def test_PRED_MAE_load_test(self):
mae = MAE()
mae.load_test(self.TEST_DATA)
assert_equal(len(mae.get_test()), len(self.TEST_DATA))
assert_equal(len(mae.get_ground_truth()), 0)
assert_raises(ValueError, mae.compute) #Raise: GT is empty!
def test_PRED_MAE_nan(self):
mae = MAE()
mae.add(2.0, nan)
assert_equal(mae.get_test(), [])
assert_equal(mae.get_ground_truth(), [])
class TestPrediction(Test):
def __init__(self):
super(TestPrediction, self).__init__()
# Prediction-based metrics: MAE, RMSE, Pearson
self.mae = MAE(self.DATA_PRED)
self.rmse = RMSE(self.DATA_PRED)
self.R = 3 # Real Rating (ground truth)
self.R_PRED = 2.1 # Predicted Rating
# test_PRED MAE
def test_PRED_MAE_compute_one(self):
assert_equal(self.mae.compute(self.R, self.R_PRED), 0.9)
def test_PRED_MAE_compute_one_empty_datasets(self):
mae = MAE()
assert_equal(mae.compute(self.R, self.R_PRED), 0.9)
def test_PRED_MAE_compute_all(self):
assert_equal(self.mae.compute(), 0.7)
def test_PRED_MAE_nan(self):
mae = MAE()
mae.add(2.0, nan)
assert_equal(mae.get_test(), [])
assert_equal(mae.get_ground_truth(), [])
def test_PRED_MAE_load(self):
mae = MAE()
mae.load(self.GT_DATA, self.TEST_DATA)
assert_equal(mae.compute(), 0.7)
def test_PRED_MAE_load_test(self):
mae = MAE()
mae.load_test(self.TEST_DATA)
assert_equal(len(mae.get_test()), len(self.TEST_DATA))
assert_equal(len(mae.get_ground_truth()), 0)
assert_raises(ValueError, mae.compute) #Raise: GT is empty!
def test_PRED_MAE_load_test_and_ground_truth(self):
mae = MAE()
mae.load_test(self.TEST_DATA)
mae.load_ground_truth(self.GT_DATA)
assert_equal(mae.compute(), 0.7)
def test_PRED_MAE_add_entry(self):
self.mae.add(1, 4) #1: GT rating, 4: Predicted rating
assert_equal(len(self.mae.get_test()), len(self.DATA_PRED)+1)
assert_equal(self.mae.compute(), 1.083333)
def test_PRED_MAE_different_list_sizes(self):
mae = MAE()
GT = [3, 1, 5, 2]
# GT list has one element less than self.TEST_DATA
mae.load(GT, self.TEST_DATA)
assert_raises(ValueError, mae.compute)
# test_PRED RMSE
def test_PRED_RMSE_compute_one(self):
#Even though rmse has data, we only compute these two param values
assert_equal(self.rmse.compute(self.R, self.R_PRED), 0.9)
def test_PRED_RMSE_compute_one_empty_datasets(self):
rmse = RMSE()
assert_equal(rmse.compute(self.R, self.R_PRED), 0.9)
def test_PRED_RMSE_compute_all(self):
assert_equal(self.rmse.compute(), 0.891067)
def test_PRED_RMSE_load_test(self):
rmse = RMSE()
self.TEST_DATA = [2.3, 0.9, 4.9, 0.9, 1.5]
rmse.load_test(self.TEST_DATA)
assert_equal(len(rmse.get_test()), len(self.TEST_DATA))
def test_PRED_RMSE_add_entry(self):
self.rmse.add(1,4)
assert_equal(len(self.rmse.get_test()), len(self.DATA_PRED)+1)
assert_equal(self.rmse.compute(), 1.470261)
def test_PRED_RMSE_different_list_sizes(self):
rmse = RMSE()
GT = [3, 1, 5, 2]
# GT list has one element less than self.TEST_DATA
rmse.load(GT, self.TEST_DATA)
assert_raises(ValueError, rmse.compute)
def test_PRED_RMSE_numpy_array(self):
rmse = RMSE()
rmse.load(array(self.GT_DATA), array(self.TEST_DATA))
assert(rmse.compute(), 0.891067)
def test_PRED_MAE_load_test(self):
mae = MAE()
mae.load_test(self.TEST_DATA)
assert_equal(len(mae.get_test()), len(self.TEST_DATA))
assert_equal(len(mae.get_ground_truth()), 0)
assert_raises(ValueError, mae.compute) #Raise: GT is empty!
