Medical Embeddings_Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Medical Embeddings and clinical trial search engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import streamlit as st  #importing streamlit liabrary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91758\\anaconda3\\anaconda\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gensim\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import FastText\n",
    "from sklearn.decomposition import PCA\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\91758\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\91758\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\91758\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt # our main display package\n",
    "import plotly.graph_objects as go\n",
    "import string # used for preprocessing\n",
    "import re # used for preprocessing\n",
    "import nltk # the Natural Language Toolkit, used for preprocessing\n",
    "import numpy as np # used for managing NaNs\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords # used for preprocessing\n",
    "from nltk.stem import WordNetLemmatizer # used for preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Dimension-covid.csv')   #for preprocessing\n",
    "df1=pd.read_csv('Dimension-covid.csv')  #for returning results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10666, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understand the data\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>Date added</th>\n",
       "      <th>Trial ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Brief title</th>\n",
       "      <th>Acronym</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Publication date</th>\n",
       "      <th>Active years</th>\n",
       "      <th>Phase</th>\n",
       "      <th>Conditions</th>\n",
       "      <th>...</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Registry</th>\n",
       "      <th>Investigators/Contacts</th>\n",
       "      <th>Sponsors/Collaborators</th>\n",
       "      <th>GRID IDs</th>\n",
       "      <th>Country of Sponsor/Collaborator</th>\n",
       "      <th>Collaborating Funders</th>\n",
       "      <th>Funder Country</th>\n",
       "      <th>Source Linkout</th>\n",
       "      <th>Dimensions URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912934</td>\n",
       "      <td>The Relationship Between Coronavirus Anxiety L...</td>\n",
       "      <td>The Relationship Between COVID-19 Anxiety Leve...</td>\n",
       "      <td>COVID-19</td>\n",
       "      <td>The aim was to determine the coronavirus anxie...</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID-19; Emotional Eating; Metabolic Syndrome</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>Dilek Küçük Alemdar</td>\n",
       "      <td>Ordu University</td>\n",
       "      <td>grid.412366.4</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912934</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912921</td>\n",
       "      <td>Effect of Palmitoylethanolamide on Proinflamma...</td>\n",
       "      <td>Effect of Palmitoylethanolamide on Proinflamma...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This is a randomized controlled trial to exami...</td>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>2020; 2021; 2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID19; Inflammation</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arizona State University</td>\n",
       "      <td>grid.215654.1</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912921</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912895</td>\n",
       "      <td>Detection of SARS-CoV-2 RNA and Biomarkers in ...</td>\n",
       "      <td>Detection of SARS-CoV-2 RNA in Coughed Droplet...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The purpose of this study is to learn how to b...</td>\n",
       "      <td>2021-04-14</td>\n",
       "      <td>2021; 2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid19</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>Larry Anderson</td>\n",
       "      <td>Emory University Hospital Midtown; Emory Unive...</td>\n",
       "      <td>grid.411929.6; grid.412162.2; grid.189967.8; g...</td>\n",
       "      <td>United States; United States; United States; U...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912895</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912817</td>\n",
       "      <td>Pain Rehabilitation Virtual Reality (PR-VR) At...</td>\n",
       "      <td>PR-VR At-home Program During and After COVID-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Poorly controlled chronic pain in teens can im...</td>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>2021; 2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pain, Chronic</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>Jennifer Stinson</td>\n",
       "      <td>Hospital for Sick Children; Stanford University</td>\n",
       "      <td>grid.42327.30; grid.168010.e</td>\n",
       "      <td>Canada; United States</td>\n",
       "      <td>Hospital for Sick Children</td>\n",
       "      <td>Canada</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912817</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912804</td>\n",
       "      <td>Perceived Health After Hospitalization for COV...</td>\n",
       "      <td>Perceived Health After Hospitalization for COV...</td>\n",
       "      <td>PRO-COVID</td>\n",
       "      <td>There are currently no precise data available ...</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2020; 2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID-19, Post-hospitalization, Disease Impact</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Centre Hospitalier Universitaire Dijon Bourgogne</td>\n",
       "      <td>grid.31151.37</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912804</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date added     Trial ID                                              Title  \\\n",
       "0  2021-06-04  NCT04912934  The Relationship Between Coronavirus Anxiety L...   \n",
       "1  2021-06-04  NCT04912921  Effect of Palmitoylethanolamide on Proinflamma...   \n",
       "2  2021-06-04  NCT04912895  Detection of SARS-CoV-2 RNA and Biomarkers in ...   \n",
       "3  2021-06-04  NCT04912817  Pain Rehabilitation Virtual Reality (PR-VR) At...   \n",
       "4  2021-06-04  NCT04912804  Perceived Health After Hospitalization for COV...   \n",
       "\n",
       "                                         Brief title    Acronym  \\\n",
       "0  The Relationship Between COVID-19 Anxiety Leve...   COVID-19   \n",
       "1  Effect of Palmitoylethanolamide on Proinflamma...        NaN   \n",
       "2  Detection of SARS-CoV-2 RNA in Coughed Droplet...        NaN   \n",
       "3    PR-VR At-home Program During and After COVID-19        NaN   \n",
       "4  Perceived Health After Hospitalization for COV...  PRO-COVID   \n",
       "\n",
       "                                            Abstract Publication date  \\\n",
       "0  The aim was to determine the coronavirus anxie...       2020-06-20   \n",
       "1  This is a randomized controlled trial to exami...       2020-10-20   \n",
       "2  The purpose of this study is to learn how to b...       2021-04-14   \n",
       "3  Poorly controlled chronic pain in teens can im...       2021-09-01   \n",
       "4  There are currently no precise data available ...       2020-06-30   \n",
       "\n",
       "       Active years Phase                                      Conditions  \\\n",
       "0              2020   NaN  COVID-19; Emotional Eating; Metabolic Syndrome   \n",
       "1  2020; 2021; 2022   NaN                           COVID19; Inflammation   \n",
       "2        2021; 2022   NaN                                         Covid19   \n",
       "3        2021; 2022   NaN                                   Pain, Chronic   \n",
       "4        2020; 2021   NaN  COVID-19, Post-hospitalization, Disease Impact   \n",
       "\n",
       "   ... Gender            Registry Investigators/Contacts  \\\n",
       "0  ...    All  ClinicalTrials.gov    Dilek Küçük Alemdar   \n",
       "1  ...    All  ClinicalTrials.gov                    NaN   \n",
       "2  ...    All  ClinicalTrials.gov         Larry Anderson   \n",
       "3  ...    All  ClinicalTrials.gov       Jennifer Stinson   \n",
       "4  ...    All  ClinicalTrials.gov                    NaN   \n",
       "\n",
       "                              Sponsors/Collaborators  \\\n",
       "0                                    Ordu University   \n",
