ToxicWikiCommentsConvolution.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import os\n",
    "# os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"   # see issue #152\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
    "\n",
    "import re\n",
    "import string\n",
    "import shutil\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import seaborn as sn\n",
    "\n",
    "import sklearn as sk\n",
    "from sklearn.feature_selection import mutual_info_classif, SelectKBest, chi2\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer, TfidfVectorizer\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.utils.class_weight import compute_sample_weight\n",
    "\n",
    "from gensim.models import KeyedVectors\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Activation, Dropout, LSTM, Input, GlobalMaxPooling1D, Conv1D, MaxPooling1D, Embedding, Flatten, Concatenate\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.preprocessing import sequence\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from tqdm import tqdm, tqdm_notebook, tnrange, trange\n",
    "import hyperas\n",
    "from hyperas import optim\n",
    "from hyperas.distributions import choice, uniform, conditional\n",
    "from hyperopt import Trials, STATUS_OK, tpe, STATUS_FAIL\n",
    "from keras_tqdm import TQDMNotebookCallback"
   ]
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       "version_major": 2,
       "version_minor": 0
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       "  Widgets Documentation</a> for setup instructions.\n",
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       "<p>\n",
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      "\n"
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   "source": [
    "tqdm.monitor_interval = 0\n",
    "tqdm_notebook().pandas()\n",
    "sn.set_context('notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cats = ['toxic','severe_toxic','obscene','threat','insult','identity_hate']\n",
    "cat_col_i = {'toxic':0,'severe_toxic':1,'obscene':2,'threat':3,'insult':4,'identity_hate':5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_pickle('CLEAN/train.P.gz', compression='gzip')\n",
    "test = pd.read_pickle('CLEAN/test.P.gz', compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"NN_INPUTS/train.seq.P\", 'rb') as f:\n",
    "    train_data = pickle.load(f)\n",
    "with open(\"NN_INPUTS/test.seq.P\", 'rb') as f:\n",
    "    test_data = pickle.load(f)\n",
    "with open(\"NN_INPUTS/embedding.matrix.P\", 'rb') as f:\n",
    "    embedding_matrix = pickle.load(f)\n",
    "with open(\"NN_INPUTS/word_index.P\", 'rb') as f:\n",
    "    word_index = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assemble the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "EMBEDDING_DIM=100\n",
    "MAX_SEQUENCE_LENGTH=400\n",
    "MAX_NUM_WORDS=25000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def custom_compute_class_weights(y):\n",
    "    cat_counts = y.sum(axis='rows')\n",
    "    total_comments = y.shape[0]\n",
    "    results = (total_comments / cat_counts).tolist()\n",
    "    result_dict = dict()\n",
    "    for i in range(len(results)):\n",
    "        result_dict[i] = results[i]\n",
    "    return result_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_train = pd.DataFrame()\n",
    "Y_train['anytoxic'] = train[cats].astype(int).sum(axis='columns') > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_model():    \n",
    "    model = Sequential()\n",
    "    model.add(Embedding(MAX_NUM_WORDS + 1,\n",
    "                        EMBEDDING_DIM,\n",
    "                        input_length=MAX_SEQUENCE_LENGTH,\n",
    "                        weights=[embedding_matrix],\n",
    "                        trainable=True\n",
    "                       )\n",
    "             )\n",
    "    # model.add(Conv1D(250, 3, activation='relu'))\n",
    "    # model.add(MaxPooling1D(3))\n",
    "    model.add(Conv1D(250, 3, activation='relu'))\n",
    "    model.add(GlobalMaxPooling1D())\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dense(1, activation='hard_sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['acc'])\n",
    "    import gc\n",
    "    gc.collect()\n",
    "    print(model.summary())\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# sample_weight = compute_sample_weight('balanced', train[cats])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_weight = custom_compute_class_weights(train[cats])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_model(cat, model):\n",
    "    model_preds = model.predict_proba(x=train_data)\n",
    "    print(classification_report(np.asarray(train[cat], dtype='float32'), model_preds>0.5))\n",
    "    return model_preds\n",
    "\n",
    "def fit_model(cat, model, epochs=5):\n",
    "    model.fit(\n",
    "        x=train_data, \n",
    "        y=np.asarray(train[cat], dtype='float32'), \n",
    "        epochs=epochs, \n",
    "        batch_size=128, \n",
    "        verbose=0,\n",
    "        class_weight=compute_class_weight('balanced', [False, True],train[cat]),\n",
    "        callbacks=[TQDMNotebookCallback(leave_inner=True, leave_outer=False)]\n",
    "    )\n",
    "    return test_model(cat, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models=dict()\n",
    "pred_dict = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, 400, 100)          2500100   \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 398, 250)          75250     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_3 (Glob (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 128)               32128     \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 2,624,119\n",
