def __init__(self, statistics, vocabulary):
"""Computes initial statistics.
:type statistics: WordStatistics
:param statistics: word statistics from the training corpus
:type vocabulary: theanolm.Vocabulary
:param vocabulary: words to include in the optimization and initial classes
"""
# count_nonzero() and any() seem to fail on the sparse matrix.
if not statistics.unigram_counts.any():
raise ValueError("Empty word unigram statistics.")
if statistics.bigram_counts.nnz == 0:
raise ValueError("Empty word bigram statistics.")
super().__init__(vocabulary)
# Create word counts.
self._word_counts = statistics.unigram_counts
logging.debug("Allocated %s for word counts.", byte_size(self._word_counts.nbytes))
self._ww_counts = statistics.bigram_counts.tocsc()
logging.debug("Allocated %s for sparse word-word counts.", byte_size(self._ww_counts.data.nbytes))
# Initialize classes.
self._word_to_class = numpy.array(vocabulary.word_id_to_class_id)
logging.debug("Allocated %s for word-to-class mapping.", byte_size(self._word_to_class.nbytes))
# Compute class counts from word counts.
logging.info("Computing class and class/word statistics.")
self._class_counts, self._cc_counts, self._cw_counts, self._wc_counts = self._compute_class_statistics(
self._word_counts, self._ww_counts, self._word_to_class
)
def __init__(self, statistics, vocabulary):
"""Computes initial statistics.
:type statistics: BigramStatistics
:param statistics: word statistics from the training corpus
:type vocabulary: theanolm.Vocabulary
:param vocabulary: words to include in the optimization and initial classes
"""
# count_nonzero() and any() seem to fail on the sparse matrix.
if not statistics.unigram_counts.any():
raise ValueError("Empty word unigram statistics.")
if statistics.bigram_counts.nnz == 0:
raise ValueError("Empty word bigram statistics.")
# Sparse classes in Theano 0.8 support only int32 indices.
super().__init__(vocabulary, 'int32')
# Create word counts.
word_counts = statistics.unigram_counts
self._word_counts = theano.shared(word_counts, 'word_counts')
logging.debug("Allocated %s for word counts.",
byte_size(word_counts.nbytes))
ww_counts_csc = statistics.bigram_counts.tocsc()
self._ww_counts = theano.shared(ww_counts_csc, 'ww_counts_csc')
logging.debug("Allocated %s for CSC word-word counts.",
byte_size(ww_counts_csc.data.nbytes))
ww_counts_csr = statistics.bigram_counts.tocsr()
self._ww_counts_csr = theano.shared(ww_counts_csr, 'ww_counts_csr')
logging.debug("Allocated %s for CSR word-word counts.",
byte_size(ww_counts_csr.data.nbytes))
# Initialize classes.
word_to_class = numpy.array(vocabulary.word_id_to_class_id)
self._word_to_class = theano.shared(word_to_class, 'word_to_class')
logging.debug("Allocated %s for word-to-class mapping.",
byte_size(word_to_class.nbytes))
# Compute class counts from word counts.
logging.info("Computing class and class/word statistics.")
class_counts, cc_counts, cw_counts, wc_counts = \
self._compute_class_statistics(word_counts,
ww_counts_csc,
word_to_class)
self._class_counts = theano.shared(class_counts, 'class_counts')
self._cc_counts = theano.shared(cc_counts, 'cc_counts')
self._cw_counts = theano.shared(cw_counts, 'cw_counts')
self._wc_counts = theano.shared(wc_counts, 'wc_counts')
# Create Theano functions.
self._create_get_word_prob_function()
self._create_evaluate_function()
self._create_move_function()
self._create_log_likelihood_function()
self._create_class_size_function()
def __init__(self, statistics, vocabulary):
"""Computes initial statistics.
:type statistics: WordStatistics
:param statistics: word statistics from the training corpus
:type vocabulary: theanolm.Vocabulary
:param vocabulary: words to include in the optimization and initial classes
"""
# count_nonzero() and any() seem to fail on the sparse matrix.
if not statistics.unigram_counts.any():
raise ValueError("Empty word unigram statistics.")
if statistics.bigram_counts.nnz == 0:
raise ValueError("Empty word bigram statistics.")
# Sparse classes in Theano 0.8 support only int32 indices.
super().__init__(vocabulary, 'int32')
# Create word counts.
word_counts = statistics.unigram_counts
self._word_counts = theano.shared(word_counts, 'word_counts')
logging.debug("Allocated %s for word counts.",
byte_size(word_counts.nbytes))
ww_counts_csc = statistics.bigram_counts.tocsc()
self._ww_counts = theano.shared(ww_counts_csc, 'ww_counts_csc')
logging.debug("Allocated %s for CSC word-word counts.",
byte_size(ww_counts_csc.data.nbytes))
ww_counts_csr = statistics.bigram_counts.tocsr()
self._ww_counts_csr = theano.shared(ww_counts_csr, 'ww_counts_csr')
logging.debug("Allocated %s for CSR word-word counts.",
byte_size(ww_counts_csr.data.nbytes))
# Initialize classes.
word_to_class = numpy.array(vocabulary.word_id_to_class_id)
self._word_to_class = theano.shared(word_to_class, 'word_to_class')
logging.debug("Allocated %s for word-to-class mapping.",
byte_size(word_to_class.nbytes))
# Compute class counts from word counts.
logging.info("Computing class and class/word statistics.")
class_counts, cc_counts, cw_counts, wc_counts = \
self._compute_class_statistics(word_counts,
ww_counts_csc,
word_to_class)
self._class_counts = theano.shared(class_counts, 'class_counts')
self._cc_counts = theano.shared(cc_counts, 'cc_counts')
self._cw_counts = theano.shared(cw_counts, 'cw_counts')
self._wc_counts = theano.shared(wc_counts, 'wc_counts')
# Create Theano functions.
self._create_get_word_prob_function()
self._create_evaluate_function()
self._create_move_function()
self._create_log_likelihood_function()
self._create_class_size_function()
def _compute_class_statistics(self, word_counts, ww_counts, word_to_class):
"""Computes class statistics from word statistics given the
word-to-class mapping.
