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lumin.nn.losses package

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lumin.nn.losses.basic_weighted module

class lumin.nn.losses.basic_weighted.WeightedMSE(weight=None)[source]

Bases: torch.nn.modules.loss.MSELoss

Class for computing Mean Squared-Error loss with optional weights per prediction. For compatability with using basic PyTorch losses, weights are passed during initialisation rather than when computing the loss.

Parameters

weight (Optional[Tensor]) – sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss

Examples::
>>> loss = WeightedMSE()
>>>
>>> loss = WeightedMSE(weights)
forward(input, target)[source]

Evaluate loss for given predictions

Parameters
  • input (Tensor) – prediction tensor

  • target (Tensor) – target tensor

Return type

Tensor

Returns

(weighted) loss

class lumin.nn.losses.basic_weighted.WeightedMAE(weight=None)[source]

Bases: torch.nn.modules.loss.L1Loss

Class for computing Mean Absolute-Error loss with optional weights per prediction. For compatability with using basic PyTorch losses, weights are passed during initialisation rather than when computing the loss.

Parameters

weight (Optional[Tensor]) – sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss

Examples::
>>> loss = WeightedMAE()
>>>
>>> loss = WeightedMAE(weights)
forward(input, target)[source]

Evaluate loss for given predictions

Parameters
  • input (Tensor) – prediction tensor

  • target (Tensor) – target tensor

Return type

Tensor

Returns

(weighted) loss

class lumin.nn.losses.basic_weighted.WeightedCCE(weight=None)[source]

Bases: torch.nn.modules.loss.NLLLoss

Class for computing Categorical Cross-Entropy loss with optional weights per prediction. For compatability with using basic PyTorch losses, weights are passed during initialisation rather than when computing the loss.

Parameters

weight (Optional[Tensor]) – sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss

Examples::
>>> loss = WeightedCCE()
>>>
>>> loss = WeightedCCE(weights)
forward(input, target)[source]

Evaluate loss for given predictions

Parameters
  • input (Tensor) – prediction tensor

  • target (Tensor) – target tensor

Return type

Tensor

Returns

(weighted) loss

lumin.nn.losses.hep_losses module

class lumin.nn.losses.hep_losses.SignificanceLoss(weight, sig_wgt=<class 'float'>, bkg_wgt=<class 'float'>, func=typing.Callable[[torch.Tensor, torch.Tensor], torch.Tensor])[source]

Bases: torch.nn.modules.module.Module

General class for implementing significance-based loss functions, e.g. Asimov Loss (https://arxiv.org/abs/1806.00322). For compatability with using basic PyTorch losses, event weights are passed during initialisation rather than when computing the loss.

Parameters
  • weight (Tensor) – sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss

  • sig_wgt – total weight of signal events

  • bkg_wgt – total weight of background events

  • func – callable which returns a float based on signal and background weights

Examples::
>>> loss = SignificanceLoss(weight, sig_weight=sig_weight,
...                         bkg_weight=bkg_weight, func=calc_ams_torch)
>>>
>>> loss = SignificanceLoss(weight, sig_weight=sig_weight,
...                         bkg_weight=bkg_weight,
...                         func=partial(calc_ams_torch, br=10))
forward(input, target)[source]

Evaluate loss for given predictions

Parameters
  • input (Tensor) – prediction tensor

  • target (Tensor) – target tensor

Return type

Tensor

Returns

(weighted) loss

Module contents

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