o yi?@sddlmZmZmZmZddlZddlmZddlmZddl m Z ddl m Z ddl mZmZddlmZGd d d eZGd d d eZd ededeeeffddZd edededefddZdededefddZd edededefddZdededefddZdGd ededefdd Zd edeeeeffd!d"Zdeded#edeeeeeeeeeeeeeeeef fd$d%Zdeded&ed'ed(ed)ed*eed+eed#edefd,d-Zd.edefd/d0Zd(ed)ed*eed1eed2eed+eed3eed4eed#ed5eedefd6d7Z ggd8fdeded9eed:eed;edeeeeeff dd?Z" @  AdIdeded#edBdCed5eedDdeeeeefff dEdFZ#dS)J)ListOptionalTupleUnionN)Tensor)Literal) _check_data_shape_to_num_outputs)_check_same_shape) _bincount dim_zero_cat)EnumStrc>eZdZdZdZdZdZededddffdd Z Z S) _MetricVariantzEnumerate for metric variants.abcvaluerrrreturncsFddtjD}t|}|dur||vr|Std|d|d)z Raises: ValueError: If required metric variant is not among the supported options. cSsg|]}|qS)lower.0imrr^/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/regression/kendall.py (sz+_MetricVariant.from_str..Nz(Invalid metric variant. Expected one of , but got .)r_member_names_superfrom_str ValueError)clsr_allowed_variantsenum_key __class__rrr !s  z_MetricVariant.from_str) __name__ __module__ __qualname____doc__ABC classmethodrr __classcell__rrr%rr$rcr ) _TestAlternativez&Enumerate for test altenative options. two-sidedlessgreaterrr2r3r4rcsNddtjD}t|dd}|dur||vr|Std|d|d) z Raises: ValueError: If required test alternative is not among the supported options. cSsg|] }|ddqS)_-)rreplacerrrrr>sz-_TestAlternative.from_str..r7r6Nz*Invalid test alternative. Expected one of rr)r1rrr r8r!)r"r_allowed_alternativesr$r%rrr 7s z_TestAlternative.from_str) r'r(r)r* TWO_SIDEDLESSGREATERr.rr r/rrr%rr10r0r1xyrcCsXt|}|j|j}}|\}}t|jdD] }||||||<q|j|jfS)zBSort sequences in an ascent order according to the sequence ``x``.r)torchcloneTsortrangeshape)r=r>permirrr_sort_on_first_sequenceFs   rGrFcCs@t||||ddk||||ddkddS)z>Count a total number of concordant pairs in a single sequence.Nr)r? logical_andsum unsqueezer=r>rFrrr_concordant_element_sumQs@rMpredstargetc*tfddtjdDdS)z.rr?catrCrDrJrSrrSr_count_concordant_pairsV*rWc Csxtt||||ddk||||ddkt||||ddk||||ddkddS)z?Count a total number of discordant pairs in a single sequences.rHNr)r? logical_orrIrJrKrLrrr_discordant_element_sum[s22rZcrP)z.rrUrSrrSr_count_discordant_pairsgrXr[FrBcCs^|r |jddj}tjd|jdtj|jd}tj||dd|ddkgdd dS)z&Convert a sequence to the rank tensor.r)dimrHdtypedeviceN) rBvaluesr?zerosrDint32r_rVintcumsum)r=rB_onesrrr_convert_sequence_to_dense_rankls0rgcCstj|jd|j|jd}tj|jd|j|jd}tj|jd|j|jd}t|jdD]<}t|dd|f}||dk}||dd||<||d|d||<||dd|d||<q.|||fS)zWGet a total number of ties and staistics for p-value calculation for a given sequence.rHr]N?)r?rbrDr^r_rCr rJ)r=tiesties_p1ties_p2r\n_tiesrrr _get_tiesus " rovariantc Cst||\}}t||}t||}tj|jd|jd}d}}d}} } } |tjkrEt |}t |dd}t |\}}} t |\}} } ||||| || | |f S)z,Obtain statistics to calculate metric value.r)r_NT)rB) rGrWr[r?tensorrDr_rr+rgro) rNrOrpconcordant_pairsdiscordant_pairsn_total preds_ties target_ties preds_ties_p1 preds_ties_p2target_ties_p1target_ties_p2rrr_get_metric_metadatas*    r{rrrscon_min_dis_pairsrtrurvc Cs|tjkr |||} | S|tjkr+||dd} | || |} |t| } | Stjdd|jD|j|jd} tjdd|jD|j|jd} t | | }d||d||d} | S)z-Calculate Kendall's tau from metric metadata.rHrhcSg|]}t|qSrlenunique)rprrrrrTz"_calculate_tau..r]cSr}rr~)rtrrrrrT) rr+r,r?sqrtrqrAr^r_minimum)rNrOrrrsr|rtrurvrptautotal_combinations denominator preds_unique target_unique min_classesrrr_calculate_taus    rt_valuecCsl|}tjjtdg|tdg|}|}|}||}| |tjt d|j |j dS)zObtain p-value for a given Tensor of t-values. Handle ``nan`` which cannot be passed into torch distributions. When t-value is ``nan``, a resulted p-value should be alson ``nan``. grinanr]) r? distributionsnormalNormalrqtoisnan nan_to_numcdfwherefloatr^r_)rr_ normal_distis_nanp_valuerrr"_get_p_value_for_t_value_from_dists , "rrwrxryrz alternativec Cs||dd|d} |tjkrd|t| d} n-||d} | ||d} | d||| 7} | ||d| |d7} |t| } | tjkrTt| } | tjtjfvr`| d9} t| }| tjkrm|d9}|S)z9Calculate p-value for Kendall's tau from metric metadata.rHrhrj r`) rr+r?rr1r:absr<r)r|rtrurwrxrvryrzrprt_value_denominator_basermt_value_denominatorrrrr_calculate_p_values      rrH concat_preds concat_target num_outputscCsNt||t||||dkr|d}|d}||||||fS)aUpdate variables required to compute Kendall rank correlation coefficient. Args: preds: Sequence of data target: Sequence of data