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test_supervised_topic_classification_EN.py
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test_supervised_topic_classification_EN.py
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import unittest
from collections import OrderedDict
import holmes_extractor as holmes
import os
script_directory = os.path.dirname(os.path.realpath(__file__))
ontology = holmes.Ontology(os.sep.join((script_directory, "test_ontology.owl")))
holmes_manager = holmes.Manager(
"en_core_web_trf",
perform_coreference_resolution=True,
ontology=ontology,
number_of_workers=1,
)
no_ontology_holmes_manager = holmes.Manager(
"en_core_web_trf", perform_coreference_resolution=True, number_of_workers=1
)
ontology2 = holmes.Ontology(os.sep.join((script_directory, "test_ontology.owl")))
no_coref_holmes_manager = holmes.Manager(
"en_core_web_trf",
perform_coreference_resolution=False,
ontology=ontology2,
number_of_workers=1,
)
def get_first_key_in_dict(dictionary: OrderedDict) -> str:
return list(dictionary.keys())[0]
class EnglishSupervisedTopicClassificationTest(unittest.TestCase):
def test_get_labels_to_classification_frequencies_direct_matching(self):
sttb = holmes_manager.get_supervised_topic_training_basis(one_hot=False)
sttb.parse_and_register_training_document("A lion chases a tiger", "animals")
sttb.parse_and_register_training_document("fast", "dummy")
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertEqual(freq["predicate-actor: chasing-lion"], {"animals": 1})
self.assertEqual(freq["predicate-patient: chasing-tiger"], {"animals": 1})
self.assertEqual(
freq["predicate-actor: chasing-lion/predicate-patient: chasing-tiger"],
{"animals": 1},
)
self.assertEqual(freq["word: lion"], {"animals": 1})
self.assertEqual(freq["word: tiger"], {"animals": 1})
def test_get_labels_to_classification_frequencies_ontology_matching(self):
sttb = holmes_manager.get_supervised_topic_training_basis(one_hot=False)
sttb.parse_and_register_training_document("A dog chases a cat", "animals")
sttb.parse_and_register_training_document("fast", "dummy")
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertEqual(freq["predicate-actor: chasing-animal"], {"animals": 1})
self.assertEqual(freq["predicate-patient: chasing-animal"], {"animals": 1})
self.assertEqual(
freq["predicate-actor: chasing-animal/predicate-patient: chasing-animal"],
{"animals": 1},
)
self.assertEqual(freq["word: animal"], {"animals": 2})
def test_get_labels_to_classification_frequencies_ontology_multiword_matching(self):
sttb = holmes_manager.get_supervised_topic_training_basis(one_hot=False)
sttb.parse_and_register_training_document(
"A gymnast jumps over a wastage horse", "gym"
)
sttb.parse_and_register_training_document("fast", "dummy")
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertEqual(freq["predicate-actor: jump-gymnast"], {"gym": 1})
self.assertEqual(freq["word: gymnast"], {"gym": 1})
self.assertEqual(freq["word: gymnastics equipment"], {"gym": 1})
def test_linked_matching_common_dependent(self):
sttb = holmes_manager.get_supervised_topic_training_basis(one_hot=False)
sttb.parse_and_register_training_document(
"A lion eats and consumes a tiger", "animals"
)
sttb.parse_and_register_training_document("fast", "dummy")
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertEqual(freq["predicate-actor: consume-lion"], {"animals": 1})
self.assertEqual(freq["predicate-actor: eat-lion"], {"animals": 1})
self.assertEqual(freq["predicate-patient: consume-tiger"], {"animals": 1})
self.assertEqual(
freq["predicate-actor: consume-lion/predicate-patient: consume-tiger"],
{"animals": 1},
)
self.assertEqual(
freq["predicate-actor: consume-lion/predicate-actor: eat-lion"],
{"animals": 1},
)
self.assertEqual(freq["word: lion"], {"animals": 1})
self.assertEqual(freq["word: tiger"], {"animals": 1})
def test_linked_matching_common_dependent_control(self):
sttb = holmes_manager.get_supervised_topic_training_basis(one_hot=False)
sttb.parse_and_register_training_document(
"A lion eats and a lion consumes", "animals"
)
sttb.parse_and_register_training_document("fast", "dummy")
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertEqual(freq["predicate-actor: consume-lion"], {"animals": 1})
