def test_example1(self):
tokens = []
tokens.append(
Token(1, b'Kiten', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2, b'Master', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(3, b':', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(4,
b'http://youtu.be/jVVD0OZk-6g',
spacy_is_punct=False,
spacy_like_url=True))
sentence_uniqueID = 'p339_s003'
text = 'Kiten Master: http://youtu.be/jVVD0OZk-6g'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_document(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 4, 1, 1, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)
def test_example6(self):
tokens = []
tokens.append(
Token(1, b'kleine', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2,
b'Elektrodrohnen',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(3, b'just', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(4, b'for', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(5, b'fun', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(6, b',', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(7, b'warum', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(8, b'nicht', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(9, b'.', spacy_is_punct=True,
spacy_like_url=False))
sentence_uniqueID = 'p339_s003'
text = 'kleine Elektrodrohnen just for fun, warum nicht.'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_document(
thf_sentence, use_sentence_length)
expected_value = [
3, 1.0 / len(tokens), 1.0 / len(tokens), 9, 0, 2, 1.0, 0.0, 0.0,
0.0
]
self.assertEqual(feature_value, expected_value)
def test_example4(self):
tokens = []
tokens.append(
Token(1, b'Hier', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2, b'eine', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(3, b'Konzept', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(4, b'-', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(5, b'Grafik', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(6, b':', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(7, b' ', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(8,
b'http://i.imgur.com/JGlqExO.jpg',
spacy_is_punct=False,
spacy_like_url=True))
sentence_uniqueID = 'p339_s003'
text = 'Hier eine Konzept-Grafik: http://i.imgur.com/JGlqExO.jpg'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_document(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 8, 1, 2, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)
def test_example5(self):
tokens = []
tokens.append(
Token(1,
b'Tempelhofparikram',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(Token(2, b'-', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(3,
b'Interreligi\xc3\xb6ser',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(4, b'Pilgerpfad', spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(5, b'auf', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(6, b'dem', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(7,
b'Tempelhofer',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(8, b'Feld', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(9, b'(', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(10,
b'http://lebensplan.com/Interreligioeser-Pilgerpfad.pdf',
spacy_is_punct=False,
spacy_like_url=True))
tokens.append(
Token(11, b')', spacy_is_punct=True, spacy_like_url=False))
sentence_uniqueID = 'p339_s003'
text = 'Tempelhofparikram - Interreligi\u00f6ser Pilgerpfad auf dem Tempelhofer Feld (http://lebensplan.com/Interreligioeser-Pilgerpfad.pdf)'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_document(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 11, 1, 3, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)
def test_example3(self):
tokens = []
tokens.append(
Token(1, b'BM', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2, b'Tester', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(3, b'#', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(4, b'1', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(5, b':', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(6, b'Kite', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(7, b'-', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(8, b'Skaten', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(9, b'auf', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(10, b'dem', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(11,
b'Tempelhofer',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(12, b'Feld', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(13, b':', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(14,
b'http://youtu.be/Jf68D61QN4A',
spacy_is_punct=False,
spacy_like_url=True))
sentence_uniqueID = 'p339_s003'
text = 'BM Tester #1: Kite-Skaten auf dem Tempelhofer Feld: http://youtu.be/Jf68D61QN4A'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_document(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 14, 1, 4, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)
def test_example2(self):
tokens = []
tokens.append(
Token(1, b'Diesen', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2, b'Vorschlag', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(3, b'gibt', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(4, b'es', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(5, b'schon', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(6, b':', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(7,
b'S\xc3\xbcdlicher',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(8, b'Zugang', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(9, b'zur', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(10,
b'Oberlandstra\xc3\x9fe',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(11, b'(', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(12, b'Hatun', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(13, b'-', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(14,
b'S\xc3\xbcr\xc3\xbcc\xc3\xbc',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(15, b'-', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(16,
b'Br\xc3\xbccke',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(17, b')', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(18, b'(', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(
19,
b'https://tempelhofer-feld.berlin.de/i/tempelhofer-feld/proposal/104-S%C3%BCdlicher_Zugang_zur_Oberlandstra%C3%9Fe_Hatu',
spacy_is_punct=False,
spacy_like_url=True))
tokens.append(
Token(20, b')', spacy_is_punct=True, spacy_like_url=False))
sentence_uniqueID = 'p339_s003'
text = 'Diesen Vorschlag gibt es schon: S\u00fcdlicher Zugang zur Oberlandstra\u00dfe (Hatun-S\u00fcr\u00fcc\u00fc-Br\u00fccke) (https://tempelhofer-feld.berlin.de/i/tempelhofer-feld/proposal/104-S%C3%BCdlicher_Zugang_zur_Oberlandstra%C3%9Fe_Hatu)'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_document(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 20, 1, 7, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)