       "1                           Arizona State University   \n",
       "2  Emory University Hospital Midtown; Emory Unive...   \n",
       "3    Hospital for Sick Children; Stanford University   \n",
       "4   Centre Hospitalier Universitaire Dijon Bourgogne   \n",
       "\n",
       "                                            GRID IDs  \\\n",
       "0                                      grid.412366.4   \n",
       "1                                      grid.215654.1   \n",
       "2  grid.411929.6; grid.412162.2; grid.189967.8; g...   \n",
       "3                       grid.42327.30; grid.168010.e   \n",
       "4                                      grid.31151.37   \n",
       "\n",
       "                     Country of Sponsor/Collaborator  \\\n",
       "0                                             Turkey   \n",
       "1                                      United States   \n",
       "2  United States; United States; United States; U...   \n",
       "3                              Canada; United States   \n",
       "4                                             France   \n",
       "\n",
       "        Collaborating Funders Funder Country  \\\n",
       "0                         NaN            NaN   \n",
       "1                         NaN            NaN   \n",
       "2                         NaN            NaN   \n",
       "3  Hospital for Sick Children         Canada   \n",
       "4                         NaN            NaN   \n",
       "\n",
       "                                Source Linkout  \\\n",
       "0  https://clinicaltrials.gov/show/NCT04912934   \n",
       "1  https://clinicaltrials.gov/show/NCT04912921   \n",
       "2  https://clinicaltrials.gov/show/NCT04912895   \n",
       "3  https://clinicaltrials.gov/show/NCT04912817   \n",
       "4  https://clinicaltrials.gov/show/NCT04912804   \n",
       "\n",
       "                                      Dimensions URL  \n",
       "0  https://app.dimensions.ai/details/clinical_tri...  \n",
       "1  https://app.dimensions.ai/details/clinical_tri...  \n",
       "2  https://app.dimensions.ai/details/clinical_tri...  \n",
       "3  https://app.dimensions.ai/details/clinical_tri...  \n",
       "4  https://app.dimensions.ai/details/clinical_tri...  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understand the data\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The aim was to determine the coronavirus anxiety levels and emotional eating status of cases with diagnosis of MetS according to diagnostic criteria published by the International Diabetic Federation (IDF) in 2005 and healthy individuals with similar BMI to MetS subjects.\\n\\nDetailed Description\\nThe aim was to determine the coronavirus anxiety levels and emotional eating status of cases with diagnosis of MetS according to diagnostic criteria published by the International Diabetic Federation (IDF) in 2005 and healthy individuals with similar BMI to MetS subjects. This study was completed with the descriptive, cross-sectional and relational screening model. The full sampling method was used in the study. The study was completed with 105 individuals with metabolic syndrome attending the diet clinic and 109 healthy individuals without MetS diagnosis also attending the diet clinic. Collection of data used anthropometric measurements and biochemical findings, blood pressure and a survey form, the Coronavirus Anxiety Scale (CAS) and Emotional Eating Scale (EES).'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Abstract'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to remove all urls\n",
    "def remove_urls(text):    \n",
    "    new_text = ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\",\" \",text).split())\n",
    "    return new_text\n",
    "\n",
    "# make all text lowercase\n",
    "def text_lowercase(text):\n",
    "    return text.lower()\n",
    "\n",
    "# remove numbers\n",
    "def remove_numbers(text):\n",
    "    result = re.sub(r'\\d+', '', text)\n",
    "    return result\n",
    "\n",
    "# remove punctuation\n",
    "def remove_punctuation(text):\n",
    "    translator = str.maketrans('', '', string.punctuation)\n",
    "    return text.translate(translator)\n",
    "\n",
    "# tokenize\n",
    "def tokenize(text):\n",
    "    text = word_tokenize(text)\n",
    "    return text\n",
    "\n",
    "# remove stopwords\n",
    "stop_words = set(stopwords.words('english'))\n",
    "def remove_stopwords(text):\n",
    "    text = [i for i in text if not i in stop_words]\n",
    "    return text\n",
    "\n",
    "# lemmatize Words \n",
    "lemmatizer = WordNetLemmatizer()\n",
    "def lemmatize(text):\n",
    "    text = [lemmatizer.lemmatize(token) for token in text]\n",
    "    return text\n",
    "\n",
    "#Creating one function so that all functions can be applied at once\n",
    "def preprocessing(text):\n",
    "    \n",
    "    text = text_lowercase(text)\n",
    "    text = remove_urls(text)\n",
    "    text = remove_numbers(text)\n",
    "    text = remove_punctuation(text)\n",
    "    text = tokenize(text)\n",
    "    text = remove_stopwords(text)\n",
    "    text = lemmatize(text)\n",
    "    text = ' '.join(text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying preprocessing and removing '\\n' character\n",
    "\n",
    "for i in range(df.shape[0]):\n",
    "    df['Abstract'][i]=preprocessing(str(df['Abstract'][i])) \n",
    "for text in df.Abstract:\n",
    "\n",
    "    text=text.replace('\\n',' ')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>Date added</th>\n",
       "      <th>Trial ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Brief title</th>\n",
       "      <th>Acronym</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Publication date</th>\n",
       "      <th>Active years</th>\n",
       "      <th>Phase</th>\n",
       "      <th>Conditions</th>\n",
       "      <th>...</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Registry</th>\n",
       "      <th>Investigators/Contacts</th>\n",
       "      <th>Sponsors/Collaborators</th>\n",
       "      <th>GRID IDs</th>\n",
       "      <th>Country of Sponsor/Collaborator</th>\n",
       "      <th>Collaborating Funders</th>\n",
       "      <th>Funder Country</th>\n",
       "      <th>Source Linkout</th>\n",
       "      <th>Dimensions URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912934</td>\n",
       "      <td>The Relationship Between Coronavirus Anxiety L...</td>\n",
       "      <td>The Relationship Between COVID-19 Anxiety Leve...</td>\n",
       "      <td>COVID-19</td>\n",
       "      <td>aim determine coronavirus anxiety level emotio...</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID-19; Emotional Eating; Metabolic Syndrome</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>Dilek Küçük Alemdar</td>\n",
       "      <td>Ordu University</td>\n",
       "      <td>grid.412366.4</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912934</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912921</td>\n",
       "      <td>Effect of Palmitoylethanolamide on Proinflamma...</td>\n",
       "      <td>Effect of Palmitoylethanolamide on Proinflamma...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>randomized controlled trial examine effect foo...</td>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>2020; 2021; 2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID19; Inflammation</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arizona State University</td>\n",
       "      <td>grid.215654.1</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912921</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912895</td>\n",
       "      <td>Detection of SARS-CoV-2 RNA and Biomarkers in ...</td>\n",
       "      <td>Detection of SARS-CoV-2 RNA in Coughed Droplet...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>purpose study learn better detect infection un...</td>\n",
       "      <td>2021-04-14</td>\n",
       "      <td>2021; 2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covid19</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>Larry Anderson</td>\n",
       "      <td>Emory University Hospital Midtown; Emory Unive...</td>\n",
       "      <td>grid.411929.6; grid.412162.2; grid.189967.8; g...</td>\n",
       "      <td>United States; United States; United States; U...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912895</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912817</td>\n",
       "      <td>Pain Rehabilitation Virtual Reality (PR-VR) At...</td>\n",
