80 lines
3.5 KiB
Python
80 lines
3.5 KiB
Python
from sklearn.linear_model import LogisticRegression
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from data.dante_loader import load_texts
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from data.features import *
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from model import AuthorshipVerificator, f1_from_counters
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from sklearn.svm import LinearSVC, SVC
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from util.color_visualization import color
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# DONE: ngrams should contain punctuation marks according to Sapkota et al. [39] in the PAN 2015 overview
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# (More recently, it was shown that character
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# n-grams corresponding to word affixes and including punctuation marks are the most
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# significant features in cross-topic authorship attribution [57].) #we have cancelled the
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# TODO: inspect the impact of chi-squared correlations against positive-only (or positive and negative) correlations for feature selection
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# TODO: sentence length (Mendenhall-style) ?
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for epistola in [1,2]:
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if epistola==1:
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authors = ['Dante','GiovanniBoccaccio','PierDellaVigna']
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else:
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authors = ['Dante', 'BenvenutoDaImola', 'FilippoVillani','GiovanniBoccaccio','GiovanniDelVirgilio',
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'GrazioloBambaglioli','GuidoDaPisa','PietroAlighieri','ZonoDeMagnalis']
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discarded = 0
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f1_scores = []
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counters = []
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for i,author in enumerate(authors):
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print('='*80)
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print('Authorship Identification for {} (complete {}/{})'.format(author, i, len(authors)))
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print('Corpus of Epistola {}'.format(epistola))
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print('='*80)
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path = '../testi_{}'.format(epistola)
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if epistola==2:
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path+='_with_GuidoDaPisa'
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positive, negative, ep_text = load_texts(path, positive_author=author, unknown_target='EpistolaXIII_{}.txt'.format(epistola))
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if len(positive) < 2:
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discarded+=1
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continue
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n_full_docs = len(positive) + len(negative)
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feature_extractor = FeatureExtractor(function_words_freq='latin',
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conjugations_freq='latin',
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features_Mendenhall=True,
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features_sentenceLengths=True,
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tfidf_feat_selection_ratio=0.1,
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wordngrams=False, n_wordngrams=(1, 2),
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charngrams=True, n_charngrams=(3, 4, 5), preserve_punctuation=False,
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split_documents=True, split_policy=split_by_sentences, window_size=3,
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normalize_features=True)
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Xtr,ytr,groups = feature_extractor.fit_transform(positive, negative)
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print(ytr)
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ep, ep_fragments = feature_extractor.transform(ep_text, return_fragments=True, window_size=3)
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print('Fitting the Verificator')
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av = AuthorshipVerificator(nfolds=10, estimator=LogisticRegression)
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av.fit(Xtr,ytr,groups)
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score_ave, score_std, tp, fp, fn, tn = av.leave_one_out(Xtr, ytr, groups, test_lowest_index_only=True, counters=True)
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# print('LOO[full-docs]={:.3f} +-{:.5f}'.format(score_ave, score_std))
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f1_scores.append(f1_from_counters(tp, fp, fn, tn))
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counters.append((tp, fp, fn, tn))
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print('F1 for {} = {:.3f}'.format(author,f1_scores[-1]))
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print('Computing macro- and micro-averages (discarded {}/{})'.format(discarded,len(authors)))
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f1_scores = np.array(f1_scores)
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counters = np.array(counters)
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macro_f1 = f1_scores.mean()
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micro_f1 = f1_from_counters(*counters.sum(axis=0).tolist())
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print('Macro-F1 = {:.3f}'.format(macro_f1))
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print('Micro-F1 = {:.3f}'.format(micro_f1))
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print()
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