o tBh @sDdZddlZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZddlmZmZmZmZmZddlmZdd lmZdd lmZGd d d ZGd ddeZGdddeZGdddeZGdddeZ GdddeZ!GdddeZ"GdddeZ#GdddeZ$GdddeZ%eeee e!e"e$e%dZ&dS) z This module contains loss classes suitable for fitting. It is not part of the public API. Specific losses are used for regression, binary classification or multiclass classification. Nxlogy) CyHalfSquaredErrorCyAbsoluteError CyPinballLossCyHalfPoissonLossCyHalfGammaLossCyHalfTweedieLossCyHalfTweedieLossIdentityCyHalfBinomialLossCyHalfMultinomialLoss)Interval IdentityLinkLogLink LogitLinkMultinomialLogit) check_scalar)ReadonlyArrayWrapper)_weighted_percentilec@seZdZdZdZdZdZdddZddZd d Z   dd d Z    dddZ   dddZ    dddZ d ddZdddZdddZejdfddZdS)!BaseLossaBase class for a loss function of 1-dimensional targets. Conventions: - y_true.shape = sample_weight.shape = (n_samples,) - y_pred.shape = raw_prediction.shape = (n_samples,) - If is_multiclass is true (multiclass classification), then y_pred.shape = raw_prediction.shape = (n_samples, n_classes) Note that this corresponds to the return value of decision_function. y_true, y_pred, sample_weight and raw_prediction must either be all float64 or all float32. gradient and hessian must be either both float64 or both float32. Note that y_pred = link.inverse(raw_prediction). Specific loss classes can inherit specific link classes to satisfy BaseLink's abstractmethods. Parameters ---------- sample_weight : {None, ndarray} If sample_weight is None, the hessian might be constant. n_classes : {None, int} The number of classes for classification, else None. Attributes ---------- closs: CyLossFunction link : BaseLink interval_y_true : Interval Valid interval for y_true interval_y_pred : Interval Valid Interval for y_pred differentiable : bool Indicates whether or not loss function is differentiable in raw_prediction everywhere. need_update_leaves_values : bool Indicates whether decision trees in gradient boosting need to uptade leave values after having been fit to the (negative) gradients. approx_hessian : bool Indicates whether the hessian is approximated or exact. If, approximated, it should be larger or equal to the exact one. constant_hessian : bool Indicates whether the hessian is one for this loss. is_multiclass : bool Indicates whether n_classes > 2 is allowed. FTNcCsB||_||_d|_d|_||_ttj tjdd|_|jj |_ dS)NF) closslinkapprox_hessianconstant_hessian n_classesrnpinfinterval_y_trueinterval_y_pred)selfrrrr"i/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/_loss/loss.py__init__}szBaseLoss.__init__cC |j|SzuReturn True if y is in the valid range of y_true. Parameters ---------- y : ndarray )rincludesr!yr"r"r#in_y_true_range zBaseLoss.in_y_true_rangecCr%)zuReturn True if y is in the valid range of y_pred. Parameters ---------- y : ndarray )r r'r(r"r"r#in_y_pred_ranger+zBaseLoss.in_y_pred_rangercCsj|dur t|}|jdkr|jddkr|d}t|}t|}|dur*t|}|jj|||||dS)aJCompute the pointwise loss value for each input. Parameters ---------- y_true : C-contiguous array of shape (n_samples,) Observed, true target values. raw_prediction : C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) Raw prediction values (in link space). sample_weight : None or C-contiguous array of shape (n_samples,) Sample weights. loss_out : None or C-contiguous array of shape (n_samples,) A