mirror of https://github.com/explosion/spaCy.git
119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from ..tokens import Doc
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from ..attrs import ORTH, POS, HEAD, DEP
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from ..compat import path2str
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import pytest
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import numpy
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import tempfile
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import shutil
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import contextlib
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import msgpack
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from pathlib import Path
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MODELS = {}
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def load_test_model(model):
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"""Load a model if it's installed as a package, otherwise skip."""
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if model not in MODELS:
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module = pytest.importorskip(model)
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MODELS[model] = module.load()
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return MODELS[model]
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@contextlib.contextmanager
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def make_tempfile(mode='r'):
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f = tempfile.TemporaryFile(mode=mode)
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yield f
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f.close()
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@contextlib.contextmanager
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def make_tempdir():
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d = Path(tempfile.mkdtemp())
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yield d
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shutil.rmtree(path2str(d))
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def get_doc(vocab, words=[], pos=None, heads=None, deps=None, tags=None, ents=None):
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"""Create Doc object from given vocab, words and annotations."""
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pos = pos or [''] * len(words)
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tags = tags or [''] * len(words)
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heads = heads or [0] * len(words)
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deps = deps or [''] * len(words)
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for value in (deps+tags+pos):
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vocab.strings.add(value)
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doc = Doc(vocab, words=words)
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attrs = doc.to_array([POS, HEAD, DEP])
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for i, (p, head, dep) in enumerate(zip(pos, heads, deps)):
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attrs[i, 0] = doc.vocab.strings[p]
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attrs[i, 1] = head
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attrs[i, 2] = doc.vocab.strings[dep]
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doc.from_array([POS, HEAD, DEP], attrs)
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if ents:
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doc.ents = [(ent_id, doc.vocab.strings[label], start, end) for ent_id, label, start, end in ents]
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if tags:
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for token in doc:
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token.tag_ = tags[token.i]
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return doc
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def apply_transition_sequence(parser, doc, sequence):
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"""Perform a series of pre-specified transitions, to put the parser in a
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desired state."""
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for action_name in sequence:
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if '-' in action_name:
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move, label = action_name.split('-')
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parser.add_label(label)
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with parser.step_through(doc) as stepwise:
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for transition in sequence:
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stepwise.transition(transition)
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def add_vecs_to_vocab(vocab, vectors):
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"""Add list of vector tuples to given vocab. All vectors need to have the
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same length. Format: [("text", [1, 2, 3])]"""
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length = len(vectors[0][1])
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vocab.reset_vectors(width=length)
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for word, vec in vectors:
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vocab.set_vector(word, vector=vec)
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return vocab
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def get_cosine(vec1, vec2):
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"""Get cosine for two given vectors"""
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return numpy.dot(vec1, vec2) / (numpy.linalg.norm(vec1) * numpy.linalg.norm(vec2))
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def assert_docs_equal(doc1, doc2):
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"""Compare two Doc objects and assert that they're equal. Tests for tokens,
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tags, dependencies and entities."""
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assert [ t.orth for t in doc1 ] == [ t.orth for t in doc2 ]
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assert [ t.pos for t in doc1 ] == [ t.pos for t in doc2 ]
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assert [ t.tag for t in doc1 ] == [ t.tag for t in doc2 ]
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assert [ t.head.i for t in doc1 ] == [ t.head.i for t in doc2 ]
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assert [ t.dep for t in doc1 ] == [ t.dep for t in doc2 ]
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if doc1.is_parsed and doc2.is_parsed:
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assert [ s for s in doc1.sents ] == [ s for s in doc2.sents ]
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assert [ t.ent_type for t in doc1 ] == [ t.ent_type for t in doc2 ]
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assert [ t.ent_iob for t in doc1 ] == [ t.ent_iob for t in doc2 ]
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assert [ ent for ent in doc1.ents ] == [ ent for ent in doc2.ents ]
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def assert_packed_msg_equal(b1, b2):
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"""Assert that two packed msgpack messages are equal."""
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msg1 = msgpack.loads(b1, encoding='utf8')
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msg2 = msgpack.loads(b2, encoding='utf8')
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assert sorted(msg1.keys()) == sorted(msg2.keys())
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for (k1, v1), (k2, v2) in zip(sorted(msg1.items()), sorted(msg2.items())):
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assert k1 == k2
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assert v1 == v2
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