class BinaryHardcodedTask(Task):
name: str
labels: Any
loss: nn.Module = None
per_sample_loss: nn.Module = None
available_func: Callable = positive_values
inputs: Any = None
activation: Optional[nn.Module] = None
decoder: Decoder = None
module: nn.Module = Identity()
metrics: Tuple[str, TorchMetric] = ()
class MultilabelClassificationTask(Task):
"""
Represents a classification task. Labels should be integers from 0 to N-1, where N is the number of classes
* activation - `nn.Softmax(dim=-1)`
* loss - `nn.CrossEntropyLoss()`
"""
name: str
labels: Any
loss: nn.Module = nn.BCEWithLogitsLoss(reduction='mean')
per_sample_loss: nn.Module = ReducedPerSample(
nn.BCEWithLogitsLoss(reduction='none'), torch.mean)
available_func: Callable = positive_values
inputs: Any = None
activation: Optional[nn.Module] = nn.Sigmoid()
decoder: Decoder = field(default_factory=MultilabelClassificationDecoder)
module: nn.Module = Identity()
metrics: Tuple[str, TorchMetric] = field(
default_factory=get_default_multilabel_classification_metrics)
class BinaryClassificationTask(Task):
"""
Represents a normal binary classification task. Labels should be between 0 and 1.
* activation - `nn.Sigmoid()`
* loss - ``nn.BCEWithLogitsLoss()`
"""
name: str
labels: Any
loss: nn.Module = nn.BCEWithLogitsLoss(reduction='mean')
per_sample_loss: nn.Module = ReducedPerSample(
nn.BCEWithLogitsLoss(reduction='none'), reduction=torch.mean)
available_func: Callable = positive_values
inputs: Any = None
activation: Optional[nn.Module] = nn.Sigmoid()
decoder: Decoder = field(default_factory=BinaryDecoder)
module: nn.Module = Identity()
metrics: Tuple[str, TorchMetric] = field(
default_factory=get_default_binary_metrics)
class ClassificationTask(Task):
"""
Represents a classification task. Labels should be integers from 0 to N-1, where N is the number of classes
* activation - `nn.Softmax()`
* loss - `nn.CrossEntropyLoss()`
"""
name: str
labels: Sequence
loss: nn.Module = nn.CrossEntropyLoss(reduction='mean')
per_sample_loss: nn.Module = ReducedPerSample(
nn.CrossEntropyLoss(reduction='none'), torch.mean)
available_func: Callable = positive_values
inputs: Sequence = None
activation: Optional[nn.Module] = nn.Softmax()
decoder: Decoder = field(default_factory=ClassificationDecoder)
module: nn.Module = Identity()
metrics: Sequence[Tuple[str, TorchMetric]] = field(
default_factory=get_default_classification_metrics)
class_names: Optional = None
top_k: Optional[int] = 5
def get_treelib_explainer(self) -> Callable:
def classification_explainer(
task_name: str, decoded: torch.Tensor, activated: torch.Tensor,
logits: torch.Tensor,
node_identifier: str) -> Tuple[Tree, Node]:
tree = Tree()
start_node = tree.create_node(task_name, node_identifier)
for i, idx in enumerate(decoded[:self.top_k]):
name = idx if self.class_names is None else self.class_names[
idx]
description = f'{i}: {name} | activated: {activated[idx]:.4f}, logits: {logits[idx]:.4f}'
tree.create_node(description,
f'{node_identifier}.{idx}',
parent=start_node)
return tree, start_node
return classification_explainer
class MultilabelClassificationTask(Task):
"""
Represents a classification task. Labels should be integers from 0 to N-1, where N is the number of classes
* activation - `nn.Sigmoid()`
* loss - `nn.CrossEntropyLoss()`
"""
name: str
labels: Sequence
loss: nn.Module = nn.BCEWithLogitsLoss(reduction='mean')
per_sample_loss: nn.Module = ReducedPerSample(
nn.BCEWithLogitsLoss(reduction='none'), torch.mean)
available_func: Callable = positive_values
inputs: Sequence = None
activation: Optional[nn.Module] = nn.Sigmoid()
decoder: Decoder = field(default_factory=MultilabelClassificationDecoder)
module: nn.Module = Identity()
metrics: Sequence[Tuple[str, TorchMetric]] = field(
default_factory=get_default_multilabel_classification_metrics)
class_names: Optional = None
def get_treelib_explainer(self) -> Callable:
def explainer(task_name: str, decoded: np.ndarray,
activated: np.ndarray, logits: np.ndarray,
node_identifier: str) -> Tuple[Tree, Node]:
tree = Tree()
start_node = tree.create_node(task_name, node_identifier)
for i, val in enumerate(decoded):
name = i if self.class_names is None else self.class_names[i]
description = f'{i}: {name} | decoded: {decoded[i]}, ' \
f'activated: {activated[i]:.4f}, ' \
f'logits: {logits[i]:.4f}'
tree.create_node(description,
f'{node_identifier}.{i}',
parent=start_node)
return tree, start_node
return explainer