def test_PRED_MAE_nan(self):
mae = MAE()
mae.add(2.0, nan)
assert_equal(mae.get_test(), [])
assert_equal(mae.get_ground_truth(), [])
class TestPrediction(Test):
def __init__(self):
super(TestPrediction, self).__init__()
# Prediction-based metrics: MAE, RMSE, Pearson
self.mae = MAE(self.DATA_PRED)
self.rmse = RMSE(self.DATA_PRED)
self.R = 3 # Real Rating (ground truth)
self.R_PRED = 2.1 # Predicted Rating
# test_PRED MAE
def test_PRED_MAE_compute_one(self):
assert_equal(self.mae.compute(self.R, self.R_PRED), 0.9)
def test_PRED_MAE_compute_one_empty_datasets(self):
mae = MAE()
assert_equal(mae.compute(self.R, self.R_PRED), 0.9)
def test_PRED_MAE_compute_all(self):
assert_equal(self.mae.compute(), 0.7)
def test_PRED_MAE_nan(self):
mae = MAE()
mae.add(2.0, nan)
assert_equal(mae.get_test(), [])
assert_equal(mae.get_ground_truth(), [])
def test_PRED_MAE_load(self):
mae = MAE()
mae.load(self.GT_DATA, self.TEST_DATA)
assert_equal(mae.compute(), 0.7)
def test_PRED_MAE_load_test(self):
mae = MAE()
mae.load_test(self.TEST_DATA)
assert_equal(len(mae.get_test()), len(self.TEST_DATA))
assert_equal(len(mae.get_ground_truth()), 0)
assert_raises(ValueError, mae.compute) #Raise: GT is empty!
def test_PRED_MAE_load_test_and_ground_truth(self):
mae = MAE()
mae.load_test(self.TEST_DATA)
mae.load_ground_truth(self.GT_DATA)
assert_equal(mae.compute(), 0.7)
def test_PRED_MAE_add_entry(self):
self.mae.add(1, 4) #1: GT rating, 4: Predicted rating
assert_equal(len(self.mae.get_test()), len(self.DATA_PRED) + 1)
assert_equal(self.mae.compute(), 1.083333)
def test_PRED_MAE_different_list_sizes(self):
mae = MAE()
GT = [3, 1, 5, 2]
# GT list has one element less than self.TEST_DATA
mae.load(GT, self.TEST_DATA)
assert_raises(ValueError, mae.compute)
# test_PRED RMSE
def test_PRED_RMSE_compute_one(self):
#Even though rmse has data, we only compute these two param values
assert_equal(self.rmse.compute(self.R, self.R_PRED), 0.9)
def test_PRED_RMSE_compute_one_empty_datasets(self):
rmse = RMSE()
assert_equal(rmse.compute(self.R, self.R_PRED), 0.9)
def test_PRED_RMSE_compute_all(self):
assert_equal(self.rmse.compute(), 0.891067)
def test_PRED_RMSE_load_test(self):
rmse = RMSE()
self.TEST_DATA = [2.3, 0.9, 4.9, 0.9, 1.5]
rmse.load_test(self.TEST_DATA)
assert_equal(len(rmse.get_test()), len(self.TEST_DATA))
def test_PRED_RMSE_add_entry(self):
self.rmse.add(1, 4)
assert_equal(len(self.rmse.get_test()), len(self.DATA_PRED) + 1)
assert_equal(self.rmse.compute(), 1.470261)
def test_PRED_RMSE_different_list_sizes(self):
rmse = RMSE()
GT = [3, 1, 5, 2]
# GT list has one element less than self.TEST_DATA
rmse.load(GT, self.TEST_DATA)
assert_raises(ValueError, rmse.compute)
def test_PRED_RMSE_numpy_array(self):
rmse = RMSE()
rmse.load(array(self.GT_DATA), array(self.TEST_DATA))
assert (rmse.compute(), 0.891067)
