Kaggle/Jigsaw Multilingual Toxic Comment Classification/Jigsaw Classification - DistilBERT with TPU and TF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src='https://raw.githubusercontent.com/dimitreOliveira/MachineLearning/master/Kaggle/Jigsaw%20Multilingual%20Toxic%20Comment%20Classification/banner.png'></center>\n",
    "\n",
    "<br>\n",
    "<center><h1>Jigsaw Multilingual Toxic Comment Classification</h1></center>\n",
    "\n",
    "<br>\n",
    "<center><h3>Jigsaw Classification - DistilBERT with TPU and TF</h3></center>\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "It only takes one toxic comment to sour an online discussion. The Conversation AI team, a research initiative founded by [Jigsaw](https://jigsaw.google.com/) and Google, builds technology to protect voices in conversation. A main area of focus is machine learning models that can identify toxicity in online conversations, where toxicity is defined as anything rude, disrespectful or otherwise likely to make someone leave a discussion. If these toxic contributions can be identified, we could have a safer, more collaborative internet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "import os,gc , warnings, transformers\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from tokenizers import BertWordPieceTokenizer\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras import optimizers, metrics, losses\n",
    "from tensorflow.keras.layers import Dense, Input, Dropout, GlobalAveragePooling1D\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler, EarlyStopping\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve, auc, classification_report\n",
    "\n",
    "def seed_everything(seed=0):\n",
    "    np.random.seed(seed)\n",
    "    tf.random.set_seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
    "\n",
    "seed = 0\n",
    "seed_everything(seed)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "# Auxiliary functions\n",
    "def plot_metrics(history, metric_list):\n",
    "    fig, axes = plt.subplots(len(metric_list), 1, sharex='col', figsize=(20, 18))\n",
    "    axes = axes.flatten()\n",
    "    \n",
    "    for index, metric in enumerate(metric_list):\n",
    "        axes[index].plot(history[metric], label='Train %s' % metric)\n",
    "        axes[index].plot(history['val_%s' % metric], label='Validation %s' % metric)\n",
    "        axes[index].legend(loc='best', fontsize=16)\n",
    "        axes[index].set_title(metric)\n",
    "\n",
    "    plt.xlabel('Epochs', fontsize=16)\n",
    "    sns.despine()\n",
    "    plt.show()\n",
    "    \n",
    "def plot_aur_curve(y_train, train_pred, y_valid, valid_pred):\n",
    "    fpr_train, tpr_train, _ = roc_curve(y_train, train_pred)\n",
    "    roc_auc_train = auc(fpr_train, tpr_train)\n",
    "    fpr_valid, tpr_valid, _ = roc_curve(y_valid, valid_pred)\n",
    "    roc_auc_valid = auc(fpr_valid, tpr_valid)\n",
    "\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr_train, tpr_train, color='blue', label='Train AUC = %0.2f' % roc_auc_train)\n",
    "    plt.plot(fpr_valid, tpr_valid, color='purple', label='ValidationAUC = %0.2f' % roc_auc_valid)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()\n",
    "    \n",
    "def plot_confusion_matrix(y_train, train_pred, y_valid, valid_pred, labels=[0, 1]):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 8))\n",
    "    train_cnf_matrix = confusion_matrix(y_train, train_pred)\n",
    "    validation_cnf_matrix = confusion_matrix(y_valid, valid_pred)\n",
    "\n",
    "    train_cnf_matrix_norm = train_cnf_matrix.astype('float') / train_cnf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "    validation_cnf_matrix_norm = validation_cnf_matrix.astype('float') / validation_cnf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    train_df_cm = pd.DataFrame(train_cnf_matrix_norm, index=labels, columns=labels)\n",
    "    validation_df_cm = pd.DataFrame(validation_cnf_matrix_norm, index=labels, columns=labels)\n",
    "\n",
    "    sns.heatmap(train_df_cm, annot=True, fmt='.2f', cmap=\"Blues\",ax=ax1).set_title('Train')\n",
    "    sns.heatmap(validation_df_cm, annot=True, fmt='.2f', cmap=sns.cubehelix_palette(8),ax=ax2).set_title('Validation')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TPU configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on TPU  grpc://10.0.0.2:8470\n",