location into which the result is stored. If None, a new array might be created. n_threads : int, default=1 Might use openmp thread parallelism. Returns ------- loss : array of shape (n_samples,) Element-wise loss function. Nrry_trueraw_prediction sample_weightloss_out n_threads)r empty_likendimshapesqueezerrloss)r!r.r/r0r1r2r"r"r#r7s  z BaseLoss.losscCs|dur|durt|}t|}ntj||jd}n |dur(tj||jd}|jdkr9|jddkr9|d}|jdkrJ|jddkrJ|d}t|}t|}|durZt|}|jj||||||dS)aCompute loss and gradient w.r.t. raw_prediction for each input. Parameters ---------- y_true : C-contiguous array of shape (n_samples,) Observed, true target values. raw_prediction : C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) Raw prediction values (in link space). sample_weight : None or C-contiguous array of shape (n_samples,) Sample weights. loss_out : None or C-contiguous array of shape (n_samples,) A location into which the loss is stored. If None, a new array might be created. gradient_out : None or C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) A location into which the gradient is stored. If None, a new array might be created. n_threads : int, default=1 Might use openmp thread parallelism. Returns ------- loss : array of shape (n_samples,) Element-wise loss function. gradient : array of shape (n_samples,) or (n_samples, n_classes) Element-wise gradients. Ndtyperr)r.r/r0r1 gradient_outr2) rr3r9r4r5r6rr loss_gradient)r!r.r/r0r1r:r2r"r"r#r;s.&    zBaseLoss.loss_gradientcCs|dur t|}|jdkr|jddkr|d}|jdkr+|jddkr+|d}t|}t|}|dur;t|}|jj|||||dS)aCompute gradient of loss w.r.t raw_prediction for each input. Parameters ---------- y_true : C-contiguous array of shape (n_samples,) Observed, true target values. raw_prediction : C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) Raw prediction values (in link space). sample_weight : None or C-contiguous array of shape (n_samples,) Sample weights. gradient_out : None or C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) A location into which the result is stored. If None, a new array might be created. n_threads : int, default=1 Might use openmp thread parallelism. Returns ------- gradient : array of shape (n_samples,) or (n_samples, n_classes) Element-wise gradients. Nrr)r.r/r0r:r2)rr3r4r5r6rrgradient)r!r.r/r0r:r2r"r"r#r< s"   zBaseLoss.gradientcCs|dur|durt|}t|}nt|}n |dur"t|}|jdkr3|jddkr3|d}|jdkrD|jddkrD|d}|jdkrU|jddkrU|d}t|}t|}|duret|}|jj||||||dS)aCompute gradient and hessian of loss w.r.t raw_prediction. Parameters ---------- y_true : C-contiguous array of shape (n_samples,) Observed, true target values. raw_prediction : C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) Raw prediction values (in link space). sample_weight : None or C-contiguous array of shape (n_samples,) Sample weights. gradient_out : None or C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) A location into which the gradient is stored. If None, a new array might be created. hessian_out : None or C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) A location into which the hessian is stored. If None, a new array might be created. n_threads : int, default=1 Might use openmp thread parallelism. Returns ------- gradient : arrays of shape (n_samples,) or (n_samples, n_classes) Element-wise gradients. hessian : arrays of shape (n_samples,) or (n_samples, n_classes) Element-wise