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55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import numpy
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from spacy.attrs import HEAD, DEP
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from spacy.symbols import nsubj, dobj, amod, nmod, conj, cc, root
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from spacy.lang.en.syntax_iterators import SYNTAX_ITERATORS
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import pytest
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from ...util import get_doc
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def test_noun_chunks_is_parsed(en_tokenizer):
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"""Test that noun_chunks raises Value Error for 'en' language if Doc is not parsed.
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To check this test, we're constructing a Doc
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with a new Vocab here and forcing is_parsed to 'False'
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to make sure the noun chunks don't run.
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"""
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doc = en_tokenizer("This is a sentence")
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doc.is_parsed = False
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with pytest.raises(ValueError):
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list(doc.noun_chunks)
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def test_en_noun_chunks_not_nested(en_vocab):
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words = ["Peter", "has", "chronic", "command", "and", "control", "issues"]
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heads = [1, 0, 4, 3, -1, -2, -5]
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deps = ["nsubj", "ROOT", "amod", "nmod", "cc", "conj", "dobj"]
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doc = get_doc(en_vocab, words=words, heads=heads, deps=deps)
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doc.from_array(
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[HEAD, DEP],
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numpy.asarray(
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[
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[1, nsubj],
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[0, root],
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[4, amod],
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[3, nmod],
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[numpy.array(-1).astype(numpy.uint64), cc],
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[numpy.array(-2).astype(numpy.uint64), conj],
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[numpy.array(-5).astype(numpy.uint64), dobj],
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],
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dtype="uint64",
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),
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)
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doc.noun_chunks_iterator = SYNTAX_ITERATORS["noun_chunks"]
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word_occurred = {}
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for chunk in doc.noun_chunks:
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for word in chunk:
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word_occurred.setdefault(word.text, 0)
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word_occurred[word.text] += 1
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for word, freq in word_occurred.items():
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assert freq == 1, (word, [chunk.text for chunk in doc.noun_chunks])
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