      "REPLICAS:  8\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n",
    "    print('Running on TPU ', tpu.master())\n",
    "except ValueError:\n",
    "    tpu = None\n",
    "\n",
    "if tpu:\n",
    "    tf.config.experimental_connect_to_cluster(tpu)\n",
    "    tf.tpu.experimental.initialize_tpu_system(tpu)\n",
    "    strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
    "else:\n",
    "    strategy = tf.distribute.get_strategy()\n",
    "\n",
    "print(\"REPLICAS: \", strategy.num_replicas_in_sync)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jigsaw toxic comment samples 223549\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>toxic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000997932d777bf</td>\n",
       "      <td>Explanation\\nWhy the edits made under my usern...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000103f0d9cfb60f</td>\n",
       "      <td>D'aww! He matches this background colour I'm s...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000113f07ec002fd</td>\n",
       "      <td>Hey man, I'm really not trying to edit war. It...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0001b41b1c6bb37e</td>\n",
       "      <td>\"\\nMore\\nI can't make any real suggestions on ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0001d958c54c6e35</td>\n",
       "      <td>You, sir, are my hero. Any chance you remember...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id                                       comment_text  toxic\n",
       "0  0000997932d777bf  Explanation\\nWhy the edits made under my usern...      0\n",
       "1  000103f0d9cfb60f  D'aww! He matches this background colour I'm s...      0\n",
       "2  000113f07ec002fd  Hey man, I'm really not trying to edit war. It...      0\n",
       "3  0001b41b1c6bb37e  \"\\nMore\\nI can't make any real suggestions on ...      0\n",
       "4  0001d958c54c6e35  You, sir, are my hero. Any chance you remember...      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jigsaw unintended bias samples 1902194\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>toxic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59848</td>\n",
       "      <td>This is so cool. It's like, 'would you want yo...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59849</td>\n",
       "      <td>Thank you!! This would make my life a lot less...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59852</td>\n",
       "      <td>This is such an urgent design problem; kudos t...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59855</td>\n",
       "      <td>Is this something I'll be able to install on m...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59856</td>\n",
       "      <td>haha you guys are a bunch of losers.</td>\n",
       "      <td>0.893617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                       comment_text     toxic\n",
       "0  59848  This is so cool. It's like, 'would you want yo...  0.000000\n",
       "1  59849  Thank you!! This would make my life a lot less...  0.000000\n",
       "2  59852  This is such an urgent design problem; kudos t...  0.000000\n",
       "3  59855  Is this something I'll be able to install on m...  0.000000\n",
       "4  59856               haha you guys are a bunch of losers.  0.893617"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_toxic = pd.read_csv(\"/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train.csv\", \n",
    "                          usecols=['id', 'comment_text', 'toxic'])\n",
    "train_bias = pd.read_csv(\"/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-unintended-bias-train.csv\", \n",
    "                         usecols=['id', 'comment_text', 'toxic'])\n",
    "valid = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/validation.csv', \n",
    "                    usecols=['id', 'comment_text', 'toxic'])\n",
    "test = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/test.csv', \n",
    "                   usecols=['id', 'content'])\n",
    "\n",
    "print('Jigsaw toxic comment samples %d' % len(train_toxic))\n",
    "display(train_toxic.head())\n",
    "print('Jigsaw unintended bias samples %d' % len(train_bias))\n",
    "display(train_bias.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_LEN = 512\n",