       "      <td>PR-VR At-home Program During and After COVID-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>poorly controlled chronic pain teen impact qua...</td>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>2021; 2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pain, Chronic</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>Jennifer Stinson</td>\n",
       "      <td>Hospital for Sick Children; Stanford University</td>\n",
       "      <td>grid.42327.30; grid.168010.e</td>\n",
       "      <td>Canada; United States</td>\n",
       "      <td>Hospital for Sick Children</td>\n",
       "      <td>Canada</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912817</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>NCT04912804</td>\n",
       "      <td>Perceived Health After Hospitalization for COV...</td>\n",
       "      <td>Perceived Health After Hospitalization for COV...</td>\n",
       "      <td>PRO-COVID</td>\n",
       "      <td>currently precise data available impact covid ...</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2020; 2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID-19, Post-hospitalization, Disease Impact</td>\n",
       "      <td>...</td>\n",
       "      <td>All</td>\n",
       "      <td>ClinicalTrials.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Centre Hospitalier Universitaire Dijon Bourgogne</td>\n",
       "      <td>grid.31151.37</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://clinicaltrials.gov/show/NCT04912804</td>\n",
       "      <td>https://app.dimensions.ai/details/clinical_tri...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date added     Trial ID                                              Title  \\\n",
       "0  2021-06-04  NCT04912934  The Relationship Between Coronavirus Anxiety L...   \n",
       "1  2021-06-04  NCT04912921  Effect of Palmitoylethanolamide on Proinflamma...   \n",
       "2  2021-06-04  NCT04912895  Detection of SARS-CoV-2 RNA and Biomarkers in ...   \n",
       "3  2021-06-04  NCT04912817  Pain Rehabilitation Virtual Reality (PR-VR) At...   \n",
       "4  2021-06-04  NCT04912804  Perceived Health After Hospitalization for COV...   \n",
       "\n",
       "                                         Brief title    Acronym  \\\n",
       "0  The Relationship Between COVID-19 Anxiety Leve...   COVID-19   \n",
       "1  Effect of Palmitoylethanolamide on Proinflamma...        NaN   \n",
       "2  Detection of SARS-CoV-2 RNA in Coughed Droplet...        NaN   \n",
       "3    PR-VR At-home Program During and After COVID-19        NaN   \n",
       "4  Perceived Health After Hospitalization for COV...  PRO-COVID   \n",
       "\n",
       "                                            Abstract Publication date  \\\n",
       "0  aim determine coronavirus anxiety level emotio...       2020-06-20   \n",
       "1  randomized controlled trial examine effect foo...       2020-10-20   \n",
       "2  purpose study learn better detect infection un...       2021-04-14   \n",
       "3  poorly controlled chronic pain teen impact qua...       2021-09-01   \n",
       "4  currently precise data available impact covid ...       2020-06-30   \n",
       "\n",
       "       Active years Phase                                      Conditions  \\\n",
       "0              2020   NaN  COVID-19; Emotional Eating; Metabolic Syndrome   \n",
       "1  2020; 2021; 2022   NaN                           COVID19; Inflammation   \n",
       "2        2021; 2022   NaN                                         Covid19   \n",
       "3        2021; 2022   NaN                                   Pain, Chronic   \n",
       "4        2020; 2021   NaN  COVID-19, Post-hospitalization, Disease Impact   \n",
       "\n",
       "   ... Gender            Registry Investigators/Contacts  \\\n",
       "0  ...    All  ClinicalTrials.gov    Dilek Küçük Alemdar   \n",
       "1  ...    All  ClinicalTrials.gov                    NaN   \n",
       "2  ...    All  ClinicalTrials.gov         Larry Anderson   \n",
       "3  ...    All  ClinicalTrials.gov       Jennifer Stinson   \n",
       "4  ...    All  ClinicalTrials.gov                    NaN   \n",
       "\n",
       "                              Sponsors/Collaborators  \\\n",
       "0                                    Ordu University   \n",
       "1                           Arizona State University   \n",
       "2  Emory University Hospital Midtown; Emory Unive...   \n",
       "3    Hospital for Sick Children; Stanford University   \n",
       "4   Centre Hospitalier Universitaire Dijon Bourgogne   \n",
       "\n",
       "                                            GRID IDs  \\\n",
       "0                                      grid.412366.4   \n",
       "1                                      grid.215654.1   \n",
       "2  grid.411929.6; grid.412162.2; grid.189967.8; g...   \n",
       "3                       grid.42327.30; grid.168010.e   \n",
       "4                                      grid.31151.37   \n",
       "\n",
       "                     Country of Sponsor/Collaborator  \\\n",
       "0                                             Turkey   \n",
       "1                                      United States   \n",
       "2  United States; United States; United States; U...   \n",
       "3                              Canada; United States   \n",
       "4                                             France   \n",
       "\n",
       "        Collaborating Funders Funder Country  \\\n",
       "0                         NaN            NaN   \n",
       "1                         NaN            NaN   \n",
       "2                         NaN            NaN   \n",
       "3  Hospital for Sick Children         Canada   \n",
       "4                         NaN            NaN   \n",
       "\n",
       "                                Source Linkout  \\\n",
       "0  https://clinicaltrials.gov/show/NCT04912934   \n",
       "1  https://clinicaltrials.gov/show/NCT04912921   \n",
       "2  https://clinicaltrials.gov/show/NCT04912895   \n",
       "3  https://clinicaltrials.gov/show/NCT04912817   \n",
       "4  https://clinicaltrials.gov/show/NCT04912804   \n",
       "\n",
       "                                      Dimensions URL  \n",
       "0  https://app.dimensions.ai/details/clinical_tri...  \n",
       "1  https://app.dimensions.ai/details/clinical_tri...  \n",
       "2  https://app.dimensions.ai/details/clinical_tri...  \n",
       "3  https://app.dimensions.ai/details/clinical_tri...  \n",
       "4  https://app.dimensions.ai/details/clinical_tri...  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'aim determine coronavirus anxiety level emotional eating status case diagnosis mets according diagnostic criterion published international diabetic federation idf healthy individual similar bmi mets subject detailed description aim determine coronavirus anxiety level emotional eating status case diagnosis mets according diagnostic criterion published international diabetic federation idf healthy individual similar bmi mets subject study completed descriptive cross sectional relational screening model full sampling method used study study completed individual metabolic syndrome attending diet clinic healthy individual without mets diagnosis also attending diet clinic collection data used anthropometric measurement biochemical finding blood pressure survey form coronavirus anxiety scale ca emotional eating scale ee'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Abstract'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying preprocessing and removing '\\n' character\n",
    "\n",
    "for i in range(df.shape[0]):\n",
    "    df['Title'][i]=preprocessing(str(df['Title'][i])) \n",
    "for text in df.Title:\n",
    "\n",
    "    text=text.replace('\\n',' ') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=[word_tokenize(word) for word in df.Abstract ]   #Tokenizing data for training purpose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('patient', 38457),\n",
       " ('covid', 38211),\n",
       " ('study', 31963),\n",
       " ('group', 12771),\n",
       " ('treatment', 12471),\n",
       " ('day', 12337),\n",
       " ('sars', 11600),\n",
       " ('cov', 11535),\n",
       " ('clinical', 11507),\n",
       " ('disease', 11395)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "cnt = Counter()\n",
    "for text in df[\"Abstract\"].values:\n",
    "    for word in text.split():\n",
    "        cnt[word] += 1\n",
    "        \n",
    "cnt.most_common(10)  #top 10 common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wordcloud in c:\\users\\91758\\anaconda3\\anaconda\\lib\\site-packages (1.6.0)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\91758\\anaconda3\\anaconda\\lib\\site-packages (from wordcloud) (3.3.4)\n",