      "Trainable params: 2,624,119\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
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       "  Widgets Documentation</a> for setup instructions.\n",
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       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
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       "<p>\n",
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     "text": [
      "159552/159571 [============================>.] - ETA: 0s             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.98      0.99    144277\n",
      "        1.0       0.82      0.99      0.90     15294\n",
      "\n",
      "avg / total       0.98      0.98      0.98    159571\n",
      "\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_4 (Embedding)      (None, 400, 100)          2500100   \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 398, 250)          75250     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_4 (Glob (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 128)               32128     \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 2,624,119\n",
      "Trainable params: 2,624,119\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
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       "<p>\n",
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       "<p>\n",
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       "<p>\n",
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       "<p>\n",
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       "</p>\n",
       "<p>\n",
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       "<p>\n",
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       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "<p>\n",
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       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
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       "<p>\n",
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     "text": [
      "159571/159571 [==============================] - 133s   \n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.99      1.00    157976\n",
      "        1.0       0.52      0.97      0.68      1595\n",
      "\n",
      "avg / total       0.99      0.99      0.99    159571\n",
      "\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_5 (Embedding)      (None, 400, 100)          2500100   \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 398, 250)          75250     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_5 (Glob (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 128)               32128     \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 2,624,119\n",
      "Trainable params: 2,624,119\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
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       "<p>\n",
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       "<p>\n",
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       "  Widgets Documentation</a> for setup instructions.\n",
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       "<p>\n",
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       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "<p>\n",
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       "<p>\n",
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       "<p>\n",
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      "159552/159571 [============================>.] - ETA: 0s             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.99      0.99    151122\n",
      "        1.0       0.81      0.99      0.89      8449\n",
      "\n",
      "avg / total       0.99      0.99      0.99    159571\n",
      "\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_6 (Embedding)      (None, 400, 100)          2500100   \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, 398, 250)          75250     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_6 (Glob (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 128)               32128     \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 2,624,119\n",
      "Trainable params: 2,624,119\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
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       "<p>\n",
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       "<p>\n",
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       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
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       "<p>\n",
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       "<p>\n",
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      "159520/159571 [============================>.] - ETA: 0s             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00    159093\n",
      "        1.0       0.00      0.00      0.00       478\n",
      "\n",
      "avg / total       0.99      1.00      1.00    159571\n",
      "\n"
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     "text": [
      "/home/mgooch/anaconda3/envs/insight_env/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_7 (Embedding)      (None, 400, 100)          2500100   \n",
      "_________________________________________________________________\n",
      "conv1d_7 (Conv1D)            (None, 398, 250)          75250     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_7 (Glob (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 128)               32128     \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 2,624,119\n",
      "Trainable params: 2,624,119\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
