o yi@sddlmZddlZddlmZddlmZddlmZdededeeeeeffd d Z dd ed ededede de defddZ ddedede de def ddZ dS))TupleN)Tensor)rank_zero_warn)_check_same_shapepredstargetreturncCstt|||jdkrtd|jtj|dd}tj||dd}||}tj||dd}|d}||||fS)zUpdates and returns variables required to compute R2 score. Checks for same shape and 1D/2D input tensors. Args: preds: Predicted tensor target: Ground truth tensor z`Expected both prediction and target to be 1D or 2D tensors, but received tensors with dimension r)dim)rndim ValueErrorshapetorchsumsize)rrsum_obssum_squared_obsresidualrssn_obsrY/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/regression/r2.py_r2_score_updates   runiform_averagerrrradjusted multioutputcCs^|dkrtd||}|||}tj|t|dd}tj|t|dd} || @} t|} d|| || | | <d| || @<|dkrM| } n%|dkrWt| } n|d krjt|} t|| | } ntd |d |d ks{t|tstd |d kr||dkrt dt | S||dkrt dt | Sdd| |d||d} | S)aComputes R2 score. Args: sum_squared_obs: Sum of square of all observations sum_obs: Sum of all observations rss: Residual sum of squares n_obs: Number of predictions or observations adjusted: number of independent regressors for calculating adjusted r2 score. 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]]) >>> sum_squared_obs, sum_obs, rss, n_obs = _r2_score_update(preds, target) >>> _r2_score_compute(sum_squared_obs, sum_obs, rss, n_obs, multioutput="raw_values") tensor([0.9654, 0.9082]) r z1Needs at least two samples to calculate r2 score.g-C6?)atolg raw_valuesrvariance_weightedzgArgument `multioutput` must be either `raw_values`, `uniform_average` or `variance_weighted`. Received .rz?`adjusted` parameter should be an integer larger or equal to 0.zdMore independent regressions than data points in adjusted r2 score. Falls back to standard r2 score.zGDivision by zero in adjusted r2 score. Falls back to standard r2 score.) r risclose zeros_like ones_likemeanr isinstanceintr UserWarning)rrrrrrmean_obstsscond_rsscond_tsscond raw_scoresr2tss_sumrrr_r2_score_compute0sH       r0cCs$t||\}}}}t||||||S)aComputes r2 score also known as `R2 Score_Coefficient Determination`_: .. math:: R^2 = 1 - \frac{SS_{res}}{SS_{tot}} where :math:`SS_{res}=\sum_i (y_i - f(x_i))^2` is the sum of residual squares, and :math:`SS_{tot}=\sum_i (y_i - \bar{y})^2` is total sum of squares. Can also calculate adjusted r2 score given by .. math:: R^2_{adj} = 1 - \frac{(1-R^2)(n-1)}{n-k-1} where the parameter :math:`k` (the number of independent regressors) should be provided as the ``adjusted`` argument. Args: preds: estimated labels target: ground truth labels adjusted: number of independent regressors for calculating adjusted r2 score. 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 Raises: ValueError: If both ``preds`` and ``targets`` are not ``1D`` or ``2D`` tensors. ValueError: If ``len(preds)`` is less than ``2`` since at least ``2`` sampels are needed to calculate r2 score. ValueError: If ``multioutput`` is not one of ``raw_values``, ``uniform_average`` or ``variance_weighted``. ValueError: If ``adjusted`` is not an ``integer`` greater than ``0``. Example: >>> from torchmetrics.functional import r2_score >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> r2_score(preds, target) tensor(0.9486) >>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]]) >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]]) >>> r2_score(preds, target, multioutput='raw_values') tensor([0.9654, 0.9082]) )rr0)rrrrrrrrrrrr2_scorezs3r1)rr) typingrrrtorchmetrics.utilitiesrtorchmetrics.utilities.checksrrr&strr0r1rrrrsH    " M