      "Requirement already satisfied: numpy>=1.6.1 in c:\\users\\91758\\anaconda3\\anaconda\\lib\\site-packages (from wordcloud) (1.20.1)\n",
      "Requirement already satisfied: pillow in c:\\users\\91758\\anaconda3\\anaconda\\lib\\site-packages (from wordcloud) (8.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\91758\\anaconda3\\anaconda\\lib\\site-packages (from matplotlib->wordcloud) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\91758\\anaconda3\\anaconda\\lib\\site-packages (from matplotlib->wordcloud) (2.8.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\91758\\anaconda3\\anaconda\\lib\\site-packages (from matplotlib->wordcloud) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\91758\\anaconda3\\anaconda\\lib\\site-packages (from matplotlib->wordcloud) (1.3.1)\n",
      "Requirement already satisfied: six in c:\\users\\91758\\anaconda3\\anaconda\\lib\\site-packages (from cycler>=0.10->matplotlib->wordcloud) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Final word cloud after all the cleaning and pre-processing\n",
    "from wordcloud import WordCloud\n",
    "from textwrap import wrap\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "#comment_words = ' '\n",
    "stopwords = set(STOPWORDS) \n",
    "from pandas.core.common import flatten\n",
    "\n",
    "# iterate through the csv file \n",
    "\n",
    "\n",
    "comment_words=flatten(x)\n",
    "comment_words=' '.join(comment_words)\n",
    "\n",
    "\n",
    "wordcloud = WordCloud(width = 800, height = 800, \n",
    "            background_color ='white', \n",
    "            stopwords = stopwords, \n",
    "            min_font_size = 10).generate(comment_words) \n",
    "\n",
    "# plot the WordCloud image                        \n",
    "plt.figure(figsize = (8, 8), facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) \n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training skipgram model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-23 12:37:34.616 INFO    gensim.models.word2vec: collecting all words and their counts\n",
      "2021-07-23 12:37:34.616 INFO    gensim.models.word2vec: PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2021-07-23 12:37:34.941 INFO    gensim.models.word2vec: PROGRESS: at sentence #10000, processed 2024809 words, keeping 39392 word types\n",
      "2021-07-23 12:37:34.954 INFO    gensim.models.word2vec: collected 39969 word types from a corpus of 2088009 raw words and 10666 sentences\n",
      "2021-07-23 12:37:34.958 INFO    gensim.models.word2vec: Creating a fresh vocabulary\n",
      "2021-07-23 12:37:35.044 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'effective_min_count=2 retains 24877 unique words (62.2407365708424%% of original 39969, drops 15092)', 'datetime': '2021-07-23T12:37:35.044949', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'prepare_vocab'}\n",
      "2021-07-23 12:37:35.044 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'effective_min_count=2 leaves 2072917 word corpus (99.2772061806247%% of original 2088009, drops 15092)', 'datetime': '2021-07-23T12:37:35.044949', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'prepare_vocab'}\n",
      "2021-07-23 12:37:35.173 INFO    gensim.models.word2vec: deleting the raw counts dictionary of 39969 items\n",
      "2021-07-23 12:37:35.173 INFO    gensim.models.word2vec: sample=0.001 downsamples 32 most-common words\n",
      "2021-07-23 12:37:35.173 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'downsampling leaves estimated 1928478.8939992087 word corpus (93.0%% of prior 2072917)', 'datetime': '2021-07-23T12:37:35.173799', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'prepare_vocab'}\n",
      "2021-07-23 12:37:35.413 INFO    gensim.models.word2vec: estimated required memory for 24877 words and 50 dimensions: 22389300 bytes\n",
      "2021-07-23 12:37:35.413 INFO    gensim.models.word2vec: resetting layer weights\n",
      "2021-07-23 12:37:35.423 INFO    gensim.utils: Word2Vec lifecycle event {'update': False, 'trim_rule': 'None', 'datetime': '2021-07-23T12:37:35.423281', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'build_vocab'}\n",
      "2021-07-23 12:37:35.424 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'training model with 3 workers on 24877 vocabulary and 50 features, using sg=1 hs=0 sample=0.001 negative=5 window=3', 'datetime': '2021-07-23T12:37:35.424284', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'train'}\n",
      "2021-07-23 12:37:36.443 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 25.51% examples, 526673 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:37.450 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 52.22% examples, 536202 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:38.448 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 77.40% examples, 528877 words/s, in_qsize 6, out_qsize 0\n",
      "2021-07-23 12:37:39.043 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:37:39.054 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:37:39.054 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:37:39.062 INFO    gensim.models.word2vec: EPOCH - 1 : training on 2088009 raw words (1927928 effective words) took 3.6s, 530421 effective words/s\n",
      "2021-07-23 12:37:40.084 INFO    gensim.models.word2vec: EPOCH 2 - PROGRESS: at 26.36% examples, 545582 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:41.083 INFO    gensim.models.word2vec: EPOCH 2 - PROGRESS: at 52.26% examples, 537180 words/s, in_qsize 6, out_qsize 0\n",
      "2021-07-23 12:37:42.093 INFO    gensim.models.word2vec: EPOCH 2 - PROGRESS: at 80.98% examples, 549472 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:42.555 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:37:42.573 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:37:42.583 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:37:42.584 INFO    gensim.models.word2vec: EPOCH - 2 : training on 2088009 raw words (1928693 effective words) took 3.5s, 548062 effective words/s\n",
      "2021-07-23 12:37:43.607 INFO    gensim.models.word2vec: EPOCH 3 - PROGRESS: at 25.80% examples, 534652 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:44.623 INFO    gensim.models.word2vec: EPOCH 3 - PROGRESS: at 54.36% examples, 553698 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:45.633 INFO    gensim.models.word2vec: EPOCH 3 - PROGRESS: at 83.01% examples, 557995 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:45.983 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:37:45.991 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:37:46.003 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:37:46.003 INFO    gensim.models.word2vec: EPOCH - 3 : training on 2088009 raw words (1928778 effective words) took 3.4s, 564077 effective words/s\n",
      "2021-07-23 12:37:47.005 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 27.85% examples, 581471 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:48.016 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 56.30% examples, 581137 words/s, in_qsize 6, out_qsize 0\n",
      "2021-07-23 12:37:49.023 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 86.75% examples, 586295 words/s, in_qsize 6, out_qsize 0\n",
      "2021-07-23 12:37:49.254 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:37:49.263 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:37:49.274 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:37:49.283 INFO    gensim.models.word2vec: EPOCH - 4 : training on 2088009 raw words (1928423 effective words) took 3.3s, 588903 effective words/s\n",
      "2021-07-23 12:37:50.288 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 27.85% examples, 579033 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:51.296 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 56.75% examples, 583903 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:52.313 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 86.77% examples, 583894 words/s, in_qsize 6, out_qsize 0\n",
      "2021-07-23 12:37:52.554 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:37:52.555 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:37:52.563 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:37:52.563 INFO    gensim.models.word2vec: EPOCH - 5 : training on 2088009 raw words (1928410 effective words) took 3.3s, 587268 effective words/s\n",
      "2021-07-23 12:37:52.563 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'training on 10440045 raw words (9642232 effective words) took 17.1s, 562359 effective words/s', 'datetime': '2021-07-23T12:37:52.563293', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'train'}\n",
