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