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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/zehsilva/4e9965fd467a6ee119f0d651df4ff34e/br_confs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gOHALr4-aYS1"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import requests\n",
"import itertools\n",
"import re\n",
"import enum\n",
"\n",
"# TODO:\n",
"# - Change the build_url to different formats for years <= 2020 (offset=1000 for each type)\n",
"# - Alternative version that looks into DBLP \n",
"\n",
"def build_url(conf,year,conf_type=0,limit=1000):\n",
" conf_type_str = \"\"\n",
" conf_site=\"\"\n",
" end_str=\"%2FConference%2F-%2FBlind_Submission\"\n",
" if conf_type==0:\n",
" conf_type_str= \"Poster\"\n",
" elif conf_type==1:\n",
" conf_type_str=\"Spotlight\"\n",
" if conf.lower()==\"uai\":\n",
" conf_type_str=\"Oral\"\n",
" elif conf_type==2:\n",
" conf_type_str=\"Oral\"\n",
" else:\n",
" conf_type_str=\"Oral\"\n",
" if conf.lower() == \"neurips_datasets\":\n",
" conf=\"NeurIPS\"\n",
" conf_type_str = \"Datasets+and+Benchmarks+\"\n",
" conf_site=f\"{conf}.cc\"\n",
" end_str=\"%2FTrack%2FDatasets_and_Benchmarks%2F-%2FSubmission\"\n",
" elif conf.lower() == \"neurips\":\n",
" conf=\"NeurIPS\"\n",
" conf_site=f\"{conf}.cc\"\n",
" if year==2022:\n",
" conf_type_str=\"Accept\"\n",
" elif conf.lower() == \"iclr\":\n",
" conf=\"ICLR\"\n",
" conf_site=f\"{conf}.cc\"\n",
" elif conf.lower() == \"uai\":\n",
" conf=\"UAI\"\n",
" conf_site=f\"auai.org%2F{conf}\"\n",
" elif conf.lower() == \"clear\":\n",
" conf=\"CLeaR\"\n",
" conf_site=f\"cclear.cc%2F{conf}\"\n",
" return f\"https://api.openreview.net/notes?content.venue={conf}+{year}+{conf_type_str}&details=replyCount&offset=0&limit={limit}&invitation={conf_site}%2F{year}{end_str}\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DocmfMYAiXQI"
},
"outputs": [],
"source": [
"def get_papers_df(url):\n",
" print(f\"Getting notes from API using url {url}\")\n",
" result = requests.get(url).json()\n",
" df = dict()\n",
" count = 1\n",
" if 'count' in result:\n",
" count = result['count']\n",
" if \"notes\" in result:\n",
" df = pd.DataFrame(result['notes'])\n",
" if \"content\" in df:\n",
" df=pd.DataFrame(list(df['content']))\n",
" return df, count\n",
"\n",
"def get_author_data(author_id):\n",
" url=f\"https://api.openreview.net/profiles?id={author_id}\"\n",
" jsondata = requests.get(url).json()\n",
" if jsondata['count']>=1:\n",
" df = pd.DataFrame(jsondata['profiles'])\n",
" df=pd.DataFrame(list(df['content']))\n",
" return df\n",
" return {}\n",
"\n",
"def name_from_fields(names_field):\n",
" return f\"{names_field[0][0]['first']} {names_field[0][0]['middle']} {names_field[0][0]['last']}\"\n",
"\n",
"def test_br(author_id):\n",
" author_data=get_author_data(author_id)\n",
" str_data = \"\"\n",
" for key in ['preferredEmail', 'homepage', 'gscholar', 'emails', 'history','emailsConfirmed']:\n",
" if key in author_data:\n",
" str_data += str(author_data[[key]].to_dict()).lower()\n",
" #str_data = str(author_data[['preferredEmail', 'homepage', 'emails', 'history','emailsConfirmed']].to_dict()).lower()\n",
" #str_data = str(author_data[['preferredEmail', 'homepage', 'gscholar', 'emails', 'history','emailsConfirmed']].to_dict()).lower()\n",
