i$lddlmZmZddlmZddlmZddlmZddl m Z ddl m Z ddl mZmZddlmZdd lmZdd lmZdd lmZGd d eZGddeZGddeZGddeZGddeZGdde ZGdde ZGddeZGddeZ GddeZ!y )!)AnyOptional) RetrievalMAP)RetrievalFallOut)RetrievalHitRate)RetrievalNormalizedDCG)RetrievalPrecision)RetrievalPrecisionRecallCurveRetrievalRecallAtFixedPrecision)RetrievalRPrecision)RetrievalRecall) RetrievalMRR)_deprecated_root_import_classc LeZdZdZ d dedeedeededdf fd ZxZ S) _RetrievalFallOutabWrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> rfo = _RetrievalFallOut(top_k=2) >>> rfo(preds, target, indexes=indexes) tensor(0.5000) Nempty_target_action ignore_indextop_kkwargsreturnc Dtddt|d|||d|y)Nr retrievalrrrrsuper__init__selfrrrr __class__s w/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/retrieval/_deprecated.pyrz_RetrievalFallOut.__init__, &&8+F s-@|chslrs)posNN __name__ __module__ __qualname____doc__strrintrr __classcell__r s@r!rr\ $)&*# t tsmt} t  t  ttr#rc LeZdZdZ d dedeedeededdf fd ZxZ S) _RetrievalHitRateabWrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([True, False, False, False, True, False, True]) >>> hr2 = _RetrievalHitRate(top_k=2) >>> hr2(preds, target, indexes=indexes) tensor(0.5000) Nrrrrrc Dtddt|d|||d|y)Nrrrrrrs r!rz_RetrievalHitRate.__init__4r"r#negNNr%r-s@r!r0r0'r.r#r0c LeZdZdZ d dedeedeededdf fd ZxZ S) _RetrievalMAPaYWrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> rmap = _RetrievalMAP() >>> rmap(preds, target, indexes=indexes) tensor(0.7917) Nrrrrrc Dtddt|d|||d|y)Nrrrrrrs r!rz_RetrievalMAP.__init__Ls+ &nkB s-@|chslrsr#r2r%r-s@r!r5r5?r.r#r5c LeZdZdZ d dedeedeededdf fd ZxZ S) _RetrievalRecalla_Wrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> r2 = _RetrievalRecall(top_k=2) >>> r2(preds, target, indexes=indexes) tensor(0.7500) Nrrrrrc Dtddt|d|||d|y)Nr rrrrrs r!rz_RetrievalRecall.__init__ds, &&7E s-@|chslrsr#r2r%r-s@r!r8r8Wr.r#r8c @eZdZdZ ddedeededdffd ZxZ S) _RetrievalRPrecisiona\Wrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> p2 = _RetrievalRPrecision() >>> p2(preds, target, indexes=indexes) tensor(0.7500) Nrrrrc Btddt|d||d|y)Nr rrrrrrrrrr s r!rz_RetrievalRPrecision.__init__|s) &&;[I f-@|f_efr#r3Nr%r-s@r!r;r;oJ $)&*g gsmg g  ggr#r;c LeZdZdZ d dedeedeededdf fd ZxZ S) _RetrievalNormalizedDCGacWrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> ndcg = _RetrievalNormalizedDCG() >>> ndcg(preds, target, indexes=indexes) tensor(0.8467) Nrrrrrc Dtddt|d|||d|y)Nrrrrrrs r!rz _RetrievalNormalizedDCG.__init__s, &&> L s-@|chslrsr#r2r%r-s@r!rBrBr.r#rBc ReZdZdZ d dedeedeedededdf fd Z xZ S) _RetrievalPrecisionabWrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> p2 = _RetrievalPrecision(top_k=2) >>> p2(preds, target, indexes=indexes) tensor(0.5000) Nrrr adaptive_krrc Ftddt|d||||d|y)Nr)rrrrFrr)rrrrrFrr s r!rz_RetrievalPrecision.__init__s7 &b+6  3%!    r#)r3NNF) r&r'r(r)r*rr+boolrrr,r-s@r!rErEsb $)&*#    sm }         r#rEc ReZdZdZ d deedededeededdf fd Z xZ S) _RetrievalPrecisionRecallCurveaWrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 0, 1, 1, 1]) >>> preds = tensor([0.4, 0.01, 0.5, 0.6, 0.2, 0.3, 0.5]) >>> target = tensor([True, False, False, True, True, False, True]) >>> r = _RetrievalPrecisionRecallCurve(max_k=4) >>> precisions, recalls, top_k = r(preds, target, indexes=indexes) >>> precisions tensor([1.0000, 0.5000, 0.6667, 0.5000]) >>> recalls tensor([0.5000, 0.5000, 1.0000, 1.0000]) >>> top_k tensor([1, 2, 3, 4]) Nmax_krFrrrrc Ftddt|d||||d|y)NrHr)rLrFrrrr)rrLrFrrrr s r!rz'_RetrievalPrecisionRecallCurve.__init__s7 &b+6  ! 3%    r#)NFr3N) r&r'r(r)rr+rIr*rrr,r-s@r!rKrKsb& $ #(&*  }  !  sm       r#rKcXeZdZdZ d dedeedededeede d dffd Z xZ S) _RetrievalRecallAtFixedPrecisionaWrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 0, 1, 1, 1]) >>> preds = tensor([0.4, 0.01, 0.5, 0.6, 0.2, 0.3, 0.5]) >>> target = tensor([True, False, False, True, True, False, True]) >>> r = _RetrievalRecallAtFixedPrecision(min_precision=0.8) >>> r(preds, target, indexes=indexes) (tensor(0.5000), tensor(1)) N min_precisionrLrFrrrrc Htddt|d|||||d|y)Nr r)rPrLrFrrrr)rrPrLrFrrrr s r!rz)_RetrievalRecallAtFixedPrecision.__init__s; &&GU  '! 3%    r#)gNFr3N) r&r'r(r)floatrr+rIr*rrr,r-s@r!rOrOso  ## #(&*   }   !  sm      r#rOc @eZdZdZ ddedeededdffd ZxZ S) _RetrievalMRRaWWrapper for deprecated import. >>> from torch import tensor >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> mrr = _RetrievalMRR() >>> mrr(preds, target, indexes=indexes) tensor(0.7500) Nrrrrc Btddt|d||d|y)NrHrr=rrr>s r!rz_RetrievalMRR.__init__s( &b+6 f-@|f_efr#r?r%r-s@r!rTrTr@r#rTN)"typingrr(torchmetrics.retrieval.average_precisionrtorchmetrics.retrieval.fall_outrtorchmetrics.retrieval.hit_ratertorchmetrics.retrieval.ndcgr torchmetrics.retrieval.precisionr -torchmetrics.retrieval.precision_recall_curver r "torchmetrics.retrieval.r_precisionr torchmetrics.retrieval.recallr &torchmetrics.retrieval.reciprocal_rankrtorchmetrics.utilities.printsrrr0r5r8r;rBrErKrOrTrr#r!ras A<<>?xB9?Gt(t0t(t0tLt0tt0g.g.t4t0 , >! %B! H 'F BgLgr#