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174 lines
7.2 KiB
Python
174 lines
7.2 KiB
Python
# -*- coding: utf-8 -*-
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import click
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import logging
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from pathlib import Path
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from dotenv import find_dotenv, load_dotenv
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import re
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import ast
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import os
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import requests
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import zipfile
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import tldextract
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import pandas as pd
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def fix_keywords(lst):
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fixed = set()
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for k in lst:
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tokens = set(k.split(','))
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for t in tokens:
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fixed.add(str.strip(t))
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fixed.discard('')
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return list(fixed)
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def extract_email_domains(lst):
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res = []
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for email in lst:
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res.append(email.split('@')[1])
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return res
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def extract_url_domains(lst):
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domains = set()
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for e in lst:
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# e[0] is a string describing the url
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# e[1] is the url or IP address
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domain = tldextract.extract(e[1]).registered_domain
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if domain == '':
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# it's an IP address
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domains.add(tldextract.extract(e[1]).domain)
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else:
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domains.add(domain)
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return domains
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@click.command()
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@click.argument('input_filepath', type=click.Path(exists=True))
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@click.argument('output_filepath', type=click.Path())
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@click.argument('external_filepath', type=click.Path())
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def main(input_filepath, output_filepath, external_filepath):
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""" Runs data processing scripts to turn raw data from (./data/raw) into
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cleaned data ready to be analyzed (saved in ./data/processed).
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"""
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logger = logging.getLogger(__name__)
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logger.info('Fetching external datasets')
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logger.info('Downloading grid.ac dataset')
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r = requests.get('https://ndownloader.figshare.com/files/27251693')
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with open(os.path.join(external_filepath, 'grid.zip'), 'wb') as f:
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f.write(r.content)
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logger.info('... unzipping')
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try:
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with zipfile.ZipFile(os.path.join(external_filepath, 'grid.zip')) as z:
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z.extractall(os.path.join(external_filepath, 'grid'))
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logger.info('... done!')
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except:
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logger.error('Invalid file')
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logger.info('Making pickle dataset from raw data')
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logger.info('Loading the zipped dataset')
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df = pd.read_csv(os.path.join(input_filepath, 'data.gz'), compression='gzip',
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sep='\t', header=None,
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names=['orcid','verified_email', 'verified_primary_email',
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'given_names', 'family_name', 'biography', 'other_names', 'urls',
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'primary_email', 'other_emails', 'keywords', 'external_ids', 'education',
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'employment', 'n_works', 'works_source', 'activation_date', 'last_update_date',
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'n_doi', 'n_arxiv', 'n_pmc', 'n_other_pids', 'other_urls'], encoding = 'utf-8',
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dtype={'orcid': 'string',
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'verified_email':'bool',
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'verified_primary_email':'bool',
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'given_names': 'string',
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'family_name': 'string',
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'biography': 'string',
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'primary_email': 'string',
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# 'activation_date': 'string',
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# 'last_update_date': 'string',
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'n_works': pd.Int16Dtype(),
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'n_doi': pd.Int16Dtype(),
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'n_arxiv': pd.Int16Dtype(),
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'n_pmc': pd.Int16Dtype(),
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'n_other_pids': pd.Int16Dtype()},
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parse_dates=['activation_date', 'last_update_date'])
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logger.info('Loading list columns')
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logger.info('... other_names')
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df['other_names'] = df[df.other_names.notna()]['other_names'].apply(lambda x: ast.literal_eval(x))
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logger.info('... keywords')
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df['keywords'] = df[df.keywords.notna()]['keywords'].apply(lambda x: ast.literal_eval(x))
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logger.info('... urls')
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df['urls'] = df[df.urls.notna()]['urls'].apply(lambda x: ast.literal_eval(x))
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df['other_urls'] = df[df.other_urls.notna()]['other_urls'].apply(lambda x: ast.literal_eval(x))
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logger.info('... other_emails')
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df['other_emails'] = df[df.other_emails.notna()]['other_emails'].apply(lambda x: ast.literal_eval(x))
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logger.info('... education')
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df['education'] = df[df.education.notna()]['education'].apply(lambda x: ast.literal_eval(x))
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logger.info('... employment')
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df['employment'] = df[df.employment.notna()]['employment'].apply(lambda x: ast.literal_eval(x))
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logger.info('... external_ids')
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df['external_ids'] = df[df.external_ids.notna()]['external_ids'].apply(lambda x: ast.literal_eval(x))
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logger.info('... works_source')
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df['works_source'] = df[df.works_source.notna()]['works_source'].apply(lambda x: ast.literal_eval(x))
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logger.info('Integrating labels from ORCID found in OpenAIRE')
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openaire_orcid = pd.read_csv(os.path.join(input_filepath, 'orcid_openaire.txt'), header=None, names=['orcid'])
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df['label'] = df.orcid.isin(openaire_orcid['orcid'])
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df['label'] = df['label'].astype('bool')
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logger.info('Fixing keywords')
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df['keywords'] = df[df.keywords.notna()]['keywords'].apply(lambda x: fix_keywords(x))
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logger.info('Extracting domains from URLs and emails')
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df['primary_email_domain'] = df[df.primary_email.notna()]['primary_email'].apply(lambda x: x.split('@')[1])
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df['other_email_domains'] = df[df.other_emails.notna()]['other_emails'].apply(lambda x: extract_email_domains(x))
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df['url_domains'] = df[df.urls.notna()]['urls'].apply(lambda x: extract_url_domains(x))
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df['other_url_domains'] = df[df.other_urls.notna()]['other_urls'].apply(lambda x: extract_url_domains(x))
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logger.info('Creating simple numeric columns')
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df['n_emails'] = df.other_emails.str.len()
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df.n_emails = df.n_emails.astype(pd.Int16Dtype())
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df['n_urls'] = df.url_domains.str.len()
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df.n_urls = df.n_urls.astype(pd.Int16Dtype())
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df['n_ids'] = df.external_ids.str.len()
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df.n_ids = df.n_ids.astype(pd.Int16Dtype())
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df['n_keywords'] = df.keywords.str.len()
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df.n_keywords = df.n_keywords.astype(pd.Int16Dtype())
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df['n_education'] = df.education.str.len()
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df.n_education = df.n_education.astype(pd.Int16Dtype())
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df['n_employment'] = df.employment.str.len()
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df.n_employment = df.n_employment.astype(pd.Int16Dtype())
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logger.info('Dropping useless columns')
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df.drop(['other_emails'], axis=1, inplace=True)
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logger.info('Serializing the dataset in ./data/processed')
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n = 1000000
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chunks = [df[i:i+n] for i in range(0, df.shape[0], n)]
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for i in range(len(chunks)):
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chunks[i].to_pickle(os.path.join(output_filepath, 'dataset.pkl.part%02d' % i))
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logger.info('DONE!')
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print(df.info())
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if __name__ == '__main__':
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log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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logging.basicConfig(level=logging.INFO, format=log_fmt)
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# not used in this stub but often useful for finding various files
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project_dir = Path(__file__).resolve().parents[2]
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# find .env automagically by walking up directories until it's found, then
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# load up the .env entries as environment variables
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load_dotenv(find_dotenv())
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main()
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