" if (\".br,\" in str_data) or (\".br]\" in str_data) or (\".br'\" in str_data) or (\".br/\" in str_data) or (\"brazil\" in str_data) or (\"brasil\" in str_data) or (\"universidade\" in str_data):\n",
" return author_data\n",
" return None\n",
"\n",
"def test_nordic(author_id):\n",
" author_data=get_author_data(author_id)\n",
" str_data = \"\"\n",
" for key in ['preferredEmail', 'homepage', 'emails','emailsConfirmed']:\n",
" if key in author_data:\n",
" str_data += str(author_data[[key]].to_dict()).lower()\n",
" #str_data = str(author_data[['preferredEmail', 'homepage', 'emails', 'history','emailsConfirmed']].to_dict()).lower()\n",
" #str_data = str(author_data[['preferredEmail', 'homepage', 'gscholar', 'emails', 'history','emailsConfirmed']].to_dict()).lower()\n",
" patterns = [\"no\", \"fi\", \"se\", \"dk\"]\n",
" countries = [\"finland\", \"finnish\", \"sweden\", \"swedish\", \"norway\", \"norwegian\", \"denmark\", \"danish\", \"suomi\", \"suomen\", \"svenska\", \"sverige\", \"dansk\", \"danmark\", \"norges\", \"norge\", \"norsk\", \"universitet\", \"yliopisto\"]\n",
" if True in [((f\".{ending},\" in str_data) or (f\".{ending}]\" in str_data) or (f\".{ending}'\" in str_data) or (f\".{ending}/\" in str_data)) for ending in patterns]:\n",
" return author_data\n",
" #if True in [country in str_data for country in countries]:\n",
" # return author_data\n",
" return None\n",
"\n",
"def unique_authors(lst_lst):\n",
" if \"authorids\" in lst_lst:\n",
" return list(set(list(itertools.chain(*lst_lst['authorids'].to_list()))))\n",
" else:\n",
" return []\n",
"\n",
"def find_brazillians_conf(conf, year):\n",
" results=[]\n",
" data = dict()\n",
" for idx,venue_type in enumerate([\"poster\", \"oral\", \"spotlight\"]):\n",
" if (conf.lower() in [\"uai\",\"clear\"]) and venue_type == \"spotlight\":\n",
" continue\n",
" if (conf.lower() in [\"neurips_datasets\"]) and (venue_type in [\"spotlight\", \"oral\"]):\n",
" continue\n",
" data[venue_type], count=get_papers_df(build_url(conf,year,idx))\n",
" if count > 1000:\n",
" n_calls = count // 1000\n",
" offset = 0\n",
" init_url = build_url(conf,year,idx)\n",
" for i in range(n_calls):\n",
" offset+=1000\n",
" new_url=init_url.replace(\"offset=0\", f\"offset={offset}\")\n",
" new_df,_ = get_papers_df(new_url)\n",
" data[venue_type] = pd.concat([data[venue_type], new_df], axis=0, ignore_index=True)\n",
" for author in unique_authors(data[venue_type]):\n",
" try:\n",
" t = test_br(author)\n",
" if t is not None:\n",
" t_dict = t.to_dict()\n",
" t_dict['venue']=f\"{conf} {year} ({venue_type})\"\n",
" print(t_dict)\n",
" results.append(t.to_dict())\n",
" except:\n",
" pass\n",
" if conf.lower()==\"neurips\" and year==2022:\n",
" break\n",
" return results\n",
"\n",
"\n",
"def find_nordics_conf(conf, year):\n",
" results=[]\n",
" data = dict()\n",
" for idx,venue_type in enumerate([\"poster\", \"oral\", \"spotlight\"]):\n",
" if (conf.lower() in [\"uai\",\"clear\"]) and venue_type == \"spotlight\":\n",
" continue\n",
" if (conf.lower() in [\"neurips_datasets\"]) and (venue_type in [\"spotlight\", \"oral\"]):\n",
" continue\n",
" data[venue_type], count=get_papers_df(build_url(conf,year,idx))\n",
" for author in unique_authors(data[venue_type]):\n",
