def get_gradient(self, word_in_topic_probs, topic_in_doc_probs):
"""
Compute gradient of regularizer w.r.t model parameters
:param word_in_topic_probs: numpy.ndarray
:param topic_in_doc_probs: numpy.ndarray
:return: gradients
"""
self._class_in_topics_probs[:, :], self._topic_in_doc_buffer[:, :], _, _ =\
compute_frequencies(word_in_doc_freqs=self._class_in_docs,
word_in_topic_probs=self._class_in_topics_probs,
topic_in_doc_probs=topic_in_doc_probs,
word_in_topics_freqs_buffer=self._class_in_topics_buffer,
topic_in_doc_freqs_buffer=self._topic_in_doc_buffer)
self._class_in_topics_probs /= self._class_in_topics_probs.sum(axis=0)
self._topic_in_doc_buffer /= topic_in_doc_probs
return self._word_in_topic_buffer, self._tau * self._topic_in_doc_buffer
def get_gradient(self, word_in_topic_probs, topic_in_doc_probs):
"""
Compute gradient of regularizer w.r.t model parameters
:param word_in_topic_probs: numpy.ndarray
:param topic_in_doc_probs: numpy.ndarray
:return: gradient
"""
_, _, topic_freqs, doc_freqs = compute_frequencies(self._word_in_doc_freqs, word_in_topic_probs,
topic_in_doc_probs, self._word_in_topic_buffer, self._topic_in_doc_buffer)
self._word_in_topic_buffer[:, :] = 0
self._topic_in_doc_buffer[:, :] = -self._tau*doc_freqs.reshape(1, -1)
self._topic_in_doc_buffer[:, :] /= topic_freqs.reshape(-1, 1)
return self._word_in_topic_buffer, self._topic_in_doc_buffer
def _do_em_iteration(self, word_in_doc_freqs, word_in_topic_probs,
topic_in_doc_probs, word_in_topic_freqs_buffer,
topic_in_doc_freqs_buffer):
word_in_topics_freqs, topic_in_doc_freqs, _, _ = compute_frequencies(
word_in_doc_freqs, word_in_topic_probs, topic_in_doc_probs,
word_in_topic_freqs_buffer, topic_in_doc_freqs_buffer)
unnormalized_word_in_topic_probs = word_in_topics_freqs
unnormalized_topic_in_doc_probs = topic_in_doc_freqs
for regularizer, weight in zip(self._regularizers,
self._regularizer_weights):
word_in_topic_probs_grad, topic_in_doc_probs_grad = \
regularizer.get_gradient(word_in_topic_probs, topic_in_doc_probs)
word_in_topic_freqs_addition = word_in_topic_probs_grad
word_in_topic_freqs_addition *= word_in_topic_probs
word_in_topic_freqs_addition *= weight
unnormalized_word_in_topic_probs += word_in_topic_freqs_addition
topic_in_doc_freqs_addition = topic_in_doc_probs_grad
topic_in_doc_freqs_addition *= topic_in_doc_probs
topic_in_doc_freqs_addition *= weight
unnormalized_topic_in_doc_probs += topic_in_doc_freqs_addition
if not self._fixed_word_in_topic_probs:
np.clip(unnormalized_word_in_topic_probs,
self._epsilon,
None,
out=unnormalized_word_in_topic_probs)
word_in_topic_probs[:, :] = unnormalized_word_in_topic_probs
word_in_topic_probs /= unnormalized_word_in_topic_probs.sum(axis=0)
np.clip(unnormalized_topic_in_doc_probs,
self._epsilon,
None,
out=unnormalized_topic_in_doc_probs)
topic_in_doc_probs[:, :] = unnormalized_topic_in_doc_probs
topic_in_doc_probs /= unnormalized_topic_in_doc_probs.sum(axis=0)
def _do_em_iteration(self, word_in_doc_freqs, word_in_topic_probs,
topic_in_doc_probs, word_in_topics_freqs_buffer,
topic_in_doc_freqs_buffer):
EPSILON = 1e-10
word_in_topics_freqs, topic_in_doc_freqs, _, _ = compute_frequencies(
word_in_doc_freqs, word_in_topic_probs, topic_in_doc_probs,
word_in_topics_freqs_buffer, topic_in_doc_freqs_buffer)
unnormalized_word_in_topic_probs = word_in_topics_freqs
unnormalized_topic_in_doc_probs = topic_in_doc_freqs
for regularizer, weight in zip(self._regularizers,
self._regularizer_weights):
word_in_topic_probs_grad, topic_in_doc_probs_grad = \
regularizer.get_gradient(word_in_topic_probs, topic_in_doc_probs)
word_in_topic_freqs_addition = word_in_topic_probs_grad
word_in_topic_freqs_addition *= word_in_topic_probs
word_in_topic_freqs_addition *= weight
unnormalized_word_in_topic_probs += word_in_topic_freqs_addition
topic_in_doc_freqs_addition = topic_in_doc_probs_grad
topic_in_doc_freqs_addition *= topic_in_doc_probs
topic_in_doc_freqs_addition *= weight
unnormalized_topic_in_doc_probs += topic_in_doc_freqs_addition
np.clip(unnormalized_word_in_topic_probs,
0,
None,
out=unnormalized_word_in_topic_probs)
np.clip(unnormalized_topic_in_doc_probs,
0,
None,
out=unnormalized_topic_in_doc_probs)
word_in_topic_prob_norm_consts = unnormalized_word_in_topic_probs.sum(
axis=0)
word_in_topic_prob_norm_const_is_not_small = (
word_in_topic_prob_norm_consts > EPSILON)
word_in_topic_probs[:, :] = unnormalized_word_in_topic_probs
word_in_topic_probs[:,
np.logical_not(
word_in_topic_prob_norm_const_is_not_small
)] = 0
word_in_topic_probs[:, word_in_topic_prob_norm_const_is_not_small] /= \
word_in_topic_prob_norm_consts[word_in_topic_prob_norm_const_is_not_small]
topic_in_doc_prob_norm_consts = unnormalized_topic_in_doc_probs.sum(
axis=0)
topic_in_doc_prob_norm_const_is_not_small = (
topic_in_doc_prob_norm_consts > EPSILON)
topic_in_doc_probs[:, :] = unnormalized_topic_in_doc_probs
topic_in_doc_probs[:,
np.logical_not(
topic_in_doc_prob_norm_const_is_not_small)] = 0
topic_in_doc_probs[:, topic_in_doc_prob_norm_const_is_not_small] /= \
topic_in_doc_prob_norm_consts[topic_in_doc_prob_norm_const_is_not_small]