mirror of
https://github.com/explosion/spaCy.git
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commit
673c864a06
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@ -5,7 +5,6 @@ import logging
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from pathlib import Path
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from pathlib import Path
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from collections import defaultdict
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from collections import defaultdict
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from gensim.models import Word2Vec
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from gensim.models import Word2Vec
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from preshed.counter import PreshCounter
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import plac
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import plac
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import spacy
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import spacy
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31
spacy/tests/regression/test_issue3869.py
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31
spacy/tests/regression/test_issue3869.py
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@ -0,0 +1,31 @@
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# coding: utf8
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from __future__ import unicode_literals
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import pytest
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from spacy.attrs import IS_ALPHA
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from spacy.lang.en import English
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@pytest.mark.parametrize(
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"sentence",
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[
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'The story was to the effect that a young American student recently called on Professor Christlieb with a letter of introduction.',
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'The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale\'s #1.',
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'The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale\'s number one',
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'Indeed, making the one who remains do all the work has installed him into a position of such insolent tyranny, it will take a month at least to reduce him to his proper proportions.',
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"It was a missed assignment, but it shouldn't have resulted in a turnover ..."
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],
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)
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def test_issue3869(sentence):
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"""Test that the Doc's count_by function works consistently"""
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nlp = English()
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doc = nlp(sentence)
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count = 0
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for token in doc:
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count += token.is_alpha
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assert count == doc.count_by(IS_ALPHA).get(1, 0)
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@ -1,6 +1,5 @@
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from cymem.cymem cimport Pool
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from cymem.cymem cimport Pool
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cimport numpy as np
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cimport numpy as np
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from preshed.counter cimport PreshCounter
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from ..vocab cimport Vocab
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from ..vocab cimport Vocab
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from ..structs cimport TokenC, LexemeC
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from ..structs cimport TokenC, LexemeC
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@ -9,6 +9,7 @@ cimport cython
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cimport numpy as np
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cimport numpy as np
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from libc.string cimport memcpy, memset
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from libc.string cimport memcpy, memset
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from libc.math cimport sqrt
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from libc.math cimport sqrt
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from collections import Counter
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import numpy
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import numpy
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import numpy.linalg
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import numpy.linalg
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@ -697,7 +698,7 @@ cdef class Doc:
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# Handle 1d case
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# Handle 1d case
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return output if len(attr_ids) >= 2 else output.reshape((self.length,))
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return output if len(attr_ids) >= 2 else output.reshape((self.length,))
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def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter counts=None):
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def count_by(self, attr_id_t attr_id, exclude=None, object counts=None):
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"""Count the frequencies of a given attribute. Produces a dict of
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"""Count the frequencies of a given attribute. Produces a dict of
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`{attribute (int): count (ints)}` frequencies, keyed by the values of
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`{attribute (int): count (ints)}` frequencies, keyed by the values of
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the given attribute ID.
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the given attribute ID.
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@ -712,19 +713,18 @@ cdef class Doc:
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cdef size_t count
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cdef size_t count
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if counts is None:
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if counts is None:
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counts = PreshCounter()
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counts = Counter()
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output_dict = True
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output_dict = True
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else:
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else:
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output_dict = False
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output_dict = False
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# Take this check out of the loop, for a bit of extra speed
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# Take this check out of the loop, for a bit of extra speed
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if exclude is None:
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if exclude is None:
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for i in range(self.length):
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for i in range(self.length):
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counts.inc(get_token_attr(&self.c[i], attr_id), 1)
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counts[get_token_attr(&self.c[i], attr_id)] += 1
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else:
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else:
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for i in range(self.length):
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for i in range(self.length):
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if not exclude(self[i]):
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if not exclude(self[i]):
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attr = get_token_attr(&self.c[i], attr_id)
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counts[get_token_attr(&self.c[i], attr_id)] += 1
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counts.inc(attr, 1)
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if output_dict:
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if output_dict:
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return dict(counts)
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return dict(counts)
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