    "BATCH_SIZE = 64 * strategy.num_replicas_in_sync\n",
    "EPOCHS = 12\n",
    "LEARNING_RATE = 1e-5 # * strategy.num_replicas_in_sync\n",
    "ES_PATIENCE = 3\n",
    "\n",
    "base_model_name = 'distilbert-base-multilingual-cased'\n",
    "model_path = 'model.h5'\n",
    "vocab_path = '/kaggle/working'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bc7ae9eda7e4814ac669ea3148cc857",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=995526.0, style=ProgressStyle(descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "tokenizer = transformers.DistilBertTokenizer.from_pretrained(base_model_name)\n",
    "tokenizer.save_pretrained(vocab_path)\n",
    "\n",
    "tokenizer = BertWordPieceTokenizer(vocab_path + '/vocab.txt', lowercase=False)\n",
    "tokenizer.enable_truncation(max_length=MAX_LEN)\n",
    "tokenizer.enable_padding(max_length=MAX_LEN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build TF datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = [x.ids for x in tokenizer.encode_batch(train_toxic['comment_text'].apply(lambda x : x).tolist())]\n",
    "x_valid = [x.ids for x in tokenizer.encode_batch(valid['comment_text'].apply(lambda x : x).tolist())]\n",
    "x_test = [x.ids for x in tokenizer.encode_batch(test['content'].apply(lambda x : x).tolist())]\n",
    "\n",
    "y_train = train_toxic['toxic'].values\n",
    "y_valid = valid['toxic'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "AUTO = tf.data.experimental.AUTOTUNE\n",
    "\n",
    "def get_training_dataset():\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
    "    dataset = dataset.repeat()\n",
    "    dataset = dataset.shuffle(2048)\n",
    "    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)\n",
    "    dataset = dataset.prefetch(AUTO)\n",
    "    return dataset\n",
    "\n",
    "def get_validation_dataset():\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((x_valid, y_valid))\n",
    "    dataset = dataset.batch(BATCH_SIZE)\n",
    "    dataset = dataset.cache()\n",
    "    dataset = dataset.prefetch(AUTO)\n",
    "    return dataset\n",
    "\n",
    "def get_test_dataset():\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(x_test)\n",
    "    dataset = dataset.batch(BATCH_SIZE)\n",
    "    dataset = dataset.prefetch(AUTO)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning rate schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate schedule: 1e-07 to 1e-05 to 1.09e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "del train_bias\n",
    "gc.collect()\n",
    "\n",
    "LR_START = 1e-7\n",
    "LR_MIN = 1e-6\n",
    "LR_MAX = LEARNING_RATE\n",
    "LR_RAMPUP_EPOCHS = 2\n",
    "LR_SUSTAIN_EPOCHS = 0\n",
    "LR_EXP_DECAY = .6\n",
    "\n",
    "def lrfn(epoch):\n",
    "    if epoch < LR_RAMPUP_EPOCHS:\n",
    "        lr = (LR_MAX - LR_START) / LR_RAMPUP_EPOCHS * epoch + LR_START\n",
    "    elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:\n",
    "        lr = LR_MAX\n",
    "    else:\n",
    "        lr = (LR_MAX - LR_MIN) * LR_EXP_DECAY**(epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS) + LR_MIN\n",
    "    return lr\n",
    "    \n",
    "rng = [i for i in range(EPOCHS)]\n",
    "y = [lrfn(x) for x in rng]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 8))\n",
    "plt.plot(rng, y)\n",
    "print(\"Learning rate schedule: {:.3g} to {:.3g} to {:.3g}\".format(y[0], max(y), y[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c15a0384f3443cd8202441c6d0d8422",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=618.0, style=ProgressStyle(description_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d54964af2e841e0acc56e7a7af5b6ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=910749124.0, style=ProgressStyle(descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_word_ids (InputLayer)  [(None, 512)]             0         \n",
      "_________________________________________________________________\n",
      "tf_distil_bert_model (TFDist ((None, 512, 768),)       134734080 \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d (Gl (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "dropout_19 (Dropout)         (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 769       \n",
      "=================================================================\n",
      "Total params: 134,734,849\n",