" try:\n",
" t = test_nordic(author)\n",
" if t is not None:\n",
" t_dict = t.to_dict()\n",
" t_dict['venue']=f\"{conf} {year} ({venue_type})\"\n",
" print(t_dict)\n",
"\n",
" results.append(t.to_dict())\n",
" except:\n",
" pass\n",
" if conf.lower()==\"neurips\" and year==2022:\n",
" break\n",
" return results\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "53p96nutSiwZ"
},
"outputs": [],
"source": [
"# get all papers\n",
"#poster=get_papers_df('https://api.openreview.net/notes?content.venue=NeurIPS+2021+Poster&details=replyCount&offset=0&limit=1000&invitation=NeurIPS.cc%2F2021%2FConference%2F-%2FBlind_Submission')\n",
"#oral=get_papers_df('https://api.openreview.net/notes?content.venue=NeurIPS+2021+Oral&details=replyCount&offset=0&limit=1000&invitation=NeurIPS.cc%2F2021%2FConference%2F-%2FBlind_Submission')\n",
"#spotlight=get_papers_df('https://api.openreview.net/notes?content.venue=NeurIPS+2021+Spotlight&details=replyCount&offset=0&limit=1000&invitation=NeurIPS.cc%2F2021%2FConference%d_Submission')\n",
"#spotlight=get_papers_df('https://api.openreview.net/notes?content.venue=NeurIPS+2021+Spotlight&details=replyCount&offset=0&limit=1000&invitation=NeurIPS.cc%2F2021%2FConference%2F-%2FBlind_Submission')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sNhgrlvVkE4u"
},
"outputs": [],
"source": [
"#author_id = unique_authors(poster.head(150))[110]\n",
"#author_data=get_author_data(author_id)\n",
"#str_data = str(author_data.to_dict()).lower()\n",
"#brs = []\n",
"#for author in unique_authors(poster):\n",
"# if test_name_br(author,2):\n",
"# t = get_author_data(author)\n",
"# print(dict(t))\n",
"# brs.append(t)"
]
},
{
"cell_type": "code",
"source": [
"br_iclr_2022 = find_brazillians_conf(\"iclr\", 2022)"
],
"metadata": {
"id": "RrJaXCkfp3Vo"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"br_iclr_2021 = find_brazillians_conf(\"iclr\", 2021)"
],
"metadata": {
"id": "CYkdKv4h63e7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"br_uai_2022 = find_brazillians_conf(\"uai\", 2022)"
],
"metadata": {
"id": "dKK3YDmN74nc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"br_neurips_2021 = find_brazillians_conf(\"neurips\", 2021)"
],
"metadata": {
"id": "ngGdsUXP76qT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"br_neurips_2022 = find_brazillians_conf(\"neurips\", 2022)"
],
"metadata": {
"id": "f7k6d8vSnT8x"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"br_neurips_2021 = find_brazillians_conf(\"neurips_datasets\", 2022)"
],
"metadata": {
"id": "uqEp8wjE79P0"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"br_clear_2021 = find_brazillians_conf(\"clear\", 2022)"
],
"metadata": {
"id": "rj6EDb2cG19q",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f8beeaeb-2c53-4642-d29d-abd0bebdac23"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Getting notes from API using url https://api.openreview.net/notes?content.venue=CLeaR+2022+Poster&details=replyCount&offset=0&limit=1000&invitation=cclear.cc%2FCLeaR%2F2022%2FConference%2F-%2FBlind_Submission\n",
