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arghyadeep99/coronaxiv-pre-processing-and-ml-model-backend.ipynb

Created August 12, 2020 21:00
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CoronaXiv Pre-Processing and ML Model Backend.ipynb
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coronaxiv-pre-processing-and-ml-model-backend.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"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": "3.8.2"
},
"colab": {
"name": "CoronaXiv Pre-Processing and ML Model Backend.ipynb",
"provenance": [],
"collapsed_sections": [
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"include_colab_link": true
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/arghyadeep99/6e27d355af0589b4f3a91dddbb65c178/coronaxiv-pre-processing-and-ml-model-backend.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XDdzZPy4ubbS",
"colab_type": "text"
},
"source": [
"## Installing Required Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rHPFzHd-ubbU",
"colab_type": "code",
"colab": {}
},
"source": [
"!pip3 install numpy pandas tqdm semanticscholar langdetect crossref altmetric sklearn torch sentence_transformers nltk tsnecuda"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zThXZKbrubbd",
"colab_type": "text"
},
"source": [
"## Necessary Imports"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "n151fBOhubbf",
"colab_type": "code",
"colab": {
"referenced_widgets": [
"72f36dea70e8486ca5bf5a084c0b5750"
]
},
"outputId": "99efc565-fc96-45de-9960-5c9a9924d8de"
},
"source": [
"import warnings\n",
"warnings.filterwarnings(action='ignore', category=DeprecationWarning)\n",
"\n",
"import os\n",
"import csv\n",
"import glob\n",
"import json\n",
"import re\n",
"import pickle\n",
"from multiprocessing import Pool\n",
"from IPython.display import display, Latex, HTML, FileLink\n",
"import joblib\n",
"import requests\n",
"import urllib\n",
"\n",
"from bs4 import BeautifulSoup\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from tqdm import tqdm_notebook\n",
"tqdm_notebook().pandas()\n",
"\n",
"import semanticscholar as sch\n",
"from langdetect import detect\n",
"from crossref.restful import Works, Journals\n",
"from altmetric import Altmetric\n",
"\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"import torch\n",
"from sentence_transformers import SentenceTransformer, models\n",
"from transformers import AutoTokenizer, AutoModelForQuestionAnswering, AutoModelWithLMHead\n",
"\n",
"from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer, TfidfTransformer\n",
"from sklearn.decomposition import NMF, LatentDirichletAllocation \n",
"from sklearn.cluster import MiniBatchKMeans, KMeans\n",
"from sklearn.metrics import silhouette_samples, silhouette_score, calinski_harabasz_score\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"from sklearn.decomposition import PCA\n",
"from nltk import word_tokenize \n",
"from nltk.stem import WordNetLemmatizer \n",
"\n",
"%matplotlib inline"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "72f36dea70e8486ca5bf5a084c0b5750",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"/home/arghyadeep99/.local/lib/python3.8/site-packages/tqdm/std.py:668: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
" from pandas import Panel\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N0oX-Briubbo",
"colab_type": "text"
},
"source": [
"## Import from Google Drive"
]
},
{
"cell_type": "code",
"metadata": {
"id": "d40Wwiwcubbp",
"colab_type": "code",
"colab": {}
},
"source": [
"from google.colab import drive\n",
"drive.mount('/gdrive')\n",
"%cd /gdrive"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "HakhNXmhubbv",
"colab_type": "text"
},
"source": [
"## Define Paths to directories"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "cnJz_5rGubbw",
"colab_type": "code",
"colab": {},
"outputId": "dfd9db51-0782-470e-bf6f-bb4ed2121d5b"
},
"source": [
"PROJECT_PATH = \"\" #ADD YOUR PROJECT PATH\n",
"DATASET_PATH = PROJECT_PATH + \"\" #ADD PATH TO DATASET\n",
"EXPORT_PATH = PROJECT_PATH + \"\" #ADD PATH TO WHERE THE EMBEDDINGS, CSVS AND NPY FILE WILL BE EXPORTED\n",
"\n",
"if not os.path.exists(EXPORT_PATH):\n",
" os.makedirs(EXPORT_PATH)\n",
"!ls $PROJECT_PATH"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: ''",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-48ad85c31a2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEXPORT_PATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEXPORT_PATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ls $PROJECT_PATH'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.8/os.py\u001b[0m in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;31m# Cannot rely on checking for EEXIST, since the operating system\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: ''"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l_IU-gURubb3",
"colab_type": "text"
},
"source": [
"## Pre-Processing "
]
},
{
"cell_type": "code",
"metadata": {
"id": "vP0BLEU-ubb5",
"colab_type": "code",
"colab": {}
},
"source": [
"#Parse all JSONs and store in a variable\n",
"all_json = glob.glob(os.path.join(DATASET_PATH, \"document_parses/**/*.json\"), recursive=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CU5tfDuBubb_",
"colab_type": "code",
"colab": {}
},
"source": [
"#Store metadata information in a variable\n",
"metadata_df = pd.read_csv(os.path.join(DATASET_PATH, 'metadata.csv'), \n",
" low_memory=False, \n",
" dtype={\n",
" 'pubmed_id': str,\n",
" 'Microsoft Academic Paper ID': str, \n",
" 'doi': str\n",
" }\n",
" )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Jks5D3-HubcG",
"colab_type": "code",
"colab": {},
"outputId": "7ec3e8ab-49c4-41ac-a1bf-c352450da070"
},
"source": [
"metadata_df.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <td>OBJECTIVE: This retrospective chart review des...</td>\n",
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" <td>11667967</td>\n",
" <td>no-cc</td>\n",
" <td>Inflammatory diseases of the respiratory tract...</td>\n",
" <td>2000-08-15</td>\n",
" <td>Vliet, Albert van der; Eiserich, Jason P; Cros...</td>\n",
" <td>Respir Res</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>document_parses/pdf_json/6b0567729c2143a66d737...</td>\n",
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" <td>PMC</td>\n",
" <td>Surfactant protein-D and pulmonary host defense</td>\n",
" <td>10.1186/rr19</td>\n",
" <td>PMC59549</td>\n",
" <td>11667972</td>\n",
" <td>no-cc</td>\n",
" <td>Surfactant protein-D (SP-D) participates in th...</td>\n",
" <td>2000-08-25</td>\n",
" <td>Crouch, Erika C</td>\n",
" <td>Respir Res</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>document_parses/pdf_json/06ced00a5fc04215949aa...</td>\n",
" <td>document_parses/pmc_json/PMC59549.xml.json</td>\n",
" <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2b73a28n</td>\n",
" <td>348055649b6b8cf2b9a376498df9bf41f7123605</td>\n",
" <td>PMC</td>\n",
" <td>Role of endothelin-1 in lung disease</td>\n",
" <td>10.1186/rr44</td>\n",
" <td>PMC59574</td>\n",
" <td>11686871</td>\n",
" <td>no-cc</td>\n",
" <td>Endothelin-1 (ET-1) is a 21 amino acid peptide...</td>\n",
" <td>2001-02-22</td>\n",
" <td>Fagan, Karen A; McMurtry, Ivan F; Rodman, David M</td>\n",
" <td>Respir Res</td>\n",
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" <td>NaN</td>\n",
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" <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
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" <td>PMC</td>\n",
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" <td>10.1186/rr61</td>\n",
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" <td>no-cc</td>\n",
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" <td>2001-05-11</td>\n",
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"4 9785vg6d 5f48792a5fa08bed9f56016f4981ae2ca6031b32 PMC \n",
"\n",
" title doi \\\n",
"0 Clinical features of culture-proven Mycoplasma... 10.1186/1471-2334-1-6 \n",
"1 Nitric oxide: a pro-inflammatory mediator in l... 10.1186/rr14 \n",
