def fit_transform(self, raw_documents, y=None, vector_source=None, stats_hdf_file=None,
cv_fold=-1, train_time_extractor=None, decode_time_extractor=None):
self.cv_fold = cv_fold
self.feature_extractor = train_time_extractor
self.decode_time_extractor = decode_time_extractor
self.thesaurus = vector_source
self.handler = get_token_handler(self.train_token_handler,
self.k,
self.sim_compressor,
self.thesaurus)
# requested stats that to go HDF, store the name so we can record stats to that name at decode time too
self.stats_hdf_file_ = stats_hdf_file
self.stats = get_stats_recorder(self.debug_level, stats_hdf_file, 'tr', cv_fold, self.k)
# a different stats recorder will be used for the testing data
# ########## BEGIN super.fit_transform ##########
# this is a modified version of super.fit_transform which works with an empty vocabulary
self._validate_vocabulary()
max_df = self.max_df
min_df = self.min_df
max_features = self.max_features
vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary_)
X = X.tocsc()
if self.binary:
X.data.fill(1)
if not self.fixed_vocabulary_:
if vocabulary:
X = self._sort_features(X, vocabulary)
n_doc = X.shape[0]
max_doc_count = (max_df
if isinstance(max_df, numbers.Integral)
else int(round(max_df * n_doc)))
min_doc_count = (min_df
if isinstance(min_df, numbers.Integral)
else int(round(min_df * n_doc)))
if max_doc_count < min_doc_count:
raise ValueError(
"max_df corresponds to < documents than min_df")
X, self.stop_words_ = self._limit_features(X, vocabulary,
max_doc_count,
min_doc_count,
max_features)
self.vocabulary_ = vocabulary
# ######### END super.fit_transform ##########
if (self.thesaurus and hasattr(self.thesaurus, 'get_nearest_neighbours') and
hasattr(self.thesaurus.get_nearest_neighbours, 'cache_info')):
logging.info('NN cache info: %s', self.thesaurus.get_nearest_neighbours.cache_info())
logging.info('Matrix shape is %r after vectorization', X.shape)
return X, self.vocabulary_
def transform(self, raw_documents):
self.feature_extractor = self.decode_time_extractor
if not hasattr(self, 'vocabulary_'):
self._check_vocabulary()
if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0:
raise ValueError("Vocabulary wasn't fitted or is empty!")
# record stats separately for the test set
self.stats = get_stats_recorder(self.debug_level, self.stats_hdf_file_, 'ev',
self.cv_fold, self.k)
if self.random_neighbour_thesaurus:
# this is a bit of hack and a waste of effort, since a thesaurus will have been loaded first
logging.info('Building random neighbour vector source with vocabulary of size %d', len(self.vocabulary_))
self.thesaurus.k = self.k
self.thesaurus.vocab = list(self.vocabulary_.keys())
self.handler = get_token_handler(self.decode_token_handler,
self.k,
self.sim_compressor,
self.thesaurus)
# todo can't populate at this stage of the pipeline, because the vocabulary might
# change if feature selection is enabled. Trying to do this will result in attempts to compose
# features that we do not know how to compose because these have not been removed by FS
# if self.thesaurus:
# logging.info('Populating vector source %s prior to transform', self.thesaurus)
# self.thesaurus.populate_vector_space(self.vocabulary_.keys())
# BEGIN a modified version of super.transform that works when vocabulary is empty
_, X = self._count_vocab(raw_documents, fixed_vocab=True)
if self.binary:
X.data.fill(1)
# END super.transform
if (self.thesaurus and hasattr(self.thesaurus, 'get_nearest_neighbours') and
hasattr(self.thesaurus.get_nearest_neighbours, 'cache_info')):
logging.info('NN cache info: %s', self.thesaurus.get_nearest_neighbours.cache_info())
return X, self.vocabulary_
