spaCy/tests/test_docs.py

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# -*- coding: utf-8 -*-
"""Sphinx doctest is just too hard. Manually paste doctest examples here"""
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import pytest
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#@pytest.mark.models
#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, its 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, its mine."
#
# o = nlp.vocab[u'back'].prob
# assert o == -7.033305644989014
# o = nlp.vocab[u'not'].prob
# assert o == -5.332601070404053
# o = nlp.vocab[u'quietly'].prob
# assert o == -11.994928359985352
#
#
#@pytest.mark.m
#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, its 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, its mine.'
#
#@pytest.mark.models
#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, its 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, its 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.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'martialed', u'counselled', u'bragged',
# u'backtracked', u'caucused', u'refiled', u'dueled', u'mused',
# u'dissented', u'yearned']
# o = [w.orth_ for w in words[100:110]]
# assert o == [u'acquits', u'cabled', u'ducked', u'sentenced',
# u'gaoled', u'perjured', u'absconded', u'bargained', u'overstayed',
# u'clerked']
#
# #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']