"{'preferredEmail': {0: '****@ed.ac.uk'}, 'gender': {0: 'Male'}, 'homepage': {0: 'https://vios.science/team/sanchez'}, 'gscholar': {0: 'https://scholar.google.com/citations?user=KPchGe4AAAAJ&hl=en'}, 'linkedin': {0: 'https://linkedin.com/in/pedro-sanches-ppsg'}, 'orcid': {0: 'https://orcid.org/0000-0003-2435-3049'}, 'emails': {0: ['****@ed.ac.uk']}, 'names': {0: [{'first': 'Pedro', 'middle': '', 'last': 'Sanchez', 'preferred': True, 'username': '~Pedro_Sanchez1'}]}, 'history': {0: [{'position': 'PhD student', 'start': 2021, 'end': 2024, 'institution': {'domain': 'ed.ac.uk', 'name': 'University of Edinburgh, University of Edinburgh'}}, {'position': 'Undergrad student', 'start': 2014, 'end': 2021, 'institution': {'domain': 'unb.br', 'name': 'Universidade de Brasília'}}]}, 'relations': {0: [{'name': 'Sotirios Tsaftaris', 'email': '****@ed.ac.uk', 'relation': 'PhD Advisor', 'start': 2021, 'end': 2024, 'readers': ['everyone']}, {'name': 'Alison ONeil', 'email': '****@mre.medical.canon', 'relation': 'PhD Advisor', 'start': 2021, 'end': 2024, 'readers': ['everyone']}]}, 'expertise': {0: [{'keywords': ['Causality'], 'start': 2021, 'end': None}, {'keywords': ['Disentanglement'], 'start': 2021, 'end': None}, {'keywords': ['Medical Imaging'], 'start': 2017, 'end': None}]}, 'emailsConfirmed': {0: ['****@ed.ac.uk']}, 'venue': 'clear 2022 (poster)'}\n",
"Getting notes from API using url https://api.openreview.net/notes?content.venue=CLeaR+2022+Spotlight&details=replyCount&offset=0&limit=1000&invitation=cclear.cc%2FCLeaR%2F2022%2FConference%2F-%2FBlind_Submission\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# busca autores brasileiros na conferencias neurips, uai e iclr, nos anos 2021 e 2022\n",
"confs = [\"neurips\", \"uai\", \"iclr\"]\n",
"years = [2021, 2022]\n",
"res = dict()\n",
"for conf in confs:\n",
" for year in years:\n",
" res[(conf,year)] = find_brazillians_conf(conf,year)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JeZPFl-o0rUT",
"outputId": "d5a9e576-df3b-4bda-80c6-399d35f8b4c7"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Getting notes from API using url https://api.openreview.net/notes?content.venue=NeurIPS+2021+Poster&details=replyCount&offset=0&limit=1000&invitation=NeurIPS.cc%2F2021%2FConference%2F-%2FBlind_Submission\n",
"Getting notes from API using url https://api.openreview.net/notes?content.venue=NeurIPS+2021+Poster&details=replyCount&offset=1000&limit=1000&invitation=NeurIPS.cc%2F2021%2FConference%2F-%2FBlind_Submission\n",
"Getting notes from API using url https://api.openreview.net/notes?content.venue=NeurIPS+2021+Poster&details=replyCount&offset=2000&limit=1000&invitation=NeurIPS.cc%2F2021%2FConference%2F-%2FBlind_Submission\n",
"{'preferredEmail': {0: '****@gmail.com'}, 'gender': {0: 'Male'}, 'homepage': {0: 'https://cottascience.github.io/'}, 'gscholar': {0: 'https://scholar.google.com.br/citations?user=0GI4MyoAAAAJ&hl=en'}, 'dblp': {0: 'https://dblp.org/pid/183/1858.html'}, 'linkedin': {0: 'https://www.linkedin.com/in/cotta/'}, 'emails': {0: ['****@purdue.edu', '****@gmail.com', '****@purdue.edu']}, 'names': {0: [{'first': 'Leonardo', 'middle': '', 'last': 'Cotta', 'preferred': True, 'username': '~Leonardo_Cotta1'}]}, 'history': {0: [{'position': 'PhD student', 'start': 2017, 'end': None, 'institution': {'domain': 'purdue.edu', 'name': 'Purdue University'}}, {'position': 'Undergrad student', 'start': 2012, 'end': 2016, 'institution': {'domain': 'ufmg.br', 'name': 