def __init__(self, sequence, predicted, expected):
self.sequence = check.check_not_empty(sequence)
self.predicted = check.check_instance(predicted, SequenceStats)
self.expected = check.check_instance(expected, SequenceStats)
check.check_length(self.predicted.values, len(self.sequence))
check.check_length(self.expected.values, len(self.sequence))
self.perplexity = perplexity(self.expected.probabilities)
def __init__(self, values, probabilities, ranks=None):
self.values = check.check_iterable(values)
self.probabilities = check.check_length(
check.check_iterable(probabilities), len(self.values))
self.ranks = check.check_length(check.check_iterable(ranks,
noneable=True),
len(self.values),
noneable=True)
def __init__(self, labels, array):
self.labels = check.check_instance(labels, Labels)
self.array = check.check_length(array, len(self.labels))
self._prediction = None
self._prediction_probability = None
self._distribution = None
self._ranked_items = None
def vector_encode(self, value, handle_oov=False):
check.check_length(value, len(self.fields))
return np.concatenate([
self.fields[i].vector_encode(v, handle_oov)
for i, v in enumerate(value)
])