blabla/WOS/wos_processing_pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import shutil\n",
    "from flashgeotext.geotext import GeoText\n",
    "import re"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\n",
    "def md5hash(s: str):\n",
    "    return hashlib.md5(s.encode('utf-8')).hexdigest()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "record_col=\"UT (Unique WOS ID)\"\n",
    "outfile = r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\WOS\\wos_extract\\wos_records_concat.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wos = pd.read_csv(outfile, sep=\"\\t\",low_memory=False)\n",
    "\n",
    "wos = wos[((wos[\"Publication Year\"]<2023)&(wos[\"Publication Year\"]>2010))].copy()\n",
    "print(f'Number of initial (valid interval) records: {len(wos)}')\n",
    "\n",
    "metrix = pd.read_excel(\"sm_journal_classification.xlsx\", sheet_name=\"Journal_Classification\")\n",
    "\n",
    "\n",
    "metrix = metrix.set_index([c for c in metrix.columns if \"issn\" not in c]).stack().reset_index()\n",
    "metrix = metrix.rename(columns={'level_6':\"issn_type\", 0:\"issn\"})\n",
    "metrix[\"issn\"]=metrix[\"issn\"].str.replace(\"-\",\"\").str.lower().str.strip()\n",
    "\n",
    "wos[\"issn\"] = wos[\"ISSN\"].str.replace(\"-\",\"\").str.lower().str.strip()\n",
    "wos[\"eissn\"] = wos[\"eISSN\"].str.replace(\"-\",\"\").str.lower().str.strip()\n",
    "wos = wos.set_index([c for c in wos.columns if \"issn\" not in c]).stack().reset_index()\n",
    "wos = wos.rename(columns={'level_71':\"issn_var\", 0:\"issn\"})\n",
    "\n",
    "wos_merge = wos.merge(metrix, on=\"issn\", how=\"left\")\n",
    "\n",
    "\n",
    "\n",
    "wos_indexed = wos_merge[~wos_merge[\"Domain_English\"].isna()]\n",
    "wos_unindexed = wos_merge[~wos_merge[record_col].isin(wos_indexed[record_col])]\n",
    "\n",
    "\n",
    "wos_unindexed = wos_unindexed.sort_values(by=[\"issn_var\"],ascending=False).drop_duplicates(subset=record_col)\n",
    "wos = wos_indexed.sort_values(by=[\"issn_var\"],ascending=False).drop_duplicates(subset=record_col)\n",
    "\n",
    "wos_postmerge = wos.copy()\n",
    "print(f'Number of METRIX filtered records: {len(wos)}')\n",
    "print(f'Number of unindexed records: {len(wos_unindexed)}')\n",
    "\n",
    "# drop entries not indexed by metrix\n",
    "# drop duplicates (based on doi)\n",
    "wos = wos[~((~wos[\"DOI\"].isna())&(wos[\"DOI\"].duplicated(False)))]\n",
    "wos = wos.drop_duplicates(subset=[\"Publication Type\",\"Document Type\",\"Authors\",\"Article Title\",\"Source Title\",\"Publication Year\"])\n",
    "print(f'Number of filtered records (dropping duplicates): {len(wos)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos[\"Domain_English\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_classifier = wos[[\"WoS Categories\",\"Research Areas\"]+list(metrix.columns)].copy().drop_duplicates()\n",
    "wos_classifier = wos_classifier.groupby([\"WoS Categories\",\"Research Areas\"], as_index=False)[[\"Domain_English\",\"Field_English\",\"SubField_English\"]].agg(\n",
    "    lambda x: pd.Series.mode(x)[0])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_to_reindex = wos_unindexed.drop(columns=list(metrix.columns))\n",
    "wos_found = wos_to_reindex.merge(wos_classifier, on=[\"WoS Categories\",\"Research Areas\"], how=\"inner\")\n",
    "# wos_found = wos_to_reindex.merge(wos_classifier, on=\"Research Areas\", how=\"inner\")\n",
    "# # wos_found = wos_to_reindex.merge(wos_classifier, on=\"WoS Categories\", how=\"inner\")\n",
    "wos_stillost = wos_unindexed[~wos_unindexed[record_col].isin(wos_found[record_col])]\n",
    "\n",
    "print(\"Found:\", wos_found[record_col].nunique(),\"\\nLost forever:\", wos_stillost[record_col].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos = pd.concat([wos,wos_found], ignore_index=True)\n",
    "print(f'Number of records (after remerge): {len(wos)}')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos[\"Domain_English\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_cat = wos.groupby(record_col)[\"WoS Categories\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "wos_cat[\"WoS Categories\"] = wos_cat[\"WoS Categories\"].str.strip()\n",
    "wos_cat[\"WoS Categories\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_subcat = wos_cat.copy()\n",
    "wos_subcat[['WoS Category', 'WoS SubCategory']] = wos_subcat[\"WoS Categories\"].str.split(\",\", expand = True, n=1)\n",
    "for c in ['WoS Category', 'WoS SubCategory',\"WoS Categories\"]:\n",
    "    wos_subcat[c] = wos_subcat[c].str.strip()\n",
    "wos_subcat.drop_duplicates(subset=[record_col,'WoS Category'])[\"WoS Category\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_areas = wos.groupby(record_col)[\"Research Areas\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "wos_areas[\"Research Areas\"] = wos_areas[\"Research Areas\"].str.strip()\n",
