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1142 lines
48 KiB
Python
1142 lines
48 KiB
Python
5 years ago
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# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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from __future__ import print_function
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from __future__ import division
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from builtins import str, bytes, dict, int
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from builtins import map, zip, filter
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from builtins import object, range
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import os
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
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import unittest
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import random
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import subprocess
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from pattern import text
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from pattern import en
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from io import open
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try:
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PATH = os.path.dirname(os.path.realpath(__file__))
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except:
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PATH = ""
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#---------------------------------------------------------------------------------------------------
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class TestInflection(unittest.TestCase):
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def setUp(self):
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pass
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def test_indefinite_article(self):
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# Assert "a" or "an".
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for article, word in (
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("an", "hour"),
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("an", "FBI"),
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("a", "bear"),
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("a", "one-liner"),
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("a", "European"),
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("a", "university"),
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("a", "uterus"),
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("an", "owl"),
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("an", "yclept"),
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("a", "year")):
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self.assertEqual(en.article(word, function=en.INDEFINITE), article)
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self.assertEqual(en.inflect.article("heir", function=en.DEFINITE), "the")
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self.assertEqual(en.inflect.referenced("ewe"), "a ewe")
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print("pattern.en.inflect.article()")
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def test_pluralize(self):
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# Assert "octopodes" for classical plural of "octopus".
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# Assert "octopuses" for modern plural.
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self.assertEqual("octopodes", en.inflect.pluralize("octopus", classical=True))
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self.assertEqual("octopuses", en.inflect.pluralize("octopus", classical=False))
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# Assert the accuracy of the pluralization algorithm.
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from pattern.db import Datasheet
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i, n = 0, 0
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for sg, pl in Datasheet.load(os.path.join(PATH, "corpora", "wordforms-en-celex.csv")):
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if en.inflect.pluralize(sg) == pl:
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i += 1
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n += 1
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self.assertTrue(float(i) / n > 0.95)
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print("pattern.en.inflect.pluralize()")
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def test_singularize(self):
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# Assert the accuracy of the singularization algorithm.
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from pattern.db import Datasheet
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i, n = 0, 0
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for sg, pl in Datasheet.load(os.path.join(PATH, "corpora", "wordforms-en-celex.csv")):
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if en.inflect.singularize(pl) == sg:
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i += 1
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n += 1
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self.assertTrue(float(i) / n > 0.95)
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print("pattern.en.inflect.singularize()")
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def test_find_lemma(self):
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# Assert the accuracy of the verb lemmatization algorithm.
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# Note: the accuracy is higher (95%) when measured on CELEX word forms
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# (probably because en.verbs has high percentage irregular verbs).
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i, n = 0, 0
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for v1, v2 in en.inflect.verbs.inflections.items():
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if en.inflect.verbs.find_lemma(v1) == v2:
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i += 1
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n += 1
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self.assertTrue(float(i) / n > 0.90)
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print("pattern.en.inflect.verbs.find_lemma()")
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def test_find_lexeme(self):
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# Assert the accuracy of the verb conjugation algorithm.
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i, n = 0, 0
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for v, lexeme1 in en.inflect.verbs.infinitives.items():
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lexeme2 = en.inflect.verbs.find_lexeme(v)
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for j in range(len(lexeme2)):
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if lexeme1[j] == lexeme2[j] or \
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lexeme1[j] == "" and \
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lexeme1[j > 5 and 10 or 0] == lexeme2[j]:
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i += 1
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n += 1
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self.assertTrue(float(i) / n > 0.90)
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print("pattern.en.inflect.verbs.find_lexeme()")
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def test_conjugate(self):
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# Assert different tenses with different conjugations.
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for (v1, v2, tense) in (
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("be", "be", en.INFINITIVE),
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("be", "am", (en.PRESENT, 1, en.SINGULAR)),
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("be", "are", (en.PRESENT, 2, en.SINGULAR)),
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("be", "is", (en.PRESENT, 3, en.SINGULAR)),
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("be", "are", (en.PRESENT, 0, en.PLURAL)),
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("be", "being", (en.PRESENT + en.PARTICIPLE,)),
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("be", "was", (en.PAST, 1, en.SINGULAR)),
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("be", "were", (en.PAST, 2, en.SINGULAR)),
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("be", "was", (en.PAST, 3, en.SINGULAR)),
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("be", "were", (en.PAST, 0, en.PLURAL)),
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("be", "were", (en.PAST, 0, None)),
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("be", "been", (en.PAST + en.PARTICIPLE,)),
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("be", "am", "1sg"),
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("be", "are", "2sg"),
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("be", "is", "3sg"),
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("be", "are", "1pl"),
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("be", "are", "2pl"),
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("be", "are", "3pl"),
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("be", "are", "pl"),
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("be", "being", "part"),
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("be", "was", "1sgp"),
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("be", "were", "2sgp"),
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("be", "was", "3sgp"),
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("be", "were", "1ppl"),
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("be", "were", "2ppl"),
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("be", "were", "3ppl"),
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("be", "were", "p"),
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("be", "were", "ppl"),
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("be", "been", "ppart"),
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("be", "am not", "1sg-"),
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("be", "aren't", "2sg-"),
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("be", "isn't", "3sg-"),
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("be", "aren't", "1pl-"),
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("be", "aren't", "2pl-"),
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("be", "aren't", "3pl-"),
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("be", "aren't", "pl-"),
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("be", "wasn't", "1sgp-"),
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("be", "weren't", "2sgp-"),
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("be", "wasn't", "3sgp-"),
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("be", "weren't", "1ppl-"),
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("be", "weren't", "2ppl-"),
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("be", "weren't", "3ppl-"),
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("be", "weren't", "ppl-"),
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("had", "have", "inf"),
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("had", "have", "1sg"),
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("had", "have", "2sg"),
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("had", "has", "3sg"),
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("had", "have", "pl"),
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("had", "having", "part"),
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("has", "had", "1sgp"),
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("has", "had", "2sgp"),
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("has", "had", "3sgp"),
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("has", "had", "ppl"),
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("has", "had", "p"),
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("has", "had", "ppart"),
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("will", "will", "1sg"),
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("will", "will", "2sg"),
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("will", "will", "3sg"),
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("will", "will", "1pl"),
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("imaginerify", "imaginerifying", "part"),
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("imaginerify", "imaginerified", "3sgp"),
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("imaginerify", None, "1sg-")):
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self.assertEqual(en.inflect.conjugate(v1, tense), v2)
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print("pattern.en.inflect.conjugate()")
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def test_lemma(self):
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# Assert the infinitive of "weren't".
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v = en.inflect.lemma("weren't")
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self.assertEqual(v, "be")
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print("pattern.en.inflect.lemma()")
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def test_lexeme(self):
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# Assert all inflections of "be".
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v = en.inflect.lexeme("be")
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self.assertEqual(v, [
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"be", "am", "are", "is", "being",
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"was", "were", "been",
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"am not", "aren't", "isn't", "wasn't", "weren't"
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])
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v = en.inflect.lexeme("imaginerify")
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self.assertEqual(v, [
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"imaginerify", "imaginerifies", "imaginerifying", "imaginerified"
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])
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print("pattern.en.inflect.lexeme()")
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def test_tenses(self):
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# Assert tense recognition.
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self.assertTrue((en.inflect.PRESENT, 1, en.inflect.SINGULAR) in en.inflect.tenses("am"))
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self.assertTrue("1sg" in en.inflect.tenses("am"))
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self.assertTrue("1sg" in en.inflect.tenses("will"))
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self.assertTrue("2sg-" in en.inflect.tenses("won't"))
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self.assertTrue("part" in en.inflect.tenses("imaginarifying"))
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print("pattern.en.inflect.tenses()")
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def test_comparative(self):
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# Assert "nice" => "nicer".
