def setUp(self):
"""Constructor for TestLinearRegression.
Loads housing data, and creates training and testing data.
"""
self.convert_numpy = ConvertNumpy()
self.linear_regression = LinearRegression()
self.predict_output = PredictOutput()
self.residual_sum_squares = ResidualSumSquares()
# Create a dictionary type to store relevant data types so that our pandas
# will read the correct information
dtype_dict = {'bathrooms': float, 'waterfront': int, 'sqft_above': int, 'sqft_living15': float,
'grade': int, 'yr_renovated': int, 'price': float, 'bedrooms': float, 'zipcode': str,
'long': float, 'sqft_lot15': float, 'sqft_living': float, 'floors': str, 'condition': int,
'lat': float, 'date': str, 'sqft_basement': int, 'yr_built': int, 'id': str, 'sqft_lot': int,
'view': int}
# Create a kc_house that encompasses all test and train data
self.kc_house = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_data.csv', dtype=dtype_dict)
# Create a kc_house_test_frame that encompasses only train data
self.kc_house_train = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_train_data.csv',
dtype=dtype_dict)
# Create a kc_house_frames that encompasses only test data
self.kc_house_test = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_test_data.csv',
dtype=dtype_dict)
class TestLinearRegression(unittest.TestCase):
"""Test for LinearRegression.
Uses housing data to test LinearRegression.
Statics:
_multiprocess_can_split_ (bool): Flag for nose tests to run tests in parallel.
"""
_multiprocess_can_split_ = True
def setUp(self):
"""Constructor for TestLinearRegression.
Loads housing data, and creates training and testing data.
"""
self.convert_numpy = ConvertNumpy()
self.linear_regression = LinearRegression()
self.predict_output = PredictOutput()
self.residual_sum_squares = ResidualSumSquares()
# Create a dictionary type to store relevant data types so that our pandas
# will read the correct information
dtype_dict = {'bathrooms': float, 'waterfront': int, 'sqft_above': int, 'sqft_living15': float,
'grade': int, 'yr_renovated': int, 'price': float, 'bedrooms': float, 'zipcode': str,
'long': float, 'sqft_lot15': float, 'sqft_living': float, 'floors': str, 'condition': int,
'lat': float, 'date': str, 'sqft_basement': int, 'yr_built': int, 'id': str, 'sqft_lot': int,
'view': int}
# Create a kc_house that encompasses all test and train data
self.kc_house = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_data.csv', dtype=dtype_dict)
# Create a kc_house_test_frame that encompasses only train data
self.kc_house_train = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_train_data.csv',
dtype=dtype_dict)
# Create a kc_house_frames that encompasses only test data
self.kc_house_test = pd.read_csv('./unit_tests/test_data/regression/kc_house/kc_house_test_data.csv',
dtype=dtype_dict)
def test_01_gradient_descent(self):
"""Test gradient descent.
Tests gradient descent and compare it to known values.
"""
# We will use sqft_living for our features
features = ['sqft_living']
# Output will use price
output = ['price']
# Convert our pandas frame to numpy
feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1)
# Create our initial weights
initial_weights = np.array([-47000., 1.])
# Step size
step_size = 7e-12
# Tolerance
tolerance = 2.5e7
# Compute our gradient descent value
final_weights = self.linear_regression.gradient_descent(feature_matrix, output,
{"initial_weights": initial_weights,
"step_size": step_size,
"tolerance": tolerance})
# Assert that the weights is correct
self.assertEquals(round(-46999.887165546708, 3), round(final_weights[0], 3))
self.assertEquals(round(281.91211917520917, 3), round(final_weights[1], 3))
def test_02_gradient_descent_multiple(self):
"""Tests gradient descent on multiple features.
Computes gradient descent on multiple input, and computes predicted model and RSS.
"""
# We will use sqft_iving, and sqft_living15
features = ['sqft_living', 'sqft_living15']
# Output will be price
output = ['price']
# Convert our pandas frame to numpy
feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1)
# Create our initial weights
initial_weights = np.array([-100000., 1., 1.])
# Step size
step_size = 4e-12
# Tolerance
tolerance = 1e9
# Compute our gradient descent value
final_weights = self.linear_regression.gradient_descent(feature_matrix, output,
{"initial_weights": initial_weights,
"step_size": step_size,
"tolerance": tolerance})
# We will use sqft_iving, and sqft_living15
test_features = ['sqft_living', 'sqft_living15']
# Output will be price
test_output = ['price']
# Convert our test pandas frame to numpy
test_feature_matrix, test_output = self.convert_numpy.convert_to_numpy(self.kc_house_test, test_features,
test_output, 1)
# Predict the output of test features
predicted_output = self.predict_output.regression(test_feature_matrix, final_weights)
# Compute RSS
rss = self.residual_sum_squares.residual_sum_squares_regression(test_output, predicted_output)
# Assert that rss is correct
self.assertEquals(round(270263443629803.41, -3), round(rss, -3))
def test_03_gradient_ascent(self):
"""Test gradient ascent.
Test gradient ascent and compare it to known values.
"""
# We will use sqft_living for our features
features = ['sqft_living']
# Output will be price
output = ['price']
# Convert our pandas frame to numpy
feature_matrix, output = self.convert_numpy.convert_to_numpy(self.kc_house_train, features, output, 1)
# Create our initial weights
initial_weights = np.array([-47000., 1.])
# Step size
step_size = 7e-12
# Tolerance
tolerance = 2.5e7
# Compute our hill climbing value
final_weights = self.linear_regression.gradient_ascent(feature_matrix, output,
{"initial_weights": initial_weights,
"step_size": step_size,
"tolerance": tolerance})
# Assert that the weights is correct
self.assertEquals(round(-47000.142201335177, 3), round(final_weights[0], 3))
self.assertEquals(round(-352.86068692252599, 3), round(final_weights[1], 3))