a  dH @sddlmZmZmZddlZddlmZddlmZddlm Z dZ eeee eeeefddd Z dee efeeeeeded d d Z deeedeeeefdddZdS))SequenceTupleUnionN)Tensor)Literal)_check_same_shape) raw_valuesuniform_averageZvariance_weighted)predstargetreturncCsnt|||d}tj||dd}||}tj||dd}tj|dd}tj||dd}|||||fS)zUpdate and returns variables required to compute Explained Variance. Checks for same shape of input tensors. Args: preds: Predicted tensor target: Ground truth tensor r)Zdim)rsizetorchsum)r r n_obs sum_errordiffsum_squared_error sum_targetsum_squared_targetr~/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/regression/explained_variance.py_explained_variance_updates  rr )rrrrr multioutputr cCs||}||||}||}||||} |dk} | dk} | | @} t|} d|| | | | | <d| | | @<|dkr| S|dkrt| St| }t| || S)a'Compute 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?grr )rZ ones_likemeanr)rrrrrrZdiff_avg numeratorZ target_avg denominatorZnonzero_numeratorZnonzero_denominatorZ valid_scoreZ output_scoresZ denom_sumrrr_explained_variance_compute.s    r)r r rr cCs<|tvrtdtt||\}}}}}t||||||S)aCompute 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.regression 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]) zFInvalid input to argument `multioutput`. Choose one of the following: )ALLOWED_MULTIOUTPUT ValueErrorrr)r r rrrrrrrrrexplained_varianceesr )r )r )typingrrrrrZtyping_extensionsrZtorchmetrics.utilities.checksrrintrrr rrrrs.      :