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self.assertEqual(en.inflect.comparative("nice"), "nicer")
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print("pattern.en.inflect.comparative()")
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def test_superlative(self):
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# Assert "nice" => "nicest"
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self.assertEqual(en.inflect.superlative("nice"), "nicest")
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# Assert "important" => "most important"
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self.assertEqual(en.inflect.superlative("important"), "most important")
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print("pattern.en.inflect.superlative()")
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#---------------------------------------------------------------------------------------------------
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class TestQuantification(unittest.TestCase):
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def setUp(self):
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pass
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def test_extract_leading_zeros(self):
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# Assert "zero zero one" => ("one", 2).
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from pattern.text.en.inflect_quantify import zshift
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v = zshift("zero zero one")
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self.assertEqual(v, ("one", 2))
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v = zshift("0 0 one")
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self.assertEqual(v, ("one", 2))
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print("pattern.en.quantify._extract_leading_zeros()")
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def test_numerals(self):
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# Assert number to numerals.
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for x, s in (
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( 1.5, "one point five"),
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( 15, "fifteen"),
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( 150, "one hundred and fifty"),
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( 151, "one hundred and fifty-one"),
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( 1510, "one thousand five hundred and ten"),
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( 15101, "fifteen thousand one hundred and one"),
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( 150101, "one hundred and fifty thousand one hundred and one"),
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(1500101, "one million, five hundred thousand one hundred and one")):
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self.assertEqual(en.numerals(x), s)
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print("pattern.en.numerals()")
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def test_number(self):
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# Assert numeric string = actual number (after rounding).
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for i in range(100):
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x = random.random()
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y = en.number(en.numerals(x, round=10))
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self.assertAlmostEqual(x, y, places=10)
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print("pattern.en.number()")
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def test_quantify(self):
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# Assert quantification algorithm.
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for a, s in (
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( 2 * ["carrot"], "a pair of carrots"),
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( 4 * ["carrot"], "several carrots"),
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( 9 * ["carrot"], "a number of carrots"),
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( 19 * ["carrot"], "a score of carrots"),
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( 23 * ["carrot"], "dozens of carrots"),
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( 201 * ["carrot"], "hundreds of carrots"),
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(1001 * ["carrot"], "thousands of carrots"),
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({"carrot": 4, "parrot": 2}, "several carrots and a pair of parrots")):
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self.assertEqual(en.quantify(a), s)
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print("pattern.en.quantify()")
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def test_reflect(self):
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self.assertEqual(en.reflect(""), "a string")
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self.assertEqual(en.reflect(["", "", ""]), "several strings")
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self.assertEqual(en.reflect(en.reflect), "a function")
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print("pattern.en.reflect()")
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#---------------------------------------------------------------------------------------------------
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class TestSpelling(unittest.TestCase):
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def test_spelling(self):
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# Assert case-sensitivity + numbers.
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for a, b in (
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( ".", "." ),
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( "?", "?" ),
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( "!", "!" ),
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( "I", "I" ),
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( "a", "a" ),
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( "42", "42" ),
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("3.14", "3.14"),
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( "The", "The" ),
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( "the", "the" )):
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self.assertEqual(en.suggest(a)[0][0], b)
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# Assert spelling suggestion accuracy.
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# Note: simply training on more text will not improve accuracy.
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i = j = 0.0
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from pattern.db import Datasheet
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for correct, wrong in Datasheet.load(os.path.join(PATH, "corpora", "spelling-birkbeck.csv")):
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for w in wrong.split(" "):
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if en.suggest(w)[0][0] == correct:
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i += 1
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else:
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j += 1
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self.assertTrue(i / (i + j) > 0.70)
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print("pattern.en.suggest()")
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#---------------------------------------------------------------------------------------------------
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||
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class TestParser(unittest.TestCase):
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def setUp(self):
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pass
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def test_tokenize(self):
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# Assert list with two sentences.
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# The tokenizer should at least handle common abbreviations and punctuation.
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v = en.tokenize("The cat is eating (e.g., a fish). Yum!")
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self.assertEqual(v, ["The cat is eating ( e.g. , a fish ) .", "Yum !"])
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print("pattern.en.tokenize()")
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def _test_morphological_rules(self, function=en.parser.morphology.apply):
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""" For each word in WordNet that is not in Brill's lexicon,
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test if the given tagger((word, "NN")) yields an improved (word, tag).
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Returns the relative scores for nouns, verbs, adjectives and adverbs.
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"""
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scores = []
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for tag, lexicon in (
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("NN", en.wordnet.NOUNS),
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("VB", en.wordnet.VERBS),
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("JJ", en.wordnet.ADJECTIVES),
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("RB", en.wordnet.ADVERBS)):
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i, n = 0, 0
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for word in lexicon():
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word = word.replace("_", " ")
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if word not in en.lexicon:
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if function([word, "NN"])[1].startswith(tag):
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i += 1
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n += 1
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scores.append(float(i) / n)
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return scores
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def test_default_suffix_rules(self):
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# Assert part-of-speech tag for unknown tokens.
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for a, b in (
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(["eating", "NN"], ["eating", "VBG"]),
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|
|
(["tigers", "NN"], ["tigers", "NNS"]),
|
||
|
|
(["really", "NN"], ["really", "RB"]),
|
||
|
|
(["foolish", "NN"], ["foolish", "JJ"])):
|
||
|
|
self.assertEqual(text._suffix_rules(a), b)
|
||
|
|
# Test with words in WordNet that are not in Brill's lexicon.
|
||
|
|
# Given are the scores for detection of nouns, verbs, adjectives and adverbs.
|
||
|
|
# The baseline should increase (not decrease) when the algorithm is modified.
|
||
|
|
v = self._test_morphological_rules(function=text._suffix_rules)
|
||
|
|
self.assertTrue(v[0] > 0.91) # NN
|
||
|
|
self.assertTrue(v[1] > 0.23) # VB
|
||
|
|
self.assertTrue(v[2] > 0.38) # JJ
|
||
|
|
self.assertTrue(v[3] > 0.60) # RB
|
||
|
|
print("pattern.text._suffix_rules()")
|
||
|
|
|
||
|
|
def test_apply_morphological_rules(self):
|
||
|
|
# Assert part-of-speech tag for unknown tokens (Brill's lexical rules).
|
||
|
|
v = self._test_morphological_rules(function=en.parser.morphology.apply)
|
||
|
|
self.assertTrue(v[0] > 0.85) # NN
|
||
|
|
self.assertTrue(v[1] > 0.19) # VB
|
||
|
|
self.assertTrue(v[2] > 0.65) # JJ
|
||
|
|
self.assertTrue(v[3] > 0.59) # RB
|
||
|
|
print("pattern.en.parser.morphology.apply()")
|
||
|
|
|
||
|
|
def test_apply_context_rules(self):
|
||
|
|
# Assert part-of-speech tags based on word context.
|
||
|
|
for a, b in ( # Rule:
|
||
|
|
([["", "JJ"], ["", "JJ"], ["", ","]], [["", "JJ"], ["", "NN"], ["", ","]]), # SURROUNDTAG
|
||
|
|
([["", "NNP"], ["", "RB"]], [["", "NNP"], ["", "NNP"]]), # PREVTAG
|
||
|
|
([["", "NN"], ["", "PRP$"]], [["", "VB"], ["", "PRP$"]]), # NEXTTAG
|
||
|
|
([["phone", ""], ["", "VBZ"]], [["phone", ""], ["", "NNS"]]), # PREVWD
|
||
|
|
([["", "VB"], ["countries", ""]], [["", "JJ"], ["countries", ""]]), # NEXTWD
|
||
|
|
([["close", "VB"], ["to", ""]], [["close", "RB"], ["to", ""]]), # RBIGRAM
|
||
|
|
([["very", ""], ["much", "JJ"]], [["very", ""], ["much", "RB"]]), # LBIGRAM
|
||
|
|
([["such", "JJ"], ["as", "DT"]], [["such", "JJ"], ["as", "IN"]]), # WDNEXTWD
|
||
|
|
([["be", "VB"]], [["be", "VB"]])): # CURWD
|
||
|
|
self.assertEqual(en.parser.context.apply(a), b)
|
||
|
|
print("pattern.en.parser.context.apply()")
|
||
|
|
|
||
|
|
def test_find_tags(self):
