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/src/features/punct_distance_feature.py
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| a | b/src/features/punct_distance_feature.py | ||
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| 1 | # Base Dependencies |
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| 2 | # ---------------- |
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| 3 | import numpy |
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| 4 | |||
| 5 | # Local Dependencies |
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| 6 | # ------------------ |
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| 7 | from models import RelationCollection |
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| 8 | |||
| 9 | # 3rd-Party Dependencies |
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| 10 | # ---------------------- |
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| 11 | from sklearn.base import BaseEstimator |
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| 12 | |||
| 13 | |||
| 14 | class PunctuationFeature(BaseEstimator): |
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| 15 | """ |
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| 16 | PunctuationFeature |
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| 17 | |||
| 18 | Computes the number of punctuation characters between the two entities of a relation. |
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| 19 | |||
| 20 | Source: |
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| 21 | Alimova and Tutubalina (2020) - Multiple features for clinical relation extraction: A machine learning approach |
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| 22 | """ |
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| 23 | |||
| 24 | def __init__(self): |
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| 25 | pass |
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| 26 | |||
| 27 | def get_feature_names(self, input_features=None): |
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| 28 | return ["punct_dist"] |
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| 29 | |||
| 30 | def create_punctuation_distance_feature( |
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| 31 | self, collection: RelationCollection |
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| 32 | ) -> numpy.array: |
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| 33 | features = [] |
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| 34 | for doc in collection.middle_tokens: |
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| 35 | features.append([len(list(filter(lambda t: t.is_punct, doc)))]) |
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| 36 | |||
| 37 | return numpy.array(features) |
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| 38 | |||
| 39 | def fit(self, x: RelationCollection, y=None): |
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| 40 | return self |
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| 41 | |||
| 42 | def transform(self, x: RelationCollection) -> numpy.array: |
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| 43 | return self.create_punctuation_distance_feature(x) |
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| 44 | |||
| 45 | def fit_transform(self, x: RelationCollection, y=None) -> numpy.array: |
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| 46 | return self.create_punctuation_distance_feature(x) |