hessians. Nrr)r.r/r0r: hessian_outr2)rr3r4r5r6rrgradient_hessian)r!r.r/r0r:r=r2r"r"r#r>>s2'       zBaseLoss.gradient_hessiancCstj|j||dd|d|dS)a{Compute the weighted average loss. Parameters ---------- y_true : C-contiguous array of shape (n_samples,) Observed, true target values. raw_prediction : C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) Raw prediction values (in link space). sample_weight : None or C-contiguous array of shape (n_samples,) Sample weights. n_threads : int, default=1 Might use openmp thread parallelism. Returns ------- loss : float Mean or averaged loss function. Nr-)weights)raverager7)r!r.r/r0r2r"r"r#__call__szBaseLoss.__call__cCstj||dd}dt|jj}|jjtj krd}n|jjr%|jj}n|jj|}|jj tjkr5d}n|jj r>|jj }n|jj |}|durR|durR|j |S|j t |||S)a#Compute raw_prediction of an intercept-only model. This can be used as initial estimates of predictions, i.e. before the first iteration in fit. Parameters ---------- y_true : array-like of shape (n_samples,) Observed, true target values. sample_weight : None or array of shape (n_samples,) Sample weights. Returns ------- raw_prediction : numpy scalar or array of shape (n_classes,) Raw predictions of an intercept-only model. rr?axis N) rr@finfor9epsr lowr low_inclusivehighhigh_inclusiverclip)r!r.r0y_predrFa_mina_maxr"r"r#fit_intercept_onlys     zBaseLoss.fit_intercept_onlycCs t|S)zpCalculate term dropped in loss. With this term added, the loss of perfect predictions is zero. )r zeros_liker!r.r0r"r"r#constant_to_optimal_zeros z!BaseLoss.constant_to_optimal_zeroFcCs||tjtjfvrtd|d|jr||jf}n|f}tj|||d}|jr2tjd|d}||fStj|||d}||fS)auInitialize arrays for gradients and hessians. Unless hessians are constant, arrays are initialized with undefined values. Parameters ---------- n_samples : int The number of samples, usually passed to `fit()`. dtype : {np.float64, np.float32}, default=np.float64 The dtype of the arrays gradient and hessian. order : {'C', 'F'}, default='F' Order of the arrays gradient and hessian. The default 'F' makes the arrays contiguous along samples. Returns ------- gradient : C-contiguous array of shape (n_samples,) or array of shape (n_samples, n_classes) Empty array (allocated but not initialized) to be used as argument gradient_out. hessian : C-contiguous array of shape (n_samples,), array of shape (n_samples, n_classes) or shape (1,) Empty (allocated but not initialized) array to be used as argument hessian_out. If constant_hessian is True (e.g. `HalfSquaredError`), the array is initialized to ``1``. zCValid options for 'dtype' are np.float32 and np.float64. Got dtype=z instead.)r5r9order)r)r5r9) rfloat32float64 ValueError is_multiclassremptyrones)r! n_samplesr9rTr5r<hessianr"r"r#init_gradient_and_hessians z"BaseLoss.init_gradient_and_hessianN)NNrNNNrNr)__name__ __module__ __qualname____doc__need_update_leaves_valuesdifferentiablerXr$r*r,r7r;r<r>rArOrRrrVr]r"r"r"r#r>s<:     4 F 8 E  *rcs"eZdZdZdfdd ZZS)HalfSquaredErroraHalf squared error with identity link, for regression. Domain: y_true and y_pred all real numbers Link: y_pred = raw_prediction For a given sample x_i, half squared error is defined as:: loss(x_i) = 0.5 * (y_true_i - raw_prediction_i)**2 The factor of 0.5 simplifies the computation of gradients and results in a unit hessian (and is consistent with what is done in LightGBM). It is also half the Normal distribution deviance. Ncs"tjttd|du|_dS)Nrr)superr$rrrr!r0 __class__r"r#r$szHalfSquaredError.