|
||
|
|
# Assert part-of-speech-tag annotation.
|
||
|
|
v = en.parser.find_tags(["black", "cat"])
|
||
|
|
self.assertEqual(v, [["black", "JJ"], ["cat", "NN"]])
|
||
|
|
self.assertEqual(en.parser.find_tags(["felix"])[0][1], "NN")
|
||
|
|
self.assertEqual(en.parser.find_tags(["Felix"])[0][1], "NNP")
|
||
|
|
print("pattern.en.parser.find_tags()")
|
||
|
|
|
||
|
|
def test_find_chunks(self):
|
||
|
|
# Assert chunk tag annotation.
|
||
|
|
v = en.parser.find_chunks([["black", "JJ"], ["cat", "NN"]])
|
||
|
|
self.assertEqual(v, [["black", "JJ", "B-NP", "O"], ["cat", "NN", "I-NP", "O"]])
|
||
|
|
# Assert the accuracy of the chunker.
|
||
|
|
# For example, in "The very black cat must be really meowing really loud in the yard.":
|
||
|
|
# - "The very black" (NP)
|
||
|
|
# - "must be really meowing" (VP)
|
||
|
|
# - "really loud" (ADJP)
|
||
|
|
# - "in" (PP)
|
||
|
|
# - "the yard" (NP)
|
||
|
|
v = en.parser.find_chunks([
|
||
|
|
["", "DT"], ["", "RB"], ["", "JJ"], ["", "NN"],
|
||
|
|
["", "MD"], ["", "RB"], ["", "VBZ"], ["", "VBG"],
|
||
|
|
["", "RB"], ["", "JJ"],
|
||
|
|
["", "IN"],
|
||
|
|
["", "CD"], ["", "NNS"]
|
||
|
|
])
|
||
|
|
self.assertEqual(v, [
|
||
|
|
["", "DT", "B-NP", "O"], ["", "RB", "I-NP", "O"], ["", "JJ", "I-NP", "O"], ["", "NN", "I-NP", "O"],
|
||
|
|
["", "MD", "B-VP", "O"], ["", "RB", "I-VP", "O"], ["", "VBZ", "I-VP", "O"], ["", "VBG", "I-VP", "O"],
|
||
|
|
["", "RB", "B-ADJP", "O"], ["", "JJ", "I-ADJP", "O"],
|
||
|
|
["", "IN", "B-PP", "B-PNP"],
|
||
|
|
["", "CD", "B-NP", "I-PNP"], ["", "NNS", "I-NP", "I-PNP"]])
|
||
|
|
# Assert commas inside chunks.
|
||
|
|
# - "the big, black cat"
|
||
|
|
v = en.parser.find_chunks([
|
||
|
|
["", "DT"], ["", "JJ"], ["", ","], ["", "JJ"], ["", "NN"]
|
||
|
|
])
|
||
|
|
self.assertEqual(v, [
|
||
|
|
["", "DT", "B-NP", "O"],
|
||
|
|
["", "JJ", "I-NP", "O"],
|
||
|
|
["", ",", "I-NP", "O"],
|
||
|
|
["", "JJ", "I-NP", "O"],
|
||
|
|
["", "NN", "I-NP", "O"]
|
||
|
|
])
|
||
|
|
# - "big, black and furry"
|
||
|
|
v = en.parser.find_chunks([
|
||
|
|
["", "JJ"], ["", ","], ["", "JJ"], ["", "CC"], ["", "JJ"]
|
||
|
|
])
|
||
|
|
self.assertEqual(v, [
|
||
|
|
["", "JJ", "B-ADJP", "O"],
|
||
|
|
["", ",", "I-ADJP", "O"],
|
||
|
|
["", "JJ", "I-ADJP", "O"],
|
||
|
|
["", "CC", "I-ADJP", "O"],
|
||
|
|
["", "JJ", "I-ADJP", "O"]
|
||
|
|
])
|
||
|
|
# - big, and very black (= two chunks "big" and "very black")
|
||
|
|
v = en.parser.find_chunks([
|
||
|
|
["", "JJ"], ["", ","], ["", "CC"], ["", "RB"], ["", "JJ"]
|
||
|
|
])
|
||
|
|
self.assertEqual(v, [
|
||
|
|
["", "JJ", "B-ADJP", "O"],
|
||
|
|
["", ",", "O", "O"],
|
||
|
|
["", "CC", "O", "O"],
|
||
|
|
["", "RB", "B-ADJP", "O"],
|
||
|
|
["", "JJ", "I-ADJP", "O"]
|
||
|
|
])
|
||
|
|
# Assert cases for which we have written special rules.
|
||
|
|
# - "perhaps you" (ADVP + NP)
|
||
|
|
v = en.parser.find_chunks([["", "RB"], ["", "PRP"]])
|
||
|
|
self.assertEqual(v, [["", "RB", "B-ADVP", "O"], ["", "PRP", "B-NP", "O"]])
|
||
|
|
# - "very nice cats" (NP)
|
||
|
|
v = en.parser.find_chunks([["", "RB"], ["", "JJ"], ["", "PRP"]])
|
||
|
|
self.assertEqual(v, [["", "RB", "B-NP", "O"], ["", "JJ", "I-NP", "O"], ["", "PRP", "I-NP", "O"]])
|
||
|
|
print("pattern.en.parser.find_chunks()")
|
||
|
|
|
||
|
|
def test_find_labels(self):
|
||
|
|
# Assert relation tag annotation (SBJ/OBJ).
|
||
|
|
v = en.parser.find_labels([
|
||
|
|
["", "", "NP"], ["", "", "NP"],
|
||
|
|
["", "", "VP"], ["", "", "VP"],
|
||
|
|
["", "", "NP"]])
|
||
|
|
self.assertEqual(v, [
|
||
|
|
["", "", "NP", "NP-SBJ-1"], ["", "", "NP", "NP-SBJ-1"],
|
||
|
|
["", "", "VP", "VP-1"], ["", "", "VP", "VP-1"],
|
||
|
|
["", "", "NP", "NP-OBJ-1"]])
|
||
|
|
print("pattern.en.parser.find_labels()")
|
||
|
|
|
||
|
|
def test_find_prepositions(self):
|
||
|
|
# Assert preposition tag annotation (PP + NP).
|
||
|
|
v = en.parser.find_prepositions([
|
||
|
|
["", "", "NP"],
|
||
|
|
["", "", "VP"],
|
||
|
|
["", "", "PP"],
|
||
|
|
["", "", "NP"],
|
||
|
|
["", "", "NP"], ])
|
||
|
|
self.assertEqual(v, [
|
||
|
|
["", "", "NP", "O"],
|
||
|
|
["", "", "VP", "O"],
|
||
|
|
["", "", "PP", "B-PNP"],
|
||
|
|
["", "", "NP", "I-PNP"],
|
||
|
|
["", "", "NP", "I-PNP"]])
|
||
|
|
# Assert PNP's with consecutive PP's.
|
||
|
|
v = en.parse("The cat was looking at me from up on the roof with interest.", prepositions=True)
|
||
|
|
self.assertEqual(v,
|
||
|
|
"The/DT/B-NP/O cat/NN/I-NP/O " \
|
||
|
|
"was/VBD/B-VP/O looking/VBG/I-VP/O " \
|
||
|
|
"at/IN/B-PP/B-PNP me/PRP/B-NP/I-PNP " \
|
||
|
|
"from/IN/B-PP/B-PNP up/IN/I-PP/I-PNP on/IN/I-PP/I-PNP the/DT/B-NP/I-PNP roof/NN/I-NP/I-PNP " \
|
||
|
|
"with/IN/B-PP/B-PNP interest/NN/B-NP/I-PNP " \
|
||
|
|
"././O/O"
|
||
|
|
)
|
||
|
|
print("pattern.en.parser.find_prepositions()")
|
||
|
|
|
||
|
|
def test_find_lemmata(self):
|
||
|
|
# Assert lemmata for nouns and verbs.
|
||
|
|
v = en.parser.find_lemmata([["cats", "NNS"], ["wearing", "VBG"], ["hats", "NNS"]])
|
||
|
|
self.assertEqual(v, [
|
||
|
|
["cats", "NNS", "cat"],
|
||
|
|
["wearing", "VBG", "wear"],
|
||
|
|
["hats", "NNS", "hat"]])
|
||
|
|
print("pattern.en.parser.find_lemmata()")
|
||
|
|
|
||
|
|
def test_named_entity_recognition(self):
|
||
|
|
# Assert named entities.
|
||
|
|
v = en.parser.parse("Arnold Schwarzenegger is cool.", chunks=False)
|
||
|
|
self.assertEqual(v,
|
||
|
|
"Arnold/NNP-PERS Schwarzenegger/NNP-PERS is/VBZ cool/JJ ./."
