mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
# -*- coding: utf-8 -*-
|
||
"""Sphinx doctest is just too hard. Manually paste doctest examples here"""
|
||
from spacy.en.attrs import IS_LOWER
|
||
|
||
|
||
def test_1():
|
||
import spacy.en
|
||
from spacy.parts_of_speech import ADV
|
||
# Load the pipeline, and call it with some text.
|
||
nlp = spacy.en.English()
|
||
tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’",
|
||
tag=True, parse=False)
|
||
o = u''.join(tok.string.upper() if tok.pos == ADV else tok.string for tok in tokens)
|
||
assert u"‘Give it BACK,’ he pleaded ABJECTLY, ‘it’s mine.’"
|
||
|
||
o = nlp.vocab[u'back'].prob
|
||
assert o == -7.403977394104004
|
||
o = nlp.vocab[u'not'].prob
|
||
assert o == -5.407193660736084
|
||
o = nlp.vocab[u'quietly'].prob
|
||
assert o == -11.07155704498291
|
||
|
||
|
||
def test2():
|
||
import spacy.en
|
||
from spacy.parts_of_speech import ADV
|
||
nlp = spacy.en.English()
|
||
# Find log probability of Nth most frequent word
|
||
probs = [lex.prob for lex in nlp.vocab]
|
||
probs.sort()
|
||
is_adverb = lambda tok: tok.pos == ADV and tok.prob < probs[-1000]
|
||
tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’")
|
||
o = u''.join(tok.string.upper() if is_adverb(tok) else tok.string for tok in tokens)
|
||
o == u'‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’'
|
||
|
||
nlp.vocab[u'back'].prob
|
||
-7.403977394104004
|
||
nlp.vocab[u'not'].prob
|
||
-5.407193660736084
|
||
nlp.vocab[u'quietly'].prob
|
||
-11.07155704498291
|
||
|
||
|
||
def test3():
|
||
import spacy.en
|
||
from spacy.parts_of_speech import ADV
|
||
nlp = spacy.en.English()
|
||
# Find log probability of Nth most frequent word
|
||
probs = [lex.prob for lex in nlp.vocab]
|
||
probs.sort()
|
||
is_adverb = lambda tok: tok.pos == ADV and tok.prob < probs[-1000]
|
||
tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’")
|
||
o = u''.join(tok.string.upper() if is_adverb(tok) else tok.string for tok in tokens)
|
||
assert o == u'‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’'
|
||
|
||
pleaded = tokens[7]
|
||
assert pleaded.repvec.shape == (300,)
|
||
o = pleaded.repvec[:5]
|
||
assert sum(o) != 0
|
||
from numpy import dot
|
||
from numpy.linalg import norm
|
||
|
||
cosine = lambda v1, v2: dot(v1, v2) / (norm(v1) * norm(v2))
|
||
words = [w for w in nlp.vocab if w.check(IS_LOWER) and w.has_repvec]
|
||
words.sort(key=lambda w: cosine(w.repvec, pleaded.repvec))
|
||
words.reverse()
|
||
o = [w.orth_ for w in words[0:20]]
|
||
assert o == [u'pleaded', u'pled', u'plead', u'confessed', u'interceded',
|
||
u'pleads', u'testified', u'conspired', u'motioned', u'demurred',
|
||
u'countersued', u'remonstrated', u'begged', u'apologised',
|
||
u'consented', u'acquiesced', u'petitioned', u'quarreled',
|
||
u'appealed', u'pleading']
|
||
o = [w.orth_ for w in words[50:60]]
|
||
assert o == [u'counselled', u'bragged', u'backtracked', u'caucused', u'refiled',
|
||
u'dueled', u'mused', u'dissented', u'yearned', u'confesses']
|
||
o = [w.orth_ for w in words[100:110]]
|
||
assert o == [u'cabled', u'ducked', u'sentenced', u'perjured', u'absconded',
|
||
u'bargained', u'overstayed', u'clerked', u'confided', u'sympathizes']
|
||
#o = [w.orth_ for w in words[1000:1010]]
|
||
#assert o == [u'scorned', u'baled', u'righted', u'requested', u'swindled',
|
||
# u'posited', u'firebombed', u'slimed', u'deferred', u'sagged']
|
||
#o = [w.orth_ for w in words[50000:50010]]
|
||
#assert o == [u'fb', u'ford', u'systems', u'puck', u'anglers', u'ik', u'tabloid',
|
||
# u'dirty', u'rims', u'artists']
|