def setUp(self):
"""Constructor for TestLogisticRegression.
Loads Amazon data, and creates training and testing data.
"""
self.convert_numpy = ConvertNumpy()
self.log_likelhood = LogLikelihood()
self.predict_output = PredictOutput()
self.logistic_regression = LogisticRegression()
self.confusion_matrix = ConfusionMatrix()
# Load the important words
self.important_words = json.load(open('./unit_tests/test_data/classification/amazon/important_words.json', 'r'))
# Load the amazon baby subset
self.review_frame = pd.read_csv('./unit_tests/test_data/classification/amazon/amazon_baby_subset.csv')
# Review needs to be text
self.review_frame['review'].astype(str)
# Clean up the punctuations
self.review_frame['review_clean'] = self.review_frame.apply(
axis=1,
func=lambda row: str(row["review"]).translate(str.maketrans({key: None for key in string.punctuation})))
# Remove any nan text
self.review_frame['review_clean'] = self.review_frame.apply(
axis=1,
func=lambda row: '' if row["review_clean"] == "nan" else row["review_clean"])
# Count the number of words that appears in each review, and make an indepedent column
for word in self.important_words:
self.review_frame[word] = self.review_frame['review_clean'].apply(lambda s, w=word: s.split().count(w))
# Load training data
self.train_frame = pd.read_csv('./unit_tests/test_data/classification/amazon/amazon_baby_subset_train_mod2.csv')
class TestLogisticRegression(unittest.TestCase):
"""Tests for LogisticRegression class.
Uses Amazon data to test logistic regression.
Statics:
_multiprocess_can_split_ (bool): Flag for nose tests to run tests in parallel.
"""
_multiprocess_can_split_ = True
def setUp(self):
"""Constructor for TestLogisticRegression.
Loads Amazon data, and creates training and testing data.
"""
self.convert_numpy = ConvertNumpy()
self.log_likelhood = LogLikelihood()
self.predict_output = PredictOutput()
self.logistic_regression = LogisticRegression()
self.confusion_matrix = ConfusionMatrix()
# Load the important words
self.important_words = json.load(open('./unit_tests/test_data/classification/amazon/important_words.json', 'r'))
# Load the amazon baby subset
self.review_frame = pd.read_csv('./unit_tests/test_data/classification/amazon/amazon_baby_subset.csv')
# Review needs to be text
self.review_frame['review'].astype(str)
# Clean up the punctuations
self.review_frame['review_clean'] = self.review_frame.apply(
axis=1,
func=lambda row: str(row["review"]).translate(str.maketrans({key: None for key in string.punctuation})))
# Remove any nan text
self.review_frame['review_clean'] = self.review_frame.apply(
axis=1,
func=lambda row: '' if row["review_clean"] == "nan" else row["review_clean"])
# Count the number of words that appears in each review, and make an indepedent column
for word in self.important_words:
self.review_frame[word] = self.review_frame['review_clean'].apply(lambda s, w=word: s.split().count(w))
# Load training data
self.train_frame = pd.read_csv('./unit_tests/test_data/classification/amazon/amazon_baby_subset_train_mod2.csv')
def test_01_gradient_ascent(self):
"""Test gradient ascent algorithm.
Tests the gradient ascent algorithm and compare it with known values.