|
||
|
|
)
|
||
|
|
print("pattern.en.parser.entities.apply()")
|
||
|
|
|
||
|
|
def test_parse(self):
|
||
|
|
# Assert parsed output with Penn Treebank II tags (slash-formatted).
|
||
|
|
# 1) "the black cat" is a noun phrase, "on the mat" is a prepositional noun phrase.
|
||
|
|
v = en.parser.parse("The black cat sat on the mat.")
|
||
|
|
self.assertEqual(v,
|
||
|
|
"The/DT/B-NP/O black/JJ/I-NP/O cat/NN/I-NP/O " + \
|
||
|
|
"sat/VBD/B-VP/O " + \
|
||
|
|
"on/IN/B-PP/B-PNP the/DT/B-NP/I-PNP mat/NN/I-NP/I-PNP ././O/O"
|
||
|
|
)
|
||
|
|
# 2) "the black cat" is the subject, "a fish" is the object.
|
||
|
|
v = en.parser.parse("The black cat is eating a fish.", relations=True)
|
||
|
|
self.assertEqual(v,
|
||
|
|
"The/DT/B-NP/O/NP-SBJ-1 black/JJ/I-NP/O/NP-SBJ-1 cat/NN/I-NP/O/NP-SBJ-1 " + \
|
||
|
|
"is/VBZ/B-VP/O/VP-1 eating/VBG/I-VP/O/VP-1 " + \
|
||
|
|
"a/DT/B-NP/O/NP-OBJ-1 fish/NN/I-NP/O/NP-OBJ-1 ././O/O/O"
|
||
|
|
)
|
||
|
|
# 3) "chasing" and "mice" lemmata are "chase" and "mouse".
|
||
|
|
v = en.parser.parse("The black cat is chasing mice.", lemmata=True)
|
||
|
|
self.assertEqual(v,
|
||
|
|
"The/DT/B-NP/O/the black/JJ/I-NP/O/black cat/NN/I-NP/O/cat " + \
|
||
|
|
"is/VBZ/B-VP/O/be chasing/VBG/I-VP/O/chase " + \
|
||
|
|
"mice/NNS/B-NP/O/mouse ././O/O/."
|
||
|
|
)
|
||
|
|
# 4) Assert str.
|
||
|
|
self.assertTrue(isinstance(v, str))
|
||
|
|
# 5) Assert str for faulty input (bytestring with unicode characters).
|
||
|
|
self.assertTrue(isinstance(en.parse("ø ü"), str))
|
||
|
|
self.assertTrue(isinstance(en.parse("ø ü", tokenize=True, tags=False, chunks=False), str))
|
||
|
|
self.assertTrue(isinstance(en.parse("ø ü", tokenize=False, tags=False, chunks=False), str))
|
||
|
|
self.assertTrue(isinstance(en.parse("o u", encoding="ascii"), str))
|
||
|
|
# 6) Assert optional parameters (i.e., setting all to False).
|
||
|
|
self.assertEqual(en.parse("ø ü.", tokenize=True, tags=False, chunks=False), "ø ü .")
|
||
|
|
self.assertEqual(en.parse("ø ü.", tokenize=False, tags=False, chunks=False), "ø ü.")
|
||
|
|
# 7) Assert the accuracy of the English tagger.
|
||
|
|
i, n = 0, 0
|
||
|
|
for corpus, a in (("tagged-en-wsj.txt", (0.968, 0.945)), ("tagged-en-oanc.txt", (0.929, 0.932))):
|
||
|
|
for sentence in open(os.path.join(PATH, "corpora", corpus)).readlines():
|
||
|
|
sentence = sentence.strip()
|
||
|
|
s1 = [w.split("/") for w in sentence.split(" ")]
|
||
|
|
s2 = [[w for w, pos in s1]]
|
||
|
|
s2 = en.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].split("-")[0]:
|
||
|
|
i += 1
|
||
|
|
n += 1
|
||
|
|
#print(corpus, float(i) / n)
|
||
|
|
self.assertTrue(float(i) / n > (en.parser.model and a[0] or a[1]))
|
||
|
|
print("pattern.en.parse()")
|
||
|
|
|
||
|
|
def test_tagged_string(self):
|
||
|
|
# Assert splitable TaggedString with language and tags properties.
|
||
|
|
v = en.parser.parse("The black cat sat on the mat.", relations=True, lemmata=True)
|
||
|
|
self.assertEqual(v.language, "en")
|
||
|
|
self.assertEqual(v.tags,
|
||
|
|
["word", "part-of-speech", "chunk", "preposition", "relation", "lemma"])
|
||
|
|
self.assertEqual(v.split(text.TOKENS)[0][0],
|
||
|
|
["The", "DT", "B-NP", "O", "NP-SBJ-1", "the"])
|
||
|
|
print("pattern.en.parse().split()")
|
||
|
|
|
||
|
|
def test_parsetree(self):
|
||
|
|
# Assert parsetree(s) == Text.
|
||
|
|
v = en.parsetree("The cat purs.")
|
||
|
|
self.assertTrue(isinstance(v, en.Text))
|
||
|
|
print("pattern.en.parsetree()")
|
||
|
|
|
||
|
|
def test_split(self):
|
||
|
|
# Assert split(parse(s)) == Text.
|
||
|
|
v = en.split(en.parse("The cat purs."))
|
||
|
|
self.assertTrue(isinstance(v, en.Text))
|
||
|
|
print("pattern.en.split()")
|
||
|
|
|
||
|
|
def test_tag(self):
|
||
|
|
# Assert [("black", "JJ"), ("cats", "NNS")].
|
||
|
|
v = en.tag("black cats")
|
||
|
|
self.assertEqual(v, [("black", "JJ"), ("cats", "NNS")])
|
||
|
|
v = en.tag("")
|
||
|
|
self.assertEqual(v, [])
|
||
|
|
print("pattern.en.tag()")
|
||
|
|
|
||
|
|
def test_ngrams(self):
|
||
|
|
# Assert n-grams with and without punctuation marks / sentence marks.
|
||
|
|
s = "The cat is napping."
|
||
|
|
v1 = en.ngrams(s, n=2)
|
||
|
|
v2 = en.ngrams(s, n=3, punctuation=en.PUNCTUATION.strip("."))
|
||
|
|
self.assertEqual(v1, [("The", "cat"), ("cat", "is"), ("is", "napping")])
|
||
|
|
self.assertEqual(v2, [("The", "cat", "is"), ("cat", "is", "napping"), ("is", "napping", ".")])
|
||
|
|
s = "The cat purrs. The dog barks."
|
||
|
|
v1 = en.ngrams(s, n=2)
|
||
|
|
v2 = en.ngrams(s, n=2, continuous=True)
|
||
|
|
self.assertEqual(v1, [("The", "cat"), ("cat", "purrs"), ("The", "dog"), ("dog", "barks")])
|
||
|
|
self.assertEqual(v2, [("The", "cat"), ("cat", "purrs"), ("purrs", "The"), ("The", "dog"), ("dog", "barks")])
|
||
|
|
print("pattern.en.ngrams()")
|
||
|
|
|
||
|
|
def test_command_line(self):
|
||
|
|
# Assert parsed output from the command-line (example from the documentation).
|
||
|
|
p = ["python", "-m", "pattern.en", "-s", "Nice cat.", "-OTCRL"]
|
||
|
|
p = subprocess.Popen(p, stdout=subprocess.PIPE)
|
||
|
|
p.wait()
|
||
|
|
v = p.stdout.read().decode('utf-8')
|
||
|
|
v = v.strip()
|
||
|
|
self.assertEqual(v, "Nice/JJ/B-NP/O/O/nice cat/NN/I-NP/O/O/cat ././O/O/O/.")
