class TestWeightedLogisticRegression(unittest.TestCase):
"""Tests WeightedLogisticRegression class.
Uses Amazon data to test WeightedLogisticRegression class.
Statics:
_multiprocess_can_split_ (bool): Flag for nose tests to run tests in parallel.
"""
_multiprocess_can_split_ = True
def setUp(self):
"""Constructor for TestWeightedLogisticRegression.
Loads Amazon data, and creates training and testing data.
"""
self.convert_numpy = ConvertNumpy()
self.predict = PredictOutput()
self.ada = AdaBoost()
self.accuracy = Accuracy()
self.weighted_logistic_regression = WeightedLogisticRegression()
# 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))
def test_01_gradient_ascent(self):
"""Tests 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)
# Create weight list for training data
weights_list = np.array([1]*len(self.review_frame))
# Compute the coefficients
coefficients = self.weighted_logistic_regression.gradient_ascent(feature_matrix, sentiment,
{"initial_coefficients": np.zeros(194),
"weights_list": weights_list,
"step_size": 1e-7, "max_iter": 30})
# Assert the coefficients
self.assertEqual([round(i, 5) for i in coefficients[0:20]],
[round(i, 5) for i in [0.00020000000000000001, 0.0014300000000000001, -0.00131,
0.0068900000000000003, 0.0068500000000000002, 0.00034000000000000002,
-0.0062399999999999999, -0.00059000000000000003, 0.0067099999999999998,
0.0046600000000000001, 0.00042999999999999999, 0.0020300000000000001,
0.0030300000000000001, -0.00332, 0.0015, -0.00011, 0.00115,
-0.0021700000000000001, -0.00139, -0.0046600000000000001]])
# Compute predictions
predictions = self.predict.logistic_regression(feature_matrix, coefficients)
# Accuracy has to match 0.74356999999999995
self.assertEqual(round(self.accuracy.general(predictions, sentiment), 5),
round(0.74356999999999995, 5))
def test_02_adaboost(self):
"""Tests adaboost algorithm.
Tests the adaboost algorithm with weighted logistic regression.
"""
# 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)
# Create 15 weighted logistic regression
weights, models = self.ada.logistic_regression(feature_matrix, sentiment,
iterations=15,
model_dict={"predict_method": self.predict.logistic_regression,
"model": self.weighted_logistic_regression,
"model_method": "gradient_ascent",
"model_parameters": {"step_size": 1e-7,
"max_iter": 30,
"initial_coefficients":
np.zeros(194)}})
# Get the predictions of each dataset in the test data
predictions = self.predict.adaboost_logistic_regression(self.predict.logistic_regression,
models, weights, feature_matrix)
# Assert the predictions
self.assertEqual(list(predictions)[0:20],
[1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1])
# Accuracy has to match 0.77612999999999999
self.assertEqual(round(self.accuracy.general(predictions, sentiment), 5),
round(0.77612999999999999, 5))
