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#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import argparse
import pandas as pd
from pyserini.collection import Collection, Cord19Article
def load(old_path, new_path):
empty_date = dict()
normal_old_dates = dict()
normal_new_dates = dict()
cnt = 0
collection_old = Collection('Cord19AbstractCollection', old_path)
collection_new = Collection('Cord19AbstractCollection', new_path)
articles = collection_old.__next__()
# iterate through raw old collection
for (i, d) in enumerate(articles):
article = Cord19Article(d.raw)
metadata = article.metadata()
date = metadata['publish_time']
if len(date) == 0:
empty_date.setdefault(article.cord_uid(), [])
empty_date[article.cord_uid()].append(article.metadata()["doi"])
empty_date[article.cord_uid()].append(len(article.title()))
else:
normal_old_dates.setdefault(article.cord_uid(), [])
normal_old_dates[article.cord_uid()].append(article.metadata()["doi"])
normal_old_dates[article.cord_uid()].append(len(article.title()))
normal_old_dates[article.cord_uid()].append(date)
cnt = cnt + 1
if cnt % 1000 == 0:
print(f'{cnt} articles read... in old data')
cnt = 0
articles = collection_new.__next__()
# iterate through raw new collection
for (i, d) in enumerate(articles):
article = Cord19Article(d.raw)
metadata = article.metadata()
date = metadata['publish_time']
if len(date) != 0:
normal_new_dates.setdefault(article.cord_uid(), [])
normal_new_dates[article.cord_uid()].append(article.metadata()["doi"])
normal_new_dates[article.cord_uid()].append(len(article.title()))
normal_new_dates[article.cord_uid()].append(date)
cnt = cnt + 1
if cnt % 1000 == 0:
print(f'{cnt} articles read... in new data')
#create df for old and new collection and groupby publish_date column, record the size of each group in column counts
normal_old_dates_df = pd.DataFrame([([k] + v) for k, v in normal_old_dates.items()])
normal_old_dates_df = normal_old_dates_df.loc[:, [0, 1, 2, 3]]
normal_old_dates_df.columns = ['docid', 'DOI', 'title', 'publish_date']
df1 = pd.DataFrame(normal_old_dates_df)
date_df = df1.sort_values('publish_date').groupby('publish_date')
date_df_counts = date_df.size().reset_index(name='counts')
normal_new_dates_df = pd.DataFrame([([k] + v) for k, v in normal_new_dates.items()])
normal_new_dates_df = normal_new_dates_df.loc[:, [0, 1, 2, 3]]
normal_new_dates_df.columns = ['docid', 'DOI', 'title', 'publish_date']
df2 = pd.DataFrame(normal_new_dates_df)
date_new_df = df2.sort_values('publish_date').groupby('publish_date')
# date_df_counts has two columns
date_new_df_counts = date_new_df.size().reset_index(name='counts')
return date_df_counts, date_new_df_counts
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Extract Dataframes of CORD-19')
parser.add_argument('--old_path', type=str, required=True, help='Path to old collection')
parser.add_argument('--new_path', type=str, required=True, help='Path to new collection')
args = parser.parse_args()
date_df_counts, date_new_df_counts = load(args.old_path, args.new_path)
date_df_counts.to_csv('date_df_counts.csv', index=False)
date_new_df_counts.to_csv('date_new_df_counts.csv', index=False)
print(f'saved dfs to date_df_counts.csv and date_new_df_counts.csv')
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