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from inputs.fields.field import Field |
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import logging |
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logger = logging.getLogger(__name__) |
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class MapTokenField(Field): |
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"""Map token field: preocess maping tokens |
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""" |
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def __init__(self, namespace, vocab_namespace, source_key, is_counting=True): |
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"""This function sets namesapce of field, vocab namespace for indexing, dataset source key |
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Arguments: |
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namespace {str} -- namesapce of field, counter namespace if constructing a counter |
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vocab_namespace {str} -- vocab namespace for indexing |
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source_key {str} -- indicate key in text data |
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Keyword Arguments: |
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is_counting {bool} -- decide constructing a counter or not (default: {True}) |
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""" |
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super().__init__() |
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self.namespace = str(namespace) |
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self.counter_namespace = str(namespace) |
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self.vocab_namespace = str(vocab_namespace) |
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self.source_key = str(source_key) |
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self.is_counting = is_counting |
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def count_vocab_items(self, counter, sentences): |
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"""This function counts dict's values in sentences, |
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then update counter, each sentence is a dict |
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Arguments: |
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counter {dict} -- counter |
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sentences {list} -- text content after preprocessing, list of dict |
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""" |
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if self.is_counting: |
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for sentence in sentences: |
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for value in sentence[self.source_key].values(): |
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counter[self.counter_namespace][str(value)] += 1 |
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logger.info("Count sentences {} to update counter namespace {} successfully.".format( |
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self.source_key, self.counter_namespace)) |
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def index(self, instance, vocab, sentences): |
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"""This function indexes token using vocabulary, then update instance |
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Arguments: |
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instance {dict} -- numerical represenration of text data |
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vocab {Vocabulary} -- vocabulary |
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sentences {list} -- text content after preprocessing |
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""" |
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for sentence in sentences: |
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instance[self.namespace].append({ |
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key: vocab.get_token_index(value, self.vocab_namespace) |
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for key, value in sentence[self.source_key].items() |
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}) |
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logger.info("Index sentences {} to construct instance namespace {} successfully.".format( |
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self.source_key, self.namespace)) |
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