fixed embeddings remote import and dataset name in logfile
This commit is contained in:
andrea
parent
df957e8448
commit
601da33836
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@ -1,4 +1,3 @@
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from sklearn.svm import SVC
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import os, sys
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from dataset_builder import MultilingualDataset
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from learning.learners import *
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@ -6,6 +5,7 @@ from util.evaluation import *
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from optparse import OptionParser
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from util.file import exists
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from util.results import PolylingualClassificationResults
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from sklearn.svm import SVC
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parser = OptionParser()
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@ -20,7 +20,7 @@ parser.add_option("-e", "--mode-embed", dest="mode_embed",
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help="Set the embedding to be used [none, pretrained, supervised, both]", type=str, default='none')
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parser.add_option("-w", "--we-path", dest="we_path",
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help="Path to the polylingual word embeddings", default='/home/andreapdr/CLESA/embeddings')
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help="Path to the polylingual word embeddings", default='/home/andreapdr/CLESA/embeddings/')
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parser.add_option("-s", "--set_c", dest="set_c",type=float,
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help="Set the C parameter", default=1)
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@ -63,41 +63,6 @@ if __name__ == '__main__':
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lXtr, lytr = data.training()
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lXte, lyte = data.test()
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print(lXtr.keys())
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small_lXtr = dict()
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small_lytr = dict()
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small_lXte = dict()
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small_lyte = dict()
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small_lXtr['da'] = lXtr['da'][:50]
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small_lytr['da'] = lytr['da'][:50]
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# small_lXtr['en'] = lXtr['en'][:50]
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# small_lytr['en'] = lytr['en'][:50]
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# small_lXtr['fr'] = lXtr['fr'][:50]
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# small_lytr['fr'] = lytr['fr'][:50]
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# small_lXte['da'] = lXte['da'][:50]
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# small_lyte['da'] = lyte['da'][:50]
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# small_lXte['en'] = lXte['en'][:50]
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# small_lyte['en'] = lyte['en'][:50]
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# small_lXte['fr'] = lXte['fr'][:50]
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# small_lyte['fr'] = lyte['fr'][:50]
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# small_lXtr['it'] = lXtr['it'][:50]
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# small_lytr['it'] = lytr['it'][:50]
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# small_lXtr['es'] = lXtr['es'][:50]
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# small_lytr['es'] = lytr['es'][:50]
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# small_lXtr['de'] = lXtr['de'][:50]
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# small_lytr['de'] = lytr['de'][:50]
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# small_lXtr['pt'] = lXtr['pt'][:50]
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# small_lytr['pt'] = lytr['pt'][:50]
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# small_lXtr['nl'] = lXtr['de'][:50]
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# small_lytr['nl'] = lytr['de'][:50]
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# small_lXtr['fi'] = lXtr['fi'][:50]
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# small_lytr['fi'] = lytr['fi'][:50]
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# small_lXtr['hu'] = lXtr['hu'][:50]
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# small_lytr['hu'] = lytr['hu'][:50]
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# small_lXtr['sv'] = lXtr['sv'][:50]
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# small_lytr['sv'] = lytr['sv'][:50]
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if op.set_c != -1:
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meta_parameters = None
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@ -137,7 +102,7 @@ if __name__ == '__main__':
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n_jobs=op.n_jobs)
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print('# Fitting ...')
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classifier.fit(small_lXtr, small_lytr)
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classifier.fit(lXtr, lytr)
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print('# Evaluating ...')
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l_eval = evaluate_method(classifier, lXte, lyte)
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@ -147,5 +112,6 @@ if __name__ == '__main__':
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macrof1, microf1, macrok, microk = l_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(result_id, 'PolyEmbed_andrea', 'svm', _config_id, op.optimc, 'test_datasetname', 'not_binary', 'not_ablation', classifier.time, lang, macrof1, microf1, macrok, microk, 'nope')
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results.add_row(result_id, 'PolyEmbed_andrea', 'svm', _config_id, op.optimc, op.dataset.split('/')[-1],
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'not_binary', 'not_ablation', classifier.time, lang, macrof1, microf1, macrok, microk, 'nope')
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print('Averages: MF1, mF1, MK, mK', np.mean(np.array(metrics), axis=0))
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@ -147,10 +147,13 @@ class FastTextWikiNews(Vectors):
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url_base = 'Cant auto-download MUSE embeddings'
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path = '/storage/andrea/FUNNELING/embeddings/wiki.multi.{}.vec'
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_name = 'wiki.multi.{}.vec'
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def __init__(self, cache, language="en", **kwargs):
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url = self.url_base.format(language)
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name = self.path.format(language)
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# name = self.path.format(language)
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name = cache + self._name.format(language)
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# print(f'\n\nFASTEXTWIKI-NEW CLASS:\nurl = {url}\nname = {name}\ncache {cache}\nlanguage = {language}')
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super(FastTextWikiNews, self).__init__(name, cache=cache, url=url, **kwargs)
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@ -1,11 +1,10 @@
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import numpy as np
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import time
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from data.embeddings import WordEmbeddings, embedding_matrix, WCE_matrix
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from scipy.sparse import issparse, csr_matrix
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from scipy.sparse import issparse
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.model_selection import GridSearchCV
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from sklearn.model_selection import KFold
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# from sklearn.externals.joblib import Parallel, delayed
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from joblib import Parallel, delayed
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from sklearn.feature_extraction.text import TfidfVectorizer
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@ -28,9 +27,13 @@ class TrivialRejector:
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def fit(self, X, y):
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self.cats = y.shape[1]
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return self
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def decision_function(self, X): return np.zeros((X.shape[0],self.cats))
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def predict(self, X): return np.zeros((X.shape[0],self.cats))
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def predict_proba(self, X): return np.zeros((X.shape[0],self.cats))
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def best_params(self): return {}
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@ -429,60 +432,6 @@ class PolylingualEmbeddingsClassifier:
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return self.model.best_params()
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class FunnellingEmbeddingPolylingualClassifier:
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""" Simulated: this setting is merely for testing purposes, and is not realistic. We here assume to have a tfidf
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vectorizer for the out-of-scope languages (which is not fair)."""
