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# -*- coding: utf-8 -*-
import os, sys; sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import unittest
import subprocess
from pattern import nl
try:
PATH = os.path.dirname(os.path.realpath(__file__))
except:
PATH = ""
#---------------------------------------------------------------------------------------------------
class TestInflection(unittest.TestCase):
def setUp(self):
pass
def test_pluralize(self):
# Assert "auto's" as plural of "auto".
self.assertEqual("auto's", nl.inflect.pluralize("auto"))
# Assert the accuracy of the pluralization algorithm.
from pattern.db import Datasheet
i, n = 0, 0
for pred, attr, sg, pl in Datasheet.load(os.path.join(PATH, "corpora", "wordforms-nl-celex.csv")):
if nl.pluralize(sg) == pl:
i +=1
n += 1
self.assertTrue(float(i) / n > 0.74)
print("pattern.nl.pluralize()")
def test_singularize(self):
# Assert the accuracy of the singularization algorithm.
from pattern.db import Datasheet
i, n = 0, 0
for pred, attr, sg, pl in Datasheet.load(os.path.join(PATH, "corpora", "wordforms-nl-celex.csv")):
if nl.singularize(pl) == sg:
i +=1
n += 1
self.assertTrue(float(i) / n > 0.88)
print("pattern.nl.singularize()")
def test_attributive(self):
# Assert the accuracy of the attributive algorithm ("fel" => "felle").
from pattern.db import Datasheet
i, n = 0, 0
for pred, attr, sg, pl in Datasheet.load(os.path.join(PATH, "corpora", "wordforms-nl-celex.csv")):
if nl.attributive(pred) == attr:
i +=1
n += 1
self.assertTrue(float(i) / n > 0.96)
print("pattern.nl.attributive()")
def test_predicative(self):
# Assert the accuracy of the predicative algorithm ("felle" => "fel").
from pattern.db import Datasheet
i, n = 0, 0
for pred, attr, sg, pl in Datasheet.load(os.path.join(PATH, "corpora", "wordforms-nl-celex.csv")):
if nl.predicative(attr) == pred:
i +=1
n += 1
self.assertTrue(float(i) / n > 0.96)
print("pattern.nl.predicative()")
def test_find_lemma(self):
# Assert the accuracy of the verb lemmatization algorithm.
# Note: the accuracy is higher (90%) when measured on CELEX word forms
# (presumably because nl.inflect.verbs has high percentage irregular verbs).
i, n = 0, 0
for v1, v2 in nl.inflect.verbs.inflections.items():
if nl.inflect.verbs.find_lemma(v1) == v2:
i += 1
n += 1
self.assertTrue(float(i) / n > 0.83)
print("pattern.nl.inflect.verbs.find_lemma()")
def test_find_lexeme(self):
# Assert the accuracy of the verb conjugation algorithm.
i, n = 0, 0
for v, lexeme1 in nl.inflect.verbs.infinitives.items():
lexeme2 = nl.inflect.verbs.find_lexeme(v)
for j in range(len(lexeme2)):
if lexeme1[j] == lexeme2[j] or \
lexeme1[j] == "" and \
lexeme1[j > 5 and 10 or 0] == lexeme2[j]:
i += 1
n += 1
self.assertTrue(float(i) / n > 0.79)
print("pattern.nl.inflect.verbs.find_lexeme()")
def test_conjugate(self):