"2 Surfactant protein-D and pulmonary host defense 10.1186/rr19 \n",
"3 Role of endothelin-1 in lung disease 10.1186/rr44 \n",
"4 Gene expression in epithelial cells in respons... 10.1186/rr61 \n",
"\n",
" pmcid pubmed_id license \\\n",
"0 PMC35282 11472636 no-cc \n",
"1 PMC59543 11667967 no-cc \n",
"2 PMC59549 11667972 no-cc \n",
"3 PMC59574 11686871 no-cc \n",
"4 PMC59580 11686888 no-cc \n",
"\n",
" abstract publish_time \\\n",
"0 OBJECTIVE: This retrospective chart review des... 2001-07-04 \n",
"1 Inflammatory diseases of the respiratory tract... 2000-08-15 \n",
"2 Surfactant protein-D (SP-D) participates in th... 2000-08-25 \n",
"3 Endothelin-1 (ET-1) is a 21 amino acid peptide... 2001-02-22 \n",
"4 Respiratory syncytial virus (RSV) and pneumoni... 2001-05-11 \n",
"\n",
" authors journal mag_id \\\n",
"0 Madani, Tariq A; Al-Ghamdi, Aisha A BMC Infect Dis NaN \n",
"1 Vliet, Albert van der; Eiserich, Jason P; Cros... Respir Res NaN \n",
"2 Crouch, Erika C Respir Res NaN \n",
"3 Fagan, Karen A; McMurtry, Ivan F; Rodman, David M Respir Res NaN \n",
"4 Domachowske, Joseph B; Bonville, Cynthia A; Ro... Respir Res NaN \n",
"\n",
" who_covidence_id arxiv_id \\\n",
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"1 NaN NaN \n",
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"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
" pdf_json_files \\\n",
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"\n",
" pmc_json_files \\\n",
"0 document_parses/pmc_json/PMC35282.xml.json \n",
"1 document_parses/pmc_json/PMC59543.xml.json \n",
"2 document_parses/pmc_json/PMC59549.xml.json \n",
"3 document_parses/pmc_json/PMC59574.xml.json \n",
"4 document_parses/pmc_json/PMC59580.xml.json \n",
"\n",
" url s2_id \n",
"0 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3... NaN \n",
"1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5... NaN \n",
"2 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5... NaN \n",
"3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5... NaN \n",
"4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5... NaN "
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "48zr5a2XubcN",
"colab_type": "code",
"colab": {}
},
"source": [
"class PaperReader:\n",
" def __init__(self, file_path):\n",
" with open(file_path) as file:\n",
" content = json.load(file)\n",
" self.paper_id = content['paper_id']\n",
" self.abstract = []\n",
" self.body_text = []\n",
" # Abstract\n",
" try:\n",
" for entry in content['abstract']:\n",
" self.abstract.append(entry['text'])\n",
" except:\n",
" self.abstract.append(\"No abstract available\")\n",
" for entry in content[\"body_text\"]:\n",
" self.body_text.append(entry['text'])\n",
" self.abstract = '. '.join(self.abstract)\n",
" self.body_text = '. '.join(self.body_text)\n",
" def __repr__(self):\n",
" return f\"{self.paper_id}: {self.abstract[:200]}... {self.body_text[:200]}...\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "FggN7FjbubcS",
"colab_type": "code",
"colab": {
"referenced_widgets": [
"9555f5b76a2042bb9ea1ea1367ff7082"
]
},
"outputId": "da1b9ea7-6f5a-47e8-f62b-9909b037b563"
},
"source": [
"dict_ = {'paper_id': [], \n",
" 'abstract': [], \n",
" 'body_text': [], \n",
" 'authors': [], \n",
" 'title': [], \n",
" 'journal': [], \n",
" 'publish_time': [], \n",
" 'abstract_summary': [],\n",
" 'doi': [],\n",
" 'url': [],\n",
" 'source_x': []\n",
" }\n",
"\n",
"for idx, entry in tqdm_notebook(enumerate(all_json), total=len(all_json)):\n",
" content = PaperReader(entry)\n",
" \n",
" # get metadata information\n",
" meta_data = metadata_df.loc[metadata_df['sha'] == content.paper_id]\n",
" # no metadata, skip this paper\n",
" if len(meta_data) == 0:\n",
" continue\n",
" \n",
" dict_['paper_id'].append(content.paper_id)\n",
" dict_['abstract'].append(content.abstract)\n",
" dict_['body_text'].append(content.body_text)\n",
" \n",
" try:\n",
" authors = meta_data['authors'].values[0].split(';')\n",
" dict_['authors'].append(\". \".join(authors))\n",
" except Exception as e:\n",
" # if Null value\n",
" dict_['authors'].append(meta_data['authors'].values[0])\n",
" \n",
" # add the title information\n",
" dict_['title'].append(meta_data['title'].values[0])\n",
" \n",
" # add the journal information\n",
" dict_['journal'].append(meta_data['journal'].values[0])\n",
" \n",
" # add the publishing data\n",
" dict_['publish_time'].append(meta_data['publish_time'].values[0])\n",
" \n",
" # add doi \n",
" dict_['doi'].append(meta_data['doi'].values[0])\n",
" \n",
" # add source\n",
" dict_['source_x'].append(meta_data['source_x'].values[0])\n",
" \n",
" # add url\n",
" dict_['url'].append(meta_data['url'].values[0]) "
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9555f5b76a2042bb9ea1ea1367ff7082",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=129922.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lldrrhOBubcX",
"colab_type": "code",
"colab": {}
},
"source": [
"columns = ['paper_id', 'abstract', 'body_text', 'authors', 'title', 'journal', 'publish_time', 'doi', \n",
" 'source_x', 'url']\n",
"\n",
"df_covid = pd.DataFrame(dict_, columns=columns)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_RAkNbrBubce",
"colab_type": "code",
"colab": {}
},
"source": [
"df_covid.to_csv(os.path.join(EXPORT_PATH, 'original.csv'), index=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "oIjX5M6Wubcj",
"colab_type": "code",
"colab": {}
},
"source": [
"df_covid.drop_duplicates(['title'], inplace=True)\n",
"df_covid.dropna(subset=['body_text'], inplace=True)\n",
"df_covid = df_covid[~df_covid.title.isnull()]\n",
"df_covid.reset_index(inplace=True, drop=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "yOANjQloubcm",
"colab_type": "code",
"colab": {
"referenced_widgets": [
"d66ce07470c24cfd927fb90666e8c4d0"
]
},
"outputId": "105dea45-1dc0-4c02-f029-847274bdb1f7"
},
"source": [
"covid_terms =['covid', 'corona virus', 'coronavirus disease 19', 'sars cov 2', '2019 ncov', '2019ncov', '2019 n cov', '2019n cov',\n",
" 'ncov 2019', 'n cov 2019', 'coronavirus 2019', 'wuhan pneumonia', 'wuhan virus', 'wuhan coronavirus',\n",
" 'coronavirus 2', 'covid-19', 'SARS-CoV-2', '2019-nCov']\n",
"covid_terms = [elem.lower() for elem in covid_terms]\n",
"covid_terms = re.compile('|'.join(covid_terms))\n",
"\n",
"def checkYear(date):\n",
" return int(date[0:4])\n",
"\n",
"def checkCovid(row, covid_terms):\n",
" return bool(covid_terms.search(row['body_text'].lower())) and checkYear(row['publish_time']) > 2019\n",
"df_covid['is_covid'] = df_covid.progress_apply(checkCovid, axis=1, covid_terms=covid_terms)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d66ce07470c24cfd927fb90666e8c4d0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=66089.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XR1V0zrsubcq",
"colab_type": "code",
"colab": {}
},
"source": [
"#Keeping papers only after 2010\n",
"df_covid = df_covid[df_covid.publish_time.map(lambda d: checkYear(d) >= 2010)]\n",
"df_covid = df_covid.reset_index(drop=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "gZ5hgnG4ubcv",
"colab_type": "code",
"colab": {
"referenced_widgets": [
"3ed376939bb644b38474ffa3090c05a2"
]
},
"outputId": "e9574672-746a-4387-eb40-d760ab80d650"
},
"source": [
"def preprocessing(text):\n",
" # remove mail\n",
" text = re.sub(r'[a-z0-9._%+-]+@[a-z0-9.-]+\\.[a-z]{2,}', ' ', text)\n",
" # remove doi\n",
" text = re.sub(r'https\\:\\/\\/doi\\.org[^\\s]+', 'DOI', text)\n",
" # remove https\n",
" text = re.sub(r'(\\()?\\s?http(s)?\\:\\/\\/[^\\)]+(\\))?', ' ', text)\n",
" # remove single characters repeated at least 3 times for spacing error (e.g. s u m m a r y)\n",
" text = re.sub(r'(\\w\\s+){3,}', ' ', text)\n",
" # replace tags (e.g. [3] [4] [5]) with whitespace\n",
" text = re.sub(r'(\\[\\d+\\]\\,?\\s?){3,}(\\.|\\,)?', ' ', text)\n",
" # replace tags (e.g. [3, 4, 5]) with whitespace\n",
" text = re.sub(r'\\[[\\d\\,\\s]+\\]', ' ', text)\n",
" # replace tags (e.g. (NUM1) repeated at least 3 times with whitespace\n",
" text = re.sub(r'(\\(\\d+\\)\\s){3,}', ' ', text)\n",