__init__r^rarbrcrdr$ __classcell__r"r"rkr#rg srgcs4eZdZdZdZdZd fdd Zd ddZZS) AbsoluteErroraAbsolute error with identity link, for regression. Domain: y_true and y_pred all real numbers Link: y_pred = raw_prediction For a given sample x_i, the absolute error is defined as:: loss(x_i) = |y_true_i - raw_prediction_i| FTNcs(tjttdd|_|du|_dS)NrhT)rir$rrrrrjrkr"r#r$3szAbsoluteError.__init__cCs"|dur tj|ddSt||dS)Compute raw_prediction of an intercept-only model. This is the weighted median of the target, i.e. over the samples axis=0. NrrC2)rmedianrrQr"r"r#rO8s z AbsoluteError.fit_intercept_onlyr^ rarbrcrdrfrer$rOrnr"r"rkr#ro"s  rocs4eZdZdZdZdZd fdd Zd dd ZZS) PinballLossaQuantile loss aka pinball loss, for regression. Domain: y_true and y_pred all real numbers quantile in (0, 1) Link: y_pred = raw_prediction For a given sample x_i, the pinball loss is defined as:: loss(x_i) = rho_{quantile}(y_true_i - raw_prediction_i) rho_{quantile}(u) = u * (quantile - 1_{u<0}) = -u *(1 - quantile) if u < 0 u * quantile if u >= 0 Note: 2 * PinballLoss(quantile=0.5) equals AbsoluteError(). Additional Attributes --------------------- quantile : float The quantile to be estimated. Must be in range (0, 1). FTN?csFt|dtjddddtjtt|dtdd|_|du|_ dS) Nquantilerrneither) target_typemin_valmax_valinclude_boundaries)rwrhT) rnumbersRealrir$rfloatrrr)r!r0rwrkr"r#r$as zPinballLoss.__init__cCs4|durtj|d|jjddSt||d|jjS)rpNdrrq)r percentilerrwrrQr"r"r#rOqs zPinballLoss.fit_intercept_only)Nrvr^rtr"r"rkr#ruDs ruc,eZdZdZdfdd ZdddZZS)HalfPoissonLossaHalf Poisson deviance loss with log-link, for regression. Domain: y_true in non-negative real numbers y_pred in positive real numbers Link: y_pred = exp(raw_prediction) For a given sample x_i, half the Poisson deviance is defined as:: loss(x_i) = y_true_i * log(y_true_i/exp(raw_prediction_i)) - y_true_i + exp(raw_prediction_i) Half the Poisson deviance is actually the negative log-likelihood up to constant terms (not involving raw_prediction) and simplifies the computation of the gradients. We also skip the constant term `y_true_i * log(y_true_i) - y_true_i`. Ncs*tjttdtdtjdd|_dS)NrhrTF)rir$rrrrrrrjrkr"r#r$zHalfPoissonLoss.__init__cCs"t|||}|dur||9}|Sr^rr!r.r0termr"r"r#rRsz(HalfPoissonLoss.constant_to_optimal_zeror^rarbrcrdr$rRrnr"r"rkr#rsrcr) HalfGammaLossaVHalf Gamma deviance loss with log-link, for regression. Domain: y_true and y_pred in positive real numbers Link: y_pred = exp(raw_prediction) For a given sample x_i, half Gamma deviance loss is defined as:: loss(x_i) = log(exp(raw_prediction_i)/y_true_i) + y_true/exp(raw_prediction_i) - 1 Half the Gamma deviance is actually proportional to the negative log- likelihood up to constant terms (not involving raw_prediction) and simplifies the computation of the gradients. We also skip the constant term `-log(y_true_i) - 1`. Ncs*tjttdtdtjdd|_dS)NrhrF)rir$r rrrrrrjrkr"r#r$rzHalfGammaLoss.