Score1_metric.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa66094d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "162091a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/data/pubmed-data.tsv', sep='\\t')\n",
    "covid_query = df[(df.query_term == 'covid-19')]\n",
    "cancer_query = df[(df.query_term == 'cancer')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e89ea833",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_score1_metric(df):\n",
    "    '''\n",
    "    Function to calculate the index of the article (where it appeared in the search results).\n",
    "    Index is calculated by looking at page number and position on the page. For example,\n",
    "    if Article A is on Page 1, Position 1 (aka the first result), then its index is 1. If\n",
    "    Article B is on Page 2, Position 1, then its index is 11.\n",
    "    \n",
    "    :param: df, pandas dataframe that contains the searches of a specific query.For example,\n",
    "                if we want to calculate indexes for articles that resulted from \"covid-19\" query,\n",
    "                we would filter our original dataset for \"covid-19\" and apply this function to this\n",
    "                filtered dataframe.\n",
    "    :return: pmid_score1_info, dictionary with the key being the PMID and the value being the average index.\n",
    "    \n",
    "    '''\n",
    "    pmid_score1_info = {}\n",
    "    for i in range(len(df)):\n",
    "        pmid_list = df['PMID'].iloc[i].split(',')\n",
    "        for pmid in pmid_list:\n",
    "            page_num = df['page_num'].iloc[i]\n",
    "            location_on_page  = pmid_list.index(pmid) + 1\n",
    "            pmid_index = (page_num - 1)*10 + location_on_page\n",
    "            if pmid not in pmid_score1_info:\n",
    "                pmid_score1_info[pmid] = [1, pmid_index]\n",
    "            else:\n",
    "                pmid_score1_info[pmid][0] += 1 # [0] is the count\n",
    "                pmid_score1_info[pmid][1] += pmid_index\n",
    "\n",
    "    for pmid in pmid_score1_info:\n",
    "        score = pmid_score1_info[pmid][1]/pmid_score1_info[pmid][0]\n",
    "        pmid_score1_info[pmid] = score\n",
    "        \n",
    "    return pmid_score1_info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285ca73f",
   "metadata": {},
   "source": [
    "## Example of how to use it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba5eb0e8",
   "metadata": {},
   "outputs": [
    {
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       " '34870630': 77.0,\n",
       " ...}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_score1_metric(covid_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35f84d83",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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