|
||
|
|
print("python -m pattern.en")
|
||
|
|
|
||
|
|
#---------------------------------------------------------------------------------------------------
|
||
|
|
|
||
|
|
|
||
|
|
class TestParseTree(unittest.TestCase):
|
||
|
|
|
||
|
|
def setUp(self):
|
||
|
|
# Parse sentences to test on.
|
||
|
|
# Creating a Text creates Sentence, Chunk, PNP and Word.
|
||
|
|
# Creating a Sentence tests Sentence.append() and Sentence.parse_token().
|
||
|
|
self.text = "I'm eating pizza with a fork. What a tasty pizza!"
|
||
|
|
self.text = en.Text(en.parse(self.text, relations=True, lemmata=True))
|
||
|
|
|
||
|
|
def test_copy(self):
|
||
|
|
# Assert deepcopy of Text, Sentence, Chunk, PNP and Word.
|
||
|
|
self.text = self.text.copy()
|
||
|
|
print("pattern.en.Text.copy()")
|
||
|
|
|
||
|
|
def test_xml(self):
|
||
|
|
# Assert XML export and import.
|
||
|
|
self.text = en.Text.from_xml(self.text.xml)
|
||
|
|
print("pattern.en.Text.xml")
|
||
|
|
print("pattern.en.Text.from_xml()")
|
||
|
|
|
||
|
|
def test_text(self):
|
||
|
|
# Assert Text.
|
||
|
|
self.assertEqual(self.text.sentences[0].string, "I 'm eating pizza with a fork .")
|
||
|
|
self.assertEqual(self.text.sentences[1].string, "What a tasty pizza !")
|
||
|
|
print("pattern.en.Text")
|
||
|
|
|
||
|
|
def test_sentence(self):
|
||
|
|
# Assert Sentence.
|
||
|
|
v = self.text[0]
|
||
|
|
self.assertTrue(v.start == 0)
|
||
|
|
self.assertTrue(v.stop == 8)
|
||
|
|
self.assertTrue(v.string == "I 'm eating pizza with a fork .")
|
||
|
|
self.assertTrue(v.subjects == [self.text[0].chunks[0]])
|
||
|
|
self.assertTrue(v.verbs == [self.text[0].chunks[1]])
|
||
|
|
self.assertTrue(v.objects == [self.text[0].chunks[2]])
|
||
|
|
self.assertTrue(v.nouns == [self.text[0].words[3], self.text[0].words[6]])
|
||
|
|
# Sentence.string must be unicode.
|
||
|
|
self.assertTrue(isinstance(v.string, str))
|
||
|
|
self.assertTrue(isinstance(str(v), str))
|
||
|
|
print("pattern.en.Sentence")
|
||
|
|
|
||
|
|
def test_sentence_constituents(self):
|
||
|
|
# Assert in-order list of Chunk, PNP and Word.
|
||
|
|
v = self.text[0].constituents(pnp=True)
|
||
|
|
self.assertEqual(v, [
|
||
|
|
self.text[0].chunks[0],
|
||
|
|
self.text[0].chunks[1],
|
||
|
|
self.text[0].chunks[2],
|
||
|
|
self.text[0].pnp[0],
|
||
|
|
self.text[0].words[7],
|
||
|
|
])
|
||
|
|
print("pattern.en.Sentence.constituents()")
|
||
|
|
|
||
|
|
def test_slice(self):
|
||
|
|
# Assert sentence slice.
|
||
|
|
v = self.text[0].slice(start=4, stop=6)
|
||
|
|
self.assertTrue(v.parent == self.text[0])
|
||
|
|
self.assertTrue(v.string == "with a")
|
||
|
|
# Assert sentence slice tag integrity.
|
||
|
|
self.assertTrue(v.words[0].type == "IN")
|
||
|
|
self.assertTrue(v.words[1].chunk is None)
|
||
|
|
print("pattern.en.Slice")
|
||
|
|
|
||
|
|
def test_chunk(self):
|
||
|
|
# Assert chunk with multiple words ("a fork").
|
||
|
|
v = self.text[0].chunks[4]
|
||
|
|
self.assertTrue(v.start == 5)
|
||
|
|
self.assertTrue(v.stop == 7)
|
||
|
|
self.assertTrue(v.string == "a fork")
|
||
|
|
self.assertTrue(v.lemmata == ["a", "fork"])
|
||
|
|
self.assertTrue(v.words == [self.text[0].words[5], self.text[0].words[6]])
|
||
|
|
self.assertTrue(v.head == self.text[0].words[6])
|
||
|
|
self.assertTrue(v.type == "NP")
|
||
|
|
self.assertTrue(v.role is None)
|
||
|
|
self.assertTrue(v.pnp is not None)
|
||
|
|
# Assert chunk that is subject/object of the sentence ("pizza").
|
||
|
|
v = self.text[0].chunks[2]
|
||
|
|
self.assertTrue(v.role == "OBJ")
|
||
|
|
self.assertTrue(v.relation == 1)
|
||
|
|
self.assertTrue(v.related == [self.text[0].chunks[0], self.text[0].chunks[1]])
|
||
|
|
self.assertTrue(v.subject == self.text[0].chunks[0])
|
||
|
|
self.assertTrue(v.verb == self.text[0].chunks[1])
|
||
|
|
self.assertTrue(v.object is None)
|
||
|
|
# Assert chunk traversal.
|
||
|
|
self.assertEqual(v.nearest("VP"), self.text[0].chunks[1])
|
||
|
|
self.assertEqual(v.previous(), self.text[0].chunks[1])
|
||
|
|
self.assertEqual(v.next(), self.text[0].chunks[3])
|
||
|
|
print("pattern.en.Chunk")
|
||
|
|
|
||
|
|
def test_chunk_conjunctions(self):
|
||
|
|
# Assert list of conjunct/disjunct chunks ("black cat" AND "white cat").
|
||
|
|
v = en.Sentence(en.parse("black cat and white cat"))
|
||
|
|
self.assertEqual(v.chunk[0].conjunctions, [(v.chunk[1], en.AND)])
|
||
|
|
print("pattern.en.Chunk.conjunctions()")
|
||
|
|
|
||
|
|
def test_chunk_modifiers(self):
|
||
|
|
# Assert list of nearby adjectives and adverbs with no role, for VP.
|
||
|
|
v = en.Sentence(en.parse("Perhaps you should go."))
