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()