" # replace '1.3' with '1,3' (we need it for split later)\n",
" text = re.sub(r'(\\d+)\\.(\\d+)', ' ', text)\n",
" # remove all full stops as abbreviations (e.g. i.e. cit. and so on)\n",
" text = re.sub(r'\\.(\\s)?([^A-Z\\s])', ' \\g<1>\\g<2>', text)\n",
" # correctly spacing the tokens\n",
" text = re.sub(r' {2,}', ' ', text)\n",
" text = re.sub(r'\\.{2,}', '.', text)\n",
" # return lowercase text\n",
" return text.lower()\n",
"\n",
"df_covid['preproc_body_text'] = df_covid['body_text'].progress_apply(preprocessing)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ed376939bb644b38474ffa3090c05a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=52709.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CzeCMqmFubcz",
"colab_type": "text"
},
"source": [
"### Filtering out only English Research Papers"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "3ses4hUuubcz",
"colab_type": "code",
"colab": {},
"outputId": "27ff7785-cc7c-421a-cfd9-e6fde3aad79b"
},
"source": [
"def detect_language(row):\n",
" try:\n",
" return detect(row['preproc_body_text'])\n",
" except:\n",
" try:\n",
" return detect(row['title'])\n",
" except: \n",
" return 'NC'\n",
"\n",
"df_covid['language'] = df_covid.progress_apply(detect_language, axis=1)\n",
"df_covid = df_covid[df_covid['language'] == 'en']\n",
"df_covid = df_covid.reset_index(drop=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" cord-19-dataset\t\t covidbert-model\r\n",
"'CoronaXiv Data Preprocessing.ipynb' exported-data\r\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EoVCUiDXubc5",
"colab_type": "code",
"colab": {}
},
"source": [
"df_covid.to_csv(os.path.join(EXPORT_PATH, 'metadata.csv'), index=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uMEPsHvqubc8",
"colab_type": "code",
"colab": {}
},
"source": [
"doi_list = df_covid[~df_covid['doi'].isnull()]['doi'].unique().tolist()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Xe2V5kWoubdA",
"colab_type": "text"
},
"source": [
"### Scrapping additional metadata about citations using the Altmetrics API"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "R9Demdg9ubdA",
"colab_type": "code",
"colab": {},
"outputId": "9495f61c-b8b2-4eca-a62c-9446b34bdf7e"
},
"source": [
"scrape_altmetrics = False\n",
"\n",
"if scrape_altmetrics:\n",
" def get_altmetric_data(doi):\n",
" res = a.doi(doi)\n",
" return res\n",
" \n",
" altmetrics_data = []\n",
" a = Altmetric()\n",
"\n",
" for doi in tqdm_notebook(doi_list):\n",
" res = a.doi(doi)\n",
" altmetrics_data.append(res)\n",
"\n",
" altmetrics_data = [d for d in altmetrics_data if d is not None]\n",
" \n",
" altmetrics_columns = [\n",
" 'doi',\n",
" 'score',\n",
" 'readers_count',\n",
" 'cited_by_posts_count',\n",
" 'cited_by_tweeters_count',\n",
" 'cited_by_fbwalls_count',\n",
" 'cited_by_wikipedia_count',\n",
" 'subjects',\n",
" ]\n",
"\n",
" df_altmetrics = pd.DataFrame(altmetrics_data, columns=altmetrics_columns)\n",
" joblib.dump(df_altmetrics, os.path.join(EXPORT_PATH, 'altmetrics.joblib'))\n",
"\n",
"else:\n",
" df_altmetrics = joblib.load(os.path.join(EXPORT_PATH, 'altmetrics.joblib'))"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d06ce9bedb64c6192861e47ee33c5a0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=51071.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 'export_path' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-b77b6a0b06e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mdf_altmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maltmetrics_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maltmetrics_columns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mjoblib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_altmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexport_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'altmetrics.joblib'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'export_path' is not defined"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zHVKaNREubdE",
"colab_type": "code",
"colab": {}
},
"source": [
"df_covid = df_covid.merge(df_altmetrics, how='left', on='doi')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Ozp2mOYXubdH",
"colab_type": "code",
"colab": {}
},
"source": [
"df_covid.to_csv(os.path.join(EXPORT_PATH, 'metadata.csv'), index=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "SZEYf337ubdL",
"colab_type": "text"
},
"source": [
"### Scrapping H-Index using Scimago API"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "lQyiL13mubdL",
"colab_type": "code",
"colab": {},
"outputId": "3c6bdbe2-133a-4952-a2a8-db80d5013845"
},
"source": [
"scrape_scimago = True\n",
"df_covid = metadata\n",
"if scrape_scimago:\n",
"\n",
" def parse_page(url):\n",
" return BeautifulSoup(requests.get(url).content, 'html.parser')\n",
" \n",
" journals = df_covid.journal.dropna().unique().tolist() \n",
"\n",
" def get_h_index(url):\n",
" soup = parse_page(url)\n",
" h_index_div = soup.find('div', {'class': 'hindexnumber'})\n",
" if h_index_div:\n",
" h_index = h_index_div.text\n",
" return h_index\n",
"\n",
" def extract_info(query):\n",
" original_title = query\n",
" query = urllib.parse.quote_plus(query)\n",
" url = f\"https://www.scimagojr.com/journalsearch.php?q={query}\"\n",
" soup = parse_page(url)\n",
"\n",
" search_results = soup.find('div', {'class': 'search_results'}).find_all('a')\n",
" if search_results == []:\n",
" full_title = None\n",
" h_index = None\n",
"\n",
" else:\n",
" first_result = search_results[0]\n",
" full_title = first_result.find('span').text\n",
" url_journal = 'https://www.scimagojr.com/' + first_result['href']\n",
" h_index = get_h_index(url_journal)\n",
"\n",
" info = {\n",
" 'original_title': original_title,\n",
" 'query': query,\n",
" 'full_title': full_title,\n",
" 'h_index': h_index\n",
" }\n",
" return info\n",
"\n",
" with Pool(processes=12) as pool, tqdm_notebook(total=len(journals)) as pbar:\n",
" scimago_data = []\n",
" for info in pool.imap_unordered(extract_info, journals):\n",
" scimago_data.append(info)\n",
" pbar.update()\n",
" \n",
" df_scimago = pd.DataFrame(scimago_data)\n",
" joblib.dump(df_scimago, os.path.join(EXPORT_PATH, 'scimago.joblib'))\n",
"\n",
"else:\n",
" df_scimago = joblib.load(os.path.join(EXPORT_PATH, 'scimago.joblib'))\n",
" \n",
"\n",
"mapping_title_hindex = dict(zip(df_scimago['original_title'], df_scimago['h_index']))\n",
"df_covid['h_index'] = df_covid.journal.map(lambda j: mapping_title_hindex[j] if j in mapping_title_hindex else j)\n",
"df_covid['source_x'] = df_covid.source_x.map(lambda s: s.split(';')[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d725038c1b646aa95e908bac9988c6e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=7295.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "p-4Svaq9ubdP",
"colab_type": "code",
"colab": {}
},
"source": [
"df_covid['peer_reviewed'] = df_covid['journal'].map(lambda j: False if type(j) == float else True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "j5Xz4H9wubdU",
"colab_type": "code",
"colab": {}
},
"source": [
"df_covid.to_csv(os.path.join(EXPORT_PATH, 'metadata.csv'), index=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "W1HESBq5ubdZ",
"colab_type": "code",
"colab": {},
"outputId": "e0e65f4b-eeeb-4554-d959-00a70c6f7382"
},
"source": [
"def get_excerpt(row):\n",
" if row['abstract'] == '':\n",
" excerpt = row['title'] + ' . ' + row['preproc_body_text'][:500]\n",
" else:\n",
" len_abstract = len(row['abstract'])\n",
" if len_abstract > 500:\n",
" excerpt = row['title'] + ' . ' + preprocessing(row['abstract'][:500])\n",
" else:\n",
" excerpt = (row['title'] + ' . ' \n",
" + preprocessing(row['abstract']) + ' . ' \n",
" + row['preproc_body_text'][:500 - len_abstract])\n",
" \n",
" return excerpt\n",
"\n",
"df_covid['excerpt'] = df_covid.progress_apply(get_excerpt, axis=1)\n",
"df_covid.to_csv(os.path.join(EXPORT_PATH, 'metadata.csv'), index=False)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bf0f3fe31349433abde3ae237febcde0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=52709.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bTFBJpsTubdd",
