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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') | |