spaCy/spacy/tests/spans/test_span.py

# coding: utf-8
from __future__ import unicode_literals

from ..util import get_doc
from ...attrs import ORTH, LENGTH

import pytest


@pytest.fixture
def doc(en_tokenizer):
    text = "This is a sentence. This is another sentence. And a third."
    heads = [1, 0, 1, -2, -3, 1, 0, 1, -2, -3, 0, 1, -2, -1]
    deps = ['nsubj', 'ROOT', 'det', 'attr', 'punct', 'nsubj', 'ROOT', 'det',
            'attr', 'punct', 'ROOT', 'det', 'npadvmod', 'punct']
    tokens = en_tokenizer(text)
    return get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)


def test_spans_sent_spans(doc):
    sents = list(doc.sents)
    assert sents[0].start == 0
    assert sents[0].end == 5
    assert len(sents) == 3
    assert sum(len(sent) for sent in sents) == len(doc)


def test_spans_root(doc):
    span = doc[2:4]
    assert len(span) == 2
    assert span.text == 'a sentence'
    assert span.root.text == 'sentence'
    assert span.root.head.text == 'is'

def test_spans_string_fn(doc):
    span = doc[0:4]
    assert len(span) == 4
    assert span.text == 'This is a sentence'
    assert span.upper_ == 'THIS IS A SENTENCE'
    assert span.lower_ == 'this is a sentence'

def test_spans_root2(en_tokenizer):
    text = "through North and South Carolina"
    heads = [0, 3, -1, -2, -4]
    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
    assert doc[-2:].root.text == 'Carolina'


def test_spans_span_sent(doc):
    """Test span.sent property"""
    assert len(list(doc.sents))
    assert doc[:2].sent.root.text == 'is'
    assert doc[:2].sent.text == 'This is a sentence .'
    assert doc[6:7].sent.root.left_edge.text == 'This'


def test_spans_lca_matrix(en_tokenizer):
    """Test span's lca matrix generation"""
    tokens = en_tokenizer('the lazy dog slept')
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=[2, 1, 1, 0])
    lca = doc[:2].get_lca_matrix()
    assert(lca[0, 0] == 0)
    assert(lca[0, 1] == -1)
    assert(lca[1, 0] == -1)
    assert(lca[1, 1] == 1)


def test_spans_default_sentiment(en_tokenizer):
    """Test span.sentiment property's default averaging behaviour"""
    text = "good stuff bad stuff"
    tokens = en_tokenizer(text)
    tokens.vocab[tokens[0].text].sentiment = 3.0
    tokens.vocab[tokens[2].text].sentiment = -2.0
    doc = get_doc(tokens.vocab, [t.text for t in tokens])
    assert doc[:2].sentiment == 3.0 / 2
    assert doc[-2:].sentiment == -2. / 2
    assert doc[:-1].sentiment == (3.+-2) / 3.


def test_spans_override_sentiment(en_tokenizer):
    """Test span.sentiment property's default averaging behaviour"""
    text = "good stuff bad stuff"
    tokens = en_tokenizer(text)
    tokens.vocab[tokens[0].text].sentiment = 3.0
    tokens.vocab[tokens[2].text].sentiment = -2.0
    doc = get_doc(tokens.vocab, [t.text for t in tokens])
    doc.user_span_hooks['sentiment'] = lambda span: 10.0
    assert doc[:2].sentiment == 10.0
    assert doc[-2:].sentiment == 10.0
    assert doc[:-1].sentiment == 10.0


def test_spans_are_hashable(en_tokenizer):
    """Test spans can be hashed."""
    text = "good stuff bad stuff"
    tokens = en_tokenizer(text)
    span1 = tokens[:2]
    span2 = tokens[2:4]
    assert hash(span1) != hash(span2)
    span3 = tokens[0:2]
    assert hash(span3) == hash(span1)


def test_spans_by_character(doc):
    span1 = doc[1:-2]
    span2 = doc.char_span(span1.start_char, span1.end_char, label='GPE')
    assert span1.start_char == span2.start_char
    assert span1.end_char == span2.end_char
    assert span2.label_ == 'GPE'


def test_span_to_array(doc):
    span = doc[1:-2]
    arr = span.to_array([ORTH, LENGTH])
    assert arr.shape == (len(span), 2)
    assert arr[0, 0] == span[0].orth
    assert arr[0, 1] == len(span[0])