"colab_type": "text"
},
"source": [
"## Generating Word Embeddings using the CovidBERT Model"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "MZXPTKrXubde",
"colab_type": "code",
"colab": {},
"outputId": "875472fe-3488-43f6-f6c4-498671dab989"
},
"source": [
"word_embedding_model = models.Transformer(os.path.join(PROJECT_PATH, \"covidbert-model/\"))\n",
"\n",
"# Apply mean pooling to get one fixed sized sentence vector\n",
"pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),\n",
" pooling_mode_mean_tokens=True,\n",
" pooling_mode_cls_token=False,\n",
" pooling_mode_max_tokens=False)\n",
"\n",
"model = SentenceTransformer(modules=[word_embedding_model, pooling_model])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/home/arghyadeep99/.local/lib/python3.8/site-packages/transformers/tokenization_utils.py:828: FutureWarning: Parameter max_len is deprecated and will be removed in a future release. Use model_max_length instead.\n",
" warnings.warn(\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "gd7vRcxxubdi",
"colab_type": "code",
"colab": {},
"outputId": "3b3a089e-6e1a-4457-e279-4e1b6258e252"
},
"source": [
"excerpt_embeddings = model.encode(df_covid.excerpt.tolist(), show_progress_bar=True, batch_size=32)\n",
"excerpt_embeddings = np.array(excerpt_embeddings)\n",
"np.save(os.path.join(EXPORT_PATH, 'embeddings_excerpts.npy'), excerpt_embeddings)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Batches: 0%| | 0/1648 [00:03<?, ?it/s]\n"
],
"name": "stderr"
},
{
"output_type": "error",
"ename": "RuntimeError",
"evalue": "CUDA error: no kernel image is available for execution on the device",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-43-e578b14c0e7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexcerpt_embeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_covid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexcerpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_progress_bar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mexcerpt_embeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexcerpt_embeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEXPORT_PATH\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'embeddings_excerpts.npy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexcerpt_embeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.8/site-packages/sentence_transformers/SentenceTransformer.py\u001b[0m in \u001b[0;36mencode\u001b[0;34m(self, sentences, batch_size, show_progress_bar, output_value, convert_to_numpy)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0mout_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0membeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moutput_value\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 101\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.8/site-packages/sentence_transformers/models/Transformer.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, features)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\"\"\"Returns token_embeddings, cls_token\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0moutput_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0moutput_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput_states\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask)\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;31m# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0;31m# ourselves in which case we just need to make it broadcastable to all heads.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 706\u001b[0;31m \u001b[0mextended_attention_mask\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_extended_attention_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 708\u001b[0m \u001b[0;31m# If a 2D ou 3D attention mask is provided for the cross-attention\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mget_extended_attention_mask\u001b[0;34m(self, attention_mask, input_shape, device)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0;31m# Since we are adding it to the raw scores before the softmax, this is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[0;31m# effectively the same as removing these entirely.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m \u001b[0mextended_attention_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextended_attention_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# fp16 compatibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 214\u001b[0m \u001b[0mextended_attention_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mextended_attention_mask\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m10000.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mextended_attention_mask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: CUDA error: no kernel image is available for execution on the device"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2xenPWaVubdm",
"colab_type": "code",
"colab": {}
},
"source": [
"titles_embeddings = model.encode(df_covid.title.tolist(), show_progress_bar=True, batch_size=32)\n",
"titles_embeddings = np.array(titles_embeddings)\n",
"np.save(os.path.join(EXPORT_PATH, 'embeddings_titles.npy'), titles_embeddings)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "KM5MyYrfubdr",
"colab_type": "code",
"colab": {}
},
"source": [
"embeddings = np.load(os.path.join(EXPORT_PATH,'embeddings_excerpts.npy'))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dPVoSTqAubdv",
"colab_type": "code",
"colab": {},
"outputId": "137eec92-b2de-4b10-b896-095b1eda86fc"
},
"source": [
"embeddings.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(52709, 768)"
]
},
"metadata": {
"tags": []
},
"execution_count": 46
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bl4oGSipubdy",
"colab_type": "text"
},
"source": [
"## Applying PCA to reduce dimensions from 768 to 250"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "Zp43DnIBubdz",
"colab_type": "code",
"colab": {},
"outputId": "a0ce8a94-f6fd-4bd5-9161-3ac14f559eb7"
},
"source": [
"pca = PCA(n_components=250)\n",
"embeddings_reduced_pca = pca.fit_transform(embeddings)\n",
"print(f'cumulated variance : {pca.explained_variance_ratio_.sum()}')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"cumulated variance : 0.8262177109718323\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GRIXHj8oubd2",
"colab_type": "text"
},
"source": [
"## Converting PCA to tSNE representation"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "WJLsKpPGubd3",
"colab_type": "code",
"colab": {},
"outputId": "fcd510e3-144b-490b-b2db-a15f2cc8fd8d"
},
"source": [
"from tsnecuda import TSNE\n",
"import matplotlib.cm as cm\n",
"#from sklearn.manifold import TSNE\n",
"\n",
"tsne = TSNE(n_components=2, verbose=1, perplexity=150, n_iter=1000)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'\\nimport matplotlib.cm as cm\\nfrom sklearn.manifold import TSNE\\n\\ntsne = TSNE(n_components=2, verbose=1, perplexity=50, n_iter=500)\\n'"
]
},
"metadata": {
"tags": []
},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kY69NgC4ubd8",
"colab_type": "code",
"colab": {}
},
"source": [
"embeddings_reduced_tsne = tsne.fit_transform(embeddings_reduced_pca)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Qdthw5xnubd_",
"colab_type": "code",
"colab": {}
},
"source": [
"%%script false --no-raise-error\n",
"\n",
"df_tsne_coordinates = pd.DataFrame(embeddings_reduced_tsne, columns=['x1', 'x2'])\n",
"df_tsne_coordinates.to_csv(os.path.join(EXPORT_PATH, 'tsne_coordinates.csv'), index=False)\n",
"embeddings_reduced_tsne = pd.read_csv(os.path.join(EXPORT_PATH, 'tsne_coordinates.csv'))\n",
"np.save(os.path.join(EXPORT_PATH, 'tsne_coordinates.npy'), embeddings_reduced_tsne)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lgI_wawrubeE",
"colab_type": "code",
"colab": {}
},
"source": [
"embeddings_reduced_tsne = np.load(os.path.join(EXPORT_PATH,'tsne_coordinates.npy'))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0mIplKNfubeH",
