mirror of
https://github.com/explosion/spaCy.git
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82 lines
3.5 KiB
Python
82 lines
3.5 KiB
Python
# -*- coding: utf-8 -*-
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"""Sphinx doctest is just too hard. Manually paste doctest examples here"""
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import pytest
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#@pytest.mark.models
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#def test_1():
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# import spacy.en
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# from spacy.parts_of_speech import ADV
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# # Load the pipeline, and call it with some text.
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# nlp = spacy.en.English()
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# tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’",
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# tag=True, parse=False)
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# o = u''.join(tok.string.upper() if tok.pos == ADV else tok.string for tok in tokens)
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# assert u"‘Give it BACK,’ he pleaded ABJECTLY, ‘it’s mine.’"
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#
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# o = nlp.vocab[u'back'].prob
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# assert o == -7.033305644989014
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# o = nlp.vocab[u'not'].prob
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# assert o == -5.332601070404053
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# o = nlp.vocab[u'quietly'].prob
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# assert o == -11.994928359985352
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#
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#
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#@pytest.mark.m
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#def test2():
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# import spacy.en
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# from spacy.parts_of_speech import ADV
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# nlp = spacy.en.English()
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# # Find log probability of Nth most frequent word
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# probs = [lex.prob for lex in nlp.vocab]
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# probs.sort()
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# is_adverb = lambda tok: tok.pos == ADV and tok.prob < probs[-1000]
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# tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’")
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# o = u''.join(tok.string.upper() if is_adverb(tok) else tok.string for tok in tokens)
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# o == u'‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’'
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#
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#@pytest.mark.models
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#def test3():
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# import spacy.en
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# from spacy.parts_of_speech import ADV
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# nlp = spacy.en.English()
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# # Find log probability of Nth most frequent word
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# probs = [lex.prob for lex in nlp.vocab]
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# probs.sort()
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# is_adverb = lambda tok: tok.pos == ADV and tok.prob < probs[-1000]
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# tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’")
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# o = u''.join(tok.string.upper() if is_adverb(tok) else tok.string for tok in tokens)
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# assert o == u'‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’'
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#
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# pleaded = tokens[7]
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# assert pleaded.repvec.shape == (300,)
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# o = pleaded.repvec[:5]
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# assert sum(o) != 0
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# from numpy import dot
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# from numpy.linalg import norm
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#
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# cosine = lambda v1, v2: dot(v1, v2) / (norm(v1) * norm(v2))
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# words = [w for w in nlp.vocab if w.is_lower and w.has_repvec]
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# words.sort(key=lambda w: cosine(w.repvec, pleaded.repvec))
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# words.reverse()
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# o = [w.orth_ for w in words[0:20]]
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# assert o == [u'pleaded', u'pled', u'plead', u'confessed', u'interceded',
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# u'pleads', u'testified', u'conspired', u'motioned', u'demurred',
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# u'countersued', u'remonstrated', u'begged', u'apologised',
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# u'consented', u'acquiesced', u'petitioned', u'quarreled',
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# u'appealed', u'pleading']
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# o = [w.orth_ for w in words[50:60]]
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# assert o == [u'martialed', u'counselled', u'bragged',
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# u'backtracked', u'caucused', u'refiled', u'dueled', u'mused',
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# u'dissented', u'yearned']
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# o = [w.orth_ for w in words[100:110]]
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# assert o == [u'acquits', u'cabled', u'ducked', u'sentenced',
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# u'gaoled', u'perjured', u'absconded', u'bargained', u'overstayed',
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# u'clerked']
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#
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# #o = [w.orth_ for w in words[1000:1010]]
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# #assert o == [u'scorned', u'baled', u'righted', u'requested', u'swindled',
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# # u'posited', u'firebombed', u'slimed', u'deferred', u'sagged']
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# #o = [w.orth_ for w in words[50000:50010]]
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# #assert o == [u'fb', u'ford', u'systems', u'puck', u'anglers', u'ik', u'tabloid',
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# # u'dirty', u'rims', u'artists']
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