spaCy/spacy/tests/website/test_home.py

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from __future__ import unicode_literals
import pytest
import spacy
import os
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try:
xrange
except NameError:
xrange = range
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@pytest.fixture()
def token(doc):
return doc[0]
@pytest.mark.models
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def test_load_resources_and_process_text():
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from spacy.en import English
nlp = English()
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):
token = doc[0]
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sentence = next(doc.sents)
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assert token is sentence[0]
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):
hello_id = nlp.vocab.strings['Hello']
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):
assert token.shape_ == 'Xxxxx'
for lexeme in nlp.vocab:
if lexeme.is_alpha:
lexeme.shape_ = 'W'
elif lexeme.is_digit:
lexeme.shape_ = 'D'
elif lexeme.is_punct:
lexeme.shape_ = 'P'
else:
lexeme.shape_ = 'M'
assert token.shape_ == 'W'
def test_export_to_numpy_arrays(nlp, doc):
from spacy.attrs import ORTH, LIKE_URL, IS_OOV
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attr_ids = [ORTH, LIKE_URL, IS_OOV]
doc_array = doc.to_array(attr_ids)
assert doc_array.shape == (len(doc), len(attr_ids))
assert doc[0].orth == doc_array[0, 0]
assert doc[1].orth == doc_array[1, 0]
assert doc[0].like_url == doc_array[0, 1]
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):
doc = nlp("Apples and oranges are similar. Boots and hippos aren't.")
apples = doc[0]
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oranges = doc[2]
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boots = doc[6]
hippos = doc[8]
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):
from spacy.parts_of_speech import ADV
def is_adverb(token):
return token.pos == spacy.parts_of_speech.ADV
# These are data-specific, so no constants are provided. You have to look
# up the IDs from the StringStore.
NNS = nlp.vocab.strings['NNS']
NNPS = nlp.vocab.strings['NNPS']
def is_plural_noun(token):
return token.tag == NNS or token.tag == NNPS
def print_coarse_pos(token):
print(token.pos_)
def print_fine_pos(token):
print(token.tag_)
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@pytest.mark.models
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def test_syntactic_dependencies():
def dependency_labels_to_root(token):
'''Walk up the syntactic tree, collecting the arc labels.'''
dep_labels = []
while token.head is not token:
dep_labels.append(token.dep)
token = token.head
return dep_labels
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@pytest.mark.models
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def test_named_entities():
def iter_products(docs):
for doc in docs:
for ent in doc.ents:
if ent.label_ == 'PRODUCT':
yield ent
def word_is_in_entity(word):
return word.ent_type != 0
def count_parent_verb_by_person(docs):
counts = defaultdict(defaultdict(int))
for doc in docs:
for ent in doc.ents:
if ent.label_ == 'PERSON' and ent.root.head.pos == VERB:
counts[ent.orth_][ent.root.head.lemma_] += 1
return counts
def test_calculate_inline_mark_up_on_original_string():
def put_spans_around_tokens(doc, get_classes):
'''Given some function to compute class names, put each token in a
span element, with the appropriate classes computed.
All whitespace is preserved, outside of the spans. (Yes, I know HTML
won't display it. But the point is no information is lost, so you can
calculate what you need, e.g. <br /> tags, <p> tags, etc.)
'''
output = []
template = '<span classes="{classes}">{word}</span>{space}'
for token in doc:
if token.is_space:
output.append(token.orth_)
else:
output.append(
template.format(
classes=' '.join(get_classes(token)),
word=token.orth_,
space=token.whitespace_))
string = ''.join(output)
string = string.replace('\n', '')
string = string.replace('\t', ' ')
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
byte_string = doc.to_bytes()
open('moby_dick.bin', 'wb').write(byte_string)
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nlp = spacy.en.English()
for byte_string in Doc.read_bytes(open('moby_dick.bin', 'rb')):
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doc = Doc(nlp.vocab)
doc.from_bytes(byte_string)
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@pytest.mark.models
def test_multithreading(nlp):
texts = [u'One document.', u'...', u'Lots of documents']
# .pipe streams input, and produces streaming output
iter_texts = (texts[i % 3] for i in xrange(100000000))
for i, doc in enumerate(nlp.pipe(iter_texts, batch_size=50, n_threads=4)):
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assert doc.is_parsed
if i == 100:
break
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