"""
# We will use important words for the output
features = self.important_words
# Output will use sentiment
output = ['sentiment']
# Convert our pandas frame to numpy
feature_matrix, sentiment = self.convert_numpy.convert_to_numpy(self.review_frame, features, output, 1)
# Compute the coefficients
coefficients = self.logistic_regression.gradient_ascent(feature_matrix, sentiment,
{"initial_coefficients": np.zeros(194),
"step_size": 1e-7, "max_iter": 301})
# Real coefficients that we need to compare with the computed coefficients
real_coef = [5.16220157e-03, 1.55656966e-02, -8.50204675e-03,
6.65460842e-02, 6.58907629e-02, 5.01743882e-03,
-5.38601484e-02, -3.50488413e-03, 6.47945868e-02,
4.54356263e-02, 3.98353364e-03, 2.00775410e-02,
3.01350011e-02, -2.87115530e-02, 1.52161964e-02,
2.72592062e-04, 1.19448177e-02, -1.82461935e-02,
-1.21706420e-02, -4.15110334e-02, 2.76820391e-03,
1.77031999e-02, -4.39700067e-03, 4.49764014e-02,
9.90916464e-03, 8.99239081e-04, -1.36219516e-03,
1.26859357e-02, 8.26466695e-03, -2.77426972e-02,
6.10128809e-04, 1.54084501e-02, -1.32134753e-02,
-3.00512492e-02, 2.97399371e-02, 1.84087080e-02,
2.86178752e-03, -1.05768015e-02, -6.57350362e-04,
-1.01476555e-02, -4.79579528e-03, 7.50891810e-03,
4.27938289e-03, 3.06785501e-03, -2.20317661e-03,
9.57273354e-03, 9.91666827e-05, -1.98462567e-02,
1.75702722e-02, 1.55478612e-03, -1.77375440e-02,
9.78324102e-03, 1.17031606e-02, -7.35345937e-03,
-6.08714030e-03, 6.43766808e-03, 1.07159665e-02,
-3.05345476e-03, 7.17190727e-03, 5.73320003e-03,
4.60661876e-03, -5.20588421e-03, 6.71012331e-03,
9.03281814e-03, 1.74563147e-03, 6.00279979e-03,
1.20181744e-02, -1.83594607e-02, -6.91010811e-03,
-1.38687273e-02, -1.50406590e-02, 5.92353611e-03,
5.67478991e-03, -5.28786220e-03, 3.08147864e-03,
5.53751236e-03, 1.49917916e-02, -3.35666000e-04,
-3.30695153e-02, -4.78990943e-03, -6.41368859e-03,
7.99938935e-03, -8.61390444e-04, 1.68052959e-02,
1.32539901e-02, 1.72307051e-03, 2.98030675e-03,
8.58284300e-03, 1.17082481e-02, 2.80825907e-03,
2.18724016e-03, 1.68824711e-02, -4.65973741e-03,
1.51368285e-03, -1.09509122e-02, 9.17842898e-03,
-1.88572281e-04, -3.89820373e-02, -2.44821005e-02,
-1.87023714e-02, -2.13943485e-02, -1.29690465e-02,
-1.71378670e-02, -1.37566767e-02, -1.49770449e-02,
-5.10287978e-03, -2.89789761e-02, -1.48663194e-02,
-1.28088380e-02, -1.07709355e-02, -6.95286915e-03,
-5.04082164e-03, -9.25914404e-03, -2.40427481e-02,
-2.65927785e-02, -1.97320937e-03, -5.04127508e-03,
-7.00791912e-03, -3.48088523e-03, -6.40958916e-03,
-4.07497010e-03, -6.30054296e-03, -1.09187932e-02,
-1.26051900e-02, -1.66895314e-03, -7.76418781e-03,
-5.15960485e-04, -1.94199551e-03, -1.24761586e-03,
-5.01291731e-03, -9.12049191e-03, -7.22098801e-03,
-8.31782981e-03, -5.60573348e-03, -1.47098335e-02,
-9.31520819e-03, -2.22034402e-03, -7.07573098e-03,
-5.10115608e-03, -8.93572862e-03, -1.27545713e-02,
-7.04171991e-03, -9.76219676e-04, 4.12091713e-04,
8.29251160e-04, 2.64661064e-03, -7.73228782e-03,
1.53471164e-03, -7.37263060e-03, -3.73694386e-03,
-3.81416409e-03, -1.64575145e-03, -3.31887732e-03,
1.22257832e-03, 1.36699286e-05, -3.01866601e-03,
-1.02826343e-02, -1.06691327e-02, 2.23639046e-03,
-9.87424798e-03, -1.02192048e-02, -3.41330929e-03,
3.34489960e-03, -3.50984516e-03, -6.26283150e-03,
-7.22419943e-03, -5.47016154e-03, -1.25063947e-02,
-2.47805699e-03, -1.60017985e-02, -6.40098934e-03,
-4.26644386e-03, -1.55376990e-02, 2.31349237e-03,
-9.06653337e-03, -6.30012672e-03, -1.21010303e-02,