|
||
|
|
self.assertEqual(v.chunk[2].modifiers, [v.chunk[0]]) # should <=> perhaps
|
||
|
|
print("pattern.en.Chunk.modifiers")
|
||
|
|
|
||
|
|
def test_pnp(self):
|
||
|
|
# Assert PNP chunk ("with a fork").
|
||
|
|
v = self.text[0].pnp[0]
|
||
|
|
self.assertTrue(v.string == "with a fork")
|
||
|
|
self.assertTrue(v.chunks == [self.text[0].chunks[3], self.text[0].chunks[4]])
|
||
|
|
self.assertTrue(v.pp == self.text[0].chunks[3])
|
||
|
|
print("pattern.en.PNP")
|
||
|
|
|
||
|
|
def test_word(self):
|
||
|
|
# Assert word tags ("fork" => NN).
|
||
|
|
v = self.text[0].words[6]
|
||
|
|
self.assertTrue(v.index == 6)
|
||
|
|
self.assertTrue(v.string == "fork")
|
||
|
|
self.assertTrue(v.lemma == "fork")
|
||
|
|
self.assertTrue(v.type == "NN")
|
||
|
|
self.assertTrue(v.chunk == self.text[0].chunks[4])
|
||
|
|
self.assertTrue(v.pnp is not None)
|
||
|
|
for i, tags in enumerate([
|
||
|
|
["I", "PRP", "B-NP", "O", "NP-SBJ-1", "i"],
|
||
|
|
["'m", "VBP", "B-VP", "O", "VP-1", "be"],
|
||
|
|
["eating", "VBG", "I-VP", "O", "VP-1", "eat"],
|
||
|
|
["pizza", "NN", "B-NP", "O", "NP-OBJ-1", "pizza"],
|
||
|
|
["with", "IN", "B-PP", "B-PNP", "O", "with"],
|
||
|
|
["a", "DT", "B-NP", "I-PNP", "O", "a"],
|
||
|
|
["fork", "NN", "I-NP", "I-PNP", "O", "fork"],
|
||
|
|
[".", ".", "O", "O", "O", "."]]):
|
||
|
|
self.assertEqual(self.text[0].words[i].tags, tags)
|
||
|
|
print("pattern.en.Word")
|
||
|
|
|
||
|
|
def test_word_custom_tags(self):
|
||
|
|
# Assert word custom tags ("word/part-of-speech/.../some-custom-tag").
|
||
|
|
s = en.Sentence("onion/NN/FOOD", token=[en.WORD, en.POS, "semantic_type"])
|
||
|
|
v = s.words[0]
|
||
|
|
self.assertEqual(v.semantic_type, "FOOD")
|
||
|
|
self.assertEqual(v.custom_tags["semantic_type"], "FOOD")
|
||
|
|
self.assertEqual(v.copy().custom_tags["semantic_type"], "FOOD")
|
||
|
|
# Assert addition of new custom tags.
|
||
|
|
v.custom_tags["taste"] = "pungent"
|
||
|
|
self.assertEqual(s.token, [en.WORD, en.POS, "semantic_type", "taste"])
|
||
|
|
print("pattern.en.Word.custom_tags")
|
||
|
|
|
||
|
|
def test_find(self):
|
||
|
|
# Assert first item for which given function is True.
|
||
|
|
v = text.tree.find(lambda x: x > 10, [1, 2, 3, 11, 12])
|
||
|
|
self.assertEqual(v, 11)
|
||
|
|
print("pattern.text.tree.find()")
|
||
|
|
|
||
|
|
def test_zip(self):
|
||
|
|
# Assert list of zipped tuples, using default to balance uneven lists.
|
||
|
|
v = text.tree.zip([1, 2, 3], [4, 5, 6, 7], default=0)
|
||
|
|
self.assertEqual(v, [(1, 4), (2, 5), (3, 6), (0, 7)])
|
||
|
|
print("pattern.text.tree.zip()")
|
||
|
|
|
||
|
|
def test_unzip(self):
|
||
|
|
v = text.tree.unzip(1, [(1, 4), (2, 5), (3, 6)])
|
||
|
|
self.assertEqual(v, [4, 5, 6])
|
||
|
|
print("pattern.text.tree.unzip()")
|
||
|
|
|
||
|
|
def test_unique(self):
|
||
|
|
# Assert list copy with unique items.
|
||
|
|
v = text.tree.unique([1, 1, 1])
|
||
|
|
self.assertEqual(len(v), 1)
|
||
|
|
self.assertEqual(v[0], 1)
|
||
|
|
print("pattern.text.tree.unique()")
|
||
|
|
|
||
|
|
def test_map(self):
|
||
|
|
# Assert dynamic Map().
|
||
|
|
v = text.tree.Map(lambda x: x + 1, [1, 2, 3])
|
||
|
|
self.assertEqual(list(v), [2, 3, 4])
|
||
|
|
self.assertEqual(v.items[0], 1)
|
||
|
|
print("pattern.text.tree.Map()")
|
||
|
|
|
||
|
|
#---------------------------------------------------------------------------------------------------
|
||
|
|
|
||
|
|
|
||
|
|
class TestModality(unittest.TestCase):
|
||
|
|
|
||
|
|
def setUp(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def test_imperative(self):
|
||
|
|
# Assert True for sentences that are orders, commands, warnings.
|
||
|
|
from pattern.text.en.modality import imperative
|
||
|
|
for b, s in (
|
||
|
|
(True, "Do your homework!"),
|
||
|
|
(True, "Do not listen to me."),
|
||
|
|
(True, "Turn that off, will you."),
|
||
|
|
(True, "Let's help him."),
|
||
|
|
(True, "Help me!"),
|
||
|
|
(True, "You will help me."),
|
||
|
|
(False, "Do it if you think it is necessary."),
|
||
|
|
(False, "I hope you will help me."),
|
||
|
|
(False, "I can help you."),
|
||
|
|
(False, "I can help you if you let me.")):
|
||
|
|
self.assertEqual(imperative(en.Sentence(en.parse(s))), b)
|
||
|
|
print("pattern.en.modality.imperative()")
|
||
|
|
|
||
|
|
def test_conditional(self):
|
||
|
|
# Assert True for sentences that contain possible or imaginary situations.
|
||
|
|
from pattern.text.en.modality import conditional
|
||
|
|
for b, s in (
|
||
|
|
(True, "We ought to help him."),
|
||
|
|
(True, "We could help him."),
|
||
|
|
(True, "I will help you."),
|
||
|
|
(True, "I hope you will help me."),
|
||
|
|
(True, "I can help you if you let me."),
|
||
|
|
(False, "You will help me."),
|
||
|
|
(False, "I can help you.")):
|
||
|
|
self.assertEqual(conditional(en.Sentence(en.parse(s))), b)
|
||
|
|
# Assert predictive mood.
|
||
|
|
s = "I will help you."
|
||
|
|
v = conditional(en.Sentence(en.parse(s)), predictive=False)
|
||
|
|
self.assertEqual(v, False)
|
||
|
|
# Assert speculative mood.
|
||
|
|
s = "I will help you if you pay me."
|
||
|
|
v = conditional(en.Sentence(en.parse(s)), predictive=False)
|
||
|
|
self.assertEqual(v, True)
|
||
|
|
print("pattern.en.modality.conditional()")
|
||
|
|
|
||
|
|
def test_subjunctive(self):
|
||
|
|
# Assert True for sentences that contain wishes, judgments or opinions.
|
||
|
|
from pattern.text.en.modality import subjunctive
|
||
|
|
for b, s in (
|
||
|
|
(True, "I wouldn't do that if I were you."),
|
||
|
|
(True, "I wish I knew."),
|
||
|
|
(True, "I propose that you be on time."),
|
||
|
|
(True, "It is a bad idea to be late."),
|
||
|
|
(False, "I will be late.")):
|
||
|
|
self.assertEqual(subjunctive(en.Sentence(en.parse(s))), b)
|
||
|
|
print("pattern.en.modality.subjunctive()")
|
||
|
|
|
||
|
|
def test_negated(self):
|
||
|
|
# Assert True for sentences that contain "not", "n't" or "never".
|
||
|
|
for b, s in (
|
||
|
|
(True, "Not true?"),
|
||
|
|
(True, "Never true."),
|
||
|
|
(True, "Isn't true."),):
|
||
|
|
self.assertEqual(en.negated(en.Sentence(en.parse(s))), b)
|
||
|
|
print("pattern.en.negated()")
|
||
|
|
|
||
|
|
def test_mood(self):
|
||
|
|
# Assert imperative mood.
|
||
|
|
v = en.mood(en.Sentence(en.parse("Do your homework!")))
|
||
|
|
self.assertEqual(v, en.IMPERATIVE)
|
||
|
|
# Assert conditional mood.
|
||
|
|
v = en.mood(en.Sentence(en.parse("We ought to help him.")))
|
||
|
|
self.assertEqual(v, en.CONDITIONAL)
|
||
|
|
# Assert subjunctive mood.
|
||
|
|
v = en.mood(en.Sentence(en.parse("I wouldn't do that if I were you.")))