    "wos_areas[\"Research Areas\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos[[\"Article Title\",\"Keywords Plus\",\"Author Keywords\"]].sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "kw_df = pd.DataFrame()\n",
    "for c in [\"Keywords Plus\",\"Author Keywords\"]:\n",
    "    kwp = wos.groupby(record_col)[c].apply(lambda x: x.str.split(';')).explode().str.strip().str.upper()\n",
    "    kwp.name = 'keyword_all'\n",
    "    kw_df = pd.concat([kwp.reset_index(),kw_df],ignore_index=True)\n",
    "kw_df = kw_df[~kw_df[\"keyword_all\"].isna()].copy().drop(columns=\"level_1\").drop_duplicates()\n",
    "kw_df[\"keyword_all\"] = kw_df[\"keyword_all\"].apply(lambda x: re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", x))\n",
    "kw_df.head(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_kwd_concat = kw_df.groupby(record_col, as_index=False).agg({'keyword_all': '; '.join})\n",
    "wos_kwd_concat.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos.columns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "geotext = GeoText()\n",
    "\n",
    "def extract_location(input_text, key='countries'):\n",
    "    anomalies = {\"Malta\":\"Malta\",\n",
    "                 \"Mongolia\":\"Mongolia\",\n",
    "                 \"Quatar\":\"Qatar\",\n",
    "                 \"Qatar\":\"Qatar\",\n",
    "                 \"Ethiop\":\"Ethiopia\",\n",
    "                 \"Nigeria\":\"Nigeria\",\n",
    "                 \"BELAR\":\"Belarus\",\n",
    "                 \"Venezuela\":\"Venezuela\",\n",
    "                 \"Cyprus\":\"Cyprus\",\n",
    "                 \"Ecuador\":\"Ecuador\",\n",
    "                 \"U Arab\":\"United Arab Emirates\",\n",
    "                 \"Syria\":\"Syria\",\n",
    "                 \"Uganda\":\"Uganda\",\n",
    "                 \"Yemen\":\"Yemen\",\n",
    "                 \"Mali\":\"Mali\",\n",
    "                 \"Senegal\":\"Senegal\",\n",
    "                 \"Vatican\":\"Vatican\",\n",
    "                 \"Uruguay\":\"Uruguay\",\n",
    "                 \"Panama\":\"Panama\",\n",
    "                 \"Fiji\":\"Fiji\",\n",
    "                 \"Faroe\":\"Faroe Islands\",\n",
    "                 \"Macedonia\":\"Macedonia\",\n",
    "                 'Mozambique':'Mozambique',\n",
    "                 \"Kuwait\":\"Kuwait\",\n",
    "                 \"Libya\":\"Libya\",\n",
    "                 \"Turkiy\":\"Turkey\",\n",
    "                 \"Liberia\":\"Liberia\",\n",
    "                 \"Namibia\":\"Namibia\",\n",
    "                 \"Ivoire\":\"Ivory Coast\",\n",
    "                 \"Guatemala\":\"Gutemala\",\n",
    "                 \"Paraguay\":\"Paraguay\",\n",
    "                 \"Honduras\":\"Honduras\",\n",
    "                 \"Nicaragua\":\"Nicaragua\",\n",
    "                 \"Trinidad\":\"Trinidad & Tobago\",\n",
    "                 \"Liechtenstein\":\"Liechtenstein\",\n",
    "                 \"Greenland\":\"Denmark\"}\n",
    "\n",
    "    extracted = geotext.extract(input_text=input_text)\n",
    "    found = extracted[key].keys()\n",
    "    if len(sorted(found))>0:\n",
    "        return sorted(found)[0]\n",
    "    elif key=='countries':\n",
    "        for i  in ['Scotland','Wales','England', 'N Ireland']:\n",
    "            if i in input_text:\n",
    "                return 'United Kingdom'\n",
    "        for j in anomalies.keys():\n",
    "            if j in input_text:\n",
    "                return anomalies.get(j)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "with open('../eu_members.txt',\"r\") as f:\n",
    "    eu_countries=f.readline().split(\",\")\n",
    "    eu_countries=[i.strip() for i in eu_countries]\n",
    "\n",
    "def country_cleanup(country):\n",
    "    if \"USA\" in country:\n",
    "        return \"USA\"\n",
    "    elif \"China\" in country:\n",
    "        return \"China\"\n",
    "    elif country in [\"England\", \"Northern Ireland\", \"Wales\", \"Scotland\",\"N Ireland\"]:\n",
    "        return \"United Kingdom\"\n",
    "    else:\n",
    "        return country\n",
    "\n",
    "\n",
    "def country_type(country):\n",
    "    if country in eu_countries:\n",
    "        return \"EU\"\n",
    "    elif country==\"China\":\n",
    "        return \"China\"\n",
    "    elif country in [\"Switzerland\", 'Norway','United Kingdom']:\n",
    "        return \"Non-EU associate\"\n",
    "    else:\n",
    "        return \"Other\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = wos.groupby(record_col)[\"Addresses\"].apply(lambda x: x.str.split('[')).explode().reset_index().drop(columns=\"level_1\")\n",
    "\n",
    "\n",
    "locations = locations[locations[\"Addresses\"]!=\"\"].copy()\n",
    "locations[\"Address\"] = locations[\"Addresses\"].apply(lambda x:x.split(\"]\")[-1])\n",
