implemented method to compute WCE only for well represented classes -
refactored MLE class in order to support WCE, standard embeddings and combinations
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andrea
parent
56ee88220b
commit
0e66fbf197
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@ -125,9 +125,10 @@ if __name__ == '__main__':
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result_id = dataset_file + 'PolyEmbedd_andrea_' + _config_id + ('_optimC' if op.optimc else '')
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PLE_test = False
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PLE_test = True
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if PLE_test:
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ple = PolylingualEmbeddingsClassifier(wordembeddings_path='/home/moreo/CLESA/PolylingualEmbeddings',
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ple = PolylingualEmbeddingsClassifier(wordembeddings_path='/home/andreapdr/CLESA/',
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config = config,
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learner=get_learner(calibrate=False),
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c_parameters=get_params(dense=False),
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n_jobs=op.n_jobs)
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@ -143,7 +144,11 @@ if __name__ == '__main__':
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macrof1, microf1, macrok, microk = ple_eval[lang]
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metrics.append([macrof1, microf1, macrok, microk])
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print('Lang %s: macro-F1=%.3f micro-F1=%.3f' % (lang, macrof1, microf1))
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results.add_row('MLE', 'svm', 'no', config['we_type'],
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'no','no', op.optimc, op.dataset.split('/')[-1], ple.time,
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lang, macrof1, microf1, macrok, microk, '')
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print('Averages: MF1, mF1, MK, mK', np.mean(np.array(metrics), axis=0))
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exit()
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print(f'### PolyEmbedd_andrea_{_config_id}\n')
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@ -151,7 +156,7 @@ if __name__ == '__main__':
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config=config,
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first_tier_learner=get_learner(calibrate=True),
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meta_learner=get_learner(calibrate=False, kernel='rbf'),
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first_tier_parameters=None, # get_params(dense=False),-->first_tier should not be optimized
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first_tier_parameters=None, # TODO get_params(dense=False),--> first_tier should not be optimized - or not?
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meta_parameters=get_params(dense=True),
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n_jobs=op.n_jobs)
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@ -169,5 +174,5 @@ if __name__ == '__main__':
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results.add_row('PolyEmbed_andrea', 'svm', _config_id, config['we_type'],
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(config['max_label_space'], classifier.best_components),
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config['dim_reduction_unsupervised'], op.optimc, op.dataset.split('/')[-1], classifier.time,
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lang, macrof1, microf1, macrok, microk, '')
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lang, macrof1, microf1, macrok, microk, 'min_prevalence = 0')
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print('Averages: MF1, mF1, MK, mK', np.mean(np.array(metrics), axis=0))
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@ -226,6 +226,18 @@ class StorageEmbeddings:
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return
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def _add_embeddings_supervised(self, docs, labels, reduction, max_label_space, voc):
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only_well_represented_C = False # TODO testing
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if only_well_represented_C:
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labels = labels.copy()
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min_prevalence = 0
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print(f'# REDUCING LABELS TO min_prevalence = {min_prevalence} in order to compute WCE Matrix ...')