'Universidade Federal de Minas Gerais'}}]}, 'relations': {0: [{'name': 'Bruno Ribeiro', 'email': '****@cs.purdue.edu', 'relation': 'PhD Advisor', 'start': 2017, 'end': None, 'readers': ['everyone']}]}, 'expertise': {0: [{'keywords': ['deep learning', 'graphs', 'sets'], 'start': None, 'end': None}, {'keywords': ['mcmc', 'optimization', 'graphs'], 'start': None, 'end': None}]}, 'emailsConfirmed': {0: ['****@purdue.edu', '****@gmail.com', '****@purdue.edu']}, 'venue': 'neurips 2021 (poster)'}\n",
"{'preferredEmail': {0: '****@gmail.com'}, 'homepage': {0: 'http://www.cowhi.org'}, 'gscholar': {0: 'https://scholar.google.com/citations?user=XVfDYnAAAAAJ&hl=en&oi=ao'}, 'linkedin': {0: 'https://linkedin.com/in/rubenglatt'}, 'orcid': {0: 'https://orcid.org/0000-0002-4401-3810'}, 'gender': {0: 'Male'}, 'emails': {0: ['****@gmail.com', '****@llnl.gov', '****@usp.br']}, 'names': {0: [{'first': 'Ruben', 'middle': '', 'last': 'Glatt', 'preferred': True, 'username': '~Ruben_Glatt2'}]}, 'history': {0: [{'position': 'Staff Researcher', 'start': 2021, 'end': None, 'institution': {'domain': 'llnl.gov', 'name': 'Lawrence Livermore National Labs'}}, {'position': 'Postdoc', 'start': 2019, 'end': 2021, 'institution': {'domain': 'llnl.gov', 'name': 'Lawrence Livermore National Labs'}}, {'position': 'PhD student', 'start': 2015, 'end': 2019, 'institution': {'domain': 'usp.br', 'name': 'Universidade de Sao Paulo'}}, {'position': 'MS student', 'start': 2004, 'end': 2011, 'institution': {'domain': 'kit.edu', 'name': 'Karlsruhe Institute of Technology'}}]}, 'relations': {0: [{'name': 'Brenda Ng', 'email': '****@llnl.gov', 'relation': 'Postdoc Advisor', 'start': 2019, 'end': 2021, 'readers': ['everyone']}, {'name': 'Anna Helena Reali Costa', 'email': '****@usp.br', 'relation': 'PhD Advisor', 'start': 2015, 'end': 2019, 'readers': ['everyone']}]}, 'expertise': {0: [{'keywords': ['Multiagent Reinforcement Learning'], 'start': 2019, 'end': None}, {'keywords': ['Reinforcement Learning'], 'start': 2015, 'end': None}, {'keywords': ['Transfer Learning'], 'start': 2015, 'end': None}, {'keywords': ['Deep Learning'], 'start': 2014, 'end': None}]}, 'emailsConfirmed': {0: ['****@gmail.com', '****@llnl.gov']}, 'venue': 'neurips 2021 (poster)'}\n",
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"{'preferredEmail': {0: '****@gmail.com'}, 'gender': {0: 'Male'}, 'homepage': {0: 'http://buscatextual.cnpq.br/buscatextual/visualizacv.do;jsessionid=D9822F24D364299D11D8868EF46ADB21.buscatextual_0'}, 'emails': {0: ['****@dcc.ufmg.br', '****@gmail.com']}, 'names': {0: [{'first': 'Artur', 'middle': '', 'last': 'Souza', 'preferred': True, 'username': '~Artur_Souza1'}, {'first': 'Artur', 'middle': '', 'last': 'Luis Fernandes', 'username': '~Artur_Luis_Fernandes1', 'preferred': False}, {'first': 'Artur', 'middle': 'L. F.', 'last': 'Souza', 'username': '~Artur_L._F._Souza1', 'preferred': False}]}, 'history': {0: [{'position': 'PhD student', 'start': 2017, 'end': None, 'institution': {'domain': 'ufmg.br', 'name': 'Universidade Federal de Minas Gerais'}}, {'position': 'Undergrad student', 'start': 2013, 'end': 2016, 'institution': {'domain': 'ufmg.br', 'name': 'Universidade Federal de Minas Gerais'}}]}, 'relations': {0: [{'name': 'Leonardo B. Oliveira', 'email': '****@stanford.edu', 'relation': 'PhD Advisor', 'start': 2017, 'end': None, 'readers': ['everyone']}]}, 'expertise': {0: [{'keywords': ['Deep Learning'], 'start': 2018, 'end': None}]}, 'emailsConfirmed': {0: ['****@dcc.ufmg.br', '****@gmail.com']}, 'venue': 'iclr 2022 (poster)'}\n",
