def setUp(self):
# Usage:
# Constructor for TestLinearRegression
# Arguments:
# None
# Create an instance of the Convert Numpy class
self.convert_numpy = ConvertNumpy()
# Create an instance of the Linear Regression class
self.linear_regression = LinearRegression()
# Create an instance of the Predict Output Class
self.predict_output = PredictOutput()
# Create an instance of the Residual Sum Squares Class
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_frame that encompasses all test and train data
self.kc_house_frame = pd.read_csv(
'./unit_tests/test_data/kc_house/kc_house_data.csv',
dtype=dtype_dict)
# Create a kc_house_test_frame that encompasses only train data
self.kc_house_train_frame = pd.read_csv(
'./unit_tests/test_data/kc_house/kc_house_train_data.csv',
dtype=dtype_dict)
# Create a kc_house_frames that encompasses only test data
self.kc_test_frames = pd.read_csv(
'./unit_tests/test_data/kc_house/kc_house_test_data.csv',
dtype=dtype_dict)
def __init__(self):
# Usage:
# Constructor for KFoldCrossValidation, used to setup ConvertNumpy class to convert pandas
# data to numpy.
# Arguments:
# None
# Create an instance of the Convert Numpy class
self.convert_numpy = ConvertNumpy()
# Create an instance of the Predict Output Class
self.predict_output = PredictOutput()
# Create an instance of the Residual Sum Squares Class
self.residual_sum_squares = ResidualSumSquares()
