def __init__(
self,
values,
base_values = None,
data = None,
display_data = None,
instance_names = None,
feature_names = None,
output_names = None,
output_indexes = None,
lower_bounds = None,
upper_bounds = None,
main_effects = None,
hierarchical_values = None,
clustering = None
):
self.op_history = []
# cloning. TODO: better cloning :)
if issubclass(type(values), Explanation):
e = values
values = e.values
base_values = e.base_values
data = e.data
output_dims = compute_output_dims(values, base_values, data)
if len(_compute_shape(feature_names)) == 1: # TODO: should always be an alias once slicer supports per-row aliases
values_shape = _compute_shape(values)
if len(values_shape) >= 1 and len(feature_names) == values_shape[0]:
feature_names = Alias(list(feature_names), 0)
elif len(values_shape) >= 2 and len(feature_names) == values_shape[1]:
feature_names = Alias(list(feature_names), 1)
if len(_compute_shape(output_names)) == 1: # TODO: should always be an alias once slicer supports per-row aliases
values_shape = _compute_shape(values)
if len(values_shape) >= 1 and len(output_names) == values_shape[0]:
output_names = Alias(list(output_names), 0)
elif len(values_shape) >= 2 and len(output_names) == values_shape[1]:
output_names = Alias(list(output_names), 1)
self._s = Slicer(
values = values,
base_values = None if base_values is None else Obj(base_values, [0] + list(output_dims)),
data = data,
display_data = display_data,
instance_names = None if instance_names is None else Alias(instance_names, 0),
feature_names = feature_names,
output_names = output_names, # None if output_names is None else Alias(output_names, output_dims),
output_indexes = None if output_indexes is None else (output_dims, output_indexes),
lower_bounds = lower_bounds,
upper_bounds = lower_bounds,
main_effects = main_effects,
hierarchical_values = hierarchical_values,
clustering = None if clustering is None else Obj(clustering, [0])
)
def object_hook(self, obj):
if "_type" not in obj:
return obj
_type = obj["_type"]
if _type == "array":
return np.array(obj["value"])
elif _type == "explanation":
cls = getattr(import_module(obj["module"]), obj["class"])
return cls.from_components(obj["components"])
elif _type == "component":
cls = getattr(import_module(obj["module"]), obj["class"])
return cls.from_fields(obj["fields"])
elif _type == "obj":
return Obj(obj["value"], obj["dim"])
elif _type == "alias":
return Alias(obj["value"], obj["dim"])
elif _type == "dim":
return Dim(obj["dim"])
return obj
def __init__(
self,
values,
base_values=None,
data=None,
display_data=None,
instance_names=None,
feature_names=None,
output_names=None,
output_indexes=None,
lower_bounds=None,
upper_bounds=None,
main_effects=None,
hierarchical_values=None,
clustering=None,
interactions=None,
feature_groups=None,
):
self.op_history = []
# cloning. TODO: better cloning :)
if issubclass(type(values), Explanation):
e = values
values = e.values
base_values = e.base_values
data = e.data
output_dims = compute_output_dims(values, base_values, data)
if len(
_compute_shape(feature_names)
) == 1: # TODO: should always be an alias once slicer supports per-row aliases
values_shape = _compute_shape(values)
if len(values_shape) >= 1 and len(
feature_names) == values_shape[0]:
feature_names = Alias(list(feature_names), 0)
elif len(values_shape) >= 2 and len(
feature_names) == values_shape[1]:
feature_names = Alias(list(feature_names), 1)
if len(
_compute_shape(output_names)
) == 1: # TODO: should always be an alias once slicer supports per-row aliases
values_shape = _compute_shape(values)
output_names = Alias(list(output_names), output_dims[0])
# if len(values_shape) >= 1 and len(output_names) == values_shape[0]:
# output_names = Alias(list(output_names), 0)
# elif len(values_shape) >= 2 and len(output_names) == values_shape[1]:
# output_names = Alias(list(output_names), 1)
if output_names is not None and not isinstance(output_names, Alias):
l = len(_compute_shape(output_names))
if l == 0:
pass
elif l == 1:
output_names = Obj(output_names, output_dims)
elif l == 2:
output_names = Obj(output_names, [0] + list(output_dims))
else:
raise ValueError(
"shap.Explanation does not yet support output_names of order greater than 3!"