:type word_counts: numpy.ndarray
:param word_counts: word unigram counts
:type ww_counts: scipy.sparse.csc_matrix
:param ww_counts: word bigram counts
:type word_to_class: numpy.ndarray
:param word_to_class: gives the class ID of each word ID
"""
class_counts = numpy.zeros(self.num_classes, self._count_type)
logging.debug("Allocated %s for class counts.",
byte_size(class_counts.nbytes))
cc_counts = numpy.zeros(
(self.num_classes, self.num_classes), dtype=self._count_type)
logging.debug("Allocated %s for class-class counts.",
byte_size(cc_counts.nbytes))
cw_counts = numpy.zeros(
(self.num_classes, self.vocabulary_size), dtype=self._count_type)
logging.debug("Allocated %s for class-word counts.",
byte_size(cw_counts.nbytes))
wc_counts = numpy.zeros(
(self.vocabulary_size, self.num_classes), dtype=self._count_type)
logging.debug("Allocated %s for word-class counts.",
byte_size(wc_counts.nbytes))
numpy.add.at(class_counts, word_to_class, word_counts)
left_word_ids, right_word_ids = ww_counts.nonzero()
counts = ww_counts[left_word_ids, right_word_ids].flat
left_class_ids = word_to_class[left_word_ids]
right_class_ids = word_to_class[right_word_ids]
numpy.add.at(cc_counts, (left_class_ids, right_class_ids), counts)
numpy.add.at(cw_counts, (left_class_ids, right_word_ids), counts)
numpy.add.at(wc_counts, (left_word_ids, right_class_ids), counts)
return class_counts, cc_counts, cw_counts, wc_counts
def _compute_class_statistics(self, word_counts, ww_counts, word_to_class):
"""Computes class statistics from word statistics given the
word-to-class mapping.
:type word_counts: numpy.ndarray
:param word_counts: word unigram counts
:type ww_counts: scipy.sparse.csc_matrix
:param ww_counts: word bigram counts
:type word_to_class: numpy.ndarray
:param word_to_class: gives the class ID of each word ID
"""
class_counts = numpy.zeros(self.num_classes, dtype=self._count_type)
logging.debug("Allocated %s for class counts.",
byte_size(class_counts.nbytes))
cc_counts = numpy.zeros(
(self.num_classes, self.num_classes), dtype=self._count_type)
logging.debug("Allocated %s for class-class counts.",
byte_size(cc_counts.nbytes))
cw_counts = numpy.zeros(
(self.num_classes, self.vocabulary_size), dtype=self._count_type)
logging.debug("Allocated %s for class-word counts.",
byte_size(cw_counts.nbytes))
wc_counts = numpy.zeros(
(self.vocabulary_size, self.num_classes), dtype=self._count_type)
logging.debug("Allocated %s for word-class counts.",
byte_size(wc_counts.nbytes))
numpy.add.at(class_counts, word_to_class, word_counts)
left_word_ids, right_word_ids = ww_counts.nonzero()
counts = ww_counts[left_word_ids, right_word_ids].flat
left_class_ids = word_to_class[left_word_ids]
right_class_ids = word_to_class[right_word_ids]
numpy.add.at(cc_counts, (left_class_ids, right_class_ids), counts)
numpy.add.at(cw_counts, (left_class_ids, right_word_ids), counts)
numpy.add.at(wc_counts, (left_word_ids, right_class_ids), counts)
return class_counts, cc_counts, cw_counts, wc_counts
def __init__(self, statistics, vocabulary):
"""Computes initial statistics.
:type statistics: WordStatistics
:param statistics: word statistics from the training corpus
:type vocabulary: theanolm.Vocabulary
:param vocabulary: words to include in the optimization and initial classes
"""
# count_nonzero() and any() seem to fail on the sparse matrix.
if not statistics.unigram_counts.any():
raise ValueError("Empty word unigram statistics.")
if statistics.bigram_counts.nnz == 0:
raise ValueError("Empty word bigram statistics.")
super().__init__(vocabulary)
# Create word counts.
self._word_counts = statistics.unigram_counts
logging.debug("Allocated %s for word counts.",
byte_size(self._word_counts.nbytes))
self._ww_counts = statistics.bigram_counts.tocsc()
logging.debug("Allocated %s for sparse word-word counts.",
byte_size(self._ww_counts.data.nbytes))
# Initialize classes.
self._word_to_class = numpy.array(vocabulary.word_id_to_class_id)
logging.debug("Allocated %s for word-to-class mapping.",
byte_size(self._word_to_class.nbytes))
# Compute class counts from word counts.
logging.info("Computing class and class/word statistics.")
self._class_counts, self._cc_counts, self._cw_counts, self._wc_counts = \
self._compute_class_statistics(self._word_counts,
self._ww_counts,
self._word_to_class)