      "2021-07-23 12:37:52.563 INFO    gensim.utils: Word2Vec lifecycle event {'params': 'Word2Vec(vocab=24877, vector_size=50, alpha=0.025)', 'datetime': '2021-07-23T12:37:52.563293', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'created'}\n",
      "2021-07-23 12:37:52.563 INFO    gensim.utils: Word2Vec lifecycle event {'fname_or_handle': 'skipgramx1.bin', 'separately': 'None', 'sep_limit': 10485760, 'ignore': frozenset(), 'datetime': '2021-07-23T12:37:52.563293', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'saving'}\n",
      "2021-07-23 12:37:52.563 INFO    gensim.utils: not storing attribute cum_table\n",
      "2021-07-23 12:37:52.584 INFO    gensim.utils: saved skipgramx1.bin\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word2Vec(vocab=24877, vector_size=50, alpha=0.025)\n"
     ]
    }
   ],
   "source": [
    "# training the model\n",
    "\n",
    "skipgram = Word2Vec(x, vector_size =50, window = 3, min_count=2,sg = 1)\n",
    "print(skipgram)\n",
    "skipgram.save('skipgramx1.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-23 12:37:52.603 INFO    gensim.models.word2vec: collecting all words and their counts\n",
      "2021-07-23 12:37:52.609 INFO    gensim.models.word2vec: PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2021-07-23 12:37:52.914 INFO    gensim.models.word2vec: PROGRESS: at sentence #10000, processed 2024809 words, keeping 39392 word types\n",
      "2021-07-23 12:37:52.931 INFO    gensim.models.word2vec: collected 39969 word types from a corpus of 2088009 raw words and 10666 sentences\n",
      "2021-07-23 12:37:52.933 INFO    gensim.models.word2vec: Creating a fresh vocabulary\n",
      "2021-07-23 12:37:53.020 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'effective_min_count=2 retains 24877 unique words (62.2407365708424%% of original 39969, drops 15092)', 'datetime': '2021-07-23T12:37:53.020940', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'prepare_vocab'}\n",
      "2021-07-23 12:37:53.020 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'effective_min_count=2 leaves 2072917 word corpus (99.2772061806247%% of original 2088009, drops 15092)', 'datetime': '2021-07-23T12:37:53.020940', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'prepare_vocab'}\n",
      "2021-07-23 12:37:53.153 INFO    gensim.models.word2vec: deleting the raw counts dictionary of 39969 items\n",
      "2021-07-23 12:37:53.153 INFO    gensim.models.word2vec: sample=0.001 downsamples 32 most-common words\n",
      "2021-07-23 12:37:53.153 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'downsampling leaves estimated 1928478.8939992087 word corpus (93.0%% of prior 2072917)', 'datetime': '2021-07-23T12:37:53.153630', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'prepare_vocab'}\n",
      "2021-07-23 12:37:53.363 INFO    gensim.models.word2vec: estimated required memory for 24877 words and 100 dimensions: 32340100 bytes\n",
      "2021-07-23 12:37:53.363 INFO    gensim.models.word2vec: resetting layer weights\n",
      "2021-07-23 12:37:53.374 INFO    gensim.utils: Word2Vec lifecycle event {'update': False, 'trim_rule': 'None', 'datetime': '2021-07-23T12:37:53.374923', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'build_vocab'}\n",
      "2021-07-23 12:37:53.374 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'training model with 3 workers on 24877 vocabulary and 100 features, using sg=1 hs=0 sample=0.001 negative=5 window=1', 'datetime': '2021-07-23T12:37:53.374923', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'train'}\n",
      "2021-07-23 12:37:54.394 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 44.17% examples, 920640 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:55.394 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 98.35% examples, 941627 words/s, in_qsize 4, out_qsize 0\n",
      "2021-07-23 12:37:55.413 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:37:55.413 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:37:55.413 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:37:55.413 INFO    gensim.models.word2vec: EPOCH - 1 : training on 2088009 raw words (1928793 effective words) took 2.0s, 945512 effective words/s\n",
      "2021-07-23 12:37:56.425 INFO    gensim.models.word2vec: EPOCH 2 - PROGRESS: at 41.74% examples, 878876 words/s, in_qsize 6, out_qsize 0\n",
      "2021-07-23 12:37:57.428 INFO    gensim.models.word2vec: EPOCH 2 - PROGRESS: at 85.87% examples, 874494 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:57.596 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:37:57.596 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:37:57.596 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:37:57.596 INFO    gensim.models.word2vec: EPOCH - 2 : training on 2088009 raw words (1928505 effective words) took 2.2s, 885566 effective words/s\n",
      "2021-07-23 12:37:58.613 INFO    gensim.models.word2vec: EPOCH 3 - PROGRESS: at 44.17% examples, 926295 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:37:59.606 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:37:59.624 INFO    gensim.models.word2vec: EPOCH 3 - PROGRESS: at 99.77% examples, 948788 words/s, in_qsize 1, out_qsize 1\n",
      "2021-07-23 12:37:59.624 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:37:59.633 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:37:59.633 INFO    gensim.models.word2vec: EPOCH - 3 : training on 2088009 raw words (1928182 effective words) took 2.0s, 949601 effective words/s\n",
      "2021-07-23 12:38:00.642 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 40.37% examples, 851350 words/s, in_qsize 6, out_qsize 0\n",
      "2021-07-23 12:38:01.642 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 86.20% examples, 879713 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:38:01.806 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:38:01.806 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:38:01.806 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:38:01.806 INFO    gensim.models.word2vec: EPOCH - 4 : training on 2088009 raw words (1928508 effective words) took 2.2s, 888191 effective words/s\n",
      "2021-07-23 12:38:02.820 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 44.17% examples, 926398 words/s, in_qsize 5, out_qsize 0\n",
      "2021-07-23 12:38:03.823 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 92.26% examples, 920953 words/s, in_qsize 6, out_qsize 0\n",
      "2021-07-23 12:38:03.892 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:38:03.900 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:38:03.908 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:38:03.909 INFO    gensim.models.word2vec: EPOCH - 5 : training on 2088009 raw words (1928291 effective words) took 2.1s, 920110 effective words/s\n",
      "2021-07-23 12:38:03.909 INFO    gensim.utils: Word2Vec lifecycle event {'msg': 'training on 10440045 raw words (9642279 effective words) took 10.5s, 915639 effective words/s', 'datetime': '2021-07-23T12:38:03.909316', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'train'}\n",
      "2021-07-23 12:38:03.909 INFO    gensim.utils: Word2Vec lifecycle event {'params': 'Word2Vec(vocab=24877, vector_size=100, alpha=0.025)', 'datetime': '2021-07-23T12:38:03.909316', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'created'}\n",
      "2021-07-23 12:38:03.913 INFO    gensim.utils: Word2Vec lifecycle event {'fname_or_handle': 'skipgramx11.bin', 'separately': 'None', 'sep_limit': 10485760, 'ignore': frozenset(), 'datetime': '2021-07-23T12:38:03.913959', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'saving'}\n",
      "2021-07-23 12:38:03.915 INFO    gensim.utils: not storing attribute cum_table\n",
      "2021-07-23 12:38:03.947 INFO    gensim.utils: saved skipgramx11.bin\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word2Vec(vocab=24877, vector_size=100, alpha=0.025)\n"
     ]
    }
   ],
   "source": [
    "# training the model\n",
    "\n",
    "skipgram = Word2Vec(x, vector_size =100, window = 1, min_count=2,sg = 1)\n",
    "print(skipgram)\n",
    "skipgram.save('skipgramx11.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-23 12:38:03.958 INFO    gensim.utils: loading Word2Vec object from skipgramx11.bin\n",
      "2021-07-23 12:38:03.978 INFO    gensim.utils: loading wv recursively from skipgramx11.bin.wv.* with mmap=None\n",
      "2021-07-23 12:38:03.978 INFO    gensim.utils: setting ignored attribute cum_table to None\n",