|
||
|
|
self.assertEqual(v, en.SUBJUNCTIVE)
|
||
|
|
# Assert indicative mood.
|
||
|
|
v = en.mood(en.Sentence(en.parse("The weather is nice today.")))
|
||
|
|
self.assertEqual(v, en.INDICATIVE)
|
||
|
|
print("pattern.en.mood()")
|
||
|
|
|
||
|
|
def test_modality(self):
|
||
|
|
# Assert -1.0 => +1.0 representing the degree of certainty.
|
||
|
|
v = en.modality(en.Sentence(en.parse("I wish it would stop raining.")))
|
||
|
|
self.assertTrue(v < 0)
|
||
|
|
v = en.modality(en.Sentence(en.parse("It will surely stop raining soon.")))
|
||
|
|
self.assertTrue(v > 0)
|
||
|
|
# Assert the accuracy of the modality algorithm.
|
||
|
|
# Given are the scores for the CoNLL-2010 Shared Task 1 Wikipedia uncertainty data:
|
||
|
|
# http://www.inf.u-szeged.hu/rgai/conll2010st/tasks.html#task1
|
||
|
|
# The baseline should increase (not decrease) when the algorithm is modified.
|
||
|
|
from pattern.db import Datasheet
|
||
|
|
from pattern.metrics import test
|
||
|
|
sentences = []
|
||
|
|
for certain, sentence in Datasheet.load(os.path.join(PATH, "corpora", "uncertainty-conll2010.csv")):
|
||
|
|
sentence = en.parse(sentence, chunks=False, light=True)
|
||
|
|
sentence = en.Sentence(sentence)
|
||
|
|
sentences.append((sentence, int(certain) > 0))
|
||
|
|
A, P, R, F = test(lambda sentence: en.modality(sentence) > 0.5, sentences)
|
||
|
|
#print(A, P, R, F)
|
||
|
|
self.assertTrue(A > 0.69)
|
||
|
|
self.assertTrue(P > 0.72)
|
||
|
|
self.assertTrue(R > 0.63)
|
||
|
|
self.assertTrue(F > 0.68)
|
||
|
|
print("pattern.en.modality()")
|
||
|
|
|
||
|
|
#---------------------------------------------------------------------------------------------------
|
||
|
|
|
||
|
|
|
||
|
|
class TestSentiment(unittest.TestCase):
|
||
|
|
|
||
|
|
def setUp(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def test_sentiment_avg(self):
|
||
|
|
# Assert 2.5.
|
||
|
|
from pattern.text import avg
|
||
|
|
v = avg([1, 2, 3, 4])
|
||
|
|
self.assertEqual(v, 2.5)
|
||
|
|
print("pattern.text.avg")
|
||
|
|
|
||
|
|
def test_sentiment(self):
|
||
|
|
# Assert < 0 for negative adjectives and > 0 for positive adjectives.
|
||
|
|
self.assertTrue(en.sentiment("wonderful")[0] > 0)
|
||
|
|
self.assertTrue(en.sentiment("horrible")[0] < 0)
|
||
|
|
self.assertTrue(en.sentiment(en.wordnet.synsets("horrible", pos="JJ")[0])[0] < 0)
|
||
|
|
self.assertTrue(en.sentiment(en.Text(en.parse("A bad book. Really horrible.")))[0] < 0)
|
||
|
|
# Assert that :) and :( are recognized.
|
||
|
|
self.assertTrue(en.sentiment(":)")[0] > 0)
|
||
|
|
self.assertTrue(en.sentiment(":(")[0] < 0)
|
||
|
|
# Assert the accuracy of the sentiment analysis (for the positive class).
|
||
|
|
# Given are the scores for Pang & Lee's polarity dataset v2.0:
|
||
|
|
# http://www.cs.cornell.edu/people/pabo/movie-review-data/
|
||
|
|
# 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-en-pang&lee1.csv")):
|
||
|
|
reviews.append((review, int(score) > 0))
|
||
|
|
from time import time
|
||
|
|
t = time()
|
||
|
|
A, P, R, F = test(lambda review: en.positive(review), reviews)
|
||
|
|
#print(A, P, R, F)
|
||
|
|
self.assertTrue(A > 0.752)
|
||
|
|
self.assertTrue(P > 0.772)
|
||
|
|
self.assertTrue(R > 0.715)
|
||
|
|
self.assertTrue(F > 0.743)
|
||
|
|
# Assert the accuracy of the sentiment analysis on short text (for the positive class).
|
||
|
|
# Given are the scores for Pang & Lee's sentence polarity dataset v1.0:
|
||
|
|
# http://www.cs.cornell.edu/people/pabo/movie-review-data/
|
||
|
|
reviews = []
|
||
|
|
for score, review in Datasheet.load(os.path.join(PATH, "corpora", "polarity-en-pang&lee2.csv")):
|
||
|
|
reviews.append((review, int(score) > 0))
|
||
|
|
A, P, R, F = test(lambda review: en.positive(review), reviews)
|
||
|
|
#print(A, P, R, F)
|
||
|
|
self.assertTrue(A > 0.654)
|
||
|
|
self.assertTrue(P > 0.660)
|
||
|
|
self.assertTrue(R > 0.636)
|
||
|
|
self.assertTrue(F > 0.648)
|
||
|
|
print("pattern.en.sentiment()")
|
||
|
|
|
||
|
|
def test_sentiment_twitter(self):
|
||
|
|
sanders = os.path.join(PATH, "corpora", "polarity-en-sanders.csv")
|
||
|
|
if os.path.exists(sanders):
|
||
|
|
# Assert the accuracy of the sentiment analysis on tweets.
|
||
|
|
# Given are the scores for Sanders Twitter Sentiment Corpus:
|
||
|
|
# http://www.sananalytics.com/lab/twitter-sentiment/
|
||
|
|
# Positive + neutral is taken as polarity >= 0.0,
|
||
|
|
# Negative is taken as polarity < 0.0.
|
||
|
|
# Since there are a lot of neutral cases,
|
||
|
|
# and the algorithm predicts 0.0 by default (i.e., majority class) the results are good.
|
||
|
|
# Distinguishing negative from neutral from positive is a much harder task
|
||
|
|
from pattern.db import Datasheet
|
||
|
|
from pattern.metrics import test
|
||
|
|
reviews = []
|
||
|
|
for i, id, date, tweet, polarity, topic in Datasheet.load(sanders):
|
||
|
|
if polarity != "irrelevant":
|
||
|
|
reviews.append((tweet, polarity in ("positive", "neutral")))
|
||
|
|
A, P, R, F = test(lambda review: en.positive(review, threshold=0.0), reviews)
|
||
|
|
#print(A, P, R, F)
|
||
|
|
self.assertTrue(A > 0.824)
|
||
|
|
self.assertTrue(P > 0.879)
|
||
|
|
self.assertTrue(R > 0.911)
|
||
|
|
self.assertTrue(F > 0.895)
|
||
|
|
|
||
|
|
def test_sentiment_assessment(self):
|
||
|
|
# Assert that en.sentiment() has a fine-grained "assessments" property.
|
||
|
|
v = en.sentiment("A warm and pleasant day.").assessments
|
||
|
|
self.assertTrue(v[1][0][0] == "pleasant")
|
||
|
|
self.assertTrue(v[1][1] > 0)
|
||
|
|
print("pattern.en.sentiment().assessments")
|
||
|
|
|
||
|
|
def test_polarity(self):
|
||
|
|
# Assert that en.polarity() yields en.sentiment()[0].
|
||
|
|
s = "A great day!"
|
||
|
|
self.assertTrue(en.polarity(s) == en.sentiment(s)[0])
|
||
|
|
print("pattern.en.polarity()")
|
||
|
|
|
||
|
|
def test_subjectivity(self):
|
||
|
|
# Assert that en.subjectivity() yields en.sentiment()[1].
|
||
|
|
s = "A great day!"