    "locations[\"Authors_of_address\"] = locations[\"Addresses\"].apply(lambda x:x.split(\"]\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "len(locations)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "locations[\"Address\"] = locations[\"Address\"].str.strip().str.strip(\";\")\n",
    "locations = locations.groupby([record_col,\"Authors_of_address\"])[\"Address\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_2\")\n",
    "locations.head(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# import dask.dataframe as dd\n",
    "#\n",
    "# locations_ddf = dd.from_pandas(locations, npartitions=4)  # convert pandas DataFrame to Dask DataFrame\n",
    "# loc_compute = locations_ddf.groupby([record_col,\"Authors_of_address\"])[\"Address\"].apply(lambda x: x.str.split(';')).explode().compute()  # compute the result"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# locations_test = locations.head(1000)\n",
    "# locations_test = locations_test.groupby([record_col,\"Authors_of_address\"])[\"Address\"].str.split(';').explode()\n",
    "# locations_test"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "# locations[\"Country\"]=locations['Address'].apply(lambda x: extract_location(input_text=x, key='countries'))\n",
    "locations[\"Country\"]=locations['Address'].apply(lambda x: x.split(\",\")[-1].strip(\" \").strip(\";\").strip(\" \"))\n",
    "locations[\"Country\"]=locations['Country'].apply(lambda x: country_cleanup(x))\n",
    "locations[\"City\"]=locations['Address'].apply(lambda x: extract_location(input_text=x, key='cities'))\n",
    "locations[\"Country_Type\"] = locations[\"Country\"].apply(lambda x: country_type(x))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "scope_types = [\"EU\",\"China\",\"Non-EU associate\"]\n",
    "locations=locations[locations[\"Country_Type\"].isin(scope_types)]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "univ_locations = locations[[record_col,\"Address\",\"Country\",\"City\",\"Country_Type\"]].copy()\n",
    "univ_locations[\"Institution\"] = univ_locations[\"Address\"].apply(lambda x: x.split(\",\")[0])\n",
    "univ_locations = univ_locations.drop_duplicates()\n",
    "univ_locations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_locations = locations.groupby([record_col,\"Country\",\"Country_Type\"])[\"Authors_of_address\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_3\")\n",
    "author_locations[\"Author_name\"] = author_locations[\"Authors_of_address\"].str.strip()\n",
    "author_locations = author_locations.drop(columns=\"Authors_of_address\")\n",
    "author_locations[\"author_str_id\"] = author_locations[\"Author_name\"].apply(lambda x:''.join(filter(str.isalnum, x.lower())))\n",
    "author_locations[\"author_str_id\"] = author_locations[\"author_str_id\"].apply(md5hash)\n",
    "author_locations = author_locations.drop(columns=\"Author_name\")\n",
    "author_locations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "author_locations[author_locations['author_str_id'].duplicated(False)]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_primary_region = author_locations.sort_values(by=\"Country_Type\").drop_duplicates(subset=[record_col,\"author_str_id\"])\n",
    "# author_primary_region\n",
    "\n",
    "china=author_primary_region[author_primary_region[\"Country_Type\"]==\"China\"][record_col].unique()\n",
    "eu=author_primary_region[author_primary_region[\"Country_Type\"]==\"EU\"][record_col].unique()\n",
    "assoc=author_primary_region[author_primary_region[\"Country_Type\"]==\"Non-EU associate\"][record_col].unique()\n",
    "\n",
    "\n",
    "# records that have distinct authors with different country affiliations\n",
    "valid_scope = wos[((wos[record_col].isin(china))\n",
    "         &\n",
    "         ((wos[record_col].isin(eu))\n",
    "         |\n",
    "         (wos[record_col].isin(assoc))))][record_col].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "author_primary_region.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'Number of records: {len(wos)}')\n",
    "print(f'Number of valid cooperation records: {len(valid_scope)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos = wos[wos[record_col].isin(valid_scope)]\n",
    "locations = locations[locations[record_col].isin(valid_scope)]\n",
    "univ_locations = univ_locations[univ_locations[record_col].isin(valid_scope)]\n",
    "author_locations = author_locations[author_locations[record_col].isin(valid_scope)]\n",
    "author_primary_region = author_locations[author_locations[record_col].isin(valid_scope)]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "affiliations = wos.groupby(record_col)[\"Affiliations\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "affiliations[\"Affiliations\"] = affiliations[\"Affiliations\"].str.strip().str.upper().fillna(\"UNKNOWN\")\n",