-3.02578875e-03, -6.76289718e-03, -5.65498722e-03,
-6.87050239e-03, -1.18950595e-02, -1.86489236e-04,
-1.15230476e-02, 2.81533219e-03, -8.10150295e-03,
-1.00062131e-02, 4.02037651e-03, -5.44300346e-03,
2.85818985e-03, 1.19885003e-04, -6.47587687e-03,
-1.14493516e-03, -7.09205934e-03]
# Loop through each value, the coefficients must be the same
for pred_coef, coef in zip(coefficients, real_coef):
# Assert that both values are the same
self.assertEqual(round(pred_coef, 5), round(coef, 5))
# Get the output of the logistic regression with threshold 0
output = self.predict_output.logistic_regression(feature_matrix, coefficients, 0)
# Generate a confusion matrix
confusion_matrix = self.confusion_matrix.confusion_matrix(sentiment, output)
# Assert the values are to be expected
self.assertEqual(confusion_matrix, {'false_negatives': 7311, 'true_negatives': 20635,
'true_positives': 19268, 'false_positives': 5858})
# Assert that the precision is correct
self.assertEqual(round(self.confusion_matrix.precision(sentiment, output), 5),
round(0.7249332179540239, 5))
# Assert that the recall is correct
self.assertEqual(round(self.confusion_matrix.recall(sentiment, output), 5),
round(0.7668550505452519, 5))
def test_02_stochastic_gradient_ascent(self):
"""Test stochastic gradient descent for logistic regression.
Tests stochastic gradient descent and test it with some known values.
"""
# We will use important words for the output
features = self.important_words
# Output will use sentiment
output = ['sentiment']
# Convert our pandas frame to numpy
feature_matrix, sentiment = self.convert_numpy.convert_to_numpy(self.train_frame, features, output, 1)
# Compute the coefficients
coefficients = self.logistic_regression.stochastic_gradient_ascent(feature_matrix, sentiment,
{"initial_coefficients": np.zeros(194),
"step_size": 5e-1,
"batch_size": 1,
"max_iter": 10})
# Real coefficients that we need to compare with the computed coefficients
real_coef = [0.26845909, 0.05510662, -0.78232359, 0.24929641, 0.1213813,
-0.13194118, -0.42110769, 0.23944013, 0.52334226, 0.30746343,
1.46697311, 0.15734639, 0.24112255, -0.22849175, -0.48095714,
0., 0.05984944, -0.41942527, -0.48095714, 0.10654088,
0., 0.06153186, -0.41942527, 0.43843464, 0.,
0.21719583, 0., 0.84326475, 0.28108825, 0.28108825,
0., 0., 0.24611428, -0.19986888, 0.15734639,
0., 0., -0.48095714, 0.12623269, 0.,
0.28108825, 0.07542718, 0., -0.42110769, 0.15734639,
-0.48095714, 0.24611428, -0.48095714, 0., 0.,
0.06153186, 0.28108825, 0., 0., 0.,
0.05984944, 0.5932902, 0.5621765, -0.48095714, 0.,
0.05984944, 0.05984944, 0.31220195, 0.11805882, 0.,
0.15085436, 0.24611428, 0., 0., 0.,
0.06153186, 0.12623269, 0., 0., 0.,
0., 0., 0., -0.35472444, 0.12623269,
0., 0., 0.68023532, 0.28108825, 0.06153186,
0.0311137, 0.35651543, 0., 0.28108825, 0.,
0.05984944, 0., 0.35651543, 0.28108825, 0.,
0., 0., -0.90206483, 0.07542718, -0.48095714,
0., 0., -0.48095714, 0., 0.,
0., -0.25, 0.0311137, 0., 0.28108825,
0., 0., 0., 0., 0.,
0., 0.34262011, -0.48095714, 0.28108825, 0.,
0., 0., 0., 0., 0.06153186,
0.12623269, 0.05984944, 0., 0., 0.,
0., 0.12623269, 0., 0., 0.12623269,
0.07542718, 0.15085436, 0.07542718, -0.68082602, 0.,
0., 0., 0.05984944, 0., 0.,
0.28108825, 0., -0.25, 0., 0.,
0.07542718, 0., 0., 0.28108825, 0.,
0., 0., 0., 0., 0.,
0.06153186, 0.0311137, 0., -0.48095714, 0.,
0., 0., 0., 0., 0.,
0., 0.40732094, 0., 0., 0.05984944,
0., 0., 0., 0., 0.,
0., 0., 0.06153186, 0., 0.06153186,
0., -0.25, 0.05984944, 0., 0.,
0., 0., -0.96191427, 0.]