self.assertEqual(freq["predicate-actor: eat-lion"], {"animals": 1})
self.assertTrue(
"predicate-actor: consume-lion/predicate-actor: eat-lion" not in freq.keys()
)
self.assertEqual(freq["word: lion"], {"animals": 2})
def test_linked_matching_stepped_lower_first(self):
sttb = holmes_manager.get_supervised_topic_training_basis(one_hot=False)
sttb.parse_and_register_training_document("A big lion eats", "animals")
sttb.parse_and_register_training_document("fast", "dummy")
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertEqual(freq["governor-adjective: lion-big"], {"animals": 1})
self.assertEqual(freq["predicate-actor: eat-lion"], {"animals": 1})
self.assertEqual(
freq["governor-adjective: lion-big/predicate-actor: eat-lion"],
{"animals": 1},
)
self.assertEqual(freq["word: lion"], {"animals": 1})
def test_linked_matching_stepped_lower_second(self):
sttb = holmes_manager.get_supervised_topic_training_basis(one_hot=False)
sttb.parse_and_register_training_document(
"Something eats a big lion", "animals"
)
sttb.parse_and_register_training_document("fast", "dummy")
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertEqual(freq["governor-adjective: lion-big"], {"animals": 1})
self.assertEqual(freq["predicate-patient: eat-lion"], {"animals": 1})
self.assertEqual(
freq["governor-adjective: lion-big/predicate-patient: eat-lion"],
{"animals": 1},
)
self.assertEqual(freq["word: lion"], {"animals": 1})
def test_linked_matching_stepped_control(self):
sttb = no_coref_holmes_manager.get_supervised_topic_training_basis(
one_hot=False
)
sttb.parse_and_register_training_document(
"There is a big lion and the lion eats", "animals"
)
sttb.parse_and_register_training_document("fast", "dummy")
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertEqual(freq["governor-adjective: lion-big"], {"animals": 1})
self.assertEqual(freq["predicate-actor: eat-lion"], {"animals": 1})
self.assertTrue(
"governor-adjective: lion-big/predicate-actor: eat-lion" not in freq.keys()
)
self.assertEqual(freq["word: lion"], {"animals": 2})
def test_repeating_relation_through_coreference(self):
sttb = no_ontology_holmes_manager.get_supervised_topic_training_basis()
sttb.parse_and_register_training_document(
"The building was used last year. It is used this year", "test"
)
sttb.parse_and_register_training_document("fast", "dummy")
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertFalse(
"predicate-patient: use-building/predicate-patient: use-building" in freq
)
def test_one_hot(self):
sttb1 = no_coref_holmes_manager.get_supervised_topic_training_basis(
one_hot=False
)
sttb1.parse_and_register_training_document(
"A dog chases a cat. A dog chases a cat", "animals"
)
sttb1.parse_and_register_training_document(
"A dog chases a cat. A dog chases a cat", "animals"
)
sttb1.parse_and_register_training_document(
"A dog chases a cat. A dog chases a cat", "animals2"
)
sttb1.parse_and_register_training_document(
"A dog chases a cat. A dog chases a cat", "animals2"
)
sttb1.prepare()
freq1 = sttb1.labels_to_classification_frequencies
sttb2 = no_coref_holmes_manager.get_supervised_topic_training_basis(
one_hot=True
)
sttb2.parse_and_register_training_document(
"A dog chases a cat. A dog chases a cat", "animals"
)
sttb2.parse_and_register_training_document(
"A dog chases a cat. A dog chases a cat", "animals"
)
sttb2.parse_and_register_training_document(
"A dog chases a cat. A dog chases a cat", "animals2"
)
sttb2.parse_and_register_training_document(
"A dog chases a cat. A dog chases a cat", "animals2"
)
sttb2.prepare()
freq2 = sttb2.labels_to_classification_frequencies
self.assertEqual(
freq1["predicate-actor: chasing-animal/predicate-patient: chasing-animal"],
{"animals": 4, "animals2": 4},
)
self.assertEqual(
freq1["predicate-actor: chasing-animal"], {"animals": 4, "animals2": 4}
)
self.assertEqual(
freq1["predicate-patient: chasing-animal"], {"animals": 4, "animals2": 4}
)
self.assertEqual(freq1["word: animal"], {"animals": 8, "animals2": 8})
self.assertEqual(
freq2["predicate-actor: chasing-animal/predicate-patient: chasing-animal"],
{"animals": 2, "animals2": 2},
)
self.assertEqual(
freq2["predicate-actor: chasing-animal"], {"animals": 2, "animals2": 2}
)
self.assertEqual(
freq2["predicate-patient: chasing-animal"], {"animals": 2, "animals2": 2}
)