      "Trainable params: 134,734,849\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_fn():\n",
    "    base_model = transformers.TFDistilBertModel.from_pretrained(base_model_name)\n",
    "    input_word_ids = Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_word_ids')\n",
    "    sequence_output = base_model(input_word_ids)[0]\n",
    "    \n",
    "    x = GlobalAveragePooling1D()(sequence_output)\n",
    "    x = Dropout(0.25)(x)\n",
    "    output = Dense(1, activation='sigmoid', name='output')(x)\n",
    "    \n",
    "    model = Model(inputs=input_word_ids, outputs=output)\n",
    "    model.compile(optimizers.Adam(lr=LEARNING_RATE), \n",
    "                  loss=losses.BinaryCrossentropy(), \n",
    "                  metrics=['accuracy', metrics.AUC()])\n",
    "    \n",
    "    return model\n",
    "\n",
    "with strategy.scope():\n",
    "    model = model_fn()\n",
    "    \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 436 steps, validate for 16 steps\n",
      "\n",
      "Epoch 00001: LearningRateScheduler reducing learning rate to 1e-07.\n",
      "Epoch 1/12\n",
      "436/436 [==============================] - 315s 722ms/step - loss: 0.4177 - accuracy: 0.8390 - auc: 0.4859 - val_loss: 0.4414 - val_accuracy: 0.8460 - val_auc: 0.5200\n",
      "\n",
      "Epoch 00002: LearningRateScheduler reducing learning rate to 5.05e-06.\n",
      "Epoch 2/12\n",
      "436/436 [==============================] - 255s 585ms/step - loss: 0.1888 - accuracy: 0.9295 - auc: 0.9083 - val_loss: 0.4023 - val_accuracy: 0.8514 - val_auc: 0.7914\n",
      "\n",
      "Epoch 00003: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "Epoch 3/12\n",
      "436/436 [==============================] - 254s 582ms/step - loss: 0.1174 - accuracy: 0.9527 - auc: 0.9688 - val_loss: 0.5385 - val_accuracy: 0.8474 - val_auc: 0.7859\n",
      "\n",
      "Epoch 00004: LearningRateScheduler reducing learning rate to 6.4e-06.\n",
      "Epoch 4/12\n",
      "436/436 [==============================] - 254s 581ms/step - loss: 0.1002 - accuracy: 0.9589 - auc: 0.9781 - val_loss: 0.5788 - val_accuracy: 0.8480 - val_auc: 0.7780\n",
      "\n",
      "Epoch 00005: LearningRateScheduler reducing learning rate to 4.24e-06.\n",
      "Epoch 5/12\n",
      "435/436 [============================>.] - ETA: 0s - loss: 0.0926 - accuracy: 0.9620 - auc: 0.9817Restoring model weights from the end of the best epoch.\n",
      "436/436 [==============================] - 262s 601ms/step - loss: 0.0926 - accuracy: 0.9620 - auc: 0.9817 - val_loss: 0.6458 - val_accuracy: 0.8478 - val_auc: 0.7548\n",
      "Epoch 00005: early stopping\n"
     ]
    }
   ],
   "source": [
    "STEPS_PER_EPOCH = len(x_train) // BATCH_SIZE\n",
    "\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, \n",
    "                   restore_best_weights=True, verbose=1)\n",
    "lr_callback = LearningRateScheduler(lrfn, verbose=1)\n",
    "\n",
    "\n",
    "history = model.fit(x=get_training_dataset(),\n",
    "                    steps_per_epoch=STEPS_PER_EPOCH,\n",
    "                    validation_data=get_validation_dataset(),\n",
    "                    callbacks=[es, lr_callback],\n",
    "                    epochs=EPOCHS, \n",
    "                    verbose=1).history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model loss graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1296 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "plot_metrics(history, metric_list=['loss', 'accuracy', 'auc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set ROC AUC 0.4999\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.91      0.91    201868\n",
      "           1       0.10      0.09      0.09     21364\n",
      "\n",
      "    accuracy                           0.83    223232\n",
      "   macro avg       0.50      0.50      0.50    223232\n",
      "weighted avg       0.83      0.83      0.83    223232\n",
      "\n",
      "Validation set ROC AUC 0.7914\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.99      0.92      6770\n",
      "           1       0.58      0.12      0.19      1230\n",
      "\n",
      "    accuracy                           0.85      8000\n",
      "   macro avg       0.72      0.55      0.56      8000\n",
