cBfzGzEUQ/aNd18ZrTgClmtn95kJnlu3v7v+xHADVdvP74dvtzJ/Cjdus/Gnt8F/DjTl77GvATM/sh8IC7P21mU4G33X0xgLvvhuhRBXCzmR1D9Ps7qZP3Ox2Y1u5IqpDoz2QdsBUY2cU+SJJTKEgiGXC9u//6gJXRiVdOA453931m9hTRqSgB6t2944xxDbF/W+j6d7ih3WProk139rZ7fBPR6T7nx2r9ThevSQNmu3t9N+9bx7v7Fjfu/rqZTQfOBL5nZo8Df+ui+VXAFqLTfqYBndVrRI/IFnbyXDbR/ZAUpD4FSaSFwGVmlg9gZmVmNpToX6E7YoEwGZgd0PYXAefF+haGEe0o7o1C3h1H/5J262uBgnbLjxCdfQuA2F/iHa0EJnSxnWeJDs8M0XP2T8ceP0/09BDtnj+AmY0E9rn7H4EbgenAamCEmc2MtSmIdZwXEj2CaAUuJjrncEcLgX8zs4zYayfFjjAgemTR7VVKkrwUCpIw7v4I0dMfz5nZa8C9RD9UHwbSzWwl0Tlunw+ohPuITqm4Avgj8BKwqxev+w7wVzNbAmxrt/7vwMf2dzQDXwQqYh2zK4ie/z+Au68iOl1jQcfniAbKp83sVaIf1l+Krf9/wJdj6yd0UfNU4EUzewX4NvA9d28EPk50OtalwKNE/8r/BXBJbN1kDjwq2u82ot+nl2KXqf6ad4/K3gc82MlrJAVo6GwZUPaf4zezEuBFYI67b05wDVcBte5+Wy/b5wJ17u5mdiHRTudzAi2y+3r+SXQC+x1h1SDBUZ+CDDQPmFkR0Q7j7yY6EGJ+CfxLH9rPINoxbMBOolcmhcLMSon2rygQUpSOFEREpI36FEREpI1CQURE2igURESkjUJBRETaKBRERKSNQkFERNr8f+257ojQLAcWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.lr_find()\n",
    "learner.lr_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will train our model for 8 epochs using ```autofit``` with a learning rate of 0.001 for two epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a custom callback for ROC-AUC\n",
    "from tensorflow.keras.callbacks import Callback\n",
    "from sklearn.metrics import roc_auc_score\n",
    "class RocAucEvaluation(Callback):\n",
    "    def __init__(self, validation_data=(), interval=1):\n",
    "        super(Callback, self).__init__()\n",
    "\n",
    "        self.interval = interval\n",
    "        self.X_val, self.y_val = validation_data\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        if epoch % self.interval == 0:\n",
    "            y_pred = self.model.predict(self.X_val, verbose=0)\n",
    "            score = roc_auc_score(self.y_val, y_pred)\n",
    "            print(\"\\n ROC-AUC - epoch: %d - score: %.6f \\n\" % (epoch+1, score))\n",
    "RocAuc = RocAucEvaluation(validation_data=(x_test, y_test), interval=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "begin training using triangular learning rate policy with max lr of 0.001...\n",
      "Train on 143613 samples, validate on 15958 samples\n",
      "Epoch 1/2\n",
      "143613/143613 [==============================] - 2157s 15ms/step - loss: 0.0668 - acc: 0.9787 - val_loss: 0.0410 - val_acc: 0.9843\n",
      "\n",
      " ROC-AUC - epoch: 1 - score: 0.986431 \n",
      "\n",
      "Epoch 2/2\n",
      "143613/143613 [==============================] - 2142s 15ms/step - loss: 0.0398 - acc: 0.9846 - val_loss: 0.0392 - val_acc: 0.9849\n",
      "\n",
      " ROC-AUC - epoch: 2 - score: 0.989871 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fcf77338be0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train\n",
    "learner.autofit(0.001, 2, callbacks=[RocAuc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our final ROC-AUC score is **0.9899**."
   ]
  }
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