|
||
|
|
self.assertTrue(en.subjectivity(s) == en.sentiment(s)[1])
|
||
|
|
print("pattern.en.subjectivity()")
|
||
|
|
|
||
|
|
def test_positive(self):
|
||
|
|
# Assert that en.positive() yields polarity >= 0.1.
|
||
|
|
s = "A great day!"
|
||
|
|
self.assertTrue(en.positive(s))
|
||
|
|
print("pattern.en.subjectivity()")
|
||
|
|
|
||
|
|
def test_sentiwordnet(self):
|
||
|
|
# Assert < 0 for negative words and > 0 for positive words.
|
||
|
|
try:
|
||
|
|
from pattern.text.en.wordnet import SentiWordNet
|
||
|
|
lexicon = SentiWordNet()
|
||
|
|
lexicon.load()
|
||
|
|
except ImportError as e:
|
||
|
|
# SentiWordNet data file is not installed in default location, stop test.
|
||
|
|
print(e)
|
||
|
|
return
|
||
|
|
self.assertTrue(lexicon["wonderful"][0] > 0)
|
||
|
|
self.assertTrue(lexicon["horrible"][0] < 0)
|
||
|
|
print("pattern.en.sentiment.SentiWordNet")
|
||
|
|
|
||
|
|
#---------------------------------------------------------------------------------------------------
|
||
|
|
|
||
|
|
|
||
|
|
class TestWordNet(unittest.TestCase):
|
||
|
|
|
||
|
|
def setUp(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def test_normalize(self):
|
||
|
|
# Assert normalization of simple diacritics (WordNet does not store diacritics).
|
||
|
|
self.assertEqual(en.wordnet.normalize("cliché"), "cliche")
|
||
|
|
self.assertEqual(en.wordnet.normalize("façade"), "facade")
|
||
|
|
print("pattern.en.wordnet.normalize()")
|
||
|
|
|
||
|
|
def test_version(self):
|
||
|
|
print("WordNet " + en.wordnet.VERSION)
|
||
|
|
|
||
|
|
def test_synsets(self):
|
||
|
|
# Assert synsets by part-of-speech.
|
||
|
|
for word, pos in (
|
||
|
|
("cat", en.wordnet.NOUN),
|
||
|
|
("purr", en.wordnet.VERB),
|
||
|
|
("nice", en.wordnet.ADJECTIVE),
|
||
|
|
("nicely", en.wordnet.ADVERB),
|
||
|
|
("cat", "nn"),
|
||
|
|
("cat", "NNS")):
|
||
|
|
self.assertTrue(en.wordnet.synsets(word, pos) != [])
|
||
|
|
# Assert TypeError when part-of-speech is not NOUN, VERB, ADJECTIVE or ADVERB.
|
||
|
|
self.assertRaises(TypeError, en.wordnet.synsets, "cat", "unknown_pos")
|
||
|
|
print("pattern.en.wordnet.synsets()")
|
||
|
|
|
||
|
|
def test_synset(self):
|
||
|
|
v = en.wordnet.synsets("puma")[0]
|
||
|
|
# Assert Synset(id).
|
||
|
|
self.assertEqual(v, en.wordnet.Synset(v.id))
|
||
|
|
self.assertEqual(v.pos, en.wordnet.NOUN)
|
||
|
|
self.assertAlmostEqual(v.ic, 0.0, places=1)
|
||
|
|
self.assertTrue("cougar" in v.synonyms) # ["cougar", "puma", "catamount", ...]
|
||
|
|
self.assertTrue("feline" in v.gloss) # "large American feline resembling a lion"
|
||
|
|
# Assert WordNet relations.
|
||
|
|
s = en.wordnet.synsets
|
||
|
|
v = s("tree")[0]
|
||
|
|
self.assertTrue(v.hypernym in v.hypernyms())
|
||
|
|
self.assertTrue(s("woody plant")[0] in v.hypernyms())
|
||
|
|
self.assertTrue(s("entity")[0] in v.hypernyms(recursive=True))
|
||
|
|
self.assertTrue(s("beech")[0] in v.hyponyms())
|
||
|
|
self.assertTrue(s("red beech")[0] in v.hyponyms(recursive=True))
|
||
|
|
self.assertTrue(s("trunk")[0] in v.meronyms())
|
||
|
|
self.assertTrue(s("forest")[0] in v.holonyms())
|
||
|
|
# Assert Lin-similarity.
|
||
|
|
self.assertTrue(
|
||
|
|
v.similarity(s("flower")[0]) >
|
||
|
|
v.similarity(s("teapot")[0]))
|
||
|
|
print("pattern.en.wordnet.Synset")
|
||
|
|
|
||
|
|
def test_ancenstor(self):
|
||
|
|
# Assert least-common-subsumer algorithm.
|
||
|
|
v1 = en.wordnet.synsets("cat")[0]
|
||
|
|
v2 = en.wordnet.synsets("dog")[0]
|
||
|
|
self.assertTrue(en.wordnet.ancestor(v1, v2) == en.wordnet.synsets("carnivore")[0])
|
||
|
|
print("pattern.en.wordnet.ancestor()")
|
||
|
|
|
||
|
|
def test_map32(self):
|
||
|
|
# Assert sense mapping from WN 3.0 to 2.1.
|
||
|
|
self.assertEqual(en.wordnet.map32(18850, "JJ"), (19556, "JJ"))
|
||
|
|
self.assertEqual(en.wordnet.map32(1382437, "VB"), (1370230, "VB"))
|
||
|
|
print("pattern.en.wordnet.map32")
|
||
|
|
|
||
|
|
def test_sentiwordnet(self):
|
||
|
|
# Assert SentiWordNet is loaded correctly.
|
||
|
|
if en.wordnet.sentiwordnet is None:
|
||
|
|
return
|
||
|
|
try:
|
||
|
|
en.wordnet.sentiwordnet.load()
|
||
|
|
except ImportError:
|
||
|
|
return
|
||
|
|
v = en.wordnet.synsets("anguish")[0]
|
||
|
|
self.assertEqual(v.weight, (-0.625, 0.625))
|
||
|
|
v = en.wordnet.synsets("enzymology")[0]
|
||
|
|
self.assertEqual(v.weight, (0.125, 0.125))
|
||
|
|
print("pattern.en.wordnet.sentiwordnet")
|
||
|
|
|
||
|
|
#---------------------------------------------------------------------------------------------------
|
||
|
|
|
||
|
|
|
||
|
|
class TestWordlists(unittest.TestCase):
|
||
|
|
|
||
|
|
def setUp(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def test_wordlist(self):
|
||
|
|
# Assert lazy loading Wordlist.
|
||
|
|
v = en.wordlist.STOPWORDS
|
||
|
|
self.assertTrue("the" in v)
|
||
|
|
# Assert Wordlist to dict.
|
||
|
|
v = dict.fromkeys(en.wordlist.STOPWORDS, True)
|
||
|
|
self.assertTrue("the" in v)
|
||
|
|
# Assert new Wordlist by adding other Wordlists.
|
||
|
|
v = en.wordlist.STOPWORDS + en.wordlist.ACADEMIC
|
||
|
|
self.assertTrue("the" in v)
|
||
|
|
self.assertTrue("dr." in v)
|
||
|
|
print("pattern.en.wordlist.Wordlist")
|
||
|
|
|
||
|
|
#---------------------------------------------------------------------------------------------------
|
||
|
|
|
||
|
|
|
||
|
|
def suite():
|
||
|
|
suite = unittest.TestSuite()
|
||
|
|
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestInflection))
|
||
|
|
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestQuantification))
|
||
|
|
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestSpelling))
|
||
|
|
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestParser))
|
||
|
|
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestParseTree))
|
||
|
|
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestModality))
|
||
|
|
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestSentiment))
|
||
|
|
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestWordNet))
|
||
|
|
suite.addTest(unittest.TestLoader().loadTestsFromTestCase(TestWordlists))
|
||
|
|
return suite
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
|
||
|
|
result = unittest.TextTestRunner(verbosity=1).run(suite())
|
||
|
|
sys.exit(not result.wasSuccessful())
|