def test_process_selectors_numpy_array_invalid_fields(field, np_params_converter):
with pytest.raises(InvalidConstraintError):
process_selectors(
constraints=[{"type": "equality", field: None}],
params=np.arange(6),
tree_converter=np_params_converter,
param_names=list("abcdefg"),
)
def test_process_selectors_tree_invalid_fields(
field, tree_params, tree_params_converter
):
with pytest.raises(InvalidConstraintError):
process_selectors(
constraints=[{"type": "equality", field: None}],
params=tree_params,
tree_converter=tree_params_converter,
param_names=list("abcdefg"),
)
def test_process_selectors_differen_length_in_multiple_selectors(np_params_converter):
constraints = [
{
"type": "pairwise_equality",
"locs": [[1, 4], [0, 3, 5]],
}
]
with pytest.raises(InvalidConstraintError):
process_selectors(
constraints=constraints,
params=np.arange(6) + 10,
tree_converter=np_params_converter,
param_names=list("abcdefg"),
)
def test_process_selectors_tree_selector(tree_params, tree_params_converter):
calculated = process_selectors(
constraints=[{"type": "equality", "selector": lambda x: x[1]}],
params=tree_params,
tree_converter=tree_params_converter,
param_names=list("abcdefg"),
)
aae(calculated[0]["index"], np.array([6]))
def test_process_selectors_no_constraint(constraints):
calculated = process_selectors(
constraints=constraints,
params=np.arange(5),
tree_converter=None,
param_names=list("abcde"),
)
assert calculated == []
def test_process_selectors_numpy_array_loc(np_params_converter):
calculated = process_selectors(
constraints=[{"type": "equality", "loc": [1, 4]}],
params=np.arange(6) + 10,
tree_converter=np_params_converter,
param_names=list("abcdefg"),
)
aae(calculated[0]["index"], np.array([1, 4]))
def test_process_selectors_dataframe_loc(df_params, df_params_converter):
constraints = [{"type": "equality", "loc": ["b", "e"]}]
calculated = process_selectors(
constraints=constraints,
params=df_params,
tree_converter=df_params_converter,
param_names=list("abcdefg"),
)
aae(calculated[0]["index"], np.array([1, 4]))
def test_process_selectors_dataframe_locs(df_params, df_params_converter):
constraints = [{"type": "pairwise_equality", "locs": [["b", "e"], ["a", "d"]]}]
calculated = process_selectors(
constraints=constraints,
params=df_params,
tree_converter=df_params_converter,
param_names=list("abcdefg"),
)
aae(calculated[0]["indices"][0], np.array([1, 4]))
aae(calculated[0]["indices"][1], np.array([0, 3]))
def test_process_selectors_dataframe_query(df_params, df_params_converter):
q = "name == 'b' | name == 'e'"
constraints = [{"type": "equality", "query": q}]
calculated = process_selectors(
constraints=constraints,
params=df_params,
tree_converter=df_params_converter,
param_names=list("abcdefg"),
)
aae(calculated[0]["index"], np.array([1, 4]))
def test_process_selectors_dataframe_queries(df_params, df_params_converter):
queries = ["name == 'b' | name == 'e'", "name == 'a' | name == 'd'"]
constraints = [{"type": "pairwise_equality", "queries": queries}]
calculated = process_selectors(
constraints=constraints,
params=df_params,
tree_converter=df_params_converter,
param_names=list("abcdefg"),
)
aae(calculated[0]["indices"][0], np.array([1, 4]))
aae(calculated[0]["indices"][1], np.array([0, 3]))
def test_process_selectors_tree_selectors(tree_params, tree_params_converter):
constraints = [
{
"type": "pairwise_equality",
"selectors": [lambda x: x[1], lambda x: x[0][1][0]],
}
]
calculated = process_selectors(
constraints=constraints,
params=tree_params,
tree_converter=tree_params_converter,
param_names=list("abcdefg"),
)
aae(calculated[0]["indices"][0], np.array([6]))
aae(calculated[0]["indices"][1], np.array([1]))
def test_process_selectors_numpy_array_locs(np_params_converter):
constraints = [
{
"type": "pairwise_equality",
"locs": [[1, 4], [0, 3]],
}
]
calculated = process_selectors(
constraints=constraints,
params=np.arange(6) + 10,
tree_converter=np_params_converter,
param_names=list("abcdefg"),
)
aae(calculated[0]["indices"][0], np.array([1, 4]))
aae(calculated[0]["indices"][1], np.array([0, 3]))
def get_converter(
params,
constraints,
lower_bounds,
upper_bounds,
func_eval,
primary_key,
scaling,
scaling_options,
derivative_eval=None,
soft_lower_bounds=None,
soft_upper_bounds=None,
add_soft_bounds=False,
):
"""Get a converter between external and internal params and internal params.
