spaCy/spacy/tests/vectors/test_vectors.py

# coding: utf-8
from __future__ import unicode_literals

from ...vectors import Vectors

import numpy
import pytest


@pytest.fixture
def strings():
    return ["apple", "orange"]

@pytest.fixture
def data():
    return numpy.asarray([[0.0, 1.0, 2.0], [3.0, -2.0, 4.0]], dtype='f')


def test_init_vectors_with_data(strings, data):
    v = Vectors(strings, data)
    assert v.shape == data.shape

def test_init_vectors_with_width(strings):
    v = Vectors(strings, 3)
    assert v.shape == (len(strings), 3)


def test_get_vector(strings, data):
    v = Vectors(strings, data)
    assert list(v[strings[0]]) == list(data[0])
    assert list(v[strings[0]]) != list(data[1])
    assert list(v[strings[1]]) != list(data[0])


def test_set_vector(strings, data):
    orig = data.copy()
    v = Vectors(strings, data)
    assert list(v[strings[0]]) == list(orig[0])
    assert list(v[strings[0]]) != list(orig[1])
    v[strings[0]] = data[1]
    assert list(v[strings[0]]) == list(orig[1])
    assert list(v[strings[0]]) != list(orig[0])


#
#@pytest.fixture()
#def tokenizer_v(vocab):
#    return Tokenizer(vocab, {}, None, None, None)
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', ["apple and orange"])
#def test_vectors_token_vector(tokenizer_v, vectors, text):
#    doc = tokenizer_v(text)
#    assert vectors[0] == (doc[0].text, list(doc[0].vector))
#    assert vectors[1] == (doc[2].text, list(doc[2].vector))
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', ["apple", "orange"])
#def test_vectors_lexeme_vector(vocab, text):
#    lex = vocab[text]
#    assert list(lex.vector)
#    assert lex.vector_norm
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', [["apple", "and", "orange"]])
#def test_vectors_doc_vector(vocab, text):
#    doc = get_doc(vocab, text)
#    assert list(doc.vector)
#    assert doc.vector_norm
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', [["apple", "and", "orange"]])
#def test_vectors_span_vector(vocab, text):
#    span = get_doc(vocab, text)[0:2]
#    assert list(span.vector)
#    assert span.vector_norm
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', ["apple orange"])
#def test_vectors_token_token_similarity(tokenizer_v, text):
#    doc = tokenizer_v(text)
#    assert doc[0].similarity(doc[1]) == doc[1].similarity(doc[0])
#    assert 0.0 < doc[0].similarity(doc[1]) < 1.0
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text1,text2', [("apple", "orange")])
#def test_vectors_token_lexeme_similarity(tokenizer_v, vocab, text1, text2):
#    token = tokenizer_v(text1)
#    lex = vocab[text2]
#    assert token.similarity(lex) == lex.similarity(token)
#    assert 0.0 < token.similarity(lex) < 1.0
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', [["apple", "orange", "juice"]])
#def test_vectors_token_span_similarity(vocab, text):
#    doc = get_doc(vocab, text)
#    assert doc[0].similarity(doc[1:3]) == doc[1:3].similarity(doc[0])
#    assert 0.0 < doc[0].similarity(doc[1:3]) < 1.0
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', [["apple", "orange", "juice"]])
#def test_vectors_token_doc_similarity(vocab, text):
#    doc = get_doc(vocab, text)
#    assert doc[0].similarity(doc) == doc.similarity(doc[0])
#    assert 0.0 < doc[0].similarity(doc) < 1.0
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', [["apple", "orange", "juice"]])
#def test_vectors_lexeme_span_similarity(vocab, text):
#    doc = get_doc(vocab, text)
#    lex = vocab[text[0]]
#    assert lex.similarity(doc[1:3]) == doc[1:3].similarity(lex)
#    assert 0.0 < doc.similarity(doc[1:3]) < 1.0
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text1,text2', [("apple", "orange")])
#def test_vectors_lexeme_lexeme_similarity(vocab, text1, text2):
#    lex1 = vocab[text1]
#    lex2 = vocab[text2]
#    assert lex1.similarity(lex2) == lex2.similarity(lex1)
#    assert 0.0 < lex1.similarity(lex2) < 1.0
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', [["apple", "orange", "juice"]])
#def test_vectors_lexeme_doc_similarity(vocab, text):
#    doc = get_doc(vocab, text)
#    lex = vocab[text[0]]
#    assert lex.similarity(doc) == doc.similarity(lex)
#    assert 0.0 < lex.similarity(doc) < 1.0
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', [["apple", "orange", "juice"]])
#def test_vectors_span_span_similarity(vocab, text):
#    doc = get_doc(vocab, text)
#    assert doc[0:2].similarity(doc[1:3]) == doc[1:3].similarity(doc[0:2])
#    assert 0.0 < doc[0:2].similarity(doc[1:3]) < 1.0
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text', [["apple", "orange", "juice"]])
#def test_vectors_span_doc_similarity(vocab, text):
#    doc = get_doc(vocab, text)
#    assert doc[0:2].similarity(doc) == doc.similarity(doc[0:2])
#    assert 0.0 < doc[0:2].similarity(doc) < 1.0
#
#
#@pytest.mark.xfail
#@pytest.mark.parametrize('text1,text2', [
#    (["apple", "and", "apple", "pie"], ["orange", "juice"])])
#def test_vectors_doc_doc_similarity(vocab, text1, text2):
#    doc1 = get_doc(vocab, text1)
#    doc2 = get_doc(vocab, text2)
#    assert doc1.similarity(doc2) == doc2.similarity(doc1)
#    assert 0.0 < doc1.similarity(doc2) < 1.0