Embedding Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our system relies on Gensim library for embeddings, this ipynb shows some basic examples of embedding training using Gensim, for more details please refer to\n",
    "https://radimrehurek.com/gensim/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reading HealthTap data and preparing it for embedding generation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "HealthTapExcel_Part1 = pandas.read_excel(\"data/HealthTap/healthtap_medical_qna_dataset_PART1.xlsx\")[\"question\"].values.tolist()\n",
    "HealthTapExcel_Part2 = pandas.read_excel(\"data/HealthTap/healthtap_medical_qna_dataset_PART2.xlsx\")[\"question\"].values.tolist()\n",
    "HealthTapExcel_Part3 = pandas.read_excel(\"data/HealthTap/healthtap_medical_qna_dataset_PART3.xlsx\")[\"question\"].values.tolist()\n",
    "\n",
    "HealthTapDataFull = HealthTapExcel_Part1+HealthTapExcel_Part2+HealthTapExcel_Part3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gensim wants Double list of Sentences and tokenized words , First list holds sentences and second list holds words of that sentence**\n",
    "\n",
    "List[x][y] where x is Sentence No and y is Word No,\n",
    "\n",
    "List[5][3] for example would be the 3rd word of 5th sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "HealthTapDataFull = [str(x).split(\" \") for x in HealthTapDataFull]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Like',\n",
       " '5',\n",
       " 'yrs',\n",
       " 'ago',\n",
       " 'to',\n",
       " 'now',\n",
       " \"I've\",\n",
       " 'had',\n",
       " 'some',\n",
       " 'reoccurring',\n",
       " 'itching',\n",
       " 'and',\n",
       " 'when',\n",
       " 'I',\n",
       " 'scratched',\n",
       " 'some',\n",
       " 'burning',\n",
       " 'n',\n",
       " 'vagina',\n",
       " 'no',\n",
       " 'pain',\n",
       " 'never',\n",
       " 'and',\n",
       " 'never',\n",
       " 'noticed',\n",
       " 'sores..is',\n",
       " 'it',\n",
       " 'herpes?']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HealthTapDataFull[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training word2vec embeddings using Gensim**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import Word2Vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Size = Specifies dimensionality of embeddings\n",
    "\n",
    "Window = Length of Continious Bag of Words window from Word2Vec Paper\n",
    "\n",
    "Min_count = Eliminates words that occur less then specified value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Word2Vec(HealthTapDataFull, size=300, window=5, min_count=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Embedding of a random word**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        0.5024255 , -2.6679456 ,  1.8089614 , -1.3950768 ,  0.2449592 ,\n",
       "        0.34671372,  2.2108152 , -0.373506  ,  2.975633  ,  1.3129283 ,\n",
       "        0.8217286 , -0.40625474,  0.26798108,  0.41780198,  1.61314   ,\n",
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       "       -0.8385396 , -1.3762621 , -1.0201815 ,  0.98048544, -0.235628  ,\n",
       "       -1.1957835 , -0.425306  , -1.8311595 ,  1.9039894 , -2.0673413 ,\n",
       "        0.4214947 ,  1.1572278 ,  0.9137055 ,  2.8436062 , -1.066208  ,\n",
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       "       -0.34669825, -0.54327536,  3.3841352 , -0.26708096, -1.4919325 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv[\"ago\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Saving HealthTap Word2Vec vectors**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Warning: This will overwrite previously trained HealthTap word2vec vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.wv.save(\"Embeddings/healthTapEmbedding.embed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training FastText embeddings using Gensim**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import FastText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = FastText(HealthTapDataFull, size=300, window=5, min_count=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Embedding of a random word**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       -7.98436821e-01, -3.20524603e-01, -8.76275837e-01,  2.72970982e-02,\n",
       "       -4.70846117e-01, -4.80772138e-01,  3.32855314e-01,  1.10067546e+00,\n",
       "       -6.33118868e-01,  9.84257877e-01, -8.65781829e-02,  1.48457184e-01,\n",
       "        4.68840182e-01,  1.47489774e+00,  3.85844372e-02,  2.16768198e-02,\n",
       "        3.38392913e-01,  1.00093532e+00, -3.49364251e-01,  1.04439640e+00,\n",
       "       -4.81697530e-01, -2.05938435e+00,  1.77176178e-01,  3.00893094e-02],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv[\"Wrong\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fast Text can also generate embeddings for words not in corpus**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.0641270e+00, -4.4317898e-01,  1.4017481e+00, -2.5567670e+00,\n",