      "2021-07-23 12:38:04.194 INFO    gensim.utils: Word2Vec lifecycle event {'fname': 'skipgramx11.bin', 'datetime': '2021-07-23T12:38:04.194220', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'loaded'}\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "skipgram = Word2Vec.load('skipgramx11.bin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Fasttext model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-23 12:38:04.205 INFO    gensim.utils: loading Word2Vec object from fast_p.bin\n",
      "2021-07-23 12:38:04.258 INFO    gensim.utils: loading wv recursively from fast_p.bin.wv.* with mmap=None\n",
      "2021-07-23 12:38:04.259 INFO    gensim.utils: loading vectors_ngrams from fast_p.bin.wv.vectors_ngrams.npy with mmap=None\n",
      "2021-07-23 12:38:05.973 INFO    gensim.utils: setting ignored attribute buckets_word to None\n",
      "2021-07-23 12:38:05.973 INFO    gensim.utils: setting ignored attribute vectors to None\n",
      "2021-07-23 12:38:06.873 INFO    gensim.utils: setting ignored attribute cum_table to None\n",
      "2021-07-23 12:38:07.058 INFO    gensim.utils: FastText lifecycle event {'fname': 'fast_p.bin', 'datetime': '2021-07-23T12:38:07.058958', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'loaded'}\n"
     ]
    }
   ],
   "source": [
    "#fast_n= FastText(x,vector_size=200, window=3, min_count=2, workers=5, min_n=1, max_n=2,sg=1)  #training fasttext Model, with skipgram\n",
    "#fast_n=fast_n.save('fast_p.bin')    #Saving our model\n",
    "fast_n = Word2Vec.load('fast_p.bin')  #Loading our pretrained model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-23 12:38:07.194 INFO    gensim.models.word2vec: collecting all words and their counts\n",
      "2021-07-23 12:38:07.195 INFO    gensim.models.word2vec: PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2021-07-23 12:38:07.551 INFO    gensim.models.word2vec: PROGRESS: at sentence #10000, processed 2024809 words, keeping 39392 word types\n",
      "2021-07-23 12:38:07.563 INFO    gensim.models.word2vec: collected 39969 word types from a corpus of 2088009 raw words and 10666 sentences\n",
      "2021-07-23 12:38:07.563 INFO    gensim.models.word2vec: Creating a fresh vocabulary\n",
      "2021-07-23 12:38:07.643 INFO    gensim.utils: FastText lifecycle event {'msg': 'effective_min_count=2 retains 24877 unique words (62.2407365708424%% of original 39969, drops 15092)', 'datetime': '2021-07-23T12:38:07.643433', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'prepare_vocab'}\n",
      "2021-07-23 12:38:07.643 INFO    gensim.utils: FastText lifecycle event {'msg': 'effective_min_count=2 leaves 2072917 word corpus (99.2772061806247%% of original 2088009, drops 15092)', 'datetime': '2021-07-23T12:38:07.643433', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'prepare_vocab'}\n",
      "2021-07-23 12:38:07.763 INFO    gensim.models.word2vec: deleting the raw counts dictionary of 39969 items\n",
      "2021-07-23 12:38:07.763 INFO    gensim.models.word2vec: sample=0.001 downsamples 32 most-common words\n",
      "2021-07-23 12:38:07.763 INFO    gensim.utils: FastText lifecycle event {'msg': 'downsampling leaves estimated 1928478.8939992087 word corpus (93.0%% of prior 2072917)', 'datetime': '2021-07-23T12:38:07.763881', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'prepare_vocab'}\n",
      "2021-07-23 12:38:08.074 INFO    gensim.models.fasttext: estimated required memory for 24877 words, 2000000 buckets and 100 dimensions: 836440980 bytes\n",
      "2021-07-23 12:38:08.074 INFO    gensim.models.word2vec: resetting layer weights\n",
      "2021-07-23 12:38:09.925 INFO    gensim.utils: FastText lifecycle event {'update': False, 'trim_rule': 'None', 'datetime': '2021-07-23T12:38:09.925456', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'build_vocab'}\n",
      "2021-07-23 12:38:09.925 INFO    gensim.utils: FastText lifecycle event {'msg': 'training model with 5 workers on 24877 vocabulary and 100 features, using sg=1 hs=0 sample=0.001 negative=5 window=2', 'datetime': '2021-07-23T12:38:09.925456', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'train'}\n",
      "2021-07-23 12:38:10.933 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 16.45% examples, 355385 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:11.945 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 32.23% examples, 337669 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:12.952 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 51.32% examples, 353282 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:13.985 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 71.13% examples, 362965 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:14.995 INFO    gensim.models.word2vec: EPOCH 1 - PROGRESS: at 95.61% examples, 370672 words/s, in_qsize 6, out_qsize 0\n",
      "2021-07-23 12:38:15.015 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 4 more threads\n",
      "2021-07-23 12:38:15.023 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 3 more threads\n",
      "2021-07-23 12:38:15.053 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:38:15.069 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:38:15.093 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:38:15.094 INFO    gensim.models.word2vec: EPOCH - 1 : training on 2088009 raw words (1928472 effective words) took 5.2s, 373492 effective words/s\n",
      "2021-07-23 12:38:16.095 INFO    gensim.models.word2vec: EPOCH 2 - PROGRESS: at 17.51% examples, 374290 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:17.107 INFO    gensim.models.word2vec: EPOCH 2 - PROGRESS: at 38.53% examples, 405609 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:18.115 INFO    gensim.models.word2vec: EPOCH 2 - PROGRESS: at 59.98% examples, 410602 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:19.152 INFO    gensim.models.word2vec: EPOCH 2 - PROGRESS: at 81.61% examples, 412609 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:19.675 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 4 more threads\n",
      "2021-07-23 12:38:19.683 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 3 more threads\n",
      "2021-07-23 12:38:19.700 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:38:19.713 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:38:19.720 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:38:19.720 INFO    gensim.models.word2vec: EPOCH - 2 : training on 2088009 raw words (1928860 effective words) took 4.6s, 417188 effective words/s\n",
      "2021-07-23 12:38:20.742 INFO    gensim.models.word2vec: EPOCH 3 - PROGRESS: at 17.51% examples, 368469 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:21.763 INFO    gensim.models.word2vec: EPOCH 3 - PROGRESS: at 37.05% examples, 382965 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:22.768 INFO    gensim.models.word2vec: EPOCH 3 - PROGRESS: at 56.75% examples, 386331 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:23.768 INFO    gensim.models.word2vec: EPOCH 3 - PROGRESS: at 76.11% examples, 388856 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:24.580 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 4 more threads\n",
      "2021-07-23 12:38:24.608 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 3 more threads\n",
      "2021-07-23 12:38:24.628 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:38:24.633 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:38:24.643 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:38:24.643 INFO    gensim.models.word2vec: EPOCH - 3 : training on 2088009 raw words (1928543 effective words) took 4.9s, 392093 effective words/s\n",
      "2021-07-23 12:38:25.655 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 17.51% examples, 370441 words/s, in_qsize 10, out_qsize 0\n",
      "2021-07-23 12:38:26.676 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 35.31% examples, 367221 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:27.698 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 53.46% examples, 364478 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:28.706 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 71.40% examples, 363426 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:29.705 INFO    gensim.models.word2vec: EPOCH 4 - PROGRESS: at 90.47% examples, 360639 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:29.915 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 4 more threads\n",
      "2021-07-23 12:38:29.915 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 3 more threads\n",
      "2021-07-23 12:38:29.956 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:38:29.964 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:38:29.974 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:38:29.974 INFO    gensim.models.word2vec: EPOCH - 4 : training on 2088009 raw words (1928683 effective words) took 5.3s, 362016 effective words/s\n",