"colab_type": "code",
"colab": {}
},
"source": [
"n_clusters = 8\n",
"kmeans = MiniBatchKMeans(n_clusters=n_clusters, batch_size=1000)\n",
"predicted_labels = kmeans.fit_predict(embeddings_reduced_pca)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "BsrpDd18ubeK",
"colab_type": "code",
"colab": {}
},
"source": [
"silhouette = silhouette_score(embeddings_reduced_pca, predicted_labels, n_jobs=10)\n",
"sample_silhouette_values = silhouette_samples(embeddings_reduced_pca, predicted_labels, n_jobs=10)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "IWa4jI4pubeO",
"colab_type": "code",
"colab": {},
"outputId": "7cee2dd2-7b9e-4039-e817-d19b8f5bb781"
},
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(1, 2)\n",
"fig.set_size_inches(18, 7)\n",
"ax1.set_xlim([-0.1, 1])\n",
"# The (n_clusters+1)*10 is for inserting blank space between silhouette plots of individual clusters, to demarcate them clearly.\n",
"ax1.set_ylim([0, len(embeddings) + (n_clusters + 1) * 10])\n",
"\n",
"y_lower = 10\n",
"for i in range(n_clusters):\n",
" # Aggregate the silhouette scores for samples belonging to cluster i, and sort them\n",
" ith_cluster_silhouette_values = sample_silhouette_values[predicted_labels == i]\n",
"\n",
" ith_cluster_silhouette_values.sort()\n",
"\n",
" size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
" y_upper = y_lower + size_cluster_i\n",
"\n",
" color = cm.nipy_spectral(float(i) / n_clusters)\n",
" ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
" 0, ith_cluster_silhouette_values,\n",
" facecolor=color, edgecolor=color, alpha=0.7)\n",
"\n",
" # Label the silhouette plots with their cluster numbers at the middle\n",
" ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
"\n",
" # Compute the new y_lower for next plot\n",
" y_lower = y_upper + 10 # 10 for the 0 samples\n",
"\n",
"ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
"ax1.set_xlabel(\"The silhouette coefficient values\")\n",
"ax1.set_ylabel(\"Cluster label\")\n",
"\n",
"# The vertical line for average silhouette score of all the values\n",
"ax1.axvline(x=silhouette, color=\"red\", linestyle=\"--\");\n",
"\n",
"ax1.set_yticks([]) # Clear the yaxis labels / ticks\n",
"ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
"\n",
"colors = cm.nipy_spectral(predicted_labels.astype(float) / n_clusters)\n",
"ax2.scatter(embeddings_reduced_tsne[:, 0], embeddings_reduced_tsne[:, 1], \n",
" marker='.', \n",
" s=30, \n",
" lw=0, \n",
" alpha=0.7,\n",
" c=colors, \n",
" edgecolor='k')\n",
"\n",
"ax2.set_title(\"tSNE representation of the data\");"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'tSNE representation of the data')"
]
},
"metadata": {
"tags": []
},
"execution_count": 53
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x504 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lWucjOfNubeV",
"colab_type": "text"
},
"source": [
"## Define Stopwords, Cleaner and Tokenizer"
]
},
{
"cell_type": "code",
"metadata": {
"id": "H9a6M-d_ubeW",
"colab_type": "code",
"colab": {}
},
"source": [
"class LemmaTokenizer:\n",
" def __init__(self):\n",
" self.wnl = WordNetLemmatizer()\n",
" def __call__(self, doc):\n",
" return [self.wnl.lemmatize(t) for t in doc.split()]\n",
"lemma_tokenizer = LemmaTokenizer() "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mnZxnoESubea",
"colab_type": "code",
"colab": {}
},
"source": [
"response = requests.get(\"https://raw.githubusercontent.com/stopwords-iso/stopwords-en/master/stopwords-en.txt\")\n",
"STOPWORDS = response.text.split('\\n')\n",
"\n",
"response_fr = requests.get(\"https://raw.githubusercontent.com/stopwords-iso/stopwords-fr/master/stopwords-fr.txt\")\n",
"STOPWORDS_FR = response_fr.text.split('\\n')\n",
"STOPWORDS += STOPWORDS_FR\n",
"\n",
"STOPWORDS += [\"et\", \"al\", \"fig\", \"figure\", \"can\", \"may\", \"also\", \"doi\", \"biorxiv\", \"preprint\",\n",
" \"copyright\", \"peer-reviewed\", \"authorfunder\", \"table\", \"license\", \"à\", \"chapter\",\n",
" \"abstract\", \"issue\", \"acute\", \"study\", \"approach\", \"inde\", \"index\", \"author\", \"unknown\"]\n",
"\n",
"STOPWORDS += ['ain', 'daren', 'hadn', 'herse', 'himse', 'itse', 'mayn', 'mightn', 'mon', 'mustn', \n",
" 'myse', 'needn', 'oughtn', 'peer', 'reviewed', 'shan']\n",
"\n",
"STOPWORDS += [\"'d\", \"'m\", \"'re\", \"'s\", \"'t\", '.', 'affect', 'change', 'doe', 'greeting', 'ha', \n",
" 'i.e', 'key', 'le', \"n't\", 'regard', 'result', 'sha', 'ti', 'wa', 'wo', 'word', \n",
" '”', 'avon', 'diver', 'fuss', 'gen', 'quantum', 'serum', 'somme', 'sou']\n",
"\n",
"STOPWORDS += ['respiratory', 'virus', 'infectious', 'disease', 'influenza', 'syndrome', 'infection', \n",
" 'coronavirus', 'covid', 'mers', 'sars', 'cov', 'viral']\n",
"\n",
"STOPWORDS += ['µ', 'µg', 'µl', 'µm', '¼', 'å', 'þ', 'β', 'β actin', 'β β', 'δ', 'ε', 'η', 'θ', \n",
" 'κ', 'κb', 'λ', 'μ', 'μg', 'μl', 'μm', 'π', 'ρ', 'σ', 'φ', 'ω', 'α', 'γ', 'medrxiv']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "U6VIq6HGubef",
"colab_type": "code",
"colab": {}
},
"source": [
"def preprocess(text):\n",
" text = text.lower()\n",
" text = re.sub(r\"http?://\\S+\", \"\", text)\n",
" text = re.sub(r\"\\s\\d+\\s\", \" \", text)\n",
" text = re.sub(r'[^\\w\\s]', ' ', text)\n",
" text = re.sub(r'\\d+', ' ', text)\n",
" return text"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "t60GCPedubej",
"colab_type": "code",
"colab": {},
"outputId": "9e3f2b0b-c5fd-4f53-f7ab-ee2dcca6b4eb"
},
"source": [
"metadata = pd.read_csv(os.path.join(EXPORT_PATH, 'metadata.csv'))\n",
"texts = metadata[['excerpt', 'body_text']].copy()\n",
"texts['cluster'] = predicted_labels\n",
"texts['excerpt'] = texts.excerpt.progress_map(preprocess)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ee5c12f94754496bbda1599d8f70718",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=52709.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "oTe0Bhxtubeo",
"colab_type": "code",
"colab": {}
},
"source": [
"def get_keywords(model, feature_names, n_top_words):\n",
" keywords = []\n",
" for topic_idx, topic in enumerate(model.components_):\n",
" keywords += [feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]]\n",
" \n",
" keywords = list(set(keywords))\n",
" return keywords"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "UImnUTREubes",
"colab_type": "code",
"colab": {},
"outputId": "32d15077-c3b6-4bdd-81a1-f7324b13bc15"
},
"source": [
"import nltk\n",
"nltk.download('wordnet')\n",
"full_vectorizer = TfidfVectorizer(max_df=0.9, \n",
" min_df=0.01,\n",
" stop_words=STOPWORDS, \n",
" ngram_range=(1, 2), \n",
" tokenizer=lemma_tokenizer)\n",
"full_vectorizer.fit(tqdm_notebook(texts['excerpt'].tolist(), leave=False))\n",
"feature_names = full_vectorizer.get_feature_names()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[nltk_data] Downloading package wordnet to\n",
"[nltk_data] /home/arghyadeep99/nltk_data...\n",
"[nltk_data] Unzipping corpora/wordnet.zip.\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
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"version_minor": 0
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"output_type": "stream",
"text": [
"/home/arghyadeep99/.local/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:484: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
" warnings.warn(\"The parameter 'token_pattern' will not be used\"\n",
"/home/arghyadeep99/.local/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:383: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['actin'] not in stop_words.\n",
" warnings.warn('Your stop_words may be inconsistent with '\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dcGyGRvrubew",
"colab_type": "text"
},
"source": [
"## Obtain Cluster Information and Keywords using Latent Dirichlet Allocation"
]
},
{
"cell_type": "code",
"metadata": {
"id": "4_lP0voaubex",
"colab_type": "code",
"colab": {},
"outputId": "811769b0-09e3-4088-e860-cb76dbd1a3fc"
},
"source": [
"cluster_info = {}\n",
"\n",
"\n",
"for k in tqdm_notebook(range(n_clusters)):\n",
"\n",
" topn = 20\n",
