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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
import matplotlib.pyplot as plt
from pyserini.collection import Collection, Cord19Article
def load(path):
empty_date = dict()
normal_dates = dict()
cnt = 0
collection = Collection('Cord19AbstractCollection', path)
articles = collection.__next__()
#iterate through raw collection
for (i, d) in enumerate(articles):
article = Cord19Article(d.raw)
# documents with empty abstract
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_dates.setdefault(article.cord_uid(), [])
normal_dates[article.cord_uid()].append(article.metadata()["doi"])
normal_dates[article.cord_uid()].append(len(article.title()))
normal_dates[article.cord_uid()].append(date)
cnt = cnt + 1
if cnt % 1000 == 0:
print(f'{cnt} articles read...')
#this df has 4 columns: docid, DOI, title, publish_date
normal_dates_df = pd.DataFrame([([k] + v) for k, v in normal_dates.items()])
normal_dates_df = normal_dates_df.loc[:, [0, 1, 2, 3]]
normal_dates_df.columns = ['docid', 'DOI', 'title', 'publish_date']
df1 = pd.DataFrame(normal_dates_df)
date_df = df1.sort_values('publish_date').groupby('publish_date')
#date_df_counts has two columns: publish_date, counts
date_df_counts = date_df.size().reset_index(name='counts')
#all dfs below have two columns: publish_date, counts (they are massaged df based on date_df_counts)
#two dfs based on year unit
only_year_filter = date_df_counts['publish_date'].str.len() == 4
with_date_filter = date_df_counts['publish_date'].str.len() > 4
only_year = date_df_counts.loc[only_year_filter].loc[date_df_counts['publish_date'] >= '2003'] #before 2003 are all under 2000
exact_year_total = date_df_counts.groupby(date_df_counts['publish_date'].str[:4])['counts'].agg('sum').reset_index(name='counts')
exact_year_total = exact_year_total.loc[exact_year_total['publish_date'] >= '2003']
#on monthly basis
exact_date = date_df_counts.loc[with_date_filter].groupby(date_df_counts['publish_date'].str[:7])['counts'].agg('sum').reset_index(name='counts')
before_2003 = exact_date.loc[exact_date['publish_date'] <= '2002-12']
between_03_19 = exact_date.loc[exact_date['publish_date'] > '2002-12'].loc[exact_date['publish_date'] <= '2019-12']
after_19 = exact_date.loc[exact_date['publish_date'] >= '2019-12']
#weekly basis after 2019-12
weekly_update_19 = date_df_counts.loc[with_date_filter].loc[date_df_counts['publish_date'] >= '2019-12'].groupby(date_df_counts['publish_date'])['counts'].agg('sum').reset_index(name='counts')
weekly_update_19['publish_date'] = pd.to_datetime(weekly_update_19['publish_date'])
weekly_update_19 = weekly_update_19.groupby(pd.Grouper(key='publish_date', freq='W'))['counts'].agg('sum').reset_index(name='counts')
return only_year, exact_year_total, before_2003, between_03_19, after_19, weekly_update_19
def plot_bars(only_year, exact_year_total, before_2003, between_03_19, after_19, weekly_update_19):
only_year.plot.bar(x='publish_date', y='counts', title='number of publishes only has year')
exact_year_total.plot.bar(x='publish_date', y='counts', title='number of publishes for all in year units')
before_2003.plot.bar(x='publish_date', y='counts', title='publish_date before 2003', figsize=(30, 10), fontsize=6)
between_03_19.plot.bar(x='publish_date', y='counts', title='between_03_19', figsize=(30, 10), fontsize=6)
after_19.plot.bar(x='publish_date', y='counts', title='after_19', figsize=(20, 10), fontsize=8)
graph_weekly_19 = weekly_update_19.loc[weekly_update_19['publish_date'] < '2020-08-09'] # omit after 2020-08-09 to make graph readable
graph_weekly_19.plot.bar(x='publish_date', y='counts', title='after 2019-12 weekly growth', figsize=(20, 10))
plt.savefig('bar_plots.pdf')
print(f'draw 6 bar plots for documents based on their publish_dates and saved into file bar_plots.pdf')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Return bar charts of temporal analysis on CORD-19')
parser.add_argument('--path', type=str, required=True, help='Path to input collection')
args = parser.parse_args()
only_year, exact_year_total, before_2003, between_03_19, after_19, weekly_update_19 = load(args.path)
plot_bars(only_year, exact_year_total, before_2003, between_03_19, after_19, weekly_update_19)
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