# Assert different tenses with different conjugations.
for (v1, v2, tense) in (
("zijn", "zijn", nl.INFINITIVE),
("zijn", "ben", (nl.PRESENT, 1, nl.SINGULAR)),
("zijn", "bent", (nl.PRESENT, 2, nl.SINGULAR)),
("zijn", "is", (nl.PRESENT, 3, nl.SINGULAR)),
("zijn", "zijn", (nl.PRESENT, 0, nl.PLURAL)),
("zijn", "zijnd", (nl.PRESENT + nl.PARTICIPLE,)),
("zijn", "was", (nl.PAST, 1, nl.SINGULAR)),
("zijn", "was", (nl.PAST, 2, nl.SINGULAR)),
("zijn", "was", (nl.PAST, 3, nl.SINGULAR)),
("zijn", "waren", (nl.PAST, 0, nl.PLURAL)),
("zijn", "was", (nl.PAST, 0, None)),
("zijn", "geweest", (nl.PAST + nl.PARTICIPLE,)),
("had", "hebben", "inf"),
("had", "heb", "1sg"),
("had", "hebt", "2sg"),
("had", "heeft", "3sg"),
("had", "hebben", "pl"),
("had", "hebbend", "part"),
("heeft", "had", "1sgp"),
("heeft", "had", "2sgp"),
("heeft", "had", "3sgp"),
("heeft", "hadden", "ppl"),
("heeft", "had", "p"),
("heeft", "gehad", "ppart"),
("smsen", "smste", "3sgp")):
self.assertEqual(nl.conjugate(v1, tense), v2)
print("pattern.nl.conjugate()")
def test_lexeme(self):
# Assert all inflections of "zijn".
v = nl.lexeme("zijn")
self.assertEqual(v, [
"zijn", "ben", "bent", "is", "zijnd", "waren", "was", "geweest"
])
print("pattern.nl.inflect.lexeme()")
def test_tenses(self):
# Assert tense recognition.
self.assertTrue((nl.PRESENT, 3, "sg") in nl.tenses("is"))
self.assertTrue("3sg" in nl.tenses("is"))
print("pattern.nl.tenses()")
#---------------------------------------------------------------------------------------------------
class TestParser(unittest.TestCase):
def setUp(self):
pass
def test_wotan2penntreebank(self):
# Assert tag translation.
for penntreebank, wotan in (
("NNP", "N(eigen,ev,neut)"),
("NNPS", "N(eigen,mv,neut)"),
("NN", "N(soort,ev,neut)"),
("NNS", "N(soort,mv,neut)"),
("VBZ", "V(refl,ott,3,ev)"),
("VBP", "V(intrans,ott,1_of_2_of_3,mv)"),
("VBD", "V(trans,ovt,1_of_2_of_3,mv)"),
("VBN", "V(trans,verl_dw,onverv)"),
("VBG", "V(intrans,teg_dw,onverv)"),
("VB", "V(intrans,inf)"),
("MD", "V(hulp_of_kopp,ott,3,ev)"),
("JJ", "Adj(attr,stell,onverv)"),
("JJR", "Adj(adv,vergr,onverv)"),
("JJS", "Adj(attr,overtr,verv_neut)"),
("RP", "Adv(deel_v)"),
("RB", "Adv(gew,geen_func,stell,onverv)"),
("DT", "Art(bep,zijd_of_mv,neut)"),
("CC", "Conj(neven)"),
("CD", "Num(hoofd,bep,zelfst,onverv)"),
("TO", "Prep(voor_inf)"),
("IN", "Prep(voor)"),
("PRP", "Pron(onbep,neut,attr)"),
("PRP$", "Pron(bez,2,ev,neut,attr)"),
(",", "Punc(komma)"),
("(", "Punc(haak_open)"),
(")", "Punc(haak_sluit)"),
(".", "Punc(punt)"),
("UH", "Int"),
("SYM", "Misc(symbool)")):
self.assertEqual(nl.wotan2penntreebank("", wotan)[1], penntreebank)
print("pattern.nl.wotan2penntreebank()")
def test_find_lemmata(self):
# Assert lemmata for nouns and verbs.
v = nl.parser.find_lemmata([["katten", "NNS"], ["droegen", "VBD"], ["hoeden", "NNS"]])
self.assertEqual(v, [
["katten", "NNS", "kat"],
["droegen", "VBD", "dragen"],
["hoeden", "NNS", "hoed"]])
print("pattern.nl.parser.find_lemmata()")
def test_parse(self):
# Assert parsed output with Penn Treebank II tags (slash-formatted).
# 1) "de zwarte kat" is a noun phrase, "op de mat" is a prepositional noun phrase.
v = nl.parser.parse("De zwarte kat zat op de mat.")