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     "text": [
      "159456/159571 [============================>.] - ETA: 0s             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.98      0.99    151694\n",
      "        1.0       0.77      0.99      0.87      7877\n",
      "\n",
      "avg / total       0.99      0.98      0.99    159571\n",
      "\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_8 (Embedding)      (None, 400, 100)          2500100   \n",
      "_________________________________________________________________\n",
      "conv1d_8 (Conv1D)            (None, 398, 250)          75250     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_8 (Glob (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 128)               32128     \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 2,624,119\n",
      "Trainable params: 2,624,119\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
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       "<p>\n",
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       "<p>\n",
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       "<p>\n",
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       "<p>\n",
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       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
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       "  Widgets Documentation</a> for setup instructions.\n",
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       "  Widgets Documentation</a> for setup instructions.\n",
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       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "HBox(children=(IntProgress(value=0, description='Epoch 13', max=159571), HTML(value='')))"
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       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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     "output_type": "stream",
     "text": [
      "159552/159571 [============================>.] - ETA: 0s             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.99      1.00    158166\n",
      "        1.0       0.61      0.95      0.74      1405\n",
      "\n",
      "avg / total       1.00      0.99      0.99    159571\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for cat in cats:\n",
    "    models[cat] = generate_model()\n",
    "    pred_dict[cat]=fit_model(cat, models[cat], epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gc\n",
    "gc.collect()\n",
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for cat in cats:\n",
    "    pred_df[cat] = pd.Series(pred_dict[cat].flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
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       "           toxic  severe_toxic   obscene  threat    insult  identity_hate\n",
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       "...          ...           ...       ...     ...       ...            ...\n",
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       "\n",
       "[159571 rows x 6 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
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    }
   ],
   "source": [
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "threat_weight = compute_class_weight('balanced', [False, True],train[cat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threat_weight[0] *= 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threat_weight[1] *= 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5.04441536e-03,   5.67868327e+03])"
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     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
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   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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     "execution_count": 174,
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   ],
   "source": [
    "models['threat'].fit(\n",
    "        x=train_data, \n",
    "        y=np.asarray(train[cat], dtype='float32'), \n",
    "        epochs=5, \n",
    "        batch_size=128, \n",
    "        verbose=0,\n",
    "        class_weight=threat_weight,\n",
    "        callbacks=[TQDMNotebookCallback(leave_inner=True, leave_outer=False)]\n",
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  },
  {
   "cell_type": "code",
   "execution_count": 175,
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     "output_type": "stream",
     "text": [
      "159488/159571 [============================>.] - ETA: 0s             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00    159093\n",
      "        1.0       0.00      0.00      0.00       478\n",
      "\n",
      "avg / total       0.99      1.00      1.00    159571\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgooch/anaconda3/envs/insight_env/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "threat_preds = test_model('threat', models['threat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_pred_df = pd.DataFrame()\n",
    "test_pred_df['id'] = test['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing: toxic\n",
      "153088/153164 [============================>.] - ETA: 0sprocessing: severe_toxic\n",
      "153120/153164 [============================>.] - ETA: 0sprocessing: obscene\n",
      "153152/153164 [============================>.] - ETA: 0sprocessing: threat\n",
      "processing: insult\n",
      "153024/153164 [============================>.] - ETA: 0sprocessing: identity_hate\n",
      "152928/153164 [============================>.] - ETA: 0s"
     ]
    }
   ],
   "source": [
    "for cat in cats:\n",
    "    print(\"\\nprocessing: %s\" % cat)\n",