      "2021-07-23 12:38:31.000 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 16.08% examples, 338747 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:32.013 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 35.39% examples, 365810 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:33.017 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 53.96% examples, 368722 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:34.016 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 71.69% examples, 366475 words/s, in_qsize 9, out_qsize 0\n",
      "2021-07-23 12:38:35.024 INFO    gensim.models.word2vec: EPOCH 5 - PROGRESS: at 92.93% examples, 368600 words/s, in_qsize 8, out_qsize 0\n",
      "2021-07-23 12:38:35.136 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 4 more threads\n",
      "2021-07-23 12:38:35.146 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 3 more threads\n",
      "2021-07-23 12:38:35.191 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 2 more threads\n",
      "2021-07-23 12:38:35.203 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 1 more threads\n",
      "2021-07-23 12:38:35.203 INFO    gensim.models.word2vec: worker thread finished; awaiting finish of 0 more threads\n",
      "2021-07-23 12:38:35.203 INFO    gensim.models.word2vec: EPOCH - 5 : training on 2088009 raw words (1928303 effective words) took 5.2s, 368543 effective words/s\n",
      "2021-07-23 12:38:35.203 INFO    gensim.utils: FastText lifecycle event {'msg': 'training on 10440045 raw words (9642861 effective words) took 25.3s, 381393 effective words/s', 'datetime': '2021-07-23T12:38:35.203660', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'train'}\n",
      "2021-07-23 12:38:36.070 INFO    gensim.utils: FastText lifecycle event {'params': 'FastText(vocab=24877, vector_size=100, alpha=0.025)', 'datetime': '2021-07-23T12:38:36.070935', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'created'}\n",
      "2021-07-23 12:38:36.070 INFO    gensim.utils: FastText lifecycle event {'fname_or_handle': 'FastText.bin', 'separately': 'None', 'sep_limit': 10485760, 'ignore': frozenset(), 'datetime': '2021-07-23T12:38:36.070935', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'saving'}\n",
      "2021-07-23 12:38:36.070 INFO    gensim.utils: storing np array 'vectors_ngrams' to FastText.bin.wv.vectors_ngrams.npy\n",
      "2021-07-23 12:38:40.643 INFO    gensim.utils: not storing attribute buckets_word\n",
      "2021-07-23 12:38:40.645 INFO    gensim.utils: not storing attribute vectors\n",
      "2021-07-23 12:38:40.645 INFO    gensim.utils: not storing attribute cum_table\n",
      "2021-07-23 12:38:40.662 INFO    gensim.utils: saved FastText.bin\n",
      "2021-07-23 12:38:40.662 INFO    gensim.utils: loading Word2Vec object from FastText.bin\n",
      "2021-07-23 12:38:40.683 INFO    gensim.utils: loading wv recursively from FastText.bin.wv.* with mmap=None\n",
      "2021-07-23 12:38:40.683 INFO    gensim.utils: loading vectors_ngrams from FastText.bin.wv.vectors_ngrams.npy with mmap=None\n",
      "2021-07-23 12:38:40.943 INFO    gensim.utils: setting ignored attribute buckets_word to None\n",
      "2021-07-23 12:38:40.943 INFO    gensim.utils: setting ignored attribute vectors to None\n",
      "2021-07-23 12:38:41.769 INFO    gensim.utils: setting ignored attribute cum_table to None\n",
      "2021-07-23 12:38:41.945 INFO    gensim.utils: FastText lifecycle event {'fname': 'FastText.bin', 'datetime': '2021-07-23T12:38:41.945732', 'gensim': '4.0.1', 'python': '3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.19041-SP0', 'event': 'loaded'}\n"
     ]
    }
   ],
   "source": [
    "FastText=FastText(x,vector_size=100, window=2, min_count=2, workers=5, min_n=1, max_n=2,sg=1)\n",
    "FastText.save('FastText.bin')    #Saving our model\n",
    "FastText = Word2Vec.load('FastText.bin')  #Loading our pretrained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model embeddings - Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('coronavirus', 0.7514869570732117),\n",
       " ('covid', 0.7044876217842102),\n",
       " ('novelty', 0.6873722672462463),\n",
       " ('evd', 0.6854491233825684),\n",
       " ('crown', 0.6740575432777405),\n",
       " ('ebola', 0.668819785118103),\n",
       " ('declares', 0.6625851988792419),\n",
       " ('coronovirus', 0.6620121002197266),\n",
       " ('virulent', 0.6609188914299011),\n",
       " ('betacoronavirus', 0.6589146852493286)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similar words - skipgram\n",
    "\n",
    "skipgram.wv.most_similar(positive=['corona'],topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('hospitalised', 0.7466251850128174),\n",
       " ('subject', 0.740435004234314),\n",
       " ('participant', 0.7355508804321289),\n",
       " ('acutely', 0.732078492641449),\n",
       " ('individual', 0.7294858694076538),\n",
       " ('moderately', 0.7208532691001892),\n",
       " ('recovering', 0.7088289856910706),\n",
       " ('survivor', 0.6963086724281311),\n",
       " ('pwcf', 0.6935908794403076),\n",
       " ('lymphocytopenia', 0.6920685172080994)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skipgram.wv.most_similar(positive=['patient'],topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('lungiq', 0.7391721606254578),\n",
       " ('leung', 0.7318173050880432),\n",
       " ('pulmonary', 0.6996168494224548),\n",
       " ('laying', 0.6874018907546997),\n",
       " ('lag', 0.6839495301246643),\n",
       " ('laung', 0.679499626159668),\n",
       " ('volunteering', 0.6774570345878601),\n",
       " ('sung', 0.6764991879463196),\n",
       " ('clungene', 0.6753350496292114),\n",
       " ('lang', 0.673167884349823)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similar words - fasttext\n",
    "\n",
    "FastText.wv.most_similar(positive=['lung'],topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('breaching', 0.9200581312179565),\n",
       " ('rebreathing', 0.918149471282959),\n",
       " ('breathe', 0.880999743938446),\n",
       " ('breaking', 0.8744059205055237),\n",
       " ('bathing', 0.8707643747329712),\n",
       " ('breath', 0.8598464131355286),\n",
       " ('breathed', 0.8542757630348206),\n",
       " ('breathable', 0.8439397811889648),\n",
       " ('babybreathe', 0.8394759893417358),\n",
       " ('threatening', 0.8362838625907898)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FastText.wv.most_similar(positive=['breathing'],topn=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA Plot - Skipgram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = skipgram.wv[skipgram.wv.index_to_key]    #stroing arrays of all words\n",
    "pca = PCA(n_components=2)                    #applying PCA to convert data into two dimensions\n",
    "result = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#words embeddings on the plot to check 2-D representation of each word\n",
    "pyplot.scatter(result[:10, 0], result[:10, 1])\n",
    "words = list(skipgram.wv.index_to_key)\n",
    "for i, word in enumerate(words[:10]):\n",
    "    pyplot.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA Plot - Fasttest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#same thing as above but with fasttext model\n",
    "X_fast = FastText.wv[FastText.wv.index_to_key]\n",
    "pca = PCA(n_components=2)\n",
    "result_fast = pca.fit_transform(X_fast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "pyplot.scatter(result_fast[:10, 0], result_fast[:10, 1])\n",
    "words = list(FastText.wv.index_to_key)\n",
    "for i, word in enumerate(words[:10]):\n",
    "    pyplot.annotate(word, xy=(result_fast[i, 0], result_fast[i, 1]))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Vectors for each abstract\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector_size=100   #defining vector size for each word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to take average of all vectors of each word in abstract, so that each abstract can represent (1,100) dimensional vector\n",
    "\n",
    "K1=[]      #creating list to store vector representation of each abstract\n",
    "\n",
    "for i in range(df.shape[0]):       #iterating through all abstract\n",
    "    K=np.zeros([vector_size,1])    #creating empty array of size (vec_size,1) to add vector representation of each word\n",
    "    count=0                        #creating count to check how many words are used from abstract\n",
    "    \n",
    "    for word in word_tokenize(df['Abstract'][i]):   #iterating through all words in given abstract\n",
    "\n",