concat_preds: List of batches of preds sequence to be concatenated concat_target: List of batches of target sequence to be concatenated num_outputs: Number of outputs in multioutput setting Raises: RuntimeError: If ``preds`` and ``target`` do not have the same shape rH)r rrKappend)rNrOrrrrrr_kendall_corrcoef_updates      rc Cst|||\ }}}}}} } } } ||} t||||| | || | }|r.t| | |||| | | || nd}|jddkrE|}|durC|nd}|dd|fS)a.Compute Kendall rank correlation coefficient, and optionally p-value of corresponding statistical test. Args: Args: preds: Sequence of data target: Sequence of data variant: Indication of which variant of Kendall's tau to be used alternative: Alternative hypothesis for for t-test. Possible values: - 'two-sided': the rank correlation is nonzero - 'less': the rank correlation is negative (less than zero) - 'greater': the rank correlation is positive (greater than zero) NrrHr`)r{rrrDsqueezeclamp)rNrOrprrrrsrurwrxrvryrzrtr|rrrrr_kendall_corrcoef_computesD  rrr2rt_testr5c Cst|tstdt|d|r|durtdt|}|r'|r't|nd}t||gg|jdkr5dn|j dd\}}t t |t |||\} } | durS| | fS| S)a Computes `Kendall Rank Correlation Coefficient`_. .. math:: tau_a = \frac{C - D}{C + D} where :math:`C` represents concordant pairs, :math:`D` stands for discordant pairs. .. math:: tau_b = \frac{C - D}{\sqrt{(C + D + T_{preds}) * (C + D + T_{target})}} where :math:`C` represents concordant pairs, :math:`D` stands for discordant pairs and :math:`T` represents a total number of ties. .. math:: tau_c = 2 * \frac{C - D}{n^2 * \frac{m - 1}{m}} where :math:`C` represents concordant pairs, :math:`D` stands for discordant pairs, :math:`n` is a total number of observations and :math:`m` is a ``min`` of unique values in ``preds`` and ``target`` sequence. Definitions according to Definition according to `The Treatment of Ties in Ranking Problems`_. Args: preds: Sequence of data of either shape ``(N,)`` or ``(N,d)`` target: Sequence of data of either shape ``(N,)`` or ``(N,d)`` variant: Indication of which variant of Kendall's tau to be used t_test: Indication whether to run t-test alternative: Alternative hypothesis for t-test. Possible values: - 'two-sided': the rank correlation is nonzero - 'less': the rank correlation is negative (less than zero) - 'greater': the rank correlation is positive (greater than zero) Return: Correlation tau statistic (Optional) p-value of corresponding statistical test (asymptotic) Raises: ValueError: If ``t_test`` is not of a type bool ValueError: If ``t_test=True`` and ``alternative=None`` Example (single output regression): >>> from torchmetrics.functional.regression import kendall_rank_corrcoef >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> target = torch.tensor([3, -0.5, 2, 1]) >>> kendall_rank_corrcoef(preds, target) tensor(0.3333) Example (multi output regression): >>> from torchmetrics.functional.regression import kendall_rank_corrcoef >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> target = torch.tensor([[3, -0.5], [2, 1]]) >>> kendall_rank_corrcoef(preds, target) tensor([1., 1.]) Example (single output regression with t-test) >>> from torchmetrics.functional.regression import kendall_rank_corrcoef >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> target = torch.tensor([3, -0.5, 2, 1]) >>> kendall_rank_corrcoef(preds, target, t_test=True, alternative='two-sided') (tensor(0.3333), tensor(0.4969)) Example (multi output regression with t-test): >>> from torchmetrics.functional.regression import kendall_rank_corrcoef >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> target = torch.tensor([[3, -0.5], [2, 1]]) >>> kendall_rank_corrcoef(preds, target, t_test=True, alternative='two-sided') (tensor([1., 1.]), tensor([nan, nan])) z>Argument `t_test` is expected to be of a type `bool`, but got rNzCArgument `alternative` is required if `t_test=True` but got `None`.rHr`)r) isinstanceboolr!typerr r1rndimrDrr ) rNrOrprr_variant _alternative_preds_targetrrrrrkendall_rank_corrcoefSs J   r)F)N)rFr2)$typingrrrrr?rtyping_extensionsr(torchmetrics.functional.regression.utilsrtorchmetrics.utilities.checksr torchmetrics.utilities.datar r torchmetrics.utilities.enumsr rr1rGrdrMrWrZr[rrgror{rrrrrrrrrrs          (     $ % >