    "affiliations = affiliations.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "affiliations[\"Affiliations\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "univ_locations[\"Institution\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "univ_locations[record_col].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "affiliations[record_col].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "univ_locations[\"Institution\"].value_counts().sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "affiliations[\"Affiliations\"].value_counts().sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wos_cat = wos.groupby(record_col)[\"WoS Categories\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "wos_cat[\"WoS Categories\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wos_areas = wos.groupby(record_col)[\"Research Areas\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n",
    "wos_areas[\"Research Areas\"] = wos_areas[\"Research Areas\"].str.strip()\n",
    "wos_areas[\"Research Areas\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[c for c in wos.columns if \"_English\" in c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrix_levels = [c for c in wos.columns if \"_English\" in c]\n",
    "for m in metrix_levels:\n",
    "    wos[m] = wos[m].replace({\"article-level classification\":\"Multidisciplinary\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrix_levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "record_countries = locations[[record_col,\"Country\"]].drop_duplicates()\n",
    "record_author_locations = author_locations[[record_col,\"author_str_id\",\"Country\"]].drop_duplicates()\n",
    "record_institution = univ_locations[[record_col,\"Institution\",\"Country\"]].drop_duplicates()\n",
    "country_types = locations[[\"Country\",\"Country_Type\"]].drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Basic network layout"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "country_collabs = record_countries.merge(record_countries, on=record_col)\n",
    "country_collabs = country_collabs[country_collabs[\"Country_x\"]!=country_collabs[\"Country_y\"]]\n",
    "country_collabs[\"weight\"] = 0.5"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "inst_collabs = record_institution.merge(record_institution, on=record_col)\n",
    "inst_collabs = inst_collabs[inst_collabs[\"Institution_x\"]!=inst_collabs[\"Institution_y\"]]\n",
    "inst_collabs[\"weight\"] = 0.5"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos.columns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "drop_cols = [ws for ws in wos.columns if ((\"uthor\" in ws or \"ddress\" in ws or \"ORCID\" in\n",
    "                                           ws or \"esearcher\" in ws or \"ditor\" in ws or \"name\" in ws or 'SEQ' in ws) and \"eyword\" not in ws)]\n",
    "drop_cols"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "outdir=\"wos_processed_data\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "os.makedirs(outdir, exist_ok=True)\n",
    "\n",
    "wos.drop(columns=drop_cols).to_excel(f\"{outdir}/wos_processed.xlsx\", index=False)\n",
    "\n",
    "record_countries.to_excel(f\"{outdir}/wos_countries.xlsx\", index=False)\n",
    "\n",
    "record_author_locations.to_excel(f\"{outdir}/wos_author_locations.xlsx\", index=False)\n",
    "\n",
    "record_institution.to_excel(f\"{outdir}/wos_institution_locations.xlsx\", index=False)\n",
    "\n",
    "kw_df.to_excel(f\"{outdir}/wos_keywords.xlsx\", index=False)\n",
    "\n",
    "country_types.to_excel(f\"{outdir}/wos_country_types.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos.drop(columns=drop_cols).to_csv(f\"{outdir}/wos_processed.csv\", index=False, sep='\\t')\n",
    "\n",
    "record_countries.to_csv(f\"{outdir}/wos_countries.csv\", index=False, sep='\\t')\n",
    "\n",
    "record_author_locations.to_csv(f\"{outdir}/wos_author_locations.csv\", index=False, sep='\\t')\n",
    "\n",
    "record_institution.to_csv(f\"{outdir}/wos_institution_locations.csv\", index=False, sep='\\t')\n",
    "\n",
    "kw_df.to_csv(f\"{outdir}/wos_keywords.csv\", index=False, sep='\\t')\n",
    "\n",
    "country_types.to_csv(f\"{outdir}/wos_country_types.csv\", index=False, sep='\\t')\n",
    "\n",
    "inst_collabs.to_csv(f\"{outdir}/wos_inst_collabs.csv\", index=False, sep='\\t')\n",
    "\n",
    "country_collabs.to_csv(f\"{outdir}/wos_country_collabs.csv\", index=False, sep='\\t')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_areas.to_csv(f\"{outdir}/wos_research_areas.csv\", index=False, sep='\\t')\n",
    "\n",
    "wos_subcat.to_csv(f\"{outdir}/wos_categories.csv\", index=False, sep='\\t')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Simple NLP part"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import shutil\n",
    "from flashgeotext.geotext import GeoText\n",
    "import re"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import spacy\n",
    "\n",
    "nlp = spacy.load('en_core_web_trf')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "outdir=\"wos_processed_data\"\n",
