examples/text/toxic_comments-fasttext.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\"; \n"
   ]
  },
  {
   "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": [
    "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: 196995\n",
      "Nrows: 143613\n",
      "143613 train sequences\n",
      "Average train sequence length: 66\n",
      "15958 test sequences\n",
      "Average test sequence length: 66\n",
      "Pad sequences (samples x time)\n",
      "x_train shape: (143613,150)\n",
      "x_test shape: (15958,150)\n",
      "y_train shape: (143613,6)\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": [
      "Is Multi-Label? True\n",
      "compiling word ID features...\n",
      "max_features is 49350\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "model = text.text_classifier('fasttext', (x_train, y_train), \n",
    "                             preproc=preproc)\n",
    "learner = ktrain.get_learner(model, train_data=(x_train, y_train), val_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating training for different learning rates... this may take a few moments...\n",
      "Epoch 1/5\n",
      " 47840/143613 [========>.....................] - ETA: 31s - loss: 0.4965 - acc: 0.7510\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": {
      "image/png": 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\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": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "early_stopping automatically enabled at patience=5\n",
      "reduce_on_plateau automatically enabled at patience=2\n",
      "\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 1/1024\n",
      "143613/143613 [==============================] - 51s 356us/step - loss: 0.1358 - acc: 0.9530 - val_loss: 0.0536 - val_acc: 0.9817\n",
      "Epoch 2/1024\n",
      "143613/143613 [==============================] - 51s 355us/step - loss: 0.0643 - acc: 0.9784 - val_loss: 0.0504 - val_acc: 0.9826\n",
      "Epoch 3/1024\n",
      "143613/143613 [==============================] - 51s 356us/step - loss: 0.0577 - acc: 0.9797 - val_loss: 0.0483 - val_acc: 0.9831\n",
      "Epoch 4/1024\n",
      "143613/143613 [==============================] - 51s 352us/step - loss: 0.0540 - acc: 0.9806 - val_loss: 0.0475 - val_acc: 0.9830\n",
      "Epoch 5/1024\n",
      "143613/143613 [==============================] - 51s 355us/step - loss: 0.0520 - acc: 0.9811 - val_loss: 0.0471 - val_acc: 0.9832\n",
      "Epoch 6/1024\n",
      "143613/143613 [==============================] - 51s 355us/step - loss: 0.0500 - acc: 0.9818 - val_loss: 0.0469 - val_acc: 0.9833\n",
      "Epoch 7/1024\n",
      "143613/143613 [==============================] - 51s 353us/step - loss: 0.0484 - acc: 0.9820 - val_loss: 0.0466 - val_acc: 0.9832\n",
      "Epoch 8/1024\n",
      "143613/143613 [==============================] - 51s 358us/step - loss: 0.0475 - acc: 0.9823 - val_loss: 0.0470 - val_acc: 0.9830\n",
      "Epoch 9/1024\n",
      "143613/143613 [==============================] - 52s 360us/step - loss: 0.0465 - acc: 0.9826 - val_loss: 0.0470 - val_acc: 0.9831\n",
      "\n",
      "Epoch 00009: Reducing Max LR on Plateau: new max lr will be 0.0005 (if not early_stopping).\n",
      "Epoch 10/1024\n",
      "143613/143613 [==============================] - 52s 359us/step - loss: 0.0441 - acc: 0.9832 - val_loss: 0.0473 - val_acc: 0.9830\n",
      "Epoch 11/1024\n",
      "143613/143613 [==============================] - 52s 359us/step - loss: 0.0432 - acc: 0.9835 - val_loss: 0.0474 - val_acc: 0.9831\n",
      "\n",
      "Epoch 00011: Reducing Max LR on Plateau: new max lr will be 0.00025 (if not early_stopping).\n",
      "Epoch 12/1024\n",
      "143613/143613 [==============================] - 51s 357us/step - loss: 0.0420 - acc: 0.9838 - val_loss: 0.0477 - val_acc: 0.9830\n",
      "Restoring model weights from the end of the best epoch\n",
      "Epoch 00012: early stopping\n",
      "Weights from best epoch have been loaded into model.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb5e7094978>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.autofit(0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}