mirror of
https://github.com/explosion/spaCy.git
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Remove old tests for old website example code
This commit is contained in:
Ines Montani
parent
eef94e3ee2
commit
d5c72c40eb
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from __future__ import unicode_literals
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import pytest
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import os
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@pytest.fixture(scope='session')
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def nlp():
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from spacy.en import English
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if os.environ.get('SPACY_DATA'):
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data_dir = os.environ.get('SPACY_DATA')
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else:
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data_dir = True
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return English(path=data_dir)
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@pytest.fixture()
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def doc(nlp):
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for word in ['Hello', ',', 'world', '.', 'Here', 'are', 'two', 'sentences', '.']:
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_ = nlp.vocab[word]
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return nlp('Hello, world. Here are two sentences.')
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from __future__ import unicode_literals
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import pytest
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from spacy.attrs import HEAD
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import numpy
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@pytest.mark.xfail
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def test_example_war_and_peace(nlp):
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# from spacy.en import English
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from spacy._doc_examples import download_war_and_peace
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unprocessed_unicode = download_war_and_peace()
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# nlp = English()
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# TODO: ImportError: No module named _doc_examples
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doc = nlp(unprocessed_unicode)
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def test_main_entry_point(nlp):
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# from spacy.en import English
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# nlp = English()
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doc = nlp('Some text.') # Applies tagger, parser, entity
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doc = nlp('Some text.', parse=False) # Applies tagger and entity, not parser
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doc = nlp('Some text.', entity=False) # Applies tagger and parser, not entity
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doc = nlp('Some text.', tag=False) # Does not apply tagger, entity or parser
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doc = nlp('') # Zero-length tokens, not an error
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# doc = nlp(b'Some text') <-- Error: need unicode
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doc = nlp(b'Some text'.decode('utf8')) # Encode to unicode first.
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@pytest.mark.models
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def test_sentence_spans(nlp):
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# from spacy.en import English
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# nlp = English()
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doc = nlp("This is a sentence. Here's another...")
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assert [s.root.orth_ for s in doc.sents] == ["is", "'s"]
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@pytest.mark.models
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def test_entity_spans(nlp):
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# from spacy.en import English
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# nlp = English()
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tokens = nlp('Mr. Best flew to New York on Saturday morning.')
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ents = list(tokens.ents)
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assert ents[0].label == 346
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assert ents[0].label_ == 'PERSON'
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assert ents[0].orth_ == 'Best'
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assert ents[0].string == ents[0].string
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@pytest.mark.models
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def test_noun_chunk_spans(nlp):
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# from spacy.en import English
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# nlp = English()
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doc = nlp('The sentence in this example has three noun chunks.')
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for chunk in doc.noun_chunks:
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print(chunk.label, chunk.orth_, '<--', chunk.root.head.orth_)
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# NP The sentence <-- has
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# NP this example <-- in
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# NP three noun chunks <-- has
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@pytest.mark.models
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def test_count_by(nlp):
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# from spacy.en import English, attrs
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# nlp = English()
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import numpy
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from spacy import attrs
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tokens = nlp('apple apple orange banana')
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assert tokens.count_by(attrs.ORTH) == {3699: 2, 3750: 1, 5965: 1}
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assert repr(tokens.to_array([attrs.ORTH])) == repr(numpy.array([[3699],
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[3699],
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[3750],
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[5965]], dtype=numpy.int32))
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@pytest.mark.models
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def test_read_bytes(nlp):
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from spacy.tokens.doc import Doc
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loc = 'test_serialize.bin'
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with open(loc, 'wb') as file_:
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file_.write(nlp(u'This is a document.').to_bytes())
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file_.write(nlp(u'This is another.').to_bytes())
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docs = []
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with open(loc, 'rb') as file_:
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for byte_string in Doc.read_bytes(file_):
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docs.append(Doc(nlp.vocab).from_bytes(byte_string))
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assert len(docs) == 2
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def test_token_span(doc):
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span = doc[4:6]
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token = span[0]
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assert token.i == 4
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@pytest.mark.models
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def test_example_i_like_new_york1(nlp):
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toks = nlp('I like New York in Autumn.')
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@pytest.fixture
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def toks(nlp):
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doc = nlp('I like New York in Autumn.')
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doc.from_array([HEAD], numpy.asarray([[1, 0, 1, -2, -3, -1, -5]], dtype='int32').T)
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return doc
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def test_example_i_like_new_york2(toks):
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i, like, new, york, in_, autumn, dot = range(len(toks))
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@pytest.fixture
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def tok(toks, tok):
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i, like, new, york, in_, autumn, dot = range(len(toks))
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return locals()[tok]
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@pytest.fixture
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def new(toks):
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return tok(toks, "new")
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@pytest.fixture
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def york(toks):
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return tok(toks, "york")
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@pytest.fixture
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def autumn(toks):
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return tok(toks, "autumn")
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@pytest.fixture
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def dot(toks):
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return tok(toks, "dot")
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def test_example_i_like_new_york3(toks, new, york):
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assert toks[new].head.orth_ == 'York'
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assert toks[york].head.orth_ == 'like'
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def test_example_i_like_new_york4(toks, new, york):
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new_york = toks[new:york+1]
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assert new_york.root.orth_ == 'York'
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def test_example_i_like_new_york5(toks, autumn, dot):
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assert toks[autumn].head.orth_ == 'in'
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assert toks[dot].head.orth_ == 'like'
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autumn_dot = toks[autumn:]
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assert autumn_dot.root.orth_ == 'Autumn'
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def test_navigating_the_parse_tree_lefts(doc):
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# TODO: where does the span object come from?
