01a_create_SPECTER_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create specter data\n",
    "Code here was adapted from specter's github page\n",
    "\n",
    "Here, we are taking our datasets and converting them to specter embeddings for training/testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json#\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First set your data directory. Recommend keeping data somewhere other than where you keep code!\n",
    "data_dir = os.path.abspath(r'C:\\Users\\aday\\OneDrive - SAGE Publishing\\PROJECT_DATA\\pubmed_case_report_classifier\\data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written train: 16974 16974\n",
      "Written dev: 2140 2140\n",
      "Written test: 2166 2166\n"
     ]
    }
   ],
   "source": [
    "datasets = ['train','dev','test']\n",
    "\n",
    "\n",
    "for dataset in datasets:\n",
    "    dataset_json = {}\n",
    "    dataset_pids = []\n",
    "    data = pd.read_csv(os.path.join(data_dir,'{}.csv'.format(dataset)), dtype=str)\n",
    "    \n",
    "    dataset_pids = list(data.doi.values)\n",
    "    for i,row in data.iterrows():\n",
    "        title = row['articletitle']\n",
    "        abstract = row['abstract']\n",
    "        doi = row['doi']\n",
    "        dataset_json[doi] = {'title': title,\n",
    "                            'abstract':abstract,\n",
    "                            'paper_id':doi}\n",
    "        doistr = '\\r\\n'.join(dataset_pids)\n",
    "        \n",
    "    output_id_path = os.path.join(data_dir,'specter_ids_{}.json'.format(dataset))\n",
    "    with open(output_id_path,'w') as f:\n",
    "        f.write(doistr)\n",
    "    output_data_path = os.path.join(data_dir,'specter_data_{}.json'.format(dataset))\n",
    "    with open(output_data_path,'w') as f:\n",
    "        json.dump(dataset_json,f)\n",
    "    print(f'Written {dataset}:', len(dataset_pids), len(dataset_json))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embed with API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write out\n",
    "# code adapted from: https://github.com/allenai/paper-embedding-public-apis\n",
    "from typing import Dict, List\n",
    "import json\n",
    "import requests\n",
    "\n",
    "URL = \"https://model-apis.semanticscholar.org/specter/v1/invoke\"\n",
    "MAX_BATCH_SIZE = 16\n",
    "\n",
    "def chunks(lst, chunk_size=MAX_BATCH_SIZE):\n",
    "    for i in range(0, len(lst), chunk_size):\n",
    "        yield lst[i : i + chunk_size]\n",
    "\n",
    "def embed(papers):\n",
    "    embeddings_by_paper_id: Dict[str, List[float]] = {}\n",
    "    for chunk in chunks(papers):\n",
    "        # Allow Python requests to convert the data above to JSON\n",
    "        response = requests.post(URL, json=chunk)\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(\"Sorry, something went wrong, please try later!\")\n",
    "        for paper in response.json()[\"preds\"]:\n",
    "            embeddings_by_paper_id[paper[\"paper_id\"]] = paper[\"embedding\"]\n",
    "    return embeddings_by_paper_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EMBEDDING: 16974\n",
      "EMBEDDING: 2140\n",
      "EMBEDDING: 2166\n",
      "Wall time: 1h 3min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "datasets = ['train','dev','test']\n",
    "\n",
    "for dataset in datasets:\n",
    "    dataset_json = []\n",
    "    data = pd.read_csv(os.path.join(data_dir,'{}.csv'.format(dataset)), dtype=str)\n",
    "    for i,row in data.iterrows():\n",
    "        title = row['articletitle']\n",
    "        abstract = row['abstract']\n",
    "        doi = row['doi']\n",
    "        # ensure that everything is right type\n",
    "        if type(abstract)==type(title)==type(doi)==str:\n",
    "            dataset_json.append({'title': title,\n",
    "                                'abstract':abstract,\n",
    "                                'paper_id':doi})\n",
    "#             doistr = '\\r\\n'.join(dataset_pids)\n",
    "    print('EMBEDDING:', len(dataset_json))\n",
    "    all_embeddings = embed(dataset_json)\n",
    "    dataset_path = os.path.join(data_dir,f'{dataset} embeddings.json')\n",
    "    with open(dataset_path, 'w') as f:\n",
    "        json.dump(all_embeddings,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check embeddings\n",
    "Embeddings should be 768-length vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2166"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_path = os.path.join(data_dir,'test embeddings.json')\n",
    "with open(test_data_path,'r') as f:\n",
    "    test_data = json.load(f)\n",
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "data = pd.read_csv(os.path.join(data_dir,'test.csv'))\n",
    "for i,row in data.iterrows():\n",
    "    doi = row['doi']\n",
    "    embedding = test_data[doi]\n",
    "    assert len(embedding)==768\n",
    "    print(len(embedding),embedding)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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