" cluster_info[k] = {}\n",
" \n",
" train = texts[texts['cluster'] != k]['excerpt'].tolist()\n",
" docs = texts[texts['cluster'] == k]['excerpt'].tolist()\n",
" \n",
" # generate tfidf vectors per clusters\n",
" \n",
" docs_vectors = full_vectorizer.transform(tqdm_notebook(docs, leave=False))\n",
" \n",
" # fit an lda on these vectors\n",
" N_topics = 2\n",
" \n",
" lda = LatentDirichletAllocation(n_components=N_topics, \n",
" max_iter=20,\n",
" learning_method='online',\n",
" learning_offset=50.,\n",
" random_state=0)\n",
"\n",
" lda.fit(docs_vectors)\n",
" \n",
" # extract the top keywords of each topic\n",
" \n",
" keywords = get_keywords(lda, feature_names, int(20 / N_topics))\n",
" \n",
" \n",
" cluster_info[k]['keywords'] = keywords\n",
"\n",
" print(f'cluster : {k}') \n",
" print(f'keywords : {keywords}')\n",
" print(\"----\\n\")"
],
"execution_count": null,
"outputs": [
{
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]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"cluster : 0\n",
"keywords : ['pneumonia', 'wuhan', 'health', 'mortality', 'clinical', 'severe', 'december', 'pandemic', 'outbreak', 'china', 'reported', 'symptom', 'patient', 'caused', 'middle east', 'middle', 'east', 'spread', 'child']\n",
"----\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
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]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"cluster : 1\n",
"keywords : ['cell', 'induced', 'replication', 'immune response', 'gene', 'cellular', 'structure', 'immune', 'innate', 'binding', 'host', 'mouse', 'interferon', 'protein', 'membrane', 'receptor', 'expression', 'response', 'rna']\n",
"----\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=10867.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"cluster : 2\n",
"keywords : ['crisis', 'public', 'health', 'model', 'hospital', 'country', 'pandemic', 'china', 'outbreak', 'epidemic', 'data', 'medical', 'patient', 'social', 'emergency', 'healthcare', 'transmission', 'spread', 'care']\n",
"----\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=9048.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"cluster : 3\n",
"keywords : ['chronic', 'child', 'clinical', 'ct', 'cell', 'method', 'symptom', 'mortality', 'hospital', 'background', 'outcome', 'patient', 'diagnosis', 'lung', 'pulmonary', 'blood', 'care', 'risk']\n",
"----\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=626.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"cluster : 4\n",
"keywords : ['crisis', 'hepatitis', 'wuhan', 'hiv', 'situation', 'surveillance', 'ct', 'design', 'management', 'initial', 'china', 'ebola', 'patient', 'der', 'contact', 'liver', 'setting', 'die', 'april', 'corona']\n",
"----\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=8575.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"cluster : 5\n",
"keywords : ['development', 'global', 'paper', 'health', 'medicine', 'learning', 'model', 'process', 'time', 'method', 'technology', 'specie', 'animal', 'human', 'network', 'data', 'social', 'science', 'pathogen', 'based']\n",
"----\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=6104.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"cluster : 6\n",
"keywords : ['development', 'pcr', 'genome', 'diarrhea', 'method', 'host', 'specie', 'porcine', 'animal', 'antibody', 'human', 'assay', 'detection', 'sequence', 'specific', 'strain', 'rt', 'sample', 'coronaviruses', 'vaccine']\n",
"----\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
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"HBox(children=(FloatProgress(value=0.0, max=3408.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"cluster : 7\n",
"keywords : ['cell', 'acid', 'peptide', 'property', 'vitro', 'method', 'compound', 'plant', 'protein', 'agent', 'antibody', 'anti', 'antiviral', 'treatment', 'inhibitor', 'application', 'activity', 'based', 'drug', 'vaccine']\n",
"----\n",
"\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CvPAAGuCube1",
"colab_type": "code",
"colab": {}
},
"source": [
"clusters_definitions = {}\n",
"for i in range(n_clusters):\n",
" clusters_definitions[f'cluster_{i}'] = ''"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "kxcbxj7aube5",
"colab_type": "code",
"colab": {}
},
"source": [
"clusters_definitions = {'0': 'Drug testing',\n",
" '1': 'Covid outbreak',\n",
" '2': 'Detection',\n",
" '3': 'Treatments',\n",
" '4': 'Genetic studies',\n",
" '5': 'Transmission',\n",
" '6': 'Crisis management',\n",
" '7': 'Immunity studies'\n",
" }\n",
"\n",
"for k in cluster_info:\n",
" cluster_info[k]['topic'] = clusters_definitions[str(k)]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NFoL54zMube8",
"colab_type": "code",
"colab": {}
},
"source": [
"joblib.dump(kmeans, os.path.join(EXPORT_PATH, 'kmeans.joblib'))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "hEh98ShnubfB",
"colab_type": "code",
"colab": {},
"outputId": "c8849639-6fab-486b-f69d-85c034db32d2"
},
"source": [
"texts['preproc_body_text'] = texts.body_text.progress_map(preprocess)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bDvAS9k9ubfH",
"colab_type": "text"
},
"source": [
"## Define TF-IDF Vectorizer"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uZLANz9zubfI",
"colab_type": "code",
"colab": {}
},
"source": [
"full_text_vectorizer = TfidfVectorizer(max_features=10000,\n",
" max_df=0.8, \n",
" min_df=0.05,\n",
" stop_words=STOPWORDS, \n",
" ngram_range=(1, 2), \n",
" tokenizer=lemma_tokenizer)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "J26cknqRubfN",
"colab_type": "code",
"colab": {},
"outputId": "b72bc28c-bcef-47f9-f06d-00c4fb819834"
},
"source": [
"vectors = full_text_vectorizer.fit_transform(tqdm_notebook(\n",
" texts['preproc_body_text'].map(lambda t: t[:40000]).tolist(), leave=False))"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=52709.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"/home/arghyadeep99/.local/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:383: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['actin'] not in stop_words.\n",
" warnings.warn('Your stop_words may be inconsistent with '\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "plMAjVYZubfS",
"colab_type": "code",
"colab": {}
},
"source": [
"feature_name_all = full_text_vectorizer.get_feature_names()\n",
"feature_name_all = np.array(feature_name_all)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "QtsOYzVnubfW",
"colab_type": "text"
},
"source": [
"## Extracting Keywords"
]
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "9pUgHNBRubfZ",
"colab_type": "code",
"colab": {},
"outputId": "2fb34896-4f25-4c93-f5ab-1a89c323d8fe"
},
"source": [
"keywords_list = []\n",
"\n",
"for i in tqdm_notebook(range(vectors.shape[0])):\n",
" row = vectors[i]\n",
" row = row.tocoo()\n",
"\n",
" tuples = zip(row.col, row.data)\n",
" tuples = sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)\n",
" \n",
" indices = [c[0] for c in tuples[:10]]\n",
" keywords = feature_name_all[indices].tolist()\n",
" \n",
" keywords_list.append(keywords)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IAQOB4a2ubfe",
"colab_type": "code",
"colab": {}
},
"source": [
"df_keywords = pd.DataFrame(keywords_list)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "BdmkuoiXubfi",
"colab_type": "code",
"colab": {}
},
"source": [
"df_keywords['paper_id'] = metadata.paper_id"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8lywYCJ6ubfl",
"colab_type": "code",
"colab": {}
},
"source": [
"df_keywords = df_keywords[['paper_id'] + list(range(10))]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "urngBP7tubfp",
"colab_type": "code",
"colab": {}
},
"source": [
"df_keywords.to_csv(os.path.join(EXPORT_PATH, 'keywords_per_doc.csv'), index=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HvmrOVffubft",
"colab_type": "code",
"colab": {}
},
"source": [
"cluster_data_front = pd.DataFrame()\n",
"cluster_data_front['key'] = range(n_clusters)\n",
"cluster_data_front['label'] = cluster_data_front['key'].map(str)\n",
