From c07eea8563c0c361842caee304ec0007d40629e6 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal@gmail.com>
Date: Wed, 26 Aug 2015 19:23:04 +0200
Subject: [PATCH] * Comment out old doc tests for now

---
 tests/test_docs.py | 155 +++++++++++++++++++++++----------------------
 1 file changed, 78 insertions(+), 77 deletions(-)

diff --git a/tests/test_docs.py b/tests/test_docs.py
index 70c8b8c63..4b0831dfd 100644
--- a/tests/test_docs.py
+++ b/tests/test_docs.py
@@ -1,80 +1,81 @@
 # -*- coding: utf-8 -*-
 """Sphinx doctest is just too hard. Manually paste doctest examples here"""
+import pytest
 
-@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, ‘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 == -7.033305644989014
-    o = nlp.vocab[u'not'].prob
-    assert o == -5.332601070404053
-    o = nlp.vocab[u'quietly'].prob
-    assert o == -11.994928359985352
-
-
-@pytest.mark.models
-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.’'
-
-@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, ‘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.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']
+#@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, ‘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 == -7.033305644989014
+#    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, ‘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.’'
+#
+#@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, ‘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.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']