spaCy/spacy/tests/util.py
2017-01-12 16:51:13 +01:00

46 lines
1.5 KiB
Python

# coding: utf-8
from __future__ import unicode_literals
from ..tokens import Doc
from ..attrs import ORTH, POS, HEAD, DEP
import numpy
def get_doc(vocab, words=[], pos=None, heads=None, deps=None, tags=None, ents=None):
"""Create Doc object from given vocab, words and annotations."""
pos = pos or [''] * len(words)
heads = heads or [0] * len(words)
deps = deps or [''] * len(words)
doc = Doc(vocab, words=words)
attrs = doc.to_array([POS, HEAD, DEP])
for i, (p, head, dep) in enumerate(zip(pos, heads, deps)):
attrs[i, 0] = doc.vocab.strings[p]
attrs[i, 1] = head
attrs[i, 2] = doc.vocab.strings[dep]
doc.from_array([POS, HEAD, DEP], attrs)
if ents:
doc.ents = [(ent_id, doc.vocab.strings[label], start, end) for ent_id, label, start, end in ents]
if tags:
for token in doc:
token.tag_ = tags[token.i]
return doc
def apply_transition_sequence(parser, doc, sequence):
"""Perform a series of pre-specified transitions, to put the parser in a
desired state."""
for action_name in sequence:
if '-' in action_name:
move, label = action_name.split('-')
parser.add_label(label)
with parser.step_through(doc) as stepwise:
for transition in sequence:
stepwise.transition(transition)
def get_cosine(vec1, vec2):
"""Get cosine for two given vectors"""
return numpy.dot(vec1, vec2) / (numpy.linalg.norm(vec1) * numpy.linalg.norm(vec2))