__init__cCs$t| d}|dur||9}|Sr`)rlogrr"r"r#rRsz&HalfGammaLoss.constant_to_optimal_zeror^rr"r"rkr#rsrcs,eZdZdZdfdd Zd ddZZS) HalfTweedieLossaHalf Tweedie deviance loss with log-link, for regression. Domain: y_true in real numbers for power <= 0 y_true in non-negative real numbers for 0 < power < 2 y_true in positive real numbers for 2 <= power y_pred in positive real numbers power in real numbers Link: y_pred = exp(raw_prediction) For a given sample x_i, half Tweedie deviance loss with p=power is defined as:: loss(x_i) = max(y_true_i, 0)**(2-p) / (1-p) / (2-p) - y_true_i * exp(raw_prediction_i)**(1-p) / (1-p) + exp(raw_prediction_i)**(2-p) / (2-p) Taking the limits for p=0, 1, 2 gives HalfSquaredError with a log link, HalfPoissonLoss and HalfGammaLoss. We also skip constant terms, but those are different for p=0, 1, 2. Therefore, the loss is not continuous in `power`. Note furthermore that although no Tweedie distribution exists for 0 < power < 1, it still gives a strictly consistent scoring function for the expectation. N?cst|dtjdtj tjdtjtt|dt d|j j dkr/t tj tjdd|_ dS|j j dkr@t dtjd d|_ dSt dtjdd|_ dS) Npowerrx)ryr|rzr{rrhrFrT)rr}r~rrrir$r rrrrrrr!r0rrkr"r#r$s"   zHalfTweedieLoss.__init__cCs|jjdkrtj||dS|jjdkrtj||dS|jjdkr*tj||dS|jj}tt|dd|d|d|}|durJ||9}|S)Nr)r.r0rr)rrrgrRrrrmaximum)r!r.r0prr"r"r#rRs"   (z(HalfTweedieLoss.constant_to_optimal_zeroNrr^rr"r"rkr#rsrcs"eZdZdZdfdd ZZS)HalfTweedieLossIdentityanHalf Tweedie deviance loss with identity link, for regression. Domain: y_true in real numbers for power <= 0 y_true in non-negative real numbers for 0 < power < 2 y_true in positive real numbers for 2 <= power y_pred in positive real numbers for power != 0 y_pred in real numbers for power = 0 power in real numbers Link: y_pred = raw_prediction For a given sample x_i, half Tweedie deviance loss with p=power is defined as:: loss(x_i) = max(y_true_i, 0)**(2-p) / (1-p) / (2-p) - y_true_i * raw_prediction_i**(1-p) / (1-p) + raw_prediction_i**(2-p) / (2-p) Note that the minimum value of this loss is 0. Note furthermore that although no Tweedie distribution exists for 0 < power < 1, it still gives a strictly consistent scoring function for the expectation. Nrcstjtt|dtd|jjdkr ttj tj dd|_ n|jjdkr0tdtj dd|_ n tdtj dd|_ |jjdkrLttj tj dd|_ dStdtj dd|_ dS)NrrhrFrT) rir$r rrrrrrrrr rrkr"r#r$"s    z HalfTweedieLossIdentity.__init__rrmr"r"rkr#rsrcs4eZdZdZd fdd Zd ddZddZZS) HalfBinomialLossaHalf Binomial deviance loss with logit link, for binary classification. This is also know as binary cross entropy, log-loss and logistic loss. Domain: y_true in [0, 1], i.e. regression on the unit interval y_pred in (0, 1), i.e. boundaries excluded Link: y_pred = expit(raw_prediction) For a given sample x_i, half Binomial deviance is defined as the negative log-likelihood of the Binomial/Bernoulli distribution and can be expressed as:: loss(x_i) = log(1 + exp(raw_pred_i)) - y_true_i * raw_pred_i See The Elements of Statistical Learning, by Hastie, Tibshirani, Friedman, section 4.4.1 (about logistic regression). Note that the formulation works for classification, y = {0, 1}, as well as logistic regression, y = [0, 1]. If you add `constant_to_optimal_zero` to the loss, you get half the Bernoulli/binomial deviance. Ncs*tjttddtdddd|_dS)NrrrrrrT)rir$r rrrrjrkr"r#r$Os zHalfBinomialLoss.