mowl.nn

EL module

class mowl.nn.elmodule.ELModule[source]

Bases: Module

Subclass of torch.nn.Module for \(\mathcal{EL}\) models. This class provides an interface for loss functions of the 7 possible normal forms existing in the \(\mathcal{EL}\) language. In case a negative version of one of the loss function exist, it must be placed inside the original loss function and be accesed through the neg parameter. More information of this can be found at Embedding the EL language

gci0_loss(gci, neg=False)[source]

Loss function for GCI0: \(C \sqsubseteq D\).

Parameters

gci (torch.Tensor) – Input tensor of shape (*,2) where C classes will be at gci[:,0] and D classes will be at gci[:,1]. It is recommended to use the ELDataset.
neg (bool, optional.) – Parameter indicating that the negative version of this loss function must be used. Defaults to False.

gci1_loss(gci, neg=False)[source]

Loss function for GCI1: \(C_1 \sqcap C_2 \sqsubseteq D\).

Parameters

gci (torch.Tensor) – Input tensor of shape (*,3) where C1 classes will be at gci[:,0], C2 classes will be at gci[:,1] and D classes will be at gci[:,2]. It is recommended to use the ELDataset.
neg (bool, optional.) – Parameter indicating that the negative version of this loss function must be used. Defaults to False.

gci2_loss(gci, neg=False)[source]

Loss function for GCI2: \(C \sqsubseteq R. D\).

Parameters

gci (torch.Tensor) – Input tensor of shape (*,3) where C classes will be at gci[:,0], R object properties will be at gci[:,1] and D classes will be at gci[:,2]. It is recommended to use the ELDataset.
neg (bool, optional.) – Parameter indicating that the negative version of this loss function must be used. Defaults to False.

gci3_loss(gci, neg=False)[source]

Loss function for GCI3: \(R. C \sqsubseteq D\).

Parameters

gci (torch.Tensor) – Input tensor of shape (*,3) where R object properties will be at gci[:,0], C classes will be at gci[:,1] and D classes will be at gci[:,2]. It is recommended to use the ELDataset.
neg (bool, optional.) – Parameter indicating that the negative version of this loss function must be used. Defaults to False.

gci0_bot_loss(gci, neg=False)[source]

Loss function for GCI0 with bottom concept: \(C \sqsubseteq \perp\).

Parameters

gci (torch.Tensor) – Input tensor of shape (*,2) where C classes will be at gci[:,0] and bottom classes will be at gci[:,1]. It is recommended to use the ELDataset.
neg (bool, optional.) – Parameter indicating that the negative version of this loss function must be used. Defaults to False.

gci1_bot_loss(gci, neg=False)[source]

Loss function for GCI1 with bottom concept: \(C_1 \sqcap C_2 \sqsubseteq \perp\).

Parameters

gci (torch.Tensor) – Input tensor of shape (*,3) where C1 classes will be at gci[:,0], C2 classes will be at gci[:,1] and bottom classes will be at gci[:,2]. It is recommended to use the ELDataset.
neg (bool, optional.) – Parameter indicating that the negative version of this loss function must be used. Defaults to False.

gci3_bot_loss(gci, neg=False)[source]

Loss function for GCI3 with bottom concept: \(R. C \sqsubseteq \perp\).

Parameters

gci (torch.Tensor) – Input tensor of shape (*,3) where R object properties will be at gci[:,0], C classes will be at gci[:,1] and bottom classes will be at gci[:,2]. It is recommended to use the ELDataset.
neg (bool, optional.) – Parameter indicating that the negative version of this loss function must be used. Defaults to False.

forward(gci, gci_name, neg=False)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.