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langs = list(docs.keys())
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well_repr_cats = np.logical_and.reduce([labels[lang].sum(axis=0)>min_prevalence for lang in langs])
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# lY = {lY[lang][:, well_repr_cats] for lang in langs} TODO not clear
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for lang in langs:
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labels[lang] = labels[lang][:, well_repr_cats]
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print(f'Target number reduced to: {labels[lang].shape[1]}\n')
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for lang in docs.keys(): # compute supervised matrices S - then apply PCA
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print(f'# [supervised-matrix] for {lang}')
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self.lang_S[lang] = get_supervised_embeddings(docs[lang], labels[lang],
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@ -461,7 +461,7 @@ class PolylingualEmbeddingsClassifier:
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}
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url: https://github.com/facebookresearch/MUSE
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"""
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def __init__(self, wordembeddings_path, learner, c_parameters=None, n_jobs=-1):
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def __init__(self, wordembeddings_path, config, learner, c_parameters=None, n_jobs=-1):
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"""
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:param wordembeddings_path: the path to the directory containing the polylingual embeddings
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:param learner: the learner
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@ -469,11 +469,15 @@ class PolylingualEmbeddingsClassifier:
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:param n_jobs: the number of concurrent threads
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"""
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self.wordembeddings_path = wordembeddings_path
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self.config = config
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self.learner = learner
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self.c_parameters=c_parameters
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self.n_jobs = n_jobs
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self.lang_tfidf = {}
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self.model = None
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self.languages = []
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self.lang_word2idx = dict()
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self.embedding_space = None
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def fit_vectorizers(self, lX):
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for lang in lX.keys():
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@ -483,6 +487,27 @@ class PolylingualEmbeddingsClassifier:
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tfidf.fit(docs)
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self.lang_tfidf[lang] = tfidf
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def vectorize(self, lX, prediction=False):
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langs = list(lX.keys())
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print(f'# tfidf-vectorizing docs')
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if prediction:
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for lang in langs:
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assert lang in self.lang_tfidf.keys(), 'no tf-idf for given language'
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tfidf_vectorizer = self.lang_tfidf[lang]
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lX[lang] = tfidf_vectorizer.transform(lX[lang])
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return self
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for lang in langs:
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tfidf_vectorizer = TfidfVectorizer(sublinear_tf=True, use_idf=True)
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self.languages.append(lang)
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tfidf_vectorizer.fit(lX[lang])
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lX[lang] = tfidf_vectorizer.transform(lX[lang])
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self.lang_word2idx[lang] = tfidf_vectorizer.vocabulary_
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self.lang_tfidf[lang] = tfidf_vectorizer
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return self
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def embed(self, docs, lang):
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assert lang in self.lang_tfidf, 'unknown language'
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tfidf_vectorizer = self.lang_tfidf[lang]
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@ -515,13 +540,17 @@ class PolylingualEmbeddingsClassifier:
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tinit = time.time()
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langs = list(lX.keys())
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WEtr, Ytr = [], []
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self.fit_vectorizers(lX) # if already fit, does nothing
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for lang in langs:
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WEtr.append(self.embed(lX[lang], lang))
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Ytr.append(ly[lang])
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# self.fit_vectorizers(lX) # if already fit, does nothing
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self.vectorize(lX)
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# config = {'unsupervised' : False, 'supervised': True}
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self.embedding_space = StorageEmbeddings(self.wordembeddings_path).fit(self.config, lX, self.lang_word2idx, ly)
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WEtr = self.embedding_space.predict(self.config, lX)
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# for lang in langs:
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# WEtr.append(self.embed(lX[lang], lang)) # todo embed with other matrices
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# Ytr.append(ly[lang])
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WEtr = np.vstack(WEtr)
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Ytr = np.vstack(Ytr)
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WEtr = np.vstack([WEtr[lang] for lang in langs])
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Ytr = np.vstack([ly[lang] for lang in langs])
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self.embed_time = time.time() - tinit
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print('fitting the WE-space of shape={}'.format(WEtr.shape))
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@ -535,8 +564,10 @@ class PolylingualEmbeddingsClassifier:
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:param lX: a dictionary {language_label: [list of preprocessed documents]}
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"""
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assert self.model is not None, 'predict called before fit'
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self.vectorize(lX, prediction=True)
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langs = list(lX.keys())
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lWEte = {lang:self.embed(lX[lang], lang) for lang in langs} # parallelizing this may consume too much memory
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lWEte = self.embedding_space.predict(self.config, lX)
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# lWEte = {lang:self.embed(lX[lang], lang) for lang in langs} # parallelizing this may consume too much memory
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return _joblib_transform_multiling(self.model.predict, lWEte, n_jobs=self.n_jobs)
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def predict_proba(self, lX):
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