This combines the following conversions:
- Flattening parameters provided as pytrees (tree_conversion)
- Enforcing constraints via reparametrizations (space_conversion)
- Scaling of the parameter space (scale_conversion)
The resulting converter can transform parameters, function outputs and derivatives.
If possible, fast paths for some or all transformations are chosen.
Args:
params (pytree): The user provided parameters.
constraints (list): The user provided constraints.
lower_bounds (pytree): The user provided lower_bounds
upper_bounds (pytree): The user provided upper bounds
func_eval (float, dict or pytree): An evaluation of ``func`` at ``params``.
Used to deterimine how the function output has to be transformed for the
optimizer.
primary_key (str): One of "value", "contributions" and "root_contributions".
Used to determine how the function and derivative output has to be
transformed for the optimzer.
scaling (bool): Whether scaling should be performed.
scaling_options (dict): User provided scaling options.
derivative_eval (dict, pytree or None): Evaluation of the derivative of
func at params. Used for consistency checks.
soft_lower_bounds (pytree): As lower_bounds
soft_upper_bounds (pytree): As upper_bounds
add_soft_bounds (bool): Whether soft bounds should be added to the
internal_params
Returns:
Converter: NamedTuple with methods to convert between internal and external
parameters, derivatives and function outputs.
InternalParams: NamedTuple with internal parameter values, lower_bounds and
upper_bounds.
"""
fast_path = _is_fast_path(
params=params,
constraints=constraints,
func_eval=func_eval,
primary_key=primary_key,
scaling=scaling,
derivative_eval=derivative_eval,
add_soft_bounds=add_soft_bounds,
)
if fast_path:
return _get_fast_path_converter(
params=params,
lower_bounds=lower_bounds,
upper_bounds=upper_bounds,
primary_key=primary_key,
)
tree_converter, internal_params = get_tree_converter(
params=params,
lower_bounds=lower_bounds,
upper_bounds=upper_bounds,
func_eval=func_eval,
derivative_eval=derivative_eval,
primary_key=primary_key,
soft_lower_bounds=soft_lower_bounds,
soft_upper_bounds=soft_upper_bounds,
add_soft_bounds=add_soft_bounds,
)
flat_constraints = process_selectors(
constraints=constraints,
params=params,
tree_converter=tree_converter,
param_names=internal_params.names,
)
space_converter, internal_params = get_space_converter(
internal_params=internal_params, internal_constraints=flat_constraints)
scale_converter, scaled_params = get_scale_converter(
internal_params=internal_params,
scaling=scaling,
scaling_options=scaling_options,
)
def _params_to_internal(params):
x_flat = tree_converter.params_flatten(params)
x_internal = space_converter.params_to_internal(x_flat)
x_scaled = scale_converter.params_to_internal(x_internal)
return x_scaled
def _params_from_internal(x, return_type="tree"):
x_unscaled = scale_converter.params_from_internal(x)
x_external = space_converter.params_from_internal(x_unscaled)
x_tree = tree_converter.params_unflatten(x_external)
if return_type == "tree":
out = x_tree
elif return_type == "tree_and_flat":
out = x_tree, x_external
elif return_type == "flat":
out = x_external
else:
msg = (
"Invalid return type: {return_type}. Must be one of 'tree', 'flat', "
"'tree_and_flat'")
raise ValueError(msg)
return out
def _derivative_to_internal(derivative_eval, x, jac_is_flat=False):
if jac_is_flat:
jacobian = derivative_eval
else:
jacobian = tree_converter.derivative_flatten(derivative_eval)
x_unscaled = scale_converter.params_from_internal(x)
jac_with_space_conversion = space_converter.derivative_to_internal(
jacobian, x_unscaled)
jac_with_unscaling = scale_converter.derivative_to_internal(
jac_with_space_conversion)
return jac_with_unscaling
def _func_to_internal(func_eval):
return tree_converter.func_flatten(func_eval)
internal_params = scaled_params._replace(
free_mask=internal_params.free_mask)
converter = Converter(
params_to_internal=_params_to_internal,
params_from_internal=_params_from_internal,
derivative_to_internal=_derivative_to_internal,
func_to_internal=_func_to_internal,
has_transforming_constraints=space_converter.
has_transforming_constraints,
)
return converter, internal_params