"{'dblp': {0: 'https://dblp.org/pid/122/2913'}, 'preferredEmail': {0: '****@gmail.com'}, 'gender': {0: 'Male'}, 'gscholar': {0: 'https://scholar.google.com/citations?user=LpWGWAwAAAAJ&hl=en'}, 'linkedin': {0: 'https://www.linkedin.com/in/andre-manoel/'}, 'orcid': {0: 'https://orcid.org/0000-0002-5455-0230'}, 'names': {0: [{'first': 'Andre', 'middle': '', 'last': 'Manoel', 'username': '~Andre_Manoel1', 'preferred': False}]}, 'emails': {0: ['****@gmail.com', '****@microsoft.com']}, 'history': {0: [{'position': 'Research Engineer', 'start': 2021, 'end': None, 'institution': {'domain': 'microsoft.com', 'name': 'Microsoft'}}, {'position': 'Data Scientist', 'start': 2020, 'end': 2021, 'institution': {'domain': 'einstein.br', 'name': 'Hospital Israelita Albert Einstein'}}, {'position': 'Machine Learning Scientist', 'start': 2018, 'end': 2020, 'institution': {'domain': 'owkin.com', 'name': 'Owkin'}}, {'position': 'Postdoc', 'start': 2017, 'end': 2018, 'institution': {'domain': 'inria.fr', 'name': 'INRIA'}}, {'position': 'Postdoc', 'start': 2016, 'end': 2017, 'institution': {'domain': 'cea.fr', 'name': 'CEA'}}, {'position': 'Postdoc', 'start': 2015, 'end': 2016, 'institution': {'domain': 'ens.fr', 'name': 'École Normale Supérieure'}}, {'position': 'PhD student', 'start': 2012, 'end': 2015, 'institution': {'domain': 'usp.br', 'name': 'Universidade de Sao Paulo'}}]}, 'relations': {0: [{'name': 'Bertrand Thirion', 'email': '****@inria.fr', 'relation': 'Postdoc Advisor', 'start': 2017, 'end': 2018, 'readers': ['everyone']}, {'name': 'Lenka Zdeborová', 'email': '****@gmail.com', 'relation': 'Postdoc Advisor', 'start': 2016, 'end': 2017, 'readers': ['everyone']}, {'name': 'Florent Krzakala', 'email': '****@ens.fr', 'relation': 'Postdoc Advisor', 'start': 2015, 'end': 2016, 'readers': ['everyone']}, {'name': 'Renato Vicente', 'email': '****@ime.usp.br', 'relation': 'PhD Advisor', 'start': 2012, 'end': 2015, 'readers': ['everyone']}]}, 'expertise': {0: [{'keywords': ['machine learning for healthcare'], 'start': 2020, 'end': None}, {'keywords': ['privacy-preserving machine learning'], 'start': 2018, 'end': None}, {'keywords': ['federated learning'], 'start': 2018, 'end': None}, {'keywords': ['message-passing algorithms'], 'start': 2012, 'end': 2018}]}, 'emailsConfirmed': {0: ['****@gmail.com', '****@microsoft.com']}, 'venue': 'iclr 2022 (poster)'}\n",
"{'preferredEmail': {0: '****@stanford.edu'}, 'gender': {0: 'Male'}, 'homepage': {0: 'https://gpoesia.com'}, 'gscholar': {0: 'https://scholar.google.com/citations?user=as5iYn4AAAAJ&hl=pt-BR'}, 'dblp': {0: 'https://dblp.org/pid/150/2695.html'}, 'emails': {0: ['****@stanford.edu']}, 'names': {0: [{'first': 'Gabriel', 'middle': '', 'last': 'Poesia', 'preferred': True, 'username': '~Gabriel_Poesia1'}]}, 'history': {0: [{'position': 'PhD student', 'start': 2019, 'end': None, 'institution': {'domain': 'stanford.edu', 'name': 'Stanford University'}}, {'position': 'MS student', 