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def __init__(self, first_tier_learner, embed_learner, meta_learner, wordembeddings_path, training_languages,
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first_tier_parameters = None, embed_parameters = None, meta_parameters = None, n_jobs=-1):
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assert first_tier_learner.probability==True and embed_learner.probability==True, \
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'both the first-tier classifier and the polyembedding classifier shoud allow calibration'
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self.training_languages = training_languages
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self.PLE = PolylingualEmbeddingsClassifier(wordembeddings_path, embed_learner,
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c_parameters=embed_parameters, n_jobs=n_jobs)
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self.Funnelling = FunnellingPolylingualClassifier(first_tier_learner, meta_learner,
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first_tier_parameters=first_tier_parameters,
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meta_parameters=meta_parameters, n_jobs=n_jobs)
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self.n_jobs = n_jobs
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def vectorize(self, lX):
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return {l:self.PLE.lang_tfidf[l].transform(lX[l]) for l in lX.keys()}
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def fit(self, lX, ly):
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"""
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:param lX: a dictionary {language_label: [list of preprocessed documents]}
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:param ly: a dictionary {language_label: ndarray of shape (ndocs, ncats) binary labels}
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:return:
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"""
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self.PLE.fit_vectorizers(lX)
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tinit = time.time()
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lX = {l: lX[l] for l in lX.keys() if l in self.training_languages}
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ly = {l: ly[l] for l in lX.keys() if l in self.training_languages}
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self.PLE.fit(lX, ly)
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lZ = self.PLE.predict_proba(lX)
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self.Funnelling.fit(self.vectorize(lX),ly,lZ,ly)
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self.time = time.time() - tinit
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return self
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def predict(self, lX):
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"""
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:param lX: a dictionary {language_label: [list of preprocessed documents]}
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"""
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lXin = {l: lX[l] for l in lX.keys() if l in self.training_languages}
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lXout = {l: lX[l] for l in lX.keys() if l not in self.training_languages}
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lZ = self.PLE.predict_proba(lXout)
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return self.Funnelling.predict(self.vectorize(lXin), lZ)
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def best_params(self):
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return {'PLE':self.PLE.best_params(), 'Funnelling':self.Funnelling.best_params()}
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class AndreaCLF(FunnellingPolylingualClassifier):
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def __init__(self,
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we_path,
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@ -509,6 +458,8 @@ class AndreaCLF(FunnellingPolylingualClassifier):
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self.lang_tfidf = {}
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self.word_embeddings = {}
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self.supervised_embeddings = {}
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self.model = None
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self.time = None
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def vectorize(self, lX, prediction=False):
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langs = list(lX.keys())
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@ -571,7 +522,7 @@ class AndreaCLF(FunnellingPolylingualClassifier):
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if supervised:
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for lang in languages:
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S = WCE_matrix(lX, ly, lang)
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S = np.squeeze(np.asarray(S)) # casting to ndarray to better visualize S while debugging
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# S = np.squeeze(np.asarray(S)) # casting to ndarray to better visualize S while debugging
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self.supervised_embeddings[lang] = S
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if unsupervised:
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_r[lang] = np.hstack((_r[lang], lX[lang].dot(S)))
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@ -590,33 +541,23 @@ class AndreaCLF(FunnellingPolylingualClassifier):
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Z, zy = self._get_zspace(lX, ly)
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# Z vectors is concatenated with doc's embedding weighted sum
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Z_embedded = dict()
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l_weighted_em = self.embed(lX, ly,
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unsupervised=self.config['unsupervised'],
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supervised=self.config['supervised'])
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if self.config['supervised'] or self.config['unsupervised']:
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# Z vectors is concatenated with doc's embedding weighted sum
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Z_embedded = dict()
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l_weighted_em = self.embed(lX, ly,
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unsupervised=self.config['unsupervised'],
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supervised=self.config['supervised'])
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# stacking Z space horizontally with unsupervised (M) and/or supervised (F) embeddings
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for lang in list(lX.keys()):
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Z_embedded[lang] = np.hstack((Z[lang], l_weighted_em[lang]))
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Z = Z_embedded
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del Z_embedded
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# stacking Z_embedded space vertically
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# _vertical_Z = np.vstack([Z_embedded[lang] for lang in self.languages])
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# _vertical_Zy = np.vstack([zy[lang] for lang in self.languages])
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# stacking Z space vertically
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_vertical_Z = np.vstack([Z[lang] for lang in self.languages])
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_vertical_Zy = np.vstack([zy[lang] for lang in self.languages])
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# zlangs = list(Z_embedded.keys()) # creo lista con embedding e poi faccio vstack su lista
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# for i, lang in enumerate(zlangs):
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# if i == 0:
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# _vertical_Z = Z_embedded[lang]
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# _vertical_Zy = zy[lang]
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# else:
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# _vertical_Z = np.vstack((_vertical_Z, Z_embedded[lang]))
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# _vertical_Zy = np.vstack((_vertical_Zy, zy[lang]))
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print('fitting the Z-space of shape={}'.format(_vertical_Z.shape))
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self.model = MonolingualClassifier(base_learner=self.meta_learner, parameters=self.meta_parameters,
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n_jobs=self.n_jobs)
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