mirror of https://github.com/explosion/spaCy.git
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
import numpy
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from thinc.api import Model
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from ..attrs import LOWER
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def extract_ngrams(ngram_size, attr=LOWER) -> Model:
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model = Model("extract_ngrams", forward)
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model.attrs["ngram_size"] = ngram_size
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model.attrs["attr"] = attr
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return model
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def forward(self, docs, is_train: bool):
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batch_keys = []
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batch_vals = []
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for doc in docs:
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unigrams = doc.to_array([self.attrs["attr"]])
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ngrams = [unigrams]
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for n in range(2, self.attrs["ngram_size"] + 1):
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ngrams.append(self.ops.ngrams(n, unigrams))
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keys = self.ops.xp.concatenate(ngrams)
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keys, vals = self.ops.xp.unique(keys, return_counts=True)
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batch_keys.append(keys)
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batch_vals.append(vals)
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# The dtype here matches what thinc is expecting -- which differs per
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# platform (by int definition). This should be fixed once the problem
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# is fixed on Thinc's side.
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lengths = self.ops.asarray([arr.shape[0] for arr in batch_keys], dtype=numpy.int_)
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batch_keys = self.ops.xp.concatenate(batch_keys)
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batch_vals = self.ops.asarray(self.ops.xp.concatenate(batch_vals), dtype="f")
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def backprop(dY):
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return dY
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return (batch_keys, batch_vals, lengths), backprop
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