    "    if cat == \"threat\":\n",
    "        test_pred_df[cat] = 0\n",
    "    else:\n",
    "        test_pred_df[cat] = models[cat].predict_proba(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>153164 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id     toxic  severe_toxic   obscene  threat    insult  \\\n",
       "0       00001cee341fdb12  1.000000      0.000000  1.000000       0  1.000000   \n",
       "1       0000247867823ef7  0.000000      0.000000  0.000000       0  0.000000   \n",
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       "5       0001ea8717f6de06  0.000000      0.000000  0.000000       0  0.000000   \n",
       "6       00024115d4cbde0f  0.000000      0.000000  0.000000       0  0.000000   \n",
       "7       000247e83dcc1211  1.000000      0.000000  0.000000       0  0.000000   \n",
       "8       00025358d4737918  0.000000      0.000000  0.000000       0  0.000000   \n",
       "9       00026d1092fe71cc  0.000000      0.000000  0.000000       0  0.000000   \n",
       "10      0002eadc3b301559  0.806089      0.000000  0.789766       0  0.000000   \n",
       "11      0002f87b16116a7f  0.000000      0.000000  0.000000       0  0.000000   \n",
       "12      0003806b11932181  0.000000      0.000000  0.000000       0  0.000000   \n",
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       "15      000634272d0d44eb  0.000000      0.000000  0.000000       0  0.000000   \n",
       "16      000663aff0fffc80  0.063926      0.000000  0.000000       0  0.000000   \n",
       "17      000689dd34e20979  0.000000      0.000000  0.000000       0  0.000000   \n",
       "18      000834769115370c  0.000000      0.000000  0.000000       0  0.000000   \n",
       "19      000844b52dee5f3f  0.424892      0.000000  0.000000       0  0.000000   \n",
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       "23      0009734200a85047  0.000000      0.000000  0.000000       0  0.000000   \n",
       "24      00097b6214686db5  0.000000      0.000000  0.000000       0  0.000000   \n",
       "25      0009aef4bd9e1697  0.000000      0.000000  0.000000       0  0.000000   \n",
       "26      000a02d807ae0254  0.000000      0.000000  0.000000       0  0.000000   \n",
       "27      000a6c6d4e89b9bc  0.000000      0.000000  0.000000       0  0.000000   \n",
       "28      000bafe2080bba82  0.766284      0.000000  0.000000       0  0.000000   \n",
       "29      000bf0a9894b2807  0.000000      0.000000  0.000000       0  0.000000   \n",
       "...                  ...       ...           ...       ...     ...       ...   \n",
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       "153151  fff9d70fe0722906  1.000000      0.000000  0.932156       0  0.498989   \n",
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       "153153  fffa3fae1890b40a  0.000000      0.000000  0.000000       0  0.000000   \n",
       "153154  fffa8a11c4378854  1.000000      0.000000  0.157815       0  0.707196   \n",
       "153155  fffac2a094c8e0e2  1.000000      0.000000  1.000000       0  1.000000   \n",
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       "153163  ffffce3fb183ee80  1.000000      0.000000  1.000000       0  0.742838   \n",
       "\n",
       "        identity_hate  \n",
       "0            0.849143  \n",
       "1            0.000000  \n",
       "2            0.000000  \n",
       "3            0.000000  \n",
       "4            0.000000  \n",
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       "10           0.000000  \n",
       "11           0.000000  \n",
       "12           0.000000  \n",
       "13           0.000000  \n",
       "14           0.000000  \n",
       "15           0.000000  \n",
       "16           0.000000  \n",
       "17           0.000000  \n",
       "18           0.000000  \n",
       "19           0.000000  \n",
       "20           0.000000  \n",
       "21           0.000000  \n",
       "22           0.000000  \n",
       "23           0.000000  \n",
       "24           0.000000  \n",
       "25           0.000000  \n",
       "26           0.000000  \n",
       "27           0.000000  \n",
       "28           0.943676  \n",
       "29           0.000000  \n",
       "...               ...  \n",
       "153134       0.000000  \n",
       "153135       0.000000  \n",
       "153136       0.000000  \n",
       "153137       0.000000  \n",
       "153138       0.000000  \n",
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       "153151       0.000000  \n",
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       "153153       0.000000  \n",
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       "153155       0.038354  \n",
       "153156       0.000000  \n",
       "153157       0.000000  \n",
       "153158       0.000000  \n",
       "153159       0.000000  \n",
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       "153161       0.000000  \n",
       "153162       0.000000  \n",
       "153163       0.000000  \n",
       "\n",
       "[153164 rows x 7 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ens_df = pd.read_csv(\"SUBMIT/ens.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "ens_df.to_csv(\"SUBMIT/convolution.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_df = pd.DataFrame()\n",
    "combined_df['id'] = test['id']\n",
    "for cat in cats:\n",
    "    if cat == 'threat':\n",
    "        combined_df[cat] = ens_df[cat]\n",
    "    else:\n",
    "        combined_df[cat] = test_pred_df[cat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_df.to_csv(\"SUBMIT/convolution_with_threat_from_ens.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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