new experimental protocol applied to continent
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@ -104,10 +104,6 @@ RANK_AT_K = -1
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REDUCE_TR = 50000
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qp.environ['SAMPLE_SIZE'] = RANK_AT_K
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data_path = {
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'first_letter_category': './first_letter_categoryDataset',
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'continent': './newExperimentalSetup'
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}
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def scape_latex(string):
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return string.replace('_', '\_')
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@ -116,14 +112,19 @@ def scape_latex(string):
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Ks = [10, 50, 100, 250, 500, 1000, 2000]
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# Ks = [500]
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for CLASS_NAME in ['first_letter_category']: #['continent']: #, 'gender', 'gender_category', 'occupations', 'source_countries', 'source_subcont_regions', 'years_category', 'relative_pageviews_category']:
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for CLASS_NAME in ['continent'] : #'years_category']: #['continent', 'first_letter_category']: #, 'gender', 'gender_category', 'occupations', 'source_countries', 'source_subcont_regions', 'years_category', 'relative_pageviews_category']:
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train_path = join(data_path[CLASS_NAME], 'train3000samples.json')
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data_path = './' + CLASS_NAME
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if CLASS_NAME in ['years_category', 'continent']:
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train_path = join(data_path, 'train500PerGroup.json')
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else:
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train_path = join(data_path, 'train3000samples.json')
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tfidf, classifier_trained = qp.util.pickled_resource(f'classifier_{CLASS_NAME}.pkl', train_classifier)
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trained=True
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experiment_prot = RetrievedSamples(data_path[CLASS_NAME],
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experiment_prot = RetrievedSamples(data_path,
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load_fn=load_json_sample,
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vectorizer=tfidf,
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max_train_lines=None,
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