def test_p_word_given_label_2016(self):
vocab = create_vocabulary('./EasyFiles/', 1)
training_data = load_training_data(vocab, './EasyFiles/')
log_probabilities = p_word_given_label(vocab, training_data, '2016')
expected_log_probabilities = {
',': -3.091042453358316,
'.': -2.3978952727983707,
'19': -3.091042453358316,
'2020': -3.091042453358316,
'a': -1.9924301646902063,
'cat': -2.3978952727983707,
'chases': -2.3978952727983707,
'dog': -2.3978952727983707,
'february': -3.091042453358316,
'hello': -2.3978952727983707,
'is': -3.091042453358316,
'it': -3.091042453358316,
'world': -2.3978952727983707,
None: -3.091042453358316
}
self.compare_dicts(log_probabilities, expected_log_probabilities)
vocab = create_vocabulary('./EasyFiles/', 2)
training_data = load_training_data(vocab, './EasyFiles/')
log_probabilities = p_word_given_label(vocab, training_data, '2016')
expected_log_probabilities = {
'.': -1.7047480922384253,
'a': -1.2992829841302609,
None: -0.6061358035703157
}
self.compare_dicts(log_probabilities, expected_log_probabilities)
def test_p_word_given_label_2020(self):
vocab = create_vocabulary('./EasyFiles/', 1)
training_data = load_training_data(vocab, './EasyFiles/')
log_probabilities = p_word_given_label(vocab, training_data, '2020')
expected_log_probabilities = {
',': -2.3513752571634776,
'.': -2.3513752571634776,
'19': -2.3513752571634776,
'2020': -2.3513752571634776,
'a': -3.044522437723423,
'cat': -3.044522437723423,
'chases': -3.044522437723423,
'dog': -3.044522437723423,
'february': -2.3513752571634776,
'hello': -3.044522437723423,
'is': -2.3513752571634776,
'it': -2.3513752571634776,
'world': -3.044522437723423,
None: -3.044522437723423
}
self.compare_dicts(log_probabilities, expected_log_probabilities)
vocab = create_vocabulary('./EasyFiles/', 2)
training_data = load_training_data(vocab, './EasyFiles/')
log_probabilities = p_word_given_label(vocab, training_data, '2020')
expected_log_probabilities = {
'.': -1.6094379124341005,
'a': -2.302585092994046,
None: -0.35667494393873267
}
self.compare_dicts(log_probabilities, expected_log_probabilities)
def test_create_vocabulary(self):
vocab = create_vocabulary('./EasyFiles/', 1)
expected_vocab = [
',', '.', '19', '2020', 'a', 'cat', 'chases', 'dog', 'february',
'hello', 'is', 'it', 'world'
]
self.assertEqual(vocab, expected_vocab)
vocab = create_vocabulary('./EasyFiles/', 2)
expected_vocab = ['.', 'a']
self.assertEqual(vocab, expected_vocab)
def test_load_training_data(self):
vocab = create_vocabulary('./EasyFiles/', 1)
training_data = load_training_data(vocab, './EasyFiles/')
expected_training_data = [{
'label': '2020',
'bow': {
'it': 1,
'is': 1,
'february': 1,
'19': 1,
',': 1,
'2020': 1,
'.': 1
}
}, {
'label': '2016',
'bow': {
'hello': 1,
'world': 1
}
}, {
'label': '2016',
'bow': {
'a': 2,
'dog': 1,
'chases': 1,
'cat': 1,
'.': 1
}
}]
self.assertCountEqual(training_data, expected_training_data)
def test_prior(self):
vocab = create_vocabulary('./corpus/training/', 2)
training_data = load_training_data(vocab, './corpus/training/')
log_probabilities = prior(training_data, ['2020', '2016'])
expected_log_probabilities = {
'2020': -0.32171182103809226,
'2016': -1.2906462863976689
}
self.compare_dicts(log_probabilities, expected_log_probabilities)
def test_create_bow(self):
vocab = create_vocabulary('./EasyFiles/', 1)
bow = create_bow(vocab, './EasyFiles/2016/1.txt')
expected_bow = {'a': 2, 'dog': 1, 'chases': 1, 'cat': 1, '.': 1}
self.assertEqual(bow, expected_bow)
bow = create_bow(vocab, './EasyFiles/2020/2.txt')
expected_bow = {
'it': 1,
'is': 1,
'february': 1,
'19': 1,
',': 1,
'2020': 1,
'.': 1
}
self.assertEqual(bow, expected_bow)
vocab = create_vocabulary('./EasyFiles/', 2)
bow = create_bow(vocab, './EasyFiles/2016/1.txt')
expected_bow = {'a': 2, None: 3, '.': 1}
self.assertEqual(bow, expected_bow)
'''
HW4 is to be written in a file called classify.py with the following interface:
create_vocabulary(training_directory: str, cutoff: int)
create_bow(vocab: dict, filepath: str)
load_training_data(vocab: list, directory: str)
prior(training_data: list, label_list: list)
p_word_given_label(vocab: list, training_data: list, label: str)
train(training_directory: str, cutoff: int)
classify(model: dict, filepath: str)
'''
__author__ = 'cs540-testers'
__credits__ = [
'Saurabh Kulkarni', 'Alex Moon', 'Stephen Jasina', 'Harrison Clark'
]
version = 'V1.1.2'
from classify import train, create_bow, load_training_data, prior, \
p_word_given_label, classify, create_vocabulary
import unittest
class TestClassify(unittest.TestCase):
def compare_dicts(self, a, b):
'''Compares two dicts that map strings to other (non-container) data'''
# Check that all elements of a are in b
for k in a:
self.assertIn(k, b)