      "weighted avg       0.82      0.85      0.81      8000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_pred = model.predict(get_training_dataset(), steps=STEPS_PER_EPOCH)\n",
    "valid_pred = model.predict(get_validation_dataset())\n",
    "train_toxic = train_toxic[:len(train_pred)]\n",
    "train_toxic['pred'] = train_pred\n",
    "valid['pred'] = valid_pred\n",
    "\n",
    "print('Train set ROC AUC %.4f' % roc_auc_score(train_toxic['toxic'], train_toxic['pred']))\n",
    "print(classification_report(train_toxic['toxic'],  np.round(train_toxic['pred'])))\n",
    "print('Validation set ROC AUC %.4f' % roc_auc_score(valid['toxic'], valid['pred']))\n",
    "print(classification_report(valid['toxic'],  np.round(valid['pred'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_aur_curve(train_toxic['toxic'], train_toxic['pred'], valid['toxic'], valid['pred'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(train_toxic['toxic'], np.round(train_toxic['pred']), \n",
    "                      valid['toxic'], np.round(valid['pred']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment_text</th>\n",
       "      <th>toxic</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Explanation\\nWhy the edits made under my usern...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D'aww! He matches this background colour I'm s...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.010190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hey man, I'm really not trying to edit war. It...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\"\\nMore\\nI can't make any real suggestions on ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.980159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>You, sir, are my hero. Any chance you remember...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.040088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>\"\\n\\nCongratulations from me as well, use the ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>COCKSUCKER BEFORE YOU PISS AROUND ON MY WORK</td>\n",
       "      <td>1</td>\n",
       "      <td>0.213530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Your vandalism to the Matt Shirvington article...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sorry if the word 'nonsense' was offensive to ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>alignment on this subject and which are contra...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        comment_text  toxic      pred\n",
       "0  Explanation\\nWhy the edits made under my usern...      0  0.003213\n",
       "1  D'aww! He matches this background colour I'm s...      0  0.010190\n",
       "2  Hey man, I'm really not trying to edit war. It...      0  0.002544\n",
       "3  \"\\nMore\\nI can't make any real suggestions on ...      0  0.980159\n",
       "4  You, sir, are my hero. Any chance you remember...      0  0.040088\n",
       "5  \"\\n\\nCongratulations from me as well, use the ...      0  0.001273\n",
       "6       COCKSUCKER BEFORE YOU PISS AROUND ON MY WORK      1  0.213530\n",
       "7  Your vandalism to the Matt Shirvington article...      0  0.001639\n",
       "8  Sorry if the word 'nonsense' was offensive to ...      0  0.008310\n",
       "9  alignment on this subject and which are contra...      0  0.008872"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set\n"
     ]
    },
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       "8  Levent Abi nasılsın? Hayat nasıl gidiyor? Umar...      0  0.050965\n",
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   ],
   "source": [
    "print('Train set')\n",
    "display(train_toxic[['comment_text', 'toxic', 'pred']].head(10))\n",
    "print('Validation set')\n",
    "display(valid[['comment_text', 'toxic', 'pred']].head(10))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test set predictions"
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     "execution_count": 17,
     "metadata": {},
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   ],
   "source": [
    "Y_test = model.predict(get_test_dataset())\n",
    "submission = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/sample_submission.csv')\n",
    "submission['toxic'] = Y_test\n",
    "submission.to_csv('submission.csv', index=False)\n",
    "submission.head(10)"
   ]
  }
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