lalithadevi commited on
Commit
c1b9bc4
1 Parent(s): 203e844

Update news_category_similar_news_prediction.py

Browse files
news_category_similar_news_prediction.py CHANGED
@@ -143,7 +143,7 @@ def predict_news_category_similar_news(old_news: pd.DataFrame, new_news: pd.Data
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  final_df['pred_proba'] = prob
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  final_df['similar_news'] = sim_news
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  final_df.reset_index(drop=True, inplace=True)
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- final_df.loc[final_df['pred_proba']<CLASSIFIER_THRESHOLD, 'category'] = 'OTHERS'
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  final_df.loc[(final_df['title'].str.contains('Pakistan')) & (final_df['category'] == 'NATION'), 'category'] = 'WORLD'
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  logger.warning('Updated category of articles having Pakistan in title and category=NATION to WORLD')
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  final_df.loc[(final_df['title'].str.contains('Zodiac Sign', case=False)) | (final_df['title'].str.contains('Horoscope', case=False)), 'category'] = 'SCIENCE'
@@ -172,7 +172,7 @@ def predict_news_category_similar_news(old_news: pd.DataFrame, new_news: pd.Data
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  final_df = pd.concat([old_news, new_news], axis=0, ignore_index=True)
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  final_df.drop_duplicates(subset='url', keep='first', inplace=True)
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  final_df.reset_index(drop=True, inplace=True)
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- final_df.loc[final_df['pred_proba']<CLASSIFIER_THRESHOLD, 'category'] = 'OTHERS'
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  final_df.loc[(final_df['title'].str.contains('Pakistan')) & (final_df['category'] == 'NATION'), 'category'] = 'WORLD'
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  logger.warning('Updated category of articles having Pakistan in title and category=NATION to WORLD')
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  final_df.loc[(final_df['title'].str.contains('Zodiac Sign', case=False)) | (final_df['title'].str.contains('Horoscope', case=False)), 'category'] = 'SCIENCE'
 
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  final_df['pred_proba'] = prob
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  final_df['similar_news'] = sim_news
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  final_df.reset_index(drop=True, inplace=True)
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+ final_df.loc[final_df['pred_proba']<CLASSIFIER_THRESHOLD, 'category'] = 'NATION'
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  final_df.loc[(final_df['title'].str.contains('Pakistan')) & (final_df['category'] == 'NATION'), 'category'] = 'WORLD'
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  logger.warning('Updated category of articles having Pakistan in title and category=NATION to WORLD')
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  final_df.loc[(final_df['title'].str.contains('Zodiac Sign', case=False)) | (final_df['title'].str.contains('Horoscope', case=False)), 'category'] = 'SCIENCE'
 
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  final_df = pd.concat([old_news, new_news], axis=0, ignore_index=True)
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  final_df.drop_duplicates(subset='url', keep='first', inplace=True)
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  final_df.reset_index(drop=True, inplace=True)
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+ final_df.loc[final_df['pred_proba']<CLASSIFIER_THRESHOLD, 'category'] = 'NATION'
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  final_df.loc[(final_df['title'].str.contains('Pakistan')) & (final_df['category'] == 'NATION'), 'category'] = 'WORLD'
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  logger.warning('Updated category of articles having Pakistan in title and category=NATION to WORLD')
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  final_df.loc[(final_df['title'].str.contains('Zodiac Sign', case=False)) | (final_df['title'].str.contains('Horoscope', case=False)), 'category'] = 'SCIENCE'