spaCy/spacy/tests/unit/test_parser.py

139 lines
4.9 KiB
Python

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import pytest
import numpy
from spacy.attrs import HEAD, DEP
@pytest.mark.models
class TestNounChunks:
@pytest.fixture(scope="class")
def ex1_en(self, EN):
example = EN.tokenizer.tokens_from_list('A base phrase should be recognized .'.split(' '))
EN.tagger.tag_from_strings(example, 'DT NN NN MD VB VBN .'.split(' '))
det,compound,nsubjpass,aux,auxpass,root,punct = tuple( EN.vocab.strings[l] for l in ['det','compound','nsubjpass','aux','auxpass','root','punct'] )
example.from_array([HEAD, DEP],
numpy.asarray(
[
[2, det],
[1, compound],
[3, nsubjpass],
[2, aux],
[1, auxpass],
[0, root],
[-1, punct]
], dtype='int32'))
return example
@pytest.fixture(scope="class")
def ex2_en(self, EN):
example = EN.tokenizer.tokens_from_list('A base phrase and a good phrase are often the same .'.split(' '))
EN.tagger.tag_from_strings(example, 'DT NN NN CC DT JJ NN VBP RB DT JJ .'.split(' '))
det,compound,nsubj,cc,amod,conj,root,advmod,attr,punct = tuple( EN.vocab.strings[l] for l in ['det','compound','nsubj','cc','amod','conj','root','advmod','attr','punct'] )
example.from_array([HEAD, DEP],
numpy.asarray(
[
[2, det],
[1, compound],
[5, nsubj],
[-1, cc],
[1, det],
[1, amod],
[-4, conj],
[0, root],
[-1, advmod],
[1, det],
[-3, attr],
[-4, punct]
], dtype='int32'))
return example
@pytest.fixture(scope="class")
def ex3_en(self, EN):
example = EN.tokenizer.tokens_from_list('A phrase with another phrase occurs .'.split(' '))
EN.tagger.tag_from_strings(example, 'DT NN IN DT NN VBZ .'.split(' '))
det,nsubj,prep,pobj,root,punct = tuple( EN.vocab.strings[l] for l in ['det','nsubj','prep','pobj','root','punct'] )
example.from_array([HEAD, DEP],
numpy.asarray(
[
[1, det],
[4, nsubj],
[-1, prep],
[1, det],
[-2, pobj],
[0, root],
[-1, punct]
], dtype='int32'))
return example
@pytest.fixture(scope="class")
def ex1_de(self, DE):
example = DE.tokenizer.tokens_from_list('Eine Tasse steht auf dem Tisch .'.split(' '))
DE.tagger.tag_from_strings(example, 'ART NN VVFIN APPR ART NN $.'.split(' '))
nk,sb,root,mo,punct = tuple( DE.vocab.strings[l] for l in ['nk','sb','root','mo','punct'])
example.from_array([HEAD, DEP],
numpy.asarray(
[
[1, nk],
[1, sb],
[0, root],
[-1, mo],
[1, nk],
[-2, nk],
[-3, punct]
], dtype='int32'))
return example
@pytest.fixture(scope="class")
def ex2_de(self, DE):
example = DE.tokenizer.tokens_from_list('Die Sängerin singt mit einer Tasse Kaffee Arien .'.split(' '))
DE.tagger.tag_from_strings(example, 'ART NN VVFIN APPR ART NN NN NN $.'.split(' '))
nk,sb,root,mo,punct,oa = tuple( DE.vocab.strings[l] for l in ['nk','sb','root','mo','punct','oa'])
example.from_array([HEAD, DEP],
numpy.asarray(
[
[1, nk],
[1, sb],
[0, root],
[-1, mo],
[1, nk],
[-2, nk],
[-1, nk],
[-5, oa],
[-6, punct]
], dtype='int32'))
return example
def test_en_standard_chunk(self, ex1_en):
chunks = list(ex1_en.noun_chunks)
assert len(chunks) == 1
assert chunks[0].string == 'A base phrase '
def test_en_coordinated_chunks(self, ex2_en):
chunks = list(ex2_en.noun_chunks)
assert len(chunks) == 2
assert chunks[0].string == 'A base phrase '
assert chunks[1].string == 'a good phrase '
def test_en_pp_chunks(self, ex3_en):
chunks = list(ex3_en.noun_chunks)
assert len(chunks) == 2
assert chunks[0].string == 'A phrase '
assert chunks[1].string == 'another phrase '
def test_de_standard_chunk(self, ex1_de):
chunks = list(ex1_de.noun_chunks)
assert len(chunks) == 2
assert chunks[0].string == 'Eine Tasse '
assert chunks[1].string == 'dem Tisch '
def test_de_extended_chunk(self, ex2_de):
chunks = list(ex2_de.noun_chunks)
assert len(chunks) == 3
assert chunks[0].string == 'Die Sängerin '
assert chunks[1].string == 'einer Tasse Kaffee '
assert chunks[2].string == 'Arien '