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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) | |