def recognize(models: dict, test_set: SinglesData, alpha_start = 0, alpha_transition = 0):
""" Recognize test word sequences from word models set
:param models: dict of trained models
{'SOMEWORD': GaussianHMM model object, 'SOMEOTHERWORD': GaussianHMM model object, ...}
:param test_set: SinglesData object
:return: (list, list) as probabilities, guesses
both lists are ordered by the test set word_id
probabilities is a list of dictionaries where each key a word and value is Log Liklihood
[{SOMEWORD': LogLvalue, 'SOMEOTHERWORD' LogLvalue, ... },
{SOMEWORD': LogLvalue, 'SOMEOTHERWORD' LogLvalue, ... },
]
guesses is a list of the best guess words ordered by the test set word_id
['WORDGUESS0', 'WORDGUESS1', 'WORDGUESS2',...]
"""
warnings.filterwarnings("ignore", category=DeprecationWarning)
logger = logging.getLogger('recognizer')
# print("Alpha start {}".format(alpha_start))
# print("Alpha transition {}".format(alpha_transition))
probabilities = []
guesses = []
# TODO implement the recognizer
# logger.debug("Models {}".format(models))
# sequences = test_set.get_all_sequences()
# logger.debug("Sequences {}".format(sequences))
# logger.debug("Test words {}".format(test_set.wordlist))
# logger.debug("Sentences {}".format(test_set._load_sentence_word_indices()))
sentences = test_set._load_sentence_word_indices()
sentences_indexes = []
for k, v in sentences.items():
sentences_indexes.append(v)
sentences_indexes
# for test_word_index, test_word in enumerate(test_set.wordlist):
# for test_word_index in range(test_set.num_items):
for sentence_indexes in sentences_indexes:
sentence_score = get_sentence_score(sentence_indexes, models, test_set, probabilities, guesses, alpha_start, alpha_transition)
logger.debug("Sentence indexes {}".format(sentence_indexes))
logger.debug("Sentence score {}".format(sentence_score))
return probabilities, guesses
def recognize_ngram(models: dict, test_set: SinglesData):
""" Recognize test word sequences from word models set
:param models: dict of trained models
{'SOMEWORD': GaussianHMM model object, 'SOMEOTHERWORD': GaussianHMM model object, ...}
:param test_set: SinglesData object
:return: (list, list) as probabilities, guesses
both lists are ordered by the test set word_id
probabilities is a list of dictionaries where each key a word and value is Log Liklihood
[{SOMEWORD': LogLvalue, 'SOMEOTHERWORD' LogLvalue, ... },
{SOMEWORD': LogLvalue, 'SOMEOTHERWORD' LogLvalue, ... },
]
guesses is a list of the best guess words ordered by the test set word_id
['WORDGUESS0', 'WORDGUESS1', 'WORDGUESS2',...]
"""
# This import is necessary to be able recognize language model in arpa files
# could be easily installed using the following command :
# pip install arpa
import arpa
import itertools
warnings.filterwarnings("ignore", category=DeprecationWarning)
# TODO implement the recognizer
probabilities = []
guesses = []
probabilities_dict = {}
guesses_dict = {}
#load the language model
lm_models = arpa.loadf('lm3_sri.lm')
lm = lm_models[0] # ARPA files may contain several models.
#for word_id in range(0, len(test_set.get_all_Xlengths())):
# probabilities_dict[word_id] = 'None'
# guesses_dict[word_id] = 'None'
for video_index in test_set._load_sentence_word_indices():
word_ids = test_set._load_sentence_word_indices()[video_index]
video_probs = collections.OrderedDict()
for word_id in word_ids:
current_sequence = test_set.get_item_sequences(word_id)
current_length = test_set.get_item_Xlengths(word_id)
probs = {}
for word, model in models.items():
try:
probs[word] = model.score(current_sequence[0],
current_length[1])
except:
print('failed for word_id {} and word: {}'.format(
word_id, word))
probs[word] = float('-inf')
if len(word_ids) > 5:
top_words = sorted(probs, key=probs.get, reverse=True)[:3]
elif len(word_ids) == 5:
top_words = sorted(probs, key=probs.get, reverse=True)[:4]
elif len(word_ids) < 5:
top_words = sorted(probs, key=probs.get, reverse=True)[:6]
probabilities_dict[word_id] = probs
video_probs[word_id] = {x: probs[x] for x in top_words}
sentences = list(itertools.product(*video_probs.values()))
sentences_prob = []
for sentence_index in range(len(sentences)):
sentence = sentences[sentence_index]
visual_prob = 0
word_index = 0
for word_id in word_ids:
word_id_probs = video_probs[word_id]
visual_prob = visual_prob + word_id_probs[sentence[word_index]]
word_index = word_index + 1
sentence_string = ''
for word in sentence:
sentence_string = sentence_string + ' ' + word
try:
language_prob = lm.log_s(sentence_string.strip())
alpha = 1
beta = 25
sentence_prob = alpha * visual_prob + beta * language_prob
sentences_prob.append(sentence_prob)
print(language_prob)
except:
print('no language for sor sentence: {}',
sentence_string.strip())
sentences_prob.append(float('-inf'))
#find the sentence with the highest prob then extract word_ids
max_sentence = sentences[sentences_prob.index(max(sentences_prob))]
word_index = 0
for word_id in word_ids:
guesses_dict[word_id] = max_sentence[word_index]
word_index = word_index + 1
for key in sorted(guesses_dict):
probabilities.append(probabilities_dict[key])
guesses.append(guesses_dict[key])
return probabilities, guesses
