def it_knows_the_index_of_its_cube_in_the_cube_set(self, cube_):
cube_.cube_index = 42
cube_partition = CubePartition(cube_)
cube_index = cube_partition.cube_index
assert cube_index == 42
def it_knows_the_only_larger_flag_state_to_help(self,
_transforms_dict_prop_,
pw_indices_dict,
expected_value):
_transforms_dict_prop_.return_value = {
"pairwise_indices": pw_indices_dict
}
assert CubePartition(None)._only_larger == expected_value
def it_interprets_the_provided_alpha_values_to_help(
self, pw_indices_dict, expected_value):
cube_partition = CubePartition(None,
{"pairwise_indices": pw_indices_dict})
alpha_values = cube_partition._alpha_values
assert alpha_values == expected_value
def but_it_raises_on_invalid_alpha_values(self, pw_indices_dict,
exception_type,
expected_message):
cube_partition = CubePartition(None,
{"pairwise_indices": pw_indices_dict})
with pytest.raises(exception_type) as e:
cube_partition._alpha_values
assert str(e.value) == expected_message
def it_can_evaluate_a_measure_expression(self, request):
single_sided_moving_avg_smoother_ = instance_mock(
request,
SingleSidedMovingAvgSmoother,
values=[[0.1, 0.2], [0.3, 0.4]])
SingleSidedMovingAvgSmoother_ = class_mock(
request,
"cr.cube.cubepart.SingleSidedMovingAvgSmoother",
return_value=single_sided_moving_avg_smoother_,
)
measure_expr = {
"function": "one_sided_moving_avg",
"base_measure": "col_percent",
"window": 2,
}
cube_partition = CubePartition(None, None)
values = cube_partition.evaluate(measure_expr)
SingleSidedMovingAvgSmoother_.assert_called_once_with(
cube_partition, measure_expr)
assert values == [[0.1, 0.2], [0.3, 0.4]]
def it_provides_the_transforms_dict_to_help(self, transforms,
expected_value):
"""Handles defaulting of transforms arg."""
assert CubePartition(None,
transforms)._transforms_dict == expected_value
def it_knows_the_secondary_alpha_value_to_help(self, _alpha_values_prop_,
alpha_values,
expected_value):
_alpha_values_prop_.return_value = alpha_values
assert CubePartition(None)._alpha_alt == expected_value
def it_knows_the_primary_alpha_value_to_help(self, _alpha_values_prop_):
"""alpha is the primary confidence-interval threshold specified by the user."""
_alpha_values_prop_.return_value = (0.042, 0.084)
assert CubePartition(None)._alpha == 0.042