'start': 2015, 'end': 2017, 'institution': {'domain': 'ufmg.br', 'name': 'Universidade Federal de Minas Gerais'}}, {'position': 'Undergrad student', 'start': 2011, 'end': 2015, 'institution': {'domain': 'ufmg.br', 'name': 'Universidade Federal de Minas Gerais'}}]}, 'relations': {0: [{'name': 'Noah Goodman', 'email': '****@stanford.edu', 'relation': 'PhD Advisor', 'start': 2019, 'end': 2025, 'readers': ['everyone']}, {'name': 'Fernando Magno', 'email': '****@ufmg.br', 'relation': 'Coauthor', 'start': 2015, 'end': 2017, 'readers': ['everyone']}]}, 'expertise': {0: [{'keywords': ['artificial intelligence'], 'start': 2018, 'end': None}, {'keywords': ['program synthesis'], 'start': 2015, 'end': None}]}, 'emailsConfirmed': {0: ['****@stanford.edu']}, 'venue': 'iclr 2022 (poster)'}\n",
"Getting notes from API using url https://api.openreview.net/notes?content.venue=ICLR+2022+Spotlight&details=replyCount&offset=0&limit=1000&invitation=ICLR.cc%2F2022%2FConference%2F-%2FBlind_Submission\n",
"Getting notes from API using url https://api.openreview.net/notes?content.venue=ICLR+2022+Oral&details=replyCount&offset=0&limit=1000&invitation=ICLR.cc%2F2022%2FConference%2F-%2FBlind_Submission\n",
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]
}
]
},
{
"cell_type": "code",
"source": [
"## cria base com chave unica para cada autor\n",
"br_unique = dict()\n",
"for keys in res:\n",
" for author in res[keys]:\n",
" n_key = hash(str(author['names']))\n",
" if n_key in br_unique:\n",
" br_unique[n_key]['conf'].append(keys)\n",
" else:\n",
" name=\" \".join([author['names'][0][0]['first'],author['names'][0][0]['middle'],author['names'][0][0]['last']]).replace(\" \", \" \")\n",
" br_unique[n_key]=author\n",
" br_unique[n_key]['conf']=[keys]\n",
" br_unique[n_key]['name']=name\n"
],
"metadata": {
"id": "UZ6l6PWfzG_K"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"## limpa a base e export pra csv\n",
"import pandas as pd\n",
"\n",
"def clean_column(colnames,df):\n",
" for col in colnames:\n",
" df[col]=[x[0] if type(x)==dict else x for x in df[col] ]\n",
"\n",
"\n",
"df = pd.DataFrame(br_unique.values())[['homepage', 'expertise', 'conf', 'name', 'dblp', 'orcid', 'gscholar', 'linkedin']]\n",
"clean_column(['homepage', 'expertise', 'dblp', 'orcid', 'gscholar', 'linkedin'],df)\n",
"df.to_csv(\"br_iclr_uai_neurips_2021_2022.csv\")"
],
"metadata": {
"id": "l3va8-RlRDXk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"## exporta para o google drive\n",
"from google.colab import auth\n",
"from google.auth import default\n",
"import gspread\n",
"import pandas as pd\n",
"#Auth\n",
"auth.authenticate_user()\n",
"creds, _ = default()\n",
"gc = gspread.authorize(creds)\n",
"title = 'br_iclr_uai_neurips_2021_2022'\n",
"from gspread_dataframe import set_with_dataframe\n",
"\n",
"gc.create(title) # if not exist\n",
"sheet = gc.open(title).sheet1\n",
"set_with_dataframe(sheet, df) "
],
"metadata": {
"id": "-PAwI-yRRwx1"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "kDY85nA2T4y4"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "GHO8woeStZow"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"colab": {
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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