self.assertEqual(v,
"De/DT/B-NP/O zwarte/JJ/I-NP/O kat/NN/I-NP/O " + \
"zat/VBD/B-VP/O " + \
"op/IN/B-PP/B-PNP de/DT/B-NP/I-PNP mat/NN/I-NP/I-PNP ././O/O"
)
# 2) "jaagt" and "vogels" lemmata are "jagen" and "vogel".
v = nl.parser.parse("De zwarte kat jaagt op vogels.", lemmata=True)
self.assertEqual(v,
"De/DT/B-NP/O/de zwarte/JJ/I-NP/O/zwart kat/NN/I-NP/O/kat " + \
"jaagt/VBZ/B-VP/O/jagen " + \
"op/IN/B-PP/B-PNP/op vogels/NNS/B-NP/I-PNP/vogel ././O/O/."
)
# Assert the accuracy of the Dutch tagger.
i, n = 0, 0
for sentence in open(os.path.join(PATH, "corpora", "tagged-nl-twnc.txt")).readlines():
sentence = sentence.decode("utf-8").strip()
s1 = [w.split("/") for w in sentence.split(" ")]
s1 = [nl.wotan2penntreebank(w, tag) for w, tag in s1]
s2 = [[w for w, pos in s1]]
s2 = nl.parse(s2, tokenize=False)
s2 = [w.split("/") for w in s2.split(" ")]
for j in range(len(s1)):
if s1[j][1] == s2[j][1]:
i += 1
n += 1
self.assertTrue(float(i) / n > 0.90)
print("pattern.nl.parser.parse()")
def test_tag(self):
# Assert [("zwarte", "JJ"), ("panters", "NNS")].
v = nl.tag("zwarte panters")
self.assertEqual(v, [("zwarte", "JJ"), ("panters", "NNS")])
print("pattern.nl.tag()")
def test_command_line(self):
# Assert parsed output from the command-line (example from the documentation).
p = ["python", "-m", "pattern.nl", "-s", "Leuke kat.", "-OTCRL"]
p = subprocess.Popen(p, stdout=subprocess.PIPE)
p.wait()
v = p.stdout.read()
v = v.strip()
self.assertEqual(v, "Leuke/JJ/B-NP/O/O/leuk kat/NN/I-NP/O/O/kat ././O/O/O/.")
print("python -m pattern.nl")
#---------------------------------------------------------------------------------------------------
class TestSentiment(unittest.TestCase):
def setUp(self):
pass
def test_sentiment(self):
# Assert < 0 for negative adjectives and > 0 for positive adjectives.
self.assertTrue(nl.sentiment("geweldig")[0] > 0)
self.assertTrue(nl.sentiment("verschrikkelijk")[0] < 0)
# Assert the accuracy of the sentiment analysis.
# Given are the scores for 3,000 book reviews.
# The baseline should increase (not decrease) when the algorithm is modified.
from pattern.db import Datasheet
from pattern.metrics import test
reviews = []
for score, review in Datasheet.load(os.path.join(PATH, "corpora", "polarity-nl-bol.com.csv")):
reviews.append((review, int(score) > 0))
A, P, R, F = test(lambda review: nl.positive(review), reviews)
#print(A, P, R, F)
self.assertTrue(A > 0.808)
self.assertTrue(P > 0.780)
self.assertTrue(R > 0.860)
self.assertTrue(F > 0.818)
print("pattern.nl.sentiment()")
#---------------------------------------------------------------------------------------------------
def suite():
suite = unittest.TestSuite()
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestInflection))
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestParser))
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestSentiment))
return suite
if __name__ == "__main__":
unittest.TextTestRunner(verbosity=1).run(suite())
|