self.assertEqual(freq2["word: animal"], {"animals": 2, "animals2": 2})
def test_multiple_document_classes(self):
sttb = holmes_manager.get_supervised_topic_training_basis(one_hot=False)
sttb.parse_and_register_training_document("A dog chases a cat", "animals")
sttb.parse_and_register_training_document("A cat chases a dog", "animals")
sttb.parse_and_register_training_document("A cat chases a horse", "animals")
sttb.parse_and_register_training_document("A cat chases a horse", "animals")
sttb.parse_and_register_training_document("A gymnast jumps over a horse", "gym")
sttb.parse_and_register_training_document(
"A gymnast jumps over a wastage horse", "gym"
)
sttb.prepare()
freq = sttb.labels_to_classification_frequencies
self.assertEqual(freq["predicate-actor: chasing-animal"], {"animals": 4})
self.assertEqual(freq["predicate-actor: jump-gymnast"], {"gym": 2})
self.assertEqual(freq["predicate-patient: chasing-animal"], {"animals": 4})
self.assertEqual(
freq["predicate-actor: chasing-animal/predicate-patient: chasing-animal"],
{"animals": 4},
)
self.assertEqual(freq["word: animal"], {"animals": 8, "gym": 2})
self.assertEqual(freq["word: gymnast"], {"gym": 2})
self.assertEqual(freq["word: gymnastics equipment"], {"animals": 2, "gym": 2})
def test_whole_scenario_with_classification_ontology(self):
sttb = holmes_manager.get_supervised_topic_training_basis(
classification_ontology=ontology, one_hot=False
)
sttb.parse_and_register_training_document("A puppy", "puppy", "d0")
sttb.parse_and_register_training_document("A pussy", "cat", "d1")
sttb.parse_and_register_training_document("A dog on a lead", "dog", "d2")
sttb.parse_and_register_training_document("Mimi Momo", "Mimi Momo", "d3")
sttb.parse_and_register_training_document("An animal", "animal", "d4")
sttb.parse_and_register_training_document("A computer", "computers", "d5")
sttb.parse_and_register_training_document("A robot", "computers", "d6")
sttb.register_additional_classification_label("parrot")
sttb.register_additional_classification_label("hound")
sttb.prepare()
self.assertEqual(
{
"Mimi Momo": ["animal", "cat"],
"dog": ["animal", "hound"],
"puppy": ["animal", "dog", "hound"],
"cat": ["animal"],
"hound": ["animal", "dog"],
},
sttb.classification_implication_dict,
)
self.assertEqual(
["Mimi Momo", "animal", "cat", "computers", "dog", "hound", "puppy"],
sttb.classifications,
)
# With so little training data, the NN does not consistently learn correctly
for i in range(20):
trainer = sttb.train(
minimum_occurrences=0,
cv_threshold=0,
max_epochs=1000,
learning_rate=0.0001,
convergence_threshold=0,
)
stc = trainer.classifier()
if (
get_first_key_in_dict(stc.parse_and_classify("You are a robot."))
== "computers"
and get_first_key_in_dict(stc.parse_and_classify("You are a cat."))
== "animal"
):
break
if i == 20:
self.assertTrue(
get_first_key_in_dict(stc.parse_and_classify("You are a robot."))
== "computers"
and get_first_key_in_dict(
stc.parse_and_classify("You are a cat.")
)
== "animal"
)
self.assertEqual(
[
"prepgovernor-noun: animal-lead",
"word: animal",
"word: computer",
"word: lead",
"word: robot",
],
list(trainer.sorted_label_dict.keys()),
)
self.assertEqual(
[{1: 1}, {1: 1}, {1: 1, 0: 1, 3: 1}, {1: 1}, {1: 1}, {2: 1}, {4: 1}],
trainer.occurrence_dicts,
)
self.assertEqual(
[
[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0],
[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0],
[1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
],
trainer.output_matrix.tolist(),
)
self.assertEqual([5, 5, 6], trainer._hidden_layer_sizes)
self.assertIsNone(
stc.parse_and_classify("My name is Charles and I like sewing.")
)
serialized_supervised_topic_classifier_model = stc.serialize_model()
stc2 = no_ontology_holmes_manager.deserialize_supervised_topic_classifier(
serialized_supervised_topic_classifier_model, verbose=True
)
self.assertEqual(
[
"prepgovernor-noun: animal-lead",
"word: animal",
"word: computer",
"word: lead",
"word: robot",
],
list(stc2.model.sorted_label_dict.keys()),
)
self.assertEqual(
get_first_key_in_dict(stc2.parse_and_classify("You are a robot.")),
"computers",
)
self.assertEqual(
get_first_key_in_dict(stc2.parse_and_classify("You are a cat.")), "animal"
)
self.assertIsNone(
stc2.parse_and_classify("My name is Charles and I like sewing.")