)
self._s = Slicer(
values=values,
base_values=None if base_values is None else Obj(
base_values, [0] + list(output_dims)),
data=data,
display_data=display_data,
instance_names=None if instance_names is None else Alias(
instance_names, 0),
feature_names=feature_names,
output_names=output_names,
output_indexes=None if output_indexes is None else
(output_dims, output_indexes),
lower_bounds=lower_bounds,
upper_bounds=upper_bounds,
main_effects=main_effects,
hierarchical_values=hierarchical_values,
clustering=None if clustering is None else Obj(clustering, [0]),
interactions=interactions,
feature_groups=feature_groups)
def __init__( # pylint: disable=too-many-arguments
self,
values,
base_values=None,
data=None,
display_data=None,
instance_names=None,
feature_names=None,
output_names=None,
output_indexes=None,
lower_bounds=None,
upper_bounds=None,
error_std=None,
main_effects=None,
hierarchical_values=None,
clustering=None,
compute_time=None):
self.op_history = []
self.compute_time = compute_time
# cloning. TODOsomeday: better cloning :)
if issubclass(type(values), Explanation):
e = values
values = e.values
base_values = e.base_values
data = e.data
self.output_dims = compute_output_dims(values, base_values, data,
output_names)
values_shape = _compute_shape(values)
if output_names is None and len(self.output_dims) == 1:
output_names = [
f"Output {i}" for i in range(values_shape[self.output_dims[0]])
]
if len(
_compute_shape(feature_names)
) == 1: # TODOsomeday: should always be an alias once slicer supports per-row aliases
if len(values_shape) >= 1 and len(
feature_names) == values_shape[0]:
feature_names = Alias(list(feature_names), 0)
elif len(values_shape) >= 2 and len(
feature_names) == values_shape[1]:
feature_names = Alias(list(feature_names), 1)
if len(
_compute_shape(output_names)
) == 1: # TODOsomeday: should always be an alias once slicer supports per-row aliases
output_names = Alias(list(output_names), self.output_dims[0])
# if len(values_shape) >= 1 and len(output_names) == values_shape[0]:
# output_names = Alias(list(output_names), 0)
# elif len(values_shape) >= 2 and len(output_names) == values_shape[1]:
# output_names = Alias(list(output_names), 1)
if output_names is not None and not isinstance(output_names, Alias):
l = len(_compute_shape(output_names))
if l == 0:
pass
elif l == 1:
output_names = Obj(output_names, self.output_dims)
elif l == 2:
output_names = Obj(output_names, [0] + list(self.output_dims))
else:
raise ValueError(
"shap.Explanation does not yet support output_names of order greater than 3!"
)
if not hasattr(base_values, "__len__") or len(base_values) == 0:
pass
elif len(_compute_shape(base_values)) == len(self.output_dims):
base_values = Obj(base_values, list(self.output_dims))
else:
base_values = Obj(base_values, [0] + list(self.output_dims))
self._s = Slicer(
values=values,
base_values=base_values,
data=list_wrap(data),
display_data=list_wrap(display_data),
instance_names=None if instance_names is None else Alias(
instance_names, 0),
feature_names=feature_names,
output_names=output_names,
output_indexes=None if output_indexes is None else
(self.output_dims, output_indexes),
lower_bounds=list_wrap(lower_bounds),
upper_bounds=list_wrap(upper_bounds),
error_std=list_wrap(error_std),
main_effects=list_wrap(main_effects),
hierarchical_values=list_wrap(hierarchical_values),
clustering=None if clustering is None else Obj(clustering, [0]))