mirror of
https://github.com/explosion/spaCy.git
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281 lines
9.0 KiB
Python
281 lines
9.0 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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import numpy
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from spacy.tokens import Doc, Span
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from spacy.vocab import Vocab
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from spacy.errors import ModelsWarning
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from spacy.attrs import ENT_TYPE, ENT_IOB
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from ..util import get_doc
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@pytest.mark.parametrize("text", [["one", "two", "three"]])
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def test_doc_api_compare_by_string_position(en_vocab, text):
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doc = Doc(en_vocab, words=text)
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# Get the tokens in this order, so their ID ordering doesn't match the idx
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token3 = doc[-1]
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token2 = doc[-2]
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token1 = doc[-1]
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token1, token2, token3 = doc
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assert token1 < token2 < token3
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assert not token1 > token2
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assert token2 > token1
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assert token2 <= token3
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assert token3 >= token1
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def test_doc_api_getitem(en_tokenizer):
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text = "Give it back! He pleaded."
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tokens = en_tokenizer(text)
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assert tokens[0].text == "Give"
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assert tokens[-1].text == "."
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with pytest.raises(IndexError):
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tokens[len(tokens)]
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def to_str(span):
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return "/".join(token.text for token in span)
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span = tokens[1:1]
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assert not to_str(span)
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span = tokens[1:4]
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assert to_str(span) == "it/back/!"
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span = tokens[1:4:1]
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assert to_str(span) == "it/back/!"
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with pytest.raises(ValueError):
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tokens[1:4:2]
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with pytest.raises(ValueError):
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tokens[1:4:-1]
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span = tokens[-3:6]
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assert to_str(span) == "He/pleaded"
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span = tokens[4:-1]
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assert to_str(span) == "He/pleaded"
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span = tokens[-5:-3]
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assert to_str(span) == "back/!"
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span = tokens[5:4]
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assert span.start == span.end == 5 and not to_str(span)
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span = tokens[4:-3]
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assert span.start == span.end == 4 and not to_str(span)
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span = tokens[:]
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assert to_str(span) == "Give/it/back/!/He/pleaded/."
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span = tokens[4:]
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assert to_str(span) == "He/pleaded/."
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span = tokens[:4]
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assert to_str(span) == "Give/it/back/!"
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span = tokens[:-3]
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assert to_str(span) == "Give/it/back/!"
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span = tokens[-3:]
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assert to_str(span) == "He/pleaded/."
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span = tokens[4:50]
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assert to_str(span) == "He/pleaded/."
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span = tokens[-50:4]
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assert to_str(span) == "Give/it/back/!"
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span = tokens[-50:-40]
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assert span.start == span.end == 0 and not to_str(span)
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span = tokens[40:50]
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assert span.start == span.end == 7 and not to_str(span)
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span = tokens[1:4]
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assert span[0].orth_ == "it"
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subspan = span[:]
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assert to_str(subspan) == "it/back/!"
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subspan = span[:2]
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assert to_str(subspan) == "it/back"
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subspan = span[1:]
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assert to_str(subspan) == "back/!"
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subspan = span[:-1]
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assert to_str(subspan) == "it/back"
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subspan = span[-2:]
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assert to_str(subspan) == "back/!"
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subspan = span[1:2]
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assert to_str(subspan) == "back"
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subspan = span[-2:-1]
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assert to_str(subspan) == "back"
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subspan = span[-50:50]
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assert to_str(subspan) == "it/back/!"
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subspan = span[50:-50]
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assert subspan.start == subspan.end == 4 and not to_str(subspan)
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@pytest.mark.parametrize(
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"text", ["Give it back! He pleaded.", " Give it back! He pleaded. "]
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)
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def test_doc_api_serialize(en_tokenizer, text):
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tokens = en_tokenizer(text)
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new_tokens = Doc(tokens.vocab).from_bytes(tokens.to_bytes())
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assert tokens.text == new_tokens.text
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assert [t.text for t in tokens] == [t.text for t in new_tokens]
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assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
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new_tokens = Doc(tokens.vocab).from_bytes(
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tokens.to_bytes(exclude=["tensor"]), exclude=["tensor"]
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)
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assert tokens.text == new_tokens.text
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assert [t.text for t in tokens] == [t.text for t in new_tokens]
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assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
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new_tokens = Doc(tokens.vocab).from_bytes(
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tokens.to_bytes(exclude=["sentiment"]), exclude=["sentiment"]
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)
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assert tokens.text == new_tokens.text
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assert [t.text for t in tokens] == [t.text for t in new_tokens]
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assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
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def test_doc_api_set_ents(en_tokenizer):
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text = "I use goggle chrone to surf the web"
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tokens = en_tokenizer(text)
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assert len(tokens.ents) == 0
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tokens.ents = [(tokens.vocab.strings["PRODUCT"], 2, 4)]
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assert len(list(tokens.ents)) == 1
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assert [t.ent_iob for t in tokens] == [0, 0, 3, 1, 0, 0, 0, 0]
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assert tokens.ents[0].label_ == "PRODUCT"
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assert tokens.ents[0].start == 2
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assert tokens.ents[0].end == 4
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def test_doc_api_sents_empty_string(en_tokenizer):
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doc = en_tokenizer("")
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doc.is_parsed = True
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sents = list(doc.sents)
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assert len(sents) == 0
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def test_doc_api_runtime_error(en_tokenizer):
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# Example that caused run-time error while parsing Reddit
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# fmt: off
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text = "67% of black households are single parent \n\n72% of all black babies born out of wedlock \n\n50% of all black kids don\u2019t finish high school"
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deps = ["nsubj", "prep", "amod", "pobj", "ROOT", "amod", "attr", "",
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"nummod", "prep", "det", "amod", "pobj", "acl", "prep", "prep",
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"pobj", "", "nummod", "prep", "det", "amod", "pobj", "aux", "neg",
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"ROOT", "amod", "dobj"]
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# fmt: on
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, words=[t.text for t in tokens], deps=deps)
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nps = []
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for np in doc.noun_chunks:
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while len(np) > 1 and np[0].dep_ not in ("advmod", "amod", "compound"):
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np = np[1:]
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if len(np) > 1:
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nps.append(np)
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with doc.retokenize() as retokenizer:
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for np in nps:
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attrs = {
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"tag": np.root.tag_,
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"lemma": np.text,
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"ent_type": np.root.ent_type_,
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}
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retokenizer.merge(np, attrs=attrs)
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def test_doc_api_right_edge(en_tokenizer):
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"""Test for bug occurring from Unshift action, causing incorrect right edge"""
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# fmt: off
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text = "I have proposed to myself, for the sake of such as live under the government of the Romans, to translate those books into the Greek tongue."