# Loop through each value, the coefficients must be the same
for pred_coef, coef in zip(coefficients, real_coef):
# Assert that both values are the same
self.assertEqual(round(pred_coef, 5), round(coef, 5))
# Get the output of the logistic regression with threshold 0
output = self.predict_output.logistic_regression(feature_matrix, coefficients, 0)
# Generate a confusion matrix
confusion_matrix = self.confusion_matrix.confusion_matrix(sentiment, output)
# Assert the values are to be expected
self.assertEqual(confusion_matrix, {'false_negatives': 6517, 'true_negatives': 11707,
'true_positives': 17331, 'false_positives': 12225})
# Assert that the precision is correct
self.assertEqual(round(self.confusion_matrix.precision(sentiment, output), 5),
round(0.72673, 5))
# Assert that the recall is correct
self.assertEqual(round(self.confusion_matrix.recall(sentiment, output), 5),
round(0.58638, 5))
def test_02_stochastic_gradient_ascent_high_iteration(self):
"""Test stochastic gradient descent for logistic regression.
Tests stochastic gradient descent and test it with some known values.
"""
# We will use important words for the output
features = self.important_words
# Output will use sentiment
output = ['sentiment']
# Convert our pandas frame to numpy
feature_matrix, sentiment = self.convert_numpy.convert_to_numpy(self.train_frame, features, output, 1)
# Compute the coefficients
coefficients = self.logistic_regression.stochastic_gradient_ascent(feature_matrix, sentiment,
{"initial_coefficients": np.zeros(194),
"step_size": 5e-1,
"batch_size": 1000,
"max_iter": 1000})
# Real coefficients that we need to compare with the computed coefficients
real_coef = [-0.06659918, 0.07516305, 0.02337901, 0.91476437, 1.25935729, -0.01093744,
-0.29808423, 0.00724611, 1.14319635, 0.58421811, -0.10388794, 0.25341405,
0.51935047, -0.16643157, 0.1581433, -0.01678466, 0.11023426, -0.07801531,
-0.11943521, -0.23901842, 0.19961916, 0.26962603, 0.00726172, 1.58116946,
-0.04749877, -0.01222728, -0.12452547, 0.2408741, 0.23996495, -0.27318487,
0.16391931, 0.46141695, -0.00520781, -0.41720674, 1.3914436, 0.59286041,
-0.01877455, -0.1177062, 0.04522629, -0.05050944, -0.1872891, 0.1119123,
0.05552736, 0.018883, -0.28821684, 0.35454167, 0.09146771, -0.15185966,
0.45980111, 0.13696004, -0.27719711, 0.37826182, 0.51482099, -0.12707594,
-0.08043197, 0.27088589, 0.20836676, -0.22217221, 0.34308818, 0.05011724,
0.01336183, -0.00422257, 0.25914879, 0.18971367, 0.11804381, 0.06478439,
0.13413068, -0.35940054, -0.04225724, -0.23574987, -0.26178573, 0.37077618,
0.266064, 0.0552738, 0.25274691, 0.15248314, 0.9721445, 0.03951392,
-0.59577998, -0.09680726, -0.13168621, 0.42806047, 0.03576358, 1.03088019,
0.52916025, -0.09516351, 0.23544152, 0.31386904, 0.50647271, 0.25383116,
0.1369185, 0.93673001, -0.06280486, 0.1670564, -0.20573152, 0.2201837,
0.12892914, -0.9711816, -0.24387714, -0.3566874, -0.65956699, -0.28473646,
-0.34083222, -0.44708957, -0.29828401, -0.52797307, -1.92693359, -0.33116364,