"cluster_data_front['topic'] = cluster_data_front.label.map(clusters_definitions)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rorSeTMmubfw",
"colab_type": "code",
"colab": {},
"outputId": "44144ac5-c5ac-44b1-8b6f-be2532c444cc"
},
"source": [
"cluster_data_front.to_json(orient='records')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'[{\"key\":0,\"label\":\"0\",\"topic\":\"Drug testing\"},{\"key\":1,\"label\":\"1\",\"topic\":\"Covid outbreak\"},{\"key\":2,\"label\":\"2\",\"topic\":\"Detection\"},{\"key\":3,\"label\":\"3\",\"topic\":\"Treatments\"},{\"key\":4,\"label\":\"4\",\"topic\":\"Genetic studies\"},{\"key\":5,\"label\":\"5\",\"topic\":\"Transmission\"},{\"key\":6,\"label\":\"6\",\"topic\":\"Crisis management\"},{\"key\":7,\"label\":\"7\",\"topic\":\"Immunity studies\"}]'"
]
},
"metadata": {
"tags": []
},
"execution_count": 74
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aP57wdhcubf0",
"colab_type": "text"
},
"source": [
"## Add cluster info, tSNE coordinates and keywords to metadata.csv"
]
},
{
"cell_type": "code",
"metadata": {
"id": "l6yjmmjEubf0",
"colab_type": "code",
"colab": {}
},
"source": [
"df_output = pd.DataFrame(embeddings_reduced_tsne, columns=['x1', 'x2'])\n",
"df_output['cluster'] = predicted_labels\n",
"df_output['paper_id'] = metadata.paper_id\n",
"\n",
"df_output = df_output[['paper_id', 'x1', 'x2', 'cluster']]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7s-HwX07ubf5",
"colab_type": "code",
"colab": {}
},
"source": [
"df_output.to_csv(os.path.join(EXPORT_PATH, 'clusters.csv'), index=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UdnhN9Izubf7",
"colab_type": "code",
"colab": {}
},
"source": [
"with open(os.path.join(EXPORT_PATH, 'clusters_info.json'), 'w') as f:\n",
" json.dump(cluster_info, f)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bEHGy-zZubf9",
"colab_type": "code",
"colab": {}
},
"source": [
"metadata.drop(['preproc_body_text'], inplace=True, axis=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bSP89Uteubf_",
"colab_type": "code",
"colab": {},
"outputId": "65f0606c-8dd2-413a-b24f-96e7a377faea"
},
"source": [
"metadata.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['paper_id', 'abstract', 'body_text', 'authors', 'title', 'journal',\n",
" 'publish_time', 'doi', 'source_x', 'url', 'is_covid', 'score',\n",
" 'readers_count', 'cited_by_posts_count', 'cited_by_tweeters_count',\n",
" 'cited_by_fbwalls_count', 'cited_by_wikipedia_count', 'subjects',\n",
" 'peer_reviewed', 'excerpt'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 102
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nkf2NAc1ubgC",
"colab_type": "code",
"colab": {}
},
"source": [
"metadata[\"x1\"] = cluster_coords[metadata[\"paper_id\"] == cluster_coords[\"paper_id\"]][\"x1\"]\n",
"metadata[\"x2\"] = cluster_coords[metadata[\"paper_id\"] == cluster_coords[\"paper_id\"]][\"x2\"]\n",
"metadata[\"cluster\"] = cluster_coords[metadata[\"paper_id\"] == cluster_coords[\"paper_id\"]][\"cluster\"]\n",
"metadata[\"cluster_name\"] = metadata[\"cluster\"].astype(str).map(clusters_definitions)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0wmJz95uubgE",
"colab_type": "code",
"colab": {}
},
"source": [
"keyword_doc = pd.read_csv(os.path.join(EXPORT_PATH, \"keywords_per_doc.csv\"))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UxUFGi_QubgI",
"colab_type": "code",
"colab": {},
"outputId": "2a68939b-36f7-4807-f623-fdbd9f0f9734"
},
"source": [
"keyword_doc.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>0</th>\n",
" <th>1</th>\n",
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" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>c9a627d5f63264b95acb95d2d3d9d46a52158b15</td>\n",
" <td>cardiac</td>\n",
" <td>patient</td>\n",
" <td>heart</td>\n",
" <td>nt</td>\n",
" <td>healthcare</td>\n",
" <td>service</td>\n",
" <td>plasma</td>\n",
" <td>risk</td>\n",
" <td>cardiovascular</td>\n",
" <td>italy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9044ecc75d28faa698b6405ad43874dbbf6b235d</td>\n",
" <td>sequence</td>\n",
" <td>italy</td>\n",
" <td>recombination</td>\n",
" <td>strain</td>\n",
" <td>genome</td>\n",
" <td>phylogenetic</td>\n",
" <td>tree</td>\n",
" <td>genotype</td>\n",
" <td>complete</td>\n",
" <td>primer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18e5ca6859a1131329eb3b2c61b66955d97aed20</td>\n",
" <td>beta</td>\n",
" <td>mouse</td>\n",
" <td>activation</td>\n",
" <td>lung</td>\n",
" <td>macrophage</td>\n",
" <td>induced</td>\n",
" <td>tuberculosis</td>\n",
" <td>secretion</td>\n",
" <td>release</td>\n",
" <td>response</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>bed493869199469ee87e5f50d139fef45f3fcf73</td>\n",
" <td>antimicrobial</td>\n",
" <td>mucosal</td>\n",
" <td>cell</td>\n",
" <td>immune</td>\n",
" <td>receptor</td>\n",
" <td>activity</td>\n",
" <td>expression</td>\n",
" <td>response</td>\n",
" <td>amino acid</td>\n",
" <td>amino</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ba7d692c043b8a27633ad72046de6045cc36e536</td>\n",
" <td>liquid</td>\n",
" <td>particle</td>\n",
" <td>air</td>\n",
" <td>flow</td>\n",
" <td>aerosol</td>\n",
" <td>sampling</td>\n",
" <td>min</td>\n",
" <td>concentration</td>\n",
" <td>collection</td>\n",
" <td>real</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" paper_id 0 1 \\\n",
"0 c9a627d5f63264b95acb95d2d3d9d46a52158b15 cardiac patient \n",
"1 9044ecc75d28faa698b6405ad43874dbbf6b235d sequence italy \n",
"2 18e5ca6859a1131329eb3b2c61b66955d97aed20 beta mouse \n",
"3 bed493869199469ee87e5f50d139fef45f3fcf73 antimicrobial mucosal \n",
"4 ba7d692c043b8a27633ad72046de6045cc36e536 liquid particle \n",
"\n",
" 2 3 4 5 6 \\\n",
"0 heart nt healthcare service plasma \n",
"1 recombination strain genome phylogenetic tree \n",
"2 activation lung macrophage induced tuberculosis \n",
"3 cell immune receptor activity expression \n",
"4 air flow aerosol sampling min \n",
"\n",
" 7 8 9 \n",
"0 risk cardiovascular italy \n",
"1 genotype complete primer \n",
"2 secretion release response \n",
"3 response amino acid amino \n",
"4 concentration collection real "
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uflg891xubgK",
"colab_type": "code",
"colab": {},
"outputId": "7ceabde2-8b96-44f4-b82f-9d6f579b26be"
},
"source": [
"keyword_doc.columns[1:]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0fIrFUcGubgN",
"colab_type": "code",
"colab": {}
},
"source": [
"keyword_doc[\"keywords\"] = keyword_doc[keyword_doc.columns[1:]].apply(lambda x: ' '.join(x.dropna().astype(str)),\n",
" axis=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "pLu6TlssubgQ",
"colab_type": "code",
"colab": {},
"outputId": "49621ba0-e6fb-464a-c575-3780cb3d70d8"
},
"source": [
"keyword_doc[\"keywords\"][0]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['cardiac',\n",
" 'patient',\n",
" 'heart',\n",
" 'nt',\n",
" 'healthcare',\n",
" 'service',\n",
" 'plasma',\n",
" 'risk',\n",
" 'cardiovascular',\n",
" 'italy']"
]
},
"metadata": {
"tags": []
},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Vz_Kl9j3ubgS",
"colab_type": "code",
"colab": {}
},
"source": [
"keyword_doc.to_csv(os.path.join(EXPORT_PATH, 'keywords_per_doc.csv'), index=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "vpX_3rfgubgU",
"colab_type": "code",
"colab": {}
},
"source": [
"metadata[\"keywords\"] = keyword_doc[\"keywords\"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YcEIdtVdubgW",
"colab_type": "code",
"colab": {},
"outputId": "40d364cc-1064-4a69-f479-1d3467fad417"
},
"source": [
"metadata[\"keywords\"].head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 cardiac patient heart nt healthcare service pl...\n",
"1 sequence italy recombination strain genome phy...\n",
"2 beta mouse activation lung macrophage induced ...\n",
"3 antimicrobial mucosal cell immune receptor act...\n",
"4 liquid particle air flow aerosol sampling min ...\n",
"Name: keywords, dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ycbp-yp1ubgZ",
"colab_type": "code",
"colab": {}
},
"source": [
