class TestLogisticRegressionL2Norm(unittest.TestCase):
"""Tests for LogisticRegressionL2Norm 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.
"""
# Create an instance of the Convert Numpy class
self.convert_numpy = ConvertNumpy()
# Create an instance of log likelihood
self.log_likelhood = LogLikelihood()
# Create an instance of the accuracy class
self.accuracy = Accuracy()
# Load the important words
self.important_words = json.load(open('./unit_tests/test_data/classification/amazon/important_words.json', 'r'))
# Create an instance of the Logistic Regression with L2 Norm class
self.logistic_regression_l2_norm = LogisticRegressionL2Norm()
# Load the amazon baby train subset
self.training_data = pd.read_csv('./unit_tests/test_data/classification/amazon/amazon_baby_subset_train.csv')
# Load the amazon baby train subset
self.validation_data = pd.read_csv('./unit_tests/test_data/'
'classification/amazon/amazon_baby_subset_validation.csv')
def test_01_gradient_ascent_no_penalty(self):
"""Tests gradient ascent algorithm.
Tests the gradient ascent algorithm but with no l2 penalty.
"""
# 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_train, label_train = self.convert_numpy.convert_to_numpy(self.training_data,
features,
output, 1)
feature_matrix_valid, label_valid = self.convert_numpy.convert_to_numpy(self.validation_data,
features,
output, 1)
# Compute the coefficients
coefficients = self.logistic_regression_l2_norm.gradient_ascent(feature_matrix_train, label_train,
{"initial_coefficients": np.zeros(194),
"step_size": 5e-6, "l2_penalty": 0,
"max_iter": 501})
# Get the accuracy
train_accuracy = self.accuracy.logistic_regression(feature_matrix_train, label_train, coefficients)
validation_accuracy = self.accuracy.logistic_regression(feature_matrix_valid, label_valid, coefficients)
# Make sure the accuraries are correct
self.assertEqual(round(0.785156157787, 5), round(train_accuracy, 5))
self.assertEqual(round(0.78143964149, 5), round(validation_accuracy, 5))
def test_02_gradient_ascent_10_penalty(self):
"""Test gradient ascent algorithm.
Tests the gradient ascent algorithm with penalty.
"""
# 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_train, label_train = self.convert_numpy.convert_to_numpy(self.training_data,
features,
output, 1)
feature_matrix_valid, label_valid = self.convert_numpy.convert_to_numpy(self.validation_data,
features,
output, 1)
# Compute the coefficients
coefficients = self.logistic_regression_l2_norm.gradient_ascent(feature_matrix_train, label_train,
{"initial_coefficients": np.zeros(194),
"step_size": 5e-6, "l2_penalty": 10,
"max_iter": 501})
# Get the accuracy
train_accuracy = self.accuracy.logistic_regression(feature_matrix_train, label_train, coefficients)
validation_accuracy = self.accuracy.logistic_regression(feature_matrix_valid, label_valid, coefficients)
# Make sure the accuracies are correct
self.assertEqual(round(0.784990911452, 5), round(train_accuracy, 5))
self.assertEqual(round(0.781719727383, 5), round(validation_accuracy, 5))
def test_03_log_likelihood(self):
"""Tests log likelihood with l2 norm.
Tests the log likelihood with l2 norm and compare it with 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_l2_norm(feature_matrix, label, coefficients, 10)
# Assert the value
self.assertEqual(round(lg, 5), round(-105.33141000000001, 5))
