a  d @sddlZddlmZmZddlZddlmZddlmZddlm Z ddl m Z m Z m Z mZmZmZdeeeed eeeefed d d Zeeed ddZdeeeed eeeefedddZdeeed eeeefedddZdS)N)OptionalUnion)Tensor)Literal)#_multiclass_confusion_matrix_update)_compute_bias_corrected_values_compute_chi_squared_drop_empty_rows_and_cols_handle_nan_in_data_nominal_input_validation&_unable_to_use_bias_correction_warningreplace)r Zdrop)predstarget num_classes nan_strategynan_replace_valuereturncCsN|jdkr|dn|}|jdkr,|dn|}t||||\}}t|||S)aCompute the bins to update the confusion matrix with for Tschuprow's T calculation. Args: preds: 1D or 2D tensor of categorical (nominal) data target: 1D or 2D tensor of categorical (nominal) data num_classes: Integer specifing the number of classes nan_strategy: Indication of whether to replace or drop ``NaN`` values nan_replace_value: Value to replace ``NaN`s when ``nan_strategy = 'replace``` Returns: Non-reduced confusion matrix )ndimZargmaxr r)rrrrrrs/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/nominal/tschuprows.py_tschuprows_t_update sr)confmatbias_correctionrc Cst|}|}t||}||}|j\}}|rt||||\}}} t|| dkrrtddtjt d|j dSt |t |d| d} n@tj||j d} tj||j d} t |t | d| d} | ddS)aCompute Tschuprow's T statistic based on a pre-computed confusion matrix. Args: confmat: Confusion matrix for observed data bias_correction: Indication of whether to use bias correction. Returns: Tschuprow's T statistic rz Tschuprow's T)Z metric_namenandevicerg?) r sumrshapertorchminr Ztensorfloatrsqrtclamp) rrZcm_sumZ chi_squaredZ phi_squaredZn_rowsZn_colsZphi_squared_correctedZrows_correctedZcols_correctedZtschuprows_t_valueZ n_rows_tensorZ n_cols_tensorrrr_tschuprows_t_compute:s"     " r'T)rrrrrrcCs:t||tt||g}t|||||}t||S)a@Compute `Tschuprow's T`_ statistic measuring the association between two categorical (nominal) data series. .. math:: T = \sqrt{\frac{\chi^2 / n}{\sqrt{(r - 1) * (k - 1)}}} where .. math:: \chi^2 = \sum_{i,j} \ frac{\left(n_{ij} - \frac{n_{i.} n_{.j}}{n}\right)^2}{\frac{n_{i.} n_{.j}}{n}} where :math:`n_{ij}` denotes the number of times the values :math:`(A_i, B_j)` are observed with :math:`A_i, B_j` represent frequencies of values in ``preds`` and ``target``, respectively. Tschuprow's T is a symmetric coefficient, i.e. :math:`T(preds, target) = T(target, preds)`. The output values lies in [0, 1] with 1 meaning the perfect association. Args: preds: 1D or 2D tensor of categorical (nominal) data: - 1D shape: (batch_size,) - 2D shape: (batch_size, num_classes) target: 1D or 2D tensor of categorical (nominal) data: - 1D shape: (batch_size,) - 2D shape: (batch_size, num_classes) bias_correction: Indication of whether to use bias correction. nan_strategy: Indication of whether to replace or drop ``NaN`` values nan_replace_value: Value to replace ``NaN``s when ``nan_strategy = 'replace'`` Returns: Tschuprow's T statistic Example: >>> from torchmetrics.functional.nominal import tschuprows_t >>> _ = torch.manual_seed(42) >>> preds = torch.randint(0, 4, (100,)) >>> target = torch.round(preds + torch.randn(100)).clamp(0, 4) >>> tschuprows_t(preds, target) tensor(0.4930) )r lenr"catuniquerr')rrrrrrrrrr tschuprows_tZs3 r+)matrixrrrrc Cst|||jd}tj|||jd}tt|dD]n\}}|dd|f|dd|f}} tt || g } t || | ||} t | ||||f<|||f<q6|S)aCompute `Tschuprow's T`_ statistic between a set of multiple variables. This can serve as a convenient tool to compute Tschuprow's T statistic for analyses of correlation between categorical variables in your dataset. Args: matrix: A tensor of categorical (nominal) data, where: - rows represent a number of data points - columns represent a number of categorical (nominal) features bias_correction: Indication of whether to use bias correction. nan_strategy: Indication of whether to replace or drop ``NaN`` values nan_replace_value: Value to replace ``NaN``s when ``nan_strategy = 'replace'`` Returns: Tschuprow's T statistic for a dataset of categorical variables Example: >>> from torchmetrics.functional.nominal import tschuprows_t_matrix >>> _ = torch.manual_seed(42) >>> matrix = torch.randint(0, 4, (200, 5)) >>> tschuprows_t_matrix(matrix) tensor([[1.0000, 0.0637, 0.0000, 0.0542, 0.1337], [0.0637, 1.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 1.0000, 0.0000, 0.0649], [0.0542, 0.0000, 0.0000, 1.0000, 0.1100], [0.1337, 0.0000, 0.0649, 0.1100, 1.0000]]) rrrN) r r!r"Zonesr itertools combinationsranger(r)r*rr') r,rrrZ num_variablesZtschuprows_t_matrix_valueijxyrrrrrtschuprows_t_matrixs$  "r4)r r)Tr r)Tr r)r-typingrrr"rZtyping_extensionsrZ7torchmetrics.functional.classification.confusion_matrixrZ%torchmetrics.functional.nominal.utilsrrr r r r intr$rboolr'r+r4rrrrsL     # ;