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span = doc[:2]
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lefts = [span.doc[i] for i in range(0, span.start)
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if span.doc[i].head in span]
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def test_navigating_the_parse_tree_rights(doc):
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span = doc[:2]
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rights = [span.doc[i] for i in range(span.end, len(span.doc))
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if span.doc[i].head in span]
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def test_string_store(doc):
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string_store = doc.vocab.strings
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for i, string in enumerate(string_store):
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assert i == string_store[string]
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from __future__ import unicode_literals
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import pytest
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import spacy
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import os
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try:
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xrange
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except NameError:
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xrange = range
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@pytest.fixture()
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def token(doc):
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return doc[0]
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@pytest.mark.models
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def test_load_resources_and_process_text():
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from spacy.en import English
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nlp = English()
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doc = nlp(u'Hello, world. Here are two sentences.')
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@pytest.mark.models
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def test_get_tokens_and_sentences(doc):
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token = doc[0]
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sentence = next(doc.sents)
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assert token is sentence[0]
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assert sentence.text == 'Hello, world.'
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@pytest.mark.models
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def test_use_integer_ids_for_any_strings(nlp, token):
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hello_id = nlp.vocab.strings['Hello']
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hello_str = nlp.vocab.strings[hello_id]
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assert token.orth == hello_id == 3125
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assert token.orth_ == hello_str == 'Hello'
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def test_get_and_set_string_views_and_flags(nlp, token):
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assert token.shape_ == 'Xxxxx'
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for lexeme in nlp.vocab:
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if lexeme.is_alpha:
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lexeme.shape_ = 'W'
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elif lexeme.is_digit:
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lexeme.shape_ = 'D'
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elif lexeme.is_punct:
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lexeme.shape_ = 'P'
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else:
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lexeme.shape_ = 'M'
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assert token.shape_ == 'W'
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def test_export_to_numpy_arrays(nlp, doc):
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from spacy.attrs import ORTH, LIKE_URL, IS_OOV
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attr_ids = [ORTH, LIKE_URL, IS_OOV]
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doc_array = doc.to_array(attr_ids)
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assert doc_array.shape == (len(doc), len(attr_ids))
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assert doc[0].orth == doc_array[0, 0]
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assert doc[1].orth == doc_array[1, 0]
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assert doc[0].like_url == doc_array[0, 1]
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assert list(doc_array[:, 1]) == [t.like_url for t in doc]
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@pytest.mark.models
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def test_word_vectors(nlp):
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doc = nlp("Apples and oranges are similar. Boots and hippos aren't.")
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apples = doc[0]
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oranges = doc[2]
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boots = doc[6]
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hippos = doc[8]
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assert apples.similarity(oranges) > boots.similarity(hippos)
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@pytest.mark.models
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def test_part_of_speech_tags(nlp):
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from spacy.parts_of_speech import ADV
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def is_adverb(token):
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return token.pos == spacy.parts_of_speech.ADV
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# These are data-specific, so no constants are provided. You have to look
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# up the IDs from the StringStore.
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NNS = nlp.vocab.strings['NNS']
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NNPS = nlp.vocab.strings['NNPS']
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def is_plural_noun(token):
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return token.tag == NNS or token.tag == NNPS
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def print_coarse_pos(token):
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print(token.pos_)
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def print_fine_pos(token):
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print(token.tag_)
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@pytest.mark.models
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def test_syntactic_dependencies():
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def dependency_labels_to_root(token):
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'''Walk up the syntactic tree, collecting the arc labels.'''
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dep_labels = []
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while token.head is not token:
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dep_labels.append(token.dep)
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token = token.head
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return dep_labels
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@pytest.mark.models
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def test_named_entities():
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def iter_products(docs):
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for doc in docs:
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for ent in doc.ents:
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if ent.label_ == 'PRODUCT':
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yield ent
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def word_is_in_entity(word):
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return word.ent_type != 0
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def count_parent_verb_by_person(docs):
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counts = defaultdict(defaultdict(int))
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for doc in docs:
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for ent in doc.ents:
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if ent.label_ == 'PERSON' and ent.root.head.pos == VERB:
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counts[ent.orth_][ent.root.head.lemma_] += 1
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return counts
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def test_calculate_inline_mark_up_on_original_string():
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def put_spans_around_tokens(doc, get_classes):
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'''Given some function to compute class names, put each token in a
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span element, with the appropriate classes computed.
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All whitespace is preserved, outside of the spans. (Yes, I know HTML
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won't display it. But the point is no information is lost, so you can
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calculate what you need, e.g. <br /> tags, <p> tags, etc.)
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'''
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output = []
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template = '<span classes="{classes}">{word}</span>{space}'
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for token in doc:
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if token.is_space:
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output.append(token.orth_)
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else:
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output.append(
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template.format(
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classes=' '.join(get_classes(token)),
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word=token.orth_,
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space=token.whitespace_))
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string = ''.join(output)
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string = string.replace('\n', '')
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string = string.replace('\t', ' ')
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return string
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@pytest.mark.models
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def test_efficient_binary_serialization(doc):
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from spacy.tokens.doc import Doc
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byte_string = doc.to_bytes()
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open('moby_dick.bin', 'wb').write(byte_string)
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nlp = spacy.en.English()
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for byte_string in Doc.read_bytes(open('moby_dick.bin', 'rb')):
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doc = Doc(nlp.vocab)
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doc.from_bytes(byte_string)
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@pytest.mark.models
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def test_multithreading(nlp):
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texts = [u'One document.', u'...', u'Lots of documents']
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# .pipe streams input, and produces streaming output
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iter_texts = (texts[i % 3] for i in xrange(100000000))
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for i, doc in enumerate(nlp.pipe(iter_texts, batch_size=50, n_threads=4)):
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assert doc.is_parsed
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if i == 100:
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break
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