standardization
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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='../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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@ -33,7 +33,7 @@ parser.add_option("-j", "--n_jobs", dest="n_jobs",type=int,
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def get_learner(calibrate=False, kernel='linear'):
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return SVC(kernel=kernel, probability=calibrate, cache_size=1000, C=op.set_c, random_state=1)
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return SVC(kernel=kernel, probability=calibrate, cache_size=1000, C=op.set_c, random_state=1, class_weight='balanced')
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def get_params(dense=False): # TODO kernel function could be useful for meta-classifier
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@ -60,6 +60,7 @@ if __name__ == '__main__':
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data = MultilingualDataset.load(op.dataset)
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data.show_dimensions()
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# data.set_view(languages=['en','it'], categories=list(range(10)))
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lXtr, lytr = data.training()
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lXte, lyte = data.test()
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@ -96,8 +97,8 @@ if __name__ == '__main__':
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classifier = AndreaCLF(op.we_path,
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config,
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first_tier_learner=get_learner(calibrate=True),
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meta_learner=get_learner(calibrate=False),
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first_tier_parameters=get_params(dense=True),
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meta_learner=get_learner(calibrate=False, kernel='rbf'),
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first_tier_parameters=get_params(dense=False),
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meta_parameters=get_params(dense=True),
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n_jobs=op.n_jobs)
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@ -1,9 +1,9 @@
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from __future__ import print_function
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import ijson
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# import ijson
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# from ijson.common import ObjectBuilder
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import os, sys
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from os.path import join
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from bz2 import BZ2File
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from ijson.common import ObjectBuilder
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import pickle
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from util.file import list_dirs, list_files, makedirs_if_not_exist
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from itertools import islice
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@ -5,7 +5,6 @@ from sklearn.preprocessing import MultiLabelBinarizer
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from data.reader.jrcacquis_reader import *
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from data.languages import lang_set, NLTK_LANGMAP, RCV2_LANGS_WITH_NLTK_STEMMING
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from data.reader.rcv_reader import fetch_RCV1, fetch_RCV2, fetch_topic_hierarchy
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from data.reader.wikipedia_tools import fetch_wikipedia_multilingual, random_wiki_sample
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from data.text_preprocessor import NLTKStemTokenizer, preprocess_documents
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import pickle
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import numpy as np
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@ -357,6 +356,9 @@ def retrieve_jrc_documents_from_dataset(datasetpath, jrc_data_home, train_years,
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# Dataset Generators
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# ----------------------------------------------------------------------------------------------------------------------
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def prepare_jrc_datasets(jrc_data_home, wiki_data_home, langs, train_years, test_years, cat_policy, most_common_cat=-1, max_wiki=5000, run=0):
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from data.reader.wikipedia_tools import fetch_wikipedia_multilingual, random_wiki_sample
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"""
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Prepare all datasets for JRC-Acquis. The datasets include the "feature-independent" version, the
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"feature-yuxtaposed" version, the monolingual version for the UpperBound, and the processed wikipedia matrices.
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@ -439,6 +441,7 @@ def prepare_jrc_datasets(jrc_data_home, wiki_data_home, langs, train_years, test
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def prepare_rcv_datasets(outpath, rcv1_data_home, rcv2_data_home, wiki_data_home, langs,
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train_for_lang=1000, test_for_lang=1000, max_wiki=5000, preprocess=True, run=0):
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from data.reader.wikipedia_tools import fetch_wikipedia_multilingual, random_wiki_sample
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"""
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Prepare all datasets for RCV1/RCV2. The datasets include the "feature-independent" version, the
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"feature-yuxtaposed" version, the monolingual version for the UpperBound, and the processed wikipedia matrices.
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@ -8,6 +8,9 @@ from sklearn.model_selection import KFold
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from joblib import Parallel, delayed
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from sklearn.feature_extraction.text import TfidfVectorizer
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from data.supervised import zscores
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from transformers.StandardizeTransformer import StandardizeTransformer
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def _sort_if_sparse(X):
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if issparse(X) and not X.has_sorted_indices:
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@ -210,13 +213,13 @@ class NaivePolylingualClassifier:
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for lang in langs:
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_sort_if_sparse(lX[lang])
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# models = Parallel(n_jobs=self.n_jobs)\
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# (delayed(MonolingualClassifier(self.base_learner, parameters=self.parameters).fit)((lX[lang]),ly[lang]) for lang in langs)
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models = [MonolingualClassifier(self.base_learner, parameters=self.parameters) for lang in langs]
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for model, lang in zip(models, langs):
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model.fit(lX[lang], ly[lang])
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models = Parallel(n_jobs=self.n_jobs)\
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(delayed(MonolingualClassifier(self.base_learner, parameters=self.parameters).fit)((lX[lang]),ly[lang]) for lang in langs)
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#
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# models = [MonolingualClassifier(self.base_learner, parameters=self.parameters) for lang in langs]
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#
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# for model, lang in zip(models, langs):
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# model.fit(lX[lang], ly[lang])
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self.model = {lang: models[i] for i, lang in enumerate(langs)}
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self.empty_categories = {lang:self.model[lang].empty_categories for lang in langs}
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@ -537,7 +540,7 @@ class AndreaCLF(FunnellingPolylingualClassifier):
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self.vectorize(lX)
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for lang in self.languages:
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print(lX[lang].shape)
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print(f'{lang}->{lX[lang].shape}')
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Z, zy = self._get_zspace(lX, ly)
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@ -552,12 +555,15 @@ class AndreaCLF(FunnellingPolylingualClassifier):
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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 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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self.standardizer = StandardizeTransformer()
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_vertical_Z = self.standardizer.fit_predict(_vertical_Z)
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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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@ -578,10 +584,10 @@ class AndreaCLF(FunnellingPolylingualClassifier):
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Z_embedded = dict()
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for lang in lX.keys():
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Z_embedded[lang] = np.hstack((lZ[lang], l_weighted_em[lang]))
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print(Z_embedded[lang].shape)
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return _joblib_transform_multiling(self.model.predict, Z_embedded, n_jobs=self.n_jobs)
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lZ = Z_embedded
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for lang in lZ.keys():
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print(lZ[lang].shape)
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lZ[lang] = self.standardizer.predict(lZ[lang])
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return _joblib_transform_multiling(self.model.predict, lZ, n_jobs=self.n_jobs)
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@ -0,0 +1,23 @@
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import numpy as np
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class StandardizeTransformer:
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def __init__(self, axis=0):
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self.axis = axis
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self.yetfit=False
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def fit(self, X):
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print('fitting Standardizer')
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std=np.std(X, axis=self.axis, ddof=1)
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self.std = np.clip(std, 1e-5, None)
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self.mean = np.mean(X, axis=self.axis)
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self.yetfit=True
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print('done')
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return self
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def predict(self, X):
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if not self.yetfit: 'transform called before fit'
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return (X - self.mean) / self.std
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def fit_predict(self, X):
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return self.fit(X).predict(X)
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