-0.43025271, -0.21284617, 0.16375567, -0.0299845, -0.30294927, -1.25019619,
-1.55092776, -0.09266983, -0.08014312, -0.07565967, -0.00950432, 0.00327247,
0.03190358, -0.04247063, -0.28205865, -0.45678176, 0.06141561, -0.2690871,
-0.05979329, -0.0019354, -0.01279985, 0.05323391, -0.35513613, -0.26639425,
-0.41094467, -0.14117863, -0.90001241, -0.33279773, 0.01621988, -0.08709595,
-0.10450457, -0.12567406, -0.61727551, -0.18663497, 0.17636203, 0.09316913,
-0.06829369, 0.1880183, -0.5078543, 0.03964466, -0.26089197, -0.07480237,
-0.05556211, -0.1450303, -0.04780934, 0.08911386, -0.15163772, 0.06213261,
-0.34512242, -0.33522342, 0.06580618, -0.44499204, -0.68623426, -0.12564489,
0.2609755, 0.09998045, -0.25098629, -0.29549973, -0.15944276, -0.47408765,
-0.03058168, -1.42253269, -0.49855378, 0.05835175, -1.17789127, -0.08226967,
-0.56793665, -0.35814271, -0.98559717, -0.16918106, -0.12477773, -0.23457722,
-0.13170106, -0.64351485, -0.01773532, -0.2686544, 0.047442, -0.34218929,
-0.48340895, 0.37866335, -0.25162177, 0.05277577, 0.01545386, -0.26267815,
-0.09903819, -0.54500151]
# Loop through each value, the coefficients must be the same
for pred_coef, coef in zip(coefficients, real_coef):
# Assert that both values are the same
self.assertEqual(round(pred_coef, 5), round(coef, 5))
# Get the output of the logistic regression with threshold 0
output = self.predict_output.logistic_regression(feature_matrix, coefficients, 0)
# Generate a confusion matrix
confusion_matrix = self.confusion_matrix.confusion_matrix(sentiment, output)
# Assert the values are to be expected
self.assertEqual(confusion_matrix, {'false_negatives': 5018, 'true_negatives': 18995,
'true_positives': 18830, 'false_positives': 4937})
# Assert that the precision is correct
self.assertEqual(round(self.confusion_matrix.precision(sentiment, output), 5),
round(0.78958, 5))
# Assert that the recall is correct
self.assertEqual(round(self.confusion_matrix.recall(sentiment, output), 5),
round(0.79228, 5))
def test_04_log_likelihood(self):
"""Test log likelihood.
Test the log likelihood algorithm, and compare it with some known values.
"""
# Generate test feature, coefficients, and label
feature_matrix = np.array([[1., 2., 3.], [1., -1., -1]])
coefficients = np.array([1., 3., -1.])
label = np.array([-1, 1])
# Compute the log likelihood
lg = self.log_likelhood.log_likelihood(feature_matrix, label, coefficients)
# Assert the value
self.assertEqual(round(lg, 5), round(-5.33141161544, 5))
def test_05_average_log_likelihood(self):
"""Test average log likelihood.
Test the average log likelihood algorithm, and compare it with some known values.
"""
# Generate test feature, coefficients, and label
feature_matrix = np.array([[1., 2., 3.], [1., -1., -1]])
coefficients = np.array([1., 3., -1.])
label = np.array([-1, 1])
# Compute the log likelihood
lg = self.log_likelhood.average_log_likelihood(feature_matrix, label, coefficients)
# Assert the value
self.assertEqual(round(lg, 5), round(-2.6657099999999998, 5))