    "        if word in list(FastText.wv.index_to_key):      #if word exists in vocab , increase count by 1 and add array of given words in our defined array K\n",
    "\n",
    "            count+=1\n",
    "            K+=FastText.wv[word].reshape(vector_size,1)\n",
    "    K=K/count                                        #taking average of given array\n",
    "    K1.append(list(K))                               #appending vector of given abstract to the initial defined list\n",
    "K1_array=np.array(K1)\n",
    "K1_array=K1_array.reshape(df.shape[0],vector_size)   #converting into array of shape(no. of rows, vector_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method 1 will take time to run. Method 2 is prefered. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mean_vector(word2vec_model, words):\n",
    "    # remove out-of-vocabulary words\n",
    "    words = [word for word in tokenize(words) if word in list(word2vec_model.wv.index_to_key)] #if word is in vocab \n",
    "    if len(words) >= 1:\n",
    "        return np.mean(word2vec_model.wv[words], axis=0)\n",
    "    else:\n",
    "        return np.array([0]*100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Abstract to vectors using skipgram Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "K1=[]                                     #defining empty list\n",
    "for i in df.Abstract:\n",
    "    K1.append(list(get_mean_vector(skipgram, i)))     #appending array to the list\n",
    "    \n",
    "    \n",
    "K11=pd.DataFrame(K1).transpose()    # Saving vectors of each abstract in data frame so that we can use directly while running code again\n",
    "K11.to_csv('skipgram-vec.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "K=pd.read_csv('skipgram-vec.csv')   #Loading our pretrained vectors of each abstract\n",
    "\n",
    "skipgram_vectors=[]                          #transforming dataframe into required array like structure as we did in above step\n",
    "for i in range(df.shape[0]):\n",
    "    skipgram_vectors.append(K[str(i)].values)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For converting Title into vectors using skipgram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "K1_title=[]\n",
    "for i in df.Title:         #instead of abstract use title\n",
    "    K1_title.append(list(get_mean_vector(skipgram, i)))    \n",
    "\n",
    "    \n",
    "K1_title=pd.DataFrame(K1).transpose()    # Saving vectors of each abstract in data frame so that we can use directly while running code again\n",
    "K1_title.to_csv('skipgram-vec-title.csv')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "K=pd.read_csv('skipgram-vec-title.csv')   #Loading our pretrained vectors of each abstract\n",
    "\n",
    "K2_title=[]                          #transforming dataframe into required array like structure as we did in above step\n",
    "for i in range(df.shape[0]):\n",
    "    K2_title.append(K[str(i)].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Abstract to vector using FastText "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "K_fast=[]                                     #defining empty list\n",
    "for i in df.Abstract:\n",
    "    K_fast.append(list(get_mean_vector(FastText, i)))     #appending array to the list\n",
    "    \n",
    "K11=pd.DataFrame(K_fast).transpose()    # Saving vectors of each abstract in data frame so that we can use directly while running code again\n",
    "K11.to_csv('FastText-vec.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('FastText-vec.csv')   #Loading our pretrained vectors of each abstract\n",
    "\n",
    "fast_vectors=[]                          #transforming dataframe into required array like structure as we did in above step\n",
    "for i in range(df.shape[0]):\n",
    "    fast_vectors.append(data[str(i)].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cosine similarity function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining function to define cosine similarity\n",
    "\n",
    "from numpy import dot\n",
    "from numpy.linalg import norm\n",
    "\n",
    "def cos_sim(a,b):\n",
    "\n",
    "    return dot(a, b)/(norm(a)*norm(b)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input query Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preprocessing input, because input should be in same form as training data set\n",
    "\n",
    "\n",
    "def preprocessing_input(query,word2vec_model):\n",
    "    query=preprocessing(query)\n",
    "    query=query.replace('\\n',' ')\n",
    "    K=get_mean_vector(word2vec_model,query)\n",
    "   \n",
    "        \n",
    "    return K   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to return 'n' similar results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_colwidth\", -1)       #this function will display full text from each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to return top n similar results\n",
    "\n",
    "#query - input query\n",
    "# p - average vectors for all abs\n",
    "#df1 - original\n",
    "\n",
    "def top_n(query,p,df1,model):    \n",
    "    print(query)\n",
    "    \n",
    "    query=preprocessing_input(query,model)    #preprocessing input to list of vectors\n",
    "                                    \n",
    "    x=[]\n",
    "    #Converting cosine similarities of overall data set with input queries into LIST\n",
    "    for i in range(len(p)):\n",
    "        x.append(cos_sim(query,p[i]))\n",
    "    \n",
    "    \n",
    " #store list in tmp to retrieve index\n",
    "    tmp=list(x)\n",
    "    \n",
    " #sort list so that largest elements are on the far right\n",
    "    \n",
    "    res = sorted(range(len(x)), key = lambda sub: x[sub])[-5:]\n",
    "    sim=[tmp[i] for i in reversed(res)]\n",
    "    print(sim)\n",
    " #get index of the 10 or n largest element\n",
    "    L=[]\n",
    "    for i in reversed(res):\n",
    "    \n",
    "        L.append(i)\n",
    "    return df1.iloc[L, [1,2,5,6]],sim     #returning dataframe (only id,title,abstract ,publication date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kidney\n",
    "#breathing\n",
    "#lung failure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# skipgram model\n",
    "\n",
    "#Input your search query here\n",
    "\n",
    "Query = \"lung failure\"\n",
    "\n",
    "Results,sim = top_n(Query,skipgram_vectors,df1,skipgram)\n",
    "Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fasttext model\n",
    "\n",
    "#Input your search query here\n",
    "\n",
    "Query = \"breathe\"\n",
    "\n",
    "Results,sim=top_n(Query,fast_vectors,df1,FastText)\n",
    "Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Streamlit App (Refer Medical.py file to run the app)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-21 19:57:17.728 \n",
      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
      "  command:\n",
      "\n",
      "    streamlit run C:\\Users\\91758\\anaconda3\\anaconda\\lib\\site-packages\\ipykernel_launcher.py [ARGUMENTS]\n"
     ]
    }
   ],
   "source": [
    "#streamlit function \n",
    "def main():\n",
    "    # Load data and models\n",
    "    data = df1     #our data which we have to display\n",
    "    model = top_n  #top_n model to return n similar results\n",
    "    \n",
    "\n",
    "    st.title(\"Medical Search engine\")      #title of our app\n",
    "    st.write('Type your query here')       #text below title\n",
    "\n",
    "    # User search\n",
    "    user_input = st.text_input(\"Search box\")   #getting input from user\n",
    "\n",
    "   \n",
    "    # Fetch results\n",
    "    if user_input:\n",
    "        \n",
    "        P,sim =model(str(user_input),K2,data)     #storing our output dataframe in P\n",
    "        #Plotly function to display our dataframe in form of plotly table\n",
    "        fig = go.Figure(data=[go.Table(header=dict(values=['ID', 'Title','Abstract','Publication Date','Similarity']),cells=dict(values=[list(P['Trial ID'].values),list(P['Title'].values), list(P['Abstract'].values),list(P['Publication date'].values),list(np.around(sim,4))],align='center'))])\n",
    "        #displying our plotly table\n",
    "        fig.update_layout(height=1700) \n",
    "        st.plotly_chart(fig) \n",
    "        # Get individual results\n",
    "    \n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Could not generate requirement for distribution -cikit-learn 0.21.3 (c:\\programdata\\anaconda3\\lib\\site-packages): Parse error at \"'-cikit-l'\": Expected W:(abcd...)\n"
     ]
    }
   ],
   "source": [
    "!pip freeze > requirements.txt"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}