__init__cCs0t||td|d|}|dur||9}|Sr`rrr"r"r#rRWsz)HalfBinomialLoss.constant_to_optimal_zerocCsx|jdkr|jddkr|d}tj|jddf|jd}|j||dddf<d|dddf|dddf<|S)a=Predict probabilities. Parameters ---------- raw_prediction : array of shape (n_samples,) or (n_samples, 1) Raw prediction values (in link space). Returns ------- proba : array of shape (n_samples, 2) Element-wise class probabilities. rrrr8N)r4r5r6rrYr9rinverse)r!r/probar"r"r# predict_proba^s   zHalfBinomialLoss.predict_probar^)rarbrcrdr$rRrrnr"r"rkr#r4s  rcsReZdZdZdZdfdd ZddZdd d Zd d Z    dddZ Z S)HalfMultinomialLossaCategorical cross-entropy loss, for multiclass classification. Domain: y_true in {0, 1, 2, 3, .., n_classes - 1} y_pred has n_classes elements, each element in (0, 1) Link: y_pred = softmax(raw_prediction) Note: We assume y_true to be already label encoded. The inverse link is softmax. But the full link function is the symmetric multinomial logit function. For a given sample x_i, the categorical cross-entropy loss is defined as the negative log-likelihood of the multinomial distribution, it generalizes the binary cross-entropy to more than 2 classes:: loss_i = log(sum(exp(raw_pred_{i, k}), k=0..n_classes-1)) - sum(y_true_{i, k} * raw_pred_{i, k}, k=0..n_classes-1) See [1]. Note that for the hessian, we calculate only the diagonal part in the classes: If the full hessian for classes k and l and sample i is H_i_k_l, we calculate H_i_k_k, i.e. k=l. Reference --------- .. [1] :arxiv:`Simon, Noah, J. Friedman and T. Hastie. "A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression". <1311.6529>` TNcs<tjtt|dtdtjdd|_tdddd|_dS)NrrTFr) rir$r rrrrrr )r!r0rrkr"r#r$szHalfMultinomialLoss.__init__cCs |j|ot|t|kSr&)rr'rallastypeintr(r"r"r#r*s z#HalfMultinomialLoss.in_y_true_rangecCstj|j|jd}t|jj}t|jD]}tj||k|dd||<t|||d|||<q|j |dddf dS)zCompute raw_prediction of an intercept-only model. This is the softmax of the weighted average of the target, i.e. over the samples axis=0. r8rrBrN) rzerosrr9rErFranger@rKrreshape)r!r.r0outrFkr"r"r#rOs z&HalfMultinomialLoss.fit_intercept_onlycCr%)a=Predict probabilities. Parameters ---------- raw_prediction : array of shape (n_samples, n_classes) Raw prediction values (in link space). Returns ------- proba : array of shape (n_samples, n_classes) Element-wise class probabilities. )rr)r!r/r"r"r#rs z!HalfMultinomialLoss.predict_probarcCs||dur|durt|}t|}nt|}n |dur"t|}t|}t|}|dur2t|}|jj||||||dS)aKCompute gradient and class probabilities fow raw_prediction. Parameters ---------- y_true : C-contiguous array of shape (n_samples,) Observed, true target values. raw_prediction : array of shape (n_samples, n_classes) Raw prediction values (in link space). sample_weight : None or C-contiguous array of shape (n_samples,) Sample weights. gradient_out : None or array of shape (n_samples, n_classes) A location into which the gradient is stored. If None, a new array might be created. proba_out : None or array of shape (n_samples, n_classes) A location into which the class probabilities are stored. If None, a new array might be created. n_threads : int, default=1 Might use openmp thread parallelism. Returns ------- gradient : array of shape (n_samples, n_classes) Element-wise gradients. proba : array of shape (n_samples, n_classes) Element-wise class probabilities. N)r.r/r0r: proba_outr2)rr3rrgradient_proba)r!r.r/r0r:rr2r"r"r#rs&$    z"HalfMultinomialLoss.gradient_proba)Nrr^r_) rarbrcrdrXr$r*rOrrrnr"r"rkr#rts"  r) squared_errorabsolute_error pinball_loss poisson_loss gamma_loss tweedie_loss binomial_lossmultinomial_loss)'rdr}numpyr scipy.specialr_lossrrrrr r r r r rrrrrrutilsrutils._readonly_array_wrapperr utils.statsrrrgrorurrrrrr_LOSSESr"r"r"r#s@ ,   P"; H.@