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heads = [2, 1, 0, -1, -1, -3, 15, 1, -2, -1, 1, -3, -1, -1, 1, -2, -1, 1,
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-2, -7, 1, -19, 1, -2, -3, 2, 1, -3, -26]
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# fmt: on
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
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assert doc[6].text == "for"
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subtree = [w.text for w in doc[6].subtree]
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assert subtree == [
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"for",
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"the",
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"sake",
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"of",
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"such",
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"as",
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"live",
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"under",
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"the",
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"government",
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"of",
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"the",
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"Romans",
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",",
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]
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assert doc[6].right_edge.text == ","
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def test_doc_api_has_vector():
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vocab = Vocab()
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vocab.reset_vectors(width=2)
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vocab.set_vector("kitten", vector=numpy.asarray([0.0, 2.0], dtype="f"))
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doc = Doc(vocab, words=["kitten"])
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assert doc.has_vector
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def test_doc_api_similarity_match():
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doc = Doc(Vocab(), words=["a"])
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assert doc.similarity(doc[0]) == 1.0
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assert doc.similarity(doc.vocab["a"]) == 1.0
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doc2 = Doc(doc.vocab, words=["a", "b", "c"])
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with pytest.warns(ModelsWarning):
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assert doc.similarity(doc2[:1]) == 1.0
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assert doc.similarity(doc2) == 0.0
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@pytest.mark.parametrize(
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"sentence,heads,lca_matrix",
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[
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(
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"the lazy dog slept",
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[2, 1, 1, 0],
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numpy.array([[0, 2, 2, 3], [2, 1, 2, 3], [2, 2, 2, 3], [3, 3, 3, 3]]),
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),
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(
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"The lazy dog slept. The quick fox jumped",
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[2, 1, 1, 0, -1, 2, 1, 1, 0],
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numpy.array(
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[
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[0, 2, 2, 3, 3, -1, -1, -1, -1],
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[2, 1, 2, 3, 3, -1, -1, -1, -1],
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[2, 2, 2, 3, 3, -1, -1, -1, -1],
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[3, 3, 3, 3, 3, -1, -1, -1, -1],
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[3, 3, 3, 3, 4, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, 5, 7, 7, 8],
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[-1, -1, -1, -1, -1, 7, 6, 7, 8],
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[-1, -1, -1, -1, -1, 7, 7, 7, 8],
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[-1, -1, -1, -1, -1, 8, 8, 8, 8],
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]
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),
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),
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],
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)
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def test_lowest_common_ancestor(en_tokenizer, sentence, heads, lca_matrix):
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tokens = en_tokenizer(sentence)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
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lca = doc.get_lca_matrix()
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assert (lca == lca_matrix).all()
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assert lca[1, 1] == 1
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assert lca[0, 1] == 2
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assert lca[1, 2] == 2
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def test_doc_is_nered(en_vocab):
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words = ["I", "live", "in", "New", "York"]
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doc = Doc(en_vocab, words=words)
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assert not doc.is_nered
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doc.ents = [Span(doc, 3, 5, label="GPE")]
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assert doc.is_nered
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# Test creating doc from array with unknown values
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arr = numpy.array([[0, 0], [0, 0], [0, 0], [384, 3], [384, 1]], dtype="uint64")
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doc = Doc(en_vocab, words=words).from_array([ENT_TYPE, ENT_IOB], arr)
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assert doc.is_nered
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# Test serialization
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new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
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assert new_doc.is_nered
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def test_doc_lang(en_vocab):
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doc = Doc(en_vocab, words=["Hello", "world"])
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assert doc.lang_ == "en"
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assert doc.lang == en_vocab.strings["en"]
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