    "record_col=\"UT (Unique WOS ID)\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "     UT (Unique WOS ID)                keyword_all\n0   WOS:000208863600013       COMPARATIVE GENOMICS\n1   WOS:000208863600013                    ANAMMOX\n2   WOS:000208863600013   KUENENIA STUTTGARTIENSIS\n3   WOS:000208863600013               METAGENOMICS\n4   WOS:000208863600013         ENRICHMENT CULTURE\n..                  ...                        ...\n95  WOS:000209672000007                   SECURITY\n96  WOS:000209672000007           TRUST EVALUATION\n97  WOS:000209672000007   WIRELESS SENSOR NETWORK \n98  WOS:000209673200006        FORMAL VERIFICATION\n99  WOS:000209673200006  STOCHASTIC MODEL CHECKING\n\n[100 rows x 2 columns]",
      "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>UT (Unique WOS ID)</th>\n      <th>keyword_all</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000208863600013</td>\n      <td>COMPARATIVE GENOMICS</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000208863600013</td>\n      <td>ANAMMOX</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000208863600013</td>\n      <td>KUENENIA STUTTGARTIENSIS</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000208863600013</td>\n      <td>METAGENOMICS</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000208863600013</td>\n      <td>ENRICHMENT CULTURE</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>WOS:000209672000007</td>\n      <td>SECURITY</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>WOS:000209672000007</td>\n      <td>TRUST EVALUATION</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>WOS:000209672000007</td>\n      <td>WIRELESS SENSOR NETWORK</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>WOS:000209673200006</td>\n      <td>FORMAL VERIFICATION</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>WOS:000209673200006</td>\n      <td>STOCHASTIC MODEL CHECKING</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kw_df.head(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [],
   "source": [
    "kw_df = pd.read_excel(f\"{outdir}/wos_keywords.xlsx\")\n",
    "wos = pd.read_excel(f\"{outdir}/wos_processed.xlsx\")\n",
    "kw_df = kw_df[~kw_df[\"keyword_all\"].isna()].copy()\n",
    "wos_kwd_concat = kw_df.groupby(record_col,as_index=False).agg({'keyword_all': '; '.join})"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "    UT (Unique WOS ID)               keyword_all\n0  WOS:000208863600013      COMPARATIVE GENOMICS\n1  WOS:000208863600013                   ANAMMOX\n2  WOS:000208863600013  KUENENIA STUTTGARTIENSIS\n3  WOS:000208863600013              METAGENOMICS\n4  WOS:000208863600013        ENRICHMENT CULTURE",
      "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>UT (Unique WOS ID)</th>\n      <th>keyword_all</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000208863600013</td>\n      <td>COMPARATIVE GENOMICS</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000208863600013</td>\n      <td>ANAMMOX</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000208863600013</td>\n      <td>KUENENIA STUTTGARTIENSIS</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000208863600013</td>\n      <td>METAGENOMICS</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000208863600013</td>\n      <td>ENRICHMENT CULTURE</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kw_df.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "kwd_nlp = pd.DataFrame(kw_df[\"keyword_all\"].drop_duplicates())\n",
    "kwd_nlp = kwd_nlp.rename(columns={\"keyword_all\":\"Document\"})\n",
    "kwd_nlp[\"Type\"] = \"kw\"\n",
    "kwd_nlp[record_col] = \"kw_\"+(kwd_nlp.index).astype(str)\n",
    "wos_nlp = wos.merge(wos_kwd_concat, on=record_col)\n",
    "wos_nlp[\"Document\"] = wos_nlp[\"keyword_all\"].fillna(\"\").str.upper()\n",
    "# wos_nlp[\"Document\"] = wos_nlp[\"Article Title\"].str.cat(wos_nlp[[\"Abstract\", \"keyword_all\"]].fillna(\"\"), sep=' - ').str.upper()\n",
    "# wos_nlp[\"Document\"] = wos_nlp[\"Article Title\"].str.cat(wos_nlp[[\"Abstract\"]].fillna(\"\"), sep=' - ').str.upper()\n",
    "wos_nlp[[record_col, \"Document\"]].drop_duplicates()\n",
    "wos_nlp[\"Type\"] = \"doc\"\n",
    "\n",
    "tnse_nlp = pd.concat([kwd_nlp,wos_nlp], ignore_index=True)\n",
    "tnse_nlp = tnse_nlp[[record_col,\"Type\",\"Document\",\"keyword_all\"]]\n",
    "# tnse_nlp = tnse_nlp.sample(1000)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "       UT (Unique WOS ID) Type                     Document keyword_all\n66311           kw_132167   kw     VERTICAL PROGRAMMABILITY         NaN\n121641          kw_354170   kw  NONLINEAR CLUSTER INVERSION         NaN\n35468            kw_59369   kw       TIME-VARIANT PARAMETER         NaN\n117421          kw_324755   kw              MULTI-INCIDENCE         NaN\n87947           kw_199369   kw  EVERGREEN BROADLEAVED TREES         NaN\n...                   ...  ...                          ...         ...\n56273           kw_105016   kw   DOUBLE ARC COORDINATE PLOT         NaN\n26548            kw_42376   kw                  MODAL SHIFT         NaN\n70903           kw_144947   kw         PRIVACY-PERSEVERANCE         NaN\n49655            kw_88641   kw                         IRAP         NaN\n104544          kw_254913   kw          COGNITIVE-PROCESSES         NaN\n\n[100 rows x 4 columns]",
