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from dataclasses import dataclass
from enum import Enum
from typing import Optional, Union
from torch_geometric.utils.mixin import CastMixin
class ExplanationType(Enum):
"""Enum class for the explanation type."""
model = 'model'
phenomenon = 'phenomenon'
class MaskType(Enum):
"""Enum class for the mask type."""
object = 'object'
common_attributes = 'common_attributes'
attributes = 'attributes'
class ModelMode(Enum):
"""Enum class for the model return type."""
binary_classification = 'binary_classification'
multiclass_classification = 'multiclass_classification'
regression = 'regression'
class ModelTaskLevel(Enum):
"""Enum class for the model task level."""
node = 'node'
edge = 'edge'
graph = 'graph'
class ModelReturnType(Enum):
"""Enum class for the model return type."""
raw = 'raw'
probs = 'probs'
log_probs = 'log_probs'
class ThresholdType(Enum):
"""Enum class for the threshold type."""
hard = 'hard'
topk = 'topk'
topk_hard = 'topk_hard'
# connected = 'connected' # TODO
@dataclass
class ExplainerConfig(CastMixin):
r"""Configuration class to store and validate high level explanation
parameters.
Args:
explanation_type (ExplanationType or str): The type of explanation to
compute. The possible values are:
- :obj:`"model"`: Explains the model prediction.
- :obj:`"phenomenon"`: Explains the phenomenon that the model
is trying to predict.
In practice, this means that the explanation algorithm will either
compute their losses with respect to the model output
(:obj:`"model"`) or the target output (:obj:`"phenomenon"`).
node_mask_type (MaskType or str, optional): The type of mask to apply
on nodes. The possible values are (default: :obj:`None`):
- :obj:`None`: Will not apply any mask on nodes.
- :obj:`"object"`: Will mask each node.
- :obj:`"common_attributes"`: Will mask each feature.
- :obj:`"attributes"`: Will mask each feature across all nodes.
edge_mask_type (MaskType or str, optional): The type of mask to apply
on edges. Has the sample possible values as :obj:`node_mask_type`.
(default: :obj:`None`)
"""
explanation_type: ExplanationType
node_mask_type: Optional[MaskType]
edge_mask_type: Optional[MaskType]
def __init__(
self,
explanation_type: Union[ExplanationType, str],
node_mask_type: Optional[Union[MaskType, str]] = None,
edge_mask_type: Optional[Union[MaskType, str]] = None,
):
if node_mask_type is not None:
node_mask_type = MaskType(node_mask_type)
if edge_mask_type is not None:
edge_mask_type = MaskType(edge_mask_type)
if edge_mask_type is not None and edge_mask_type != MaskType.object:
raise ValueError(f"'edge_mask_type' needs be None or of type "
f"'object' (got '{edge_mask_type.value}')")
if node_mask_type is None and edge_mask_type is None:
raise ValueError("Either 'node_mask_type' or 'edge_mask_type' "
"must be provided")
self.explanation_type = ExplanationType(explanation_type)
self.node_mask_type = node_mask_type
self.edge_mask_type = edge_mask_type
@dataclass
class ModelConfig(CastMixin):
r"""Configuration class to store model parameters.
Args:
mode (ModelMode or str): The mode of the model. The possible values
are:
- :obj:`"binary_classification"`: A binary classification
model.
- :obj:`"multiclass_classification"`: A multiclass
classification model.
- :obj:`"regression"`: A regression model.
task_level (ModelTaskLevel or str): The task-level of the model.
The possible values are:
- :obj:`"node"`: A node-level prediction model.
- :obj:`"edge"`: An edge-level prediction model.
- :obj:`"graph"`: A graph-level prediction model.
return_type (ModelReturnType or str, optional): The return type of the
model. The possible values are (default: :obj:`None`):
- :obj:`"raw"`: The model returns raw values.
- :obj:`"probs"`: The model returns probabilities.
- :obj:`"log_probs"`: The model returns log-probabilities.
"""
mode: ModelMode
task_level: ModelTaskLevel
return_type: ModelReturnType
def __init__(
self,
mode: Union[ModelMode, str],
task_level: Union[ModelTaskLevel, str],
return_type: Optional[Union[ModelReturnType, str]] = None,
):
self.mode = ModelMode(mode)
self.task_level = ModelTaskLevel(task_level)
if return_type is None and self.mode == ModelMode.regression:
return_type = ModelReturnType.raw
self.return_type = ModelReturnType(return_type)
if (self.mode == ModelMode.regression
and self.return_type != ModelReturnType.raw):
raise ValueError(f"A model for regression needs to return raw "
f"outputs (got {self.return_type.value})")
if (self.mode == ModelMode.binary_classification and self.return_type
not in [ModelReturnType.raw, ModelReturnType.probs]):
raise ValueError(
f"A model for binary classification needs to return raw "
f"outputs or probabilities (got {self.return_type.value})")
@dataclass
class ThresholdConfig(CastMixin):
r"""Configuration class to store and validate threshold parameters.
Args:
threshold_type (ThresholdType or str): The type of threshold to apply.
The possible values are:
- :obj:`None`: No threshold is applied.
- :obj:`"hard"`: A hard threshold is applied to each mask.
The elements of the mask with a value below the :obj:`value`
are set to :obj:`0`, the others are set to :obj:`1`.
- :obj:`"topk"`: A soft threshold is applied to each mask.
The top obj:`value` elements of each mask are kept, the
others are set to :obj:`0`.
- :obj:`"topk_hard"`: Same as :obj:`"topk"` but values are set
to :obj:`1` for all elements which are kept.
value (int or float, optional): The value to use when thresholding.
(default: :obj:`None`)
"""
type: ThresholdType
value: Union[float, int]
def __init__(
self,
threshold_type: Union[ThresholdType, str],
value: Union[float, int],
):
self.type = ThresholdType(threshold_type)
self.value = value
if not isinstance(self.value, (int, float)):
raise ValueError(f"Threshold value must be a float or int "
f"(got {type(self.value)}).")
if (self.type == ThresholdType.hard
and (self.value < 0 or self.value > 1)):
raise ValueError(f"Threshold value must be between 0 and 1 "
f"(got {self.value})")
if self.type in [ThresholdType.topk, ThresholdType.topk_hard]:
if not isinstance(self.value, int):
raise ValueError(f"Threshold value needs to be an integer "
f"(got {type(self.value)}).")
if self.value <= 0:
raise ValueError(f"Threshold value needs to be positive "
f"(got {self.value}).")
|