       "        2.5962510e+00, -2.3989165e+00,  1.8445976e-01, -1.4772114e+00,\n",
       "        6.4381802e-01, -1.2886997e+00,  4.1982040e-01, -8.4903830e-01,\n",
       "       -1.0403616e+00, -1.4109166e+00,  1.1609293e+00, -5.1313955e-01,\n",
       "        4.0730423e-01,  2.4448764e+00, -1.3329360e+00,  6.8753302e-01,\n",
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       "       -2.1085920e+00,  4.3641672e+00,  1.0798147e+00, -1.4949433e+00,\n",
       "        4.4921145e+00, -1.5892622e+00,  1.3344519e+00,  1.5045664e+00,\n",
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       "        1.8767782e+00,  3.5052559e-01, -4.9768807e-03,  1.0822990e+00,\n",
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       "       -3.6163520e-02, -7.9488552e-01, -2.1923900e+00, -1.9644425e+00,\n",
       "        4.7163129e-01,  2.2330396e+00, -2.6153376e+00,  3.3931765e-01,\n",
       "       -1.2761269e+00, -4.3565350e+00,  1.5590703e+00,  2.3647990e+00,\n",
       "        1.3635037e+00, -8.9361775e-01,  1.2673614e+00, -3.4287789e+00,\n",
       "        1.2957003e+00,  2.9936500e+00,  2.2715302e+00, -4.9477077e-01,\n",
       "        9.9752948e-02,  1.8790325e+00, -1.7869131e-01,  8.0790502e-01,\n",
       "       -1.9094288e-01, -3.2937920e-01, -2.0237422e+00,  9.9596280e-01,\n",
       "       -1.2137444e+00,  3.5878730e+00, -1.0933306e-01, -4.0377267e-02,\n",
       "        8.3232009e-01, -1.9504372e+00,  1.4739816e+00,  8.6875679e-03,\n",
       "       -1.7935240e+00,  1.3828809e+00, -8.3376187e-01,  3.8728374e-01,\n",
       "       -3.2446826e-01,  1.8422480e+00,  9.0188771e-01, -3.7799278e-01,\n",
       "       -1.3911188e+00, -1.6499782e-01,  5.6092000e-01, -1.1834619e+00,\n",
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       "       -2.5604839e+00, -6.8458766e-01, -3.1685042e+00, -2.1284161e+00,\n",
       "       -6.0764992e-01,  2.9989238e+00, -1.1192724e+00, -1.4581373e+00,\n",
       "       -1.2155505e+00, -2.6956811e+00,  2.5263208e-01,  2.8709191e-01,\n",
       "        2.2544522e+00,  3.0486979e+00, -8.8261205e-01, -2.7103431e+00,\n",
       "        5.6238872e-01, -1.0938228e+00,  3.0779104e+00, -4.1936707e-01,\n",
       "        1.4281795e+00, -5.1213777e-01,  1.6234970e+00,  2.8535812e+00,\n",
       "       -1.6040637e-01,  1.2922224e+00, -1.4271230e+00,  8.8515198e-01,\n",
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       "       -6.2089574e-01, -4.6234789e-01,  2.5351524e-01,  4.9472490e-01,\n",
       "        4.0748082e-02,  5.1322728e-01,  2.3325648e+00, -1.5117218e+00,\n",
       "        2.4750354e+00,  8.1856316e-01,  2.0301422e-01, -4.0998954e-01,\n",
       "        1.0451933e+00,  2.5916684e+00, -2.1917446e+00, -1.9210474e+00,\n",
       "        6.4884496e-01, -3.5123546e+00, -4.0569872e-01,  2.4140079e+00,\n",
       "       -1.3735969e+00,  1.4707773e+00, -2.0218868e+00,  7.7181947e-01,\n",
       "       -2.2639563e+00, -7.4374372e-01,  2.6197276e-01, -1.5998495e+00,\n",
       "       -3.1879124e-01,  3.0774355e-02,  4.7290483e-01, -1.3813479e+00,\n",
       "        3.1250730e+00,  1.7316929e+00, -2.1147316e+00,  1.1668110e+00,\n",
       "       -9.6176755e-01, -6.8407422e-01, -2.0259137e+00,  1.3174264e+00,\n",
       "       -2.2864745e+00, -3.5856742e-01,  1.4461815e+00, -1.7888542e+00,\n",
       "       -6.2166017e-01, -1.5433286e+00, -2.7117893e-01,  1.0634469e+00,\n",
       "        5.4844469e-01,  8.9615631e-01, -3.6468354e-01,  1.9813595e+00,\n",
       "       -1.4250522e+00,  1.7839050e+00, -6.9158781e-01,  3.9103317e-01,\n",
       "        2.9555416e-01,  1.6458279e+00, -1.4377997e+00,  5.7399338e-01,\n",
       "       -1.6605583e+00, -2.8347523e+00, -2.2604265e+00,  6.5009093e-01,\n",
       "        5.1438361e-01, -3.8904896e+00, -1.6715014e-01,  1.7752329e+00],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv[\"ron\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Saving FastText model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"Embeddings/HealthTapFastText\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**\n",
    "\n",
    "System automatically recognizes and integrates FastText embeddings located at\n",
    "\n",
    "\"Embeddings/HealthTapFastText\" (relative path)\n",
    "\n",
    "and\n",
    "\n",
    "Word2Vec embeddings located at\n",
    "\n",
    "\"Embeddings/healthTapEmbedding.embed\"\n",
    "\n",
    "For embeddings that are saved on these paths, one needs to merely select FastText or HealthTap options of EmbeddingHandler to use these (examples are given on Training Model Script)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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