o yi@sddlmZmZmZddlZddlmZddlmZdededeeeeeeffdd Z dd ed ed ededede defddZ ddedede deeeeffddZ dS))SequenceTupleUnionN)Tensor)_check_same_shapepredstargetreturncCsnt|||d}tj||dd}||}tj||dd}tj|dd}tj||dd}|||||fS)zUpdates and returns variables required to compute Explained Variance. Checks for same shape of input tensors. Args: preds: Predicted tensor target: Ground truth tensor r)dim)rsizetorchsum)rrn_obs sum_errordiffsum_squared_error sum_targetsum_squared_targetri/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/regression/explained_variance.py_explained_variance_updates runiform_averagerrrrr multioutputcCs||}||||}||}||||} |dk} | dk} | | @} t|} d|| | | | | <d| | | @<|dkrB| S|dkrKt| S|dkr]t| }t| || SdS)a'Computes Explained Variance. Args: n_obs: Number of predictions or observations sum_error: Sum of errors over all observations sum_squared_error: Sum of square of errors over all observations sum_target: Sum of target values sum_squared_target: Sum of squares of target values multioutput: Defines aggregation in the case of multiple output scores. Can be one of the following strings: * ``'raw_values'`` returns full set of scores * ``'uniform_average'`` scores are uniformly averaged * ``'variance_weighted'`` scores are weighted by their individual variances Example: >>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]]) >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]]) >>> n_obs, sum_error, ss_error, sum_target, ss_target = _explained_variance_update(preds, target) >>> _explained_variance_compute(n_obs, sum_error, ss_error, sum_target, ss_target, multioutput='raw_values') tensor([0.9677, 1.0000]) rg?g raw_valuesrvariance_weightedN)r ones_likemeanr )rrrrrrdiff_avg numerator target_avg denominatornonzero_numeratornonzero_denominator valid_score output_scores denom_sumrrr_explained_variance_compute,s$   r&cCs&t||\}}}}}t||||||S)aComputes explained variance. Args: preds: estimated labels target: ground truth labels multioutput: Defines aggregation in the case of multiple output scores. Can be one of the following strings): * ``'raw_values'`` returns full set of scores * ``'uniform_average'`` scores are uniformly averaged * ``'variance_weighted'`` scores are weighted by their individual variances Example: >>> from torchmetrics.functional import explained_variance >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> explained_variance(preds, target) tensor(0.9572) >>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]]) >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]]) >>> explained_variance(preds, target, multioutput='raw_values') tensor([0.9677, 1.0000]) )rr&)rrrrrrrrrrrexplained_variancedsr')r) typingrrrr rtorchmetrics.utilities.checksrintrstrr&r'rrrrs>   $ ;