      "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>UT (Unique WOS ID)</th>\n      <th>Type</th>\n      <th>Document</th>\n      <th>keyword_all</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>66311</th>\n      <td>kw_132167</td>\n      <td>kw</td>\n      <td>VERTICAL PROGRAMMABILITY</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>121641</th>\n      <td>kw_354170</td>\n      <td>kw</td>\n      <td>NONLINEAR CLUSTER INVERSION</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>35468</th>\n      <td>kw_59369</td>\n      <td>kw</td>\n      <td>TIME-VARIANT PARAMETER</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>117421</th>\n      <td>kw_324755</td>\n      <td>kw</td>\n      <td>MULTI-INCIDENCE</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>87947</th>\n      <td>kw_199369</td>\n      <td>kw</td>\n      <td>EVERGREEN BROADLEAVED TREES</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>56273</th>\n      <td>kw_105016</td>\n      <td>kw</td>\n      <td>DOUBLE ARC COORDINATE PLOT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>26548</th>\n      <td>kw_42376</td>\n      <td>kw</td>\n      <td>MODAL SHIFT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>70903</th>\n      <td>kw_144947</td>\n      <td>kw</td>\n      <td>PRIVACY-PERSEVERANCE</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>49655</th>\n      <td>kw_88641</td>\n      <td>kw</td>\n      <td>IRAP</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>104544</th>\n      <td>kw_254913</td>\n      <td>kw</td>\n      <td>COGNITIVE-PROCESSES</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tnse_nlp.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "vectors = list()\n",
    "vector_norms = list()\n",
    "\n",
    "for doc in nlp.pipe(tnse_nlp['Document'].astype('unicode').values, batch_size=300,\n",
    "                    n_process=4):\n",
    "    trf_vector = doc._.trf_data.tensors[-1].mean(axis=0)\n",
    "    trf_norm = np.linalg.norm(doc._.trf_data.tensors[-1].mean(axis=0))\n",
    "    norm_vector = trf_vector/trf_norm\n",
    "    vectors.append(norm_vector)\n",
    "    vector_norms.append(np.linalg.norm(norm_vector))\n",
    "\n",
    "tnse_nlp['vector'] = vectors\n",
    "tnse_nlp['vector_norm'] = vector_norms\n",
    "tnse_nlp['vector_norm'].plot(kind=\"hist\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "         UT (Unique WOS ID) Type   \n159915  WOS:000493345400001  doc  \\\n62232             kw_120676   kw   \n18729              kw_28349   kw   \n146728  WOS:000337736000001  doc   \n157327  WOS:000793790600002  doc   \n...                     ...  ...   \n64501             kw_126785   kw   \n114208            kw_304857   kw   \n90681             kw_207619   kw   \n117081            kw_322648   kw   \n146051  WOS:000660876800002  doc   \n\n                                                 Document   \n159915  A COOPERATIVE EFFECT-BASED DECISION SUPPORT MO...  \\\n62232                               URBAN STREET VITALITY   \n18729                 CONTINUOUS ATTRIBUTE DISCRETISATION   \n146728  VENTRICULAR FIBRILLATION AND TACHYCARDIA CLASS...   \n157327  MAPPING AND MODELLING DEFECT DATA FROM UAV CAP...   \n...                                                   ...   \n64501                                   LITTER PRODUCTION   \n114208                                       MIXING-STATE   \n90681                                         SAR-OPTICAL   \n117081                             INNATE IMMUNE-RESPONSE   \n146051  LOW-CYCLE FATIGUE LIFETIME ESTIMATION AND PRED...   \n\n                                              keyword_all   \n159915  TEAM FORMATION; COOPERATIVE EFFECT; COVERING; ...  \\\n62232                                                 NaN   \n18729                                                 NaN   \n146728  MACHINE LEARNING; PUBLIC DOMAIN ELECTROCARDIOG...   \n157327  UNMANNED AERIAL VEHICLE ; BUILDING INFORMATION...   \n...                                                   ...   \n64501                                                 NaN   \n114208                                                NaN   \n90681                                                 NaN   \n117081                                                NaN   \n146051  GAS TURBINE; LCF; COMPRESSOR; PREDICTIVE MAINT...   \n\n                                                   vector  vector_norm  \n159915  [0.037737507, 0.03163352, -0.023620829, -0.019...          1.0  \n62232   [0.05269539, -0.00761333, -0.043163303, -0.023...          1.0  \n18729   [0.048983343, -0.012124105, -0.0497743, -0.024...          1.0  \n146728  [0.041310925, 0.03034619, -0.020368228, -0.021...          1.0  \n157327  [0.04185079, 0.03162047, -0.022166232, -0.0242...          1.0  \n...                                                   ...          ...  \n64501   [0.04933314, 0.0028764526, -0.053359915, -0.03...          1.0  \n114208  [0.04587132, -0.014809725, -0.037412226, -0.02...          1.0  \n90681   [0.049859583, 0.00093559147, -0.040774263, -0....          1.0  \n117081  [0.04046586, -0.009001592, -0.043696642, -0.02...          1.0  \n146051  [0.038426127, 0.032835256, -0.015592382, -0.02...          1.0  \n\n[100 rows x 6 columns]",