)
def test_whole_scenario_with_classification_ontology_and_match_all_words(self):
sttb = holmes_manager.get_supervised_topic_training_basis(
classification_ontology=ontology, match_all_words=True, one_hot=False
)
sttb.parse_and_register_training_document("A puppy", "puppy", "d0")
sttb.parse_and_register_training_document("A pussy", "cat", "d1")
sttb.parse_and_register_training_document("A dog on a lead", "dog", "d2")
sttb.parse_and_register_training_document("Mimi Momo", "Mimi Momo", "d3")
sttb.parse_and_register_training_document("An animal", "animal", "d4")
sttb.parse_and_register_training_document("A computer", "computers", "d5")
sttb.parse_and_register_training_document("A robot", "computers", "d6")
sttb.register_additional_classification_label("parrot")
sttb.register_additional_classification_label("hound")
sttb.prepare()
self.assertEqual(
{
"Mimi Momo": ["animal", "cat"],
"dog": ["animal", "hound"],
"puppy": ["animal", "dog", "hound"],
"cat": ["animal"],
"hound": ["animal", "dog"],
},
sttb.classification_implication_dict,
)
self.assertEqual(
["Mimi Momo", "animal", "cat", "computers", "dog", "hound", "puppy"],
sttb.classifications,
)
# With so little training data, the NN does not consistently learn correctly
for i in range(20):
trainer = sttb.train(
minimum_occurrences=0,
cv_threshold=0,
max_epochs=1000,
learning_rate=0.0001,
convergence_threshold=0,
)
stc = trainer.classifier()
if (
get_first_key_in_dict(stc.parse_and_classify("You are a robot."))
== "computers"
and get_first_key_in_dict(stc.parse_and_classify("You are a cat."))
== "animal"
):
break
if i == 20:
self.assertTrue(
get_first_key_in_dict(stc.parse_and_classify("You are a robot."))
== "computers"
and get_first_key_in_dict(
stc.parse_and_classify("You are a cat.")
)
== "animal"
)
self.assertEqual(
[
"prepgovernor-noun: animal-lead",
"word: animal",
"word: computer",
"word: lead",
"word: mimi",
"word: momo",
"word: on",
"word: robot",
],
list(trainer.sorted_label_dict.keys()),
)
self.assertEqual(
[{1: 1}, {1: 1}, {0: 1, 1: 1, 3: 1, 6: 1}, {1: 1, 4: 1, 5: 1}, {1: 1}, {2: 1}, {7: 1}],
trainer.occurrence_dicts,
)
self.assertEqual(
[
[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0],
[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0],
[1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
],
trainer.output_matrix.tolist(),
)
self.assertEqual([8, 7, 7], trainer._hidden_layer_sizes)
self.assertIsNone(
stc.parse_and_classify("My name is Charles and I like sewing.")
)
serialized_supervised_topic_classifier_model = stc.serialize_model()
stc2 = no_ontology_holmes_manager.deserialize_supervised_topic_classifier(
serialized_supervised_topic_classifier_model
)
self.assertEqual(
[
"prepgovernor-noun: animal-lead",
"word: animal",
"word: computer",
"word: lead",
"word: mimi",
"word: momo",
"word: on",
"word: robot",
],
list(stc2.model.sorted_label_dict.keys()),
)
self.assertEqual(
get_first_key_in_dict(stc2.parse_and_classify("You are a robot.")),
"computers",
)
self.assertEqual(
get_first_key_in_dict(stc2.parse_and_classify("You are a cat.")), "animal"
)
self.assertIsNone(
stc2.parse_and_classify("My name is Charles and I like sewing.")
)
def test_filtering(self):
sttb = holmes_manager.get_supervised_topic_training_basis()
sttb.parse_and_register_training_document("A dog chases a cat", "animals")
sttb.parse_and_register_training_document("A cat chases a dog", "animals")
sttb.parse_and_register_training_document("A cat chases a horse", "animals")
sttb.parse_and_register_training_document("A cat chases a horse", "animals")
sttb.parse_and_register_training_document("A gymnast jumps over a horse", "gym")
sttb.parse_and_register_training_document(
"A gymnast jumps over a vaulting horse", "gym"
)
sttb.prepare()
trainer = sttb.train(minimum_occurrences=4, cv_threshold=0.0)
self.assertEqual(
list(trainer.sorted_label_dict.keys()),
[
"predicate-actor: chasing-animal",
"predicate-actor: chasing-animal/predicate-patient: chasing-animal",
"predicate-patient: chasing-animal",
"word: animal",
],
)
self.assertEqual(
set(map(lambda phr: phr.label, trainer.phraselet_infos)),
{
"predicate-actor: chasing-animal",
"predicate-patient: chasing-animal",
"word: animal",
},
)
trainer2 = sttb.train(minimum_occurrences=4, cv_threshold=1)
self.assertEqual(
list(trainer2.sorted_label_dict.keys()),
[
"predicate-actor: chasing-animal",
"predicate-actor: chasing-animal/predicate-patient: chasing-animal",
"predicate-patient: chasing-animal",
],
)
self.assertEqual(
set(map(lambda phr: phr.label, trainer2.phraselet_infos)),
{"predicate-actor: chasing-animal", "predicate-patient: chasing-animal"},
)