def test_scobit_utility_transform_without_interecepts(self):
"""
Ensures that `_scobit_utility_transform()` returns correct results
"""
# Create a set of systematic utilities that will test the function for
# correct calculations, for proper dealing with overflow, and for
# proper dealing with underflow.
# The first and third elements tests general calculation.
# The second element of index_array should lead to the transformation
# equaling the intercept for alternative 2.
# The fourth element should test what happens with underflow and should
# lead to max_comp_value + ASC 1.
# The fifth element should test what happens with overflow and should
# lead to 50 * np.exp(1)
index_array = np.array([1,
0,
-1,
50,
-50])
# Crerate the array of expected results
intercept_1 = 0
intercept_3 = 0
shape_1 = np.exp(self.fake_shapes[0])
shape_3 = np.exp(self.fake_shapes[2])
result_1 = (intercept_1 -
np.log((1.0 + np.exp(-1 * index_array[0]))**shape_1 - 1.0))
result_3 = (intercept_3 -
np.log((1.0 + np.exp(-1 * index_array[2]))**shape_3 - 1.0))
expected_results = np.array([result_1,
0,
result_3,
scobit.max_comp_value + intercept_1,
intercept_3 - 50 * np.exp(1)])[:, None]
# Use the utility transformation function
args = [index_array,
self.fake_df[self.alt_id_col].values,
self.fake_rows_to_alts,
self.fake_shapes,
None]
kwargs = {"intercept_ref_pos": self.fake_intercept_ref_pos}
func_results = scobit._scobit_utility_transform(*args, **kwargs)
# Check the correctness of the result
self.assertIsInstance(func_results, np.ndarray)
self.assertEqual(len(func_results.shape), 2)
self.assertEqual(func_results.shape[1], expected_results.shape[1])
self.assertEqual(func_results.shape[0], expected_results.shape[0])
npt.assert_allclose(expected_results, func_results)
return None
def test_scobit_utility_transform_without_interecepts(self):
"""
Ensures that `_scobit_utility_transform()` returns correct results
"""
# Create a set of systematic utilities that will test the function for
# correct calculations, for proper dealing with overflow, and for
# proper dealing with underflow.
# The first and third elements tests general calculation.
# The second element of index_array should lead to the transformation
# equaling the intercept for alternative 2.
# The fourth element should test what happens with underflow and should
# lead to max_comp_value + ASC 1.
# The fifth element should test what happens with overflow and should
# lead to 50 * np.exp(1)
index_array = np.array([1, 0, -1, 50, -50])
# Crerate the array of expected results
intercept_1 = 0
intercept_3 = 0
shape_1 = np.exp(self.fake_shapes[0])
shape_3 = np.exp(self.fake_shapes[2])
result_1 = (intercept_1 -
np.log((1.0 + np.exp(-1 * index_array[0]))**shape_1 - 1.0))
result_3 = (intercept_3 -
np.log((1.0 + np.exp(-1 * index_array[2]))**shape_3 - 1.0))
expected_results = np.array([
result_1, 0, result_3, scobit.max_comp_value + intercept_1,
intercept_3 - 50 * np.exp(1)
])[:, None]
# Use the utility transformation function
args = [
index_array, self.fake_df[self.alt_id_col].values,
self.fake_rows_to_alts, self.fake_shapes, None
]
kwargs = {"intercept_ref_pos": self.fake_intercept_ref_pos}
func_results = scobit._scobit_utility_transform(*args, **kwargs)
# Check the correctness of the result
self.assertIsInstance(func_results, np.ndarray)
self.assertEqual(len(func_results.shape), 2)
self.assertEqual(func_results.shape[1], expected_results.shape[1])
self.assertEqual(func_results.shape[0], expected_results.shape[0])
npt.assert_allclose(expected_results, func_results)
return None