      "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>UT (Unique WOS ID)</th>\n      <th>Type</th>\n      <th>Document</th>\n      <th>keyword_all</th>\n      <th>vector</th>\n      <th>vector_norm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>159915</th>\n      <td>WOS:000493345400001</td>\n      <td>doc</td>\n      <td>A COOPERATIVE EFFECT-BASED DECISION SUPPORT MO...</td>\n      <td>TEAM FORMATION; COOPERATIVE EFFECT; COVERING; ...</td>\n      <td>[0.037737507, 0.03163352, -0.023620829, -0.019...</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>62232</th>\n      <td>kw_120676</td>\n      <td>kw</td>\n      <td>URBAN STREET VITALITY</td>\n      <td>NaN</td>\n      <td>[0.05269539, -0.00761333, -0.043163303, -0.023...</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>18729</th>\n      <td>kw_28349</td>\n      <td>kw</td>\n      <td>CONTINUOUS ATTRIBUTE DISCRETISATION</td>\n      <td>NaN</td>\n      <td>[0.048983343, -0.012124105, -0.0497743, -0.024...</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>146728</th>\n      <td>WOS:000337736000001</td>\n      <td>doc</td>\n      <td>VENTRICULAR FIBRILLATION AND TACHYCARDIA CLASS...</td>\n      <td>MACHINE LEARNING; PUBLIC DOMAIN ELECTROCARDIOG...</td>\n      <td>[0.041310925, 0.03034619, -0.020368228, -0.021...</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>157327</th>\n      <td>WOS:000793790600002</td>\n      <td>doc</td>\n      <td>MAPPING AND MODELLING DEFECT DATA FROM UAV CAP...</td>\n      <td>UNMANNED AERIAL VEHICLE ; BUILDING INFORMATION...</td>\n      <td>[0.04185079, 0.03162047, -0.022166232, -0.0242...</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>64501</th>\n      <td>kw_126785</td>\n      <td>kw</td>\n      <td>LITTER PRODUCTION</td>\n      <td>NaN</td>\n      <td>[0.04933314, 0.0028764526, -0.053359915, -0.03...</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>114208</th>\n      <td>kw_304857</td>\n      <td>kw</td>\n      <td>MIXING-STATE</td>\n      <td>NaN</td>\n      <td>[0.04587132, -0.014809725, -0.037412226, -0.02...</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>90681</th>\n      <td>kw_207619</td>\n      <td>kw</td>\n      <td>SAR-OPTICAL</td>\n      <td>NaN</td>\n      <td>[0.049859583, 0.00093559147, -0.040774263, -0....</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>117081</th>\n      <td>kw_322648</td>\n      <td>kw</td>\n      <td>INNATE IMMUNE-RESPONSE</td>\n      <td>NaN</td>\n      <td>[0.04046586, -0.009001592, -0.043696642, -0.02...</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>146051</th>\n      <td>WOS:000660876800002</td>\n      <td>doc</td>\n      <td>LOW-CYCLE FATIGUE LIFETIME ESTIMATION AND PRED...</td>\n      <td>GAS TURBINE; LCF; COMPRESSOR; PREDICTIVE MAINT...</td>\n      <td>[0.038426127, 0.032835256, -0.015592382, -0.02...</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tnse_nlp.head(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "  UT (Unique WOS ID)      TNSE-X      TNSE-Y\n0               kw_0  127.197891  114.109520\n1               kw_1  -21.558281 -202.681183\n2               kw_2   15.277477  -37.555573\n3               kw_3   54.094421 -164.205536\n4               kw_4 -165.029221  -96.129143",
      "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>UT (Unique WOS ID)</th>\n      <th>TNSE-X</th>\n      <th>TNSE-Y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>kw_0</td>\n      <td>127.197891</td>\n      <td>114.109520</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>kw_1</td>\n      <td>-21.558281</td>\n      <td>-202.681183</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>kw_2</td>\n      <td>15.277477</td>\n      <td>-37.555573</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>kw_3</td>\n      <td>54.094421</td>\n      <td>-164.205536</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>kw_4</td>\n      <td>-165.029221</td>\n      <td>-96.129143</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# % matplotlib inline\n",
    "\n",
    "vector_data = pd.DataFrame(tnse_nlp[\"vector\"].to_list(), index=tnse_nlp[record_col]).reset_index()\n",
    "vector_data.head()\n",
    "\n",
    "labels = vector_data.values[:, 0]\n",
    "record_vectors = vector_data.values[:, 1:]\n",
    "\n",