"metadata.to_csv(os.path.join(EXPORT_PATH, 'metadata.csv'), index=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WTamRM_Bubgc",
"colab_type": "code",
"colab": {},
"outputId": "b7d56a9b-cedc-4a3b-f806-17c6d0cced9b"
},
"source": [
"metadata = pd.read_csv(os.path.join(EXPORT_PATH, 'metadata.csv'))\n",
"metadata.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"\"['origin', 'sequence', 'gene', 'nucleotide', 'shared', 'recombination', 'population', 'class', 'identical', 'residue']\""
]
},
"metadata": {
"tags": []
},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IHnQu-Pmubgf",
"colab_type": "code",
"colab": {},
"outputId": "d9325066-9c77-4252-b38e-4e9a9982baaa"
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"source": [
"metadata.describe()"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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" <th></th>\n",
" <th>score</th>\n",
" <th>readers_count</th>\n",
" <th>cited_by_posts_count</th>\n",
" <th>cited_by_tweeters_count</th>\n",
" <th>cited_by_fbwalls_count</th>\n",
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" <th>x2</th>\n",
" <th>cluster</th>\n",
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" <th>count</th>\n",
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" <td>32539.000000</td>\n",
" <td>30615.000000</td>\n",
" <td>5836.000000</td>\n",
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" <td>52709.000000</td>\n",
" <td>52709.000000</td>\n",
" <td>52709.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>52.947950</td>\n",
" <td>24.885461</td>\n",
" <td>70.553520</td>\n",
" <td>59.115270</td>\n",
" <td>2.419123</td>\n",
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" <td>3.053748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>400.755279</td>\n",
" <td>69.129338</td>\n",
" <td>669.659965</td>\n",
" <td>535.398548</td>\n",
" <td>11.040603</td>\n",
" <td>1.159224</td>\n",
" <td>8.998177</td>\n",
" <td>8.575326</td>\n",
" <td>2.180997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-18.728113</td>\n",
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" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-7.115292</td>\n",
" <td>-6.810529</td>\n",
" <td>1.000000</td>\n",
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" <td>6.000000</td>\n",
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" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.282119</td>\n",
" <td>0.270270</td>\n",
" <td>3.000000</td>\n",
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" <tr>\n",
" <th>75%</th>\n",
" <td>14.950000</td>\n",
" <td>28.000000</td>\n",
" <td>18.000000</td>\n",
" <td>16.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>7.048177</td>\n",
" <td>6.266664</td>\n",
" <td>5.000000</td>\n",
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" <tr>\n",
" <th>max</th>\n",
" <td>20753.978000</td>\n",
" <td>2708.000000</td>\n",
" <td>41140.000000</td>\n",
" <td>29352.000000</td>\n",
" <td>671.000000</td>\n",
" <td>18.000000</td>\n",
" <td>20.685944</td>\n",
" <td>18.294134</td>\n",
" <td>7.000000</td>\n",
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],
"text/plain": [
" score readers_count cited_by_posts_count \\\n",
"count 32539.000000 32539.000000 32539.000000 \n",
"mean 52.947950 24.885461 70.553520 \n",
"std 400.755279 69.129338 669.659965 \n",
"min 0.250000 0.000000 1.000000 \n",
"25% 1.000000 0.000000 2.000000 \n",
"50% 3.700000 0.000000 6.000000 \n",
"75% 14.950000 28.000000 18.000000 \n",
"max 20753.978000 2708.000000 41140.000000 \n",
"\n",
" cited_by_tweeters_count cited_by_fbwalls_count \\\n",
"count 30615.000000 5836.000000 \n",
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"std 535.398548 11.040603 \n",
"min 1.000000 1.000000 \n",
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"50% 5.000000 1.000000 \n",
"75% 16.000000 2.000000 \n",
"max 29352.000000 671.000000 \n",
"\n",
" cited_by_wikipedia_count x1 x2 cluster \n",
"count 1155.000000 52709.000000 52709.000000 52709.000000 \n",
"mean 1.406926 -0.008952 -0.051137 3.053748 \n",
"std 1.159224 8.998177 8.575326 2.180997 \n",
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"25% 1.000000 -7.115292 -6.810529 1.000000 \n",
"50% 1.000000 0.282119 0.270270 3.000000 \n",
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"max 18.000000 20.685944 18.294134 7.000000 "
]
},
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}
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},
{
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{
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" <td>In serving its primary function in gas exchang...</td>\n",
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" <td>Inflammasome in the Pathogenesis of Pulmonary ...</td>\n",
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" <td>2018-09-05</td>\n",
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" <td>['beta', 'mouse', 'activation', 'lung', 'macro...</td>\n",
" <td>None</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
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" <td>Chemokines are an extensive family of small pr...</td>\n",
" <td>Chemokines are a family of chemoattractant cyt...</td>\n",
" <td>Mohan, Teena. Deng, Lei. Wang, Bao-Zhong</td>\n",
" <td>CCL28 chemokine: An anchoring point bridging i...</td>\n",
" <td>International Immunopharmacology</td>\n",
" <td>2017-10-31</td>\n",
" <td>10.1016/j.intimp.2017.08.012</td>\n",
" <td>Elsevier</td>\n",
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" paper_id \\\n",
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" abstract \\\n",
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"1 NaN \n",
"2 NaN \n",
"3 Chemokines are an extensive family of small pr... \n",
"4 We present a novel bioaerosol sampling system ... \n",
"\n",
" body_text \\\n",
"0 This editorial refers to 'Characteristics and ... \n",
"1 Since 1996 a new Infectious Bronchitis virus (... \n",
"2 In serving its primary function in gas exchang... \n",
"3 Chemokines are a family of chemoattractant cyt... \n",
"4 Concerns over airborne microorganisms, called ... \n",
"\n",
" authors \\\n",
"0 Pellicori, Pierpaolo \n",
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"2 Xu, Fengying. Wen, Zongmei. Shi, Xueying. F... \n",
"3 Mohan, Teena. Deng, Lei. Wang, Bao-Zhong \n",
"4 Cho, Yu Sung. Hong, Seung Chan. Choi, Jeonga... \n",
"\n",
" title \\\n",
"0 At the heart of COVID-19 \n",
"1 Molecular investigation of a full-length genom... \n",
"2 Inflammasome in the Pathogenesis of Pulmonary ... \n",
"3 CCL28 chemokine: An anchoring point bridging i... \n",
"4 Development of an automated wet-cyclone system... \n",
"\n",
" journal publish_time \\\n",
"0 Eur Heart J 2020-05-14 \n",
"1 Virus Res 2015-12-02 \n",
"2 Inflammasomes: Clinical and Therapeutic Implic... 2018-09-05 \n",
"3 International Immunopharmacology 2017-10-31 \n",
"4 Sensors and Actuators B: Chemical 2019-04-01 \n",
"\n",
" doi source_x \\\n",
"0 10.1093/eurheartj/ehaa415 Medline \n",
"1 10.1016/j.virusres.2015.07.008 Elsevier \n",
"2 10.1007/978-3-319-89390-7_6 PMC \n",
"3 10.1016/j.intimp.2017.08.012 Elsevier \n",
"4 10.1016/j.snb.2018.12.155 Elsevier \n",
"\n",
" url ... \\\n",
"0 https://www.ncbi.nlm.nih.gov/pubmed/32405639/;... ... \n",
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" peer_reviewed excerpt \\\n",
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" keywords h_index \\\n",
"0 ['cardiac', 'patient', 'heart', 'nt', 'healthc... 286 \n",
"1 ['sequence', 'italy', 'recombination', 'strain... 114 \n",
"2 ['beta', 'mouse', 'activation', 'lung', 'macro... None \n",
"3 ['antimicrobial', 'mucosal', 'cell', 'immune',... 106 \n",
"4 ['liquid', 'particle', 'air', 'flow', 'aerosol... 184 \n",
"\n",
" cluster_name \n",
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"1 Crisis management \n",
"2 Drug testing \n",
"3 Covid outbreak \n",
"4 Transmission \n",
"\n",
"[5 rows x 26 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 89
}
]
}
]
}
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