    "tsne_model = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, random_state=42, metric='cosine')\n",
    "tnse_2d = tsne_model.fit_transform(record_vectors)\n",
    "tnse_data = pd.DataFrame(tnse_2d, index=labels).reset_index()\n",
    "tnse_data.columns = [record_col, \"TNSE-X\", \"TNSE-Y\"]\n",
    "tnse_data.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1c5cd3dbd90>"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos_plot = tnse_nlp.merge(tnse_data, on=record_col)\n",
    "\n",
    "g = sns.scatterplot(wos_plot, x=\"TNSE-X\", y=\"TNSE-Y\",\n",
    "                    hue='Type', s=1)\n",
    "g.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "# wos_plot.head()\n",
    "# wos_nlp = wos_plot[[record_col, \"Document\", \"keyword_all\", \"TNSE-X\", \"TNSE-Y\"]]\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_nlp = wos.merge(wos_kwd_concat, on=record_col)\n",
    "wos_nlp[\"Document\"] = wos_nlp[\"Article Title\"].str.cat(wos_nlp[[\"Abstract\", \"keyword_all\"]].fillna(\"\"), sep=' - ')\n",
    "# wos_kwd_test[\"BERT_KWDS\"] = wos_kwd_test[\"Document\"].map(kwd_extract)\n",
    "\n",
    "vectors = list()\n",
    "vector_norms = list()\n",
    "\n",
    "for doc in nlp.pipe(wos_nlp['Document'].astype('unicode').values, batch_size=300,\n",
    "                    n_process=4):\n",
    "    vectors.append(doc.vector)\n",
    "    vector_norms.append(doc.vector_norm)\n",
    "\n",
    "wos_nlp['vector'] = vectors\n",
    "wos_nlp['vector_norm'] = vector_norms\n",
    "wos_nlp['vector_norm'].plot(kind=\"hist\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# % matplotlib inline\n",
    "\n",
    "vector_data = pd.DataFrame(wos_nlp[\"vector\"].to_list(), index=wos_nlp[record_col]).reset_index()\n",
    "vector_data.head()\n",
    "\n",
    "labels = vector_data.values[:, 0]\n",
    "record_vectors = vector_data.values[:, 1:]\n",
    "\n",
    "tsne_model = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, random_state=42, metric='cosine')\n",
    "tnse_2d = tsne_model.fit_transform(record_vectors)\n",
    "tnse_data = pd.DataFrame(tnse_2d, index=labels).reset_index()\n",
    "tnse_data.columns = [record_col, \"TNSE-X\", \"TNSE-Y\"]\n",
    "tnse_data.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_plot = wos_nlp.merge(tnse_data, on=record_col)\n",
    "\n",
    "g = sns.scatterplot(wos_plot[wos_plot[\"Domain_English\"] != 'article-level classification'], x=\"TNSE-X\", y=\"TNSE-Y\",\n",
    "                    hue='Domain_English', s=1)\n",
    "g.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "wos_plot.head()\n",
    "wos_nlp = wos_plot[[record_col, \"Document\", \"keyword_all\", \"TNSE-X\", \"TNSE-Y\"]]\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "wos_nlp.to_excel(f\"{outdir}/wos_nlp.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_nlp.to_csv(f\"{outdir}/wos_nlp.csv\", index=False, sep='\\t')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_nlp.columns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import spacy\n",
    "\n",
    "nlp = spacy.load(\"en_core_web_lg\")\n",
    "kwd_nlp = pd.DataFrame(kw_df[\"keyword_all\"].drop_duplicates())\n",
    "# wos_kwd_test[\"BERT_KWDS\"] = wos_kwd_test[\"Document\"].map(kwd_extract)\n",
    "\n",
    "vectors = list()\n",
    "vector_norms = list()\n",
    "\n",
    "for doc in nlp.pipe(kwd_nlp['keyword_all'].astype('unicode').values, batch_size=300,\n",
    "                    n_process=4):\n",
    "    vectors.append(doc.vector)\n",
    "    vector_norms.append(doc.vector_norm)\n",
    "\n",
    "kwd_nlp['vector'] = vectors\n",
    "kwd_nlp['vector_norm'] = vector_norms\n",
    "kwd_nlp['vector_norm'].plot(kind=\"hist\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# % matplotlib inline\n",
    "\n",
    "vector_data = pd.DataFrame(kwd_nlp[\"vector\"].to_list(), index=kwd_nlp[\"keyword_all\"]).reset_index()\n",
    "vector_data.head()\n",
    "\n",
    "labels = vector_data.values[:, 0]\n",
    "record_vectors = vector_data.values[:, 1:]\n",
    "\n",
    "tsne_model = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, random_state=42, metric='cosine')\n",
    "tnse_2d = tsne_model.fit_transform(record_vectors)\n",
    "tnse_data = pd.DataFrame(tnse_2d, index=labels).reset_index()\n",
    "tnse_data.columns = [record_col, \"TNSE-X\", \"TNSE-Y\"]\n",
    "tnse_data.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "g = sns.scatterplot(tnse_data, x=\"TNSE-X\", y=\"TNSE-Y\", s=1)\n",
    "g.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wos_nlp.to_csv(f\"{outdir}/wos_nlp.csv\", index=False, sep='\\t')\n",
    "tnse_data.to_csv(f\"{outdir}/kw_nlp.csv\", index=False, sep='\\t')\n",
    "\n",
    "wos_nlp.to_excel(f\"{outdir}/wos_nlp.xlsx\", index=False)\n",
    "tnse_data.drop_duplicates(subset=record_col).to_excel(f\"{outdir}/kw_nlp.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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