""" 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. """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause # Goals: # - Provide a common private module for loss functions/classes. # - To be used in: # - LogisticRegression # - PoissonRegressor, GammaRegressor, TweedieRegressor # - HistGradientBoostingRegressor, HistGradientBoostingClassifier # - GradientBoostingRegressor, GradientBoostingClassifier # - SGDRegressor, SGDClassifier # - Replace link module of GLMs. import numbers import numpy as np from scipy.special import xlogy from ..utils import check_scalar from ..utils.stats import _weighted_percentile from ._loss import ( CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss, ) from .link import ( HalfLogitLink, IdentityLink, Interval, LogitLink, LogLink, MultinomialLogit, ) # Note: The shape of raw_prediction for multiclass classifications are # - GradientBoostingClassifier: (n_samples, n_classes) # - HistGradientBoostingClassifier: (n_classes, n_samples) # # Note: Instead of inheritance like # # class BaseLoss(BaseLink, CyLossFunction): # ... # # # Note: Naturally, we would inherit in the following order # # class HalfSquaredError(IdentityLink, CyHalfSquaredError, BaseLoss) # # But because of https://github.com/cython/cython/issues/4350 we set BaseLoss as # # the last one. This, of course, changes the MRO. # class HalfSquaredError(IdentityLink, CyHalfSquaredError, BaseLoss): # # we use composition. This way we improve maintainability by avoiding the above # mentioned Cython edge case and have easier to understand code (which method calls # which code). class BaseLoss: """Base 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. """ # For gradient boosted decision trees: # This variable indicates whether the loss requires the leaves values to # be updated once the tree has been trained. The trees are trained to # predict a Newton-Raphson step (see grower._finalize_leaf()). But for # some losses (e.g. least absolute deviation) we need to adjust the tree # values to account for the "line search" of the gradient descent # procedure. See the original paper Greedy Function Approximation: A # Gradient Boosting Machine by Friedman # (https://statweb.stanford.edu/~jhf/ftp/trebst.pdf) for the theory. differentiable = True need_update_leaves_values = False is_multiclass = False def __init__(self, closs, link, n_classes=None): self.closs = closs self.link = link self.approx_hessian = False self.constant_hessian = False self.n_classes = n_classes self.interval_y_true = Interval(-np.inf, np.inf, False, False) self.interval_y_pred = self.link.interval_y_pred def in_y_true_range(self, y): """Return True if y is in the valid range of y_true. Parameters ---------- y : ndarray """ return self.interval_y_true.includes(y) def in_y_pred_range(self, y): """Return True if y is in the valid range of y_pred. Parameters ---------- y : ndarray """ return self.interval_y_pred.includes(y) def loss( self, y_true, raw_prediction, sample_weight=None, loss_out=None, n_threads=1, ): """Compute 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. """ if loss_out is None: loss_out = np.empty_like(y_true) # Be graceful to shape (n_samples, 1) -> (n_samples,) if raw_prediction.ndim == 2 and raw_prediction.shape[1] == 1: raw_prediction = raw_prediction.squeeze(1) self.closs.loss( y_true=y_true, raw_prediction=raw_prediction, sample_weight=sample_weight, loss_out=loss_out, n_threads=n_threads, ) return loss_out def loss_gradient( self, y_true, raw_prediction, sample_weight=None, loss_out=None, gradient_out=None, n_threads=1, ): """Compute 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. """ if loss_out is None: if gradient_out is None: loss_out = np.empty_like(y_true) gradient_out = np.empty_like(raw_prediction) else: loss_out = np.empty_like(y_true, dtype=gradient_out.dtype) elif gradient_out is None: gradient_out = np.empty_like(raw_prediction, dtype=loss_out.dtype) # Be graceful to shape (n_samples, 1) -> (n_samples,) if raw_prediction.ndim == 2 and raw_prediction.shape[1] == 1: raw_prediction = raw_prediction.squeeze(1) if gradient_out.ndim == 2 and gradient_out.shape[1] == 1: gradient_out = gradient_out.squeeze(1) self.closs.loss_gradient( y_true=y_true, raw_prediction=raw_prediction, sample_weight=sample_weight, loss_out=loss_out, gradient_out=gradient_out, n_threads=n_threads, ) return loss_out, gradient_out def gradient( self, y_true, raw_prediction, sample_weight=None, gradient_out=None, n_threads=1, ): """Compute 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. """ if gradient_out is None: gradient_out = np.empty_like(raw_prediction) # Be graceful to shape (n_samples, 1) -> (n_samples,) if raw_prediction.ndim == 2 and raw_prediction.shape[1] == 1: raw_prediction = raw_prediction.squeeze(1) if gradient_out.ndim == 2 and gradient_out.shape[1] == 1: gradient_out = gradient_out.squeeze(1) self.closs.gradient( y_true=y_true, raw_prediction=raw_prediction, sample_weight=sample_weight, gradient_out=gradient_out, n_threads=n_threads, ) return gradient_out def gradient_hessian( self, y_true, raw_prediction, sample_weight=None, gradient_out=None, hessian_out=None, n_threads=1, ): """Compute 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. """ if gradient_out is None: if hessian_out is None: gradient_out = np.empty_like(raw_prediction) hessian_out = np.empty_like(raw_prediction) else: gradient_out = np.empty_like(hessian_out) elif hessian_out is None: hessian_out = np.empty_like(gradient_out) # Be graceful to shape (n_samples, 1) -> (n_samples,) if raw_prediction.ndim == 2 and raw_prediction.shape[1] == 1: raw_prediction = raw_prediction.squeeze(1) if gradient_out.ndim == 2 and gradient_out.shape[1] == 1: gradient_out = gradient_out.squeeze(1) if hessian_out.ndim == 2 and hessian_out.shape[1] == 1: hessian_out = hessian_out.squeeze(1) self.closs.gradient_hessian( y_true=y_true, raw_prediction=raw_prediction, sample_weight=sample_weight, gradient_out=gradient_out, hessian_out=hessian_out, n_threads=n_threads, ) return gradient_out, hessian_out def __call__(self, y_true, raw_prediction, sample_weight=None, n_threads=1): """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. """ return np.average( self.loss( y_true=y_true, raw_prediction=raw_prediction, sample_weight=None, loss_out=None, n_threads=n_threads, ), weights=sample_weight, ) def fit_intercept_only(self, y_true, sample_weight=None): """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. """ # As default, take weighted average of the target over the samples # axis=0 and then transform into link-scale (raw_prediction). y_pred = np.average(y_true, weights=sample_weight, axis=0) eps = 10 * np.finfo(y_pred.dtype).eps if self.interval_y_pred.low == -np.inf: a_min = None elif self.interval_y_pred.low_inclusive: a_min = self.interval_y_pred.low else: a_min = self.interval_y_pred.low + eps if self.interval_y_pred.high == np.inf: a_max = None elif self.interval_y_pred.high_inclusive: a_max = self.interval_y_pred.high else: a_max = self.interval_y_pred.high - eps if a_min is None and a_max is None: return self.link.link(y_pred) else: return self.link.link(np.clip(y_pred, a_min, a_max)) def constant_to_optimal_zero(self, y_true, sample_weight=None): """Calculate term dropped in loss. With this term added, the loss of perfect predictions is zero. """ return np.zeros_like(y_true) def init_gradient_and_hessian(self, n_samples, dtype=np.float64, order="F"): """Initialize 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``. """ if dtype not in (np.float32, np.float64): raise ValueError( "Valid options for 'dtype' are np.float32 and np.float64. " f"Got dtype={dtype} instead." ) if self.is_multiclass: shape = (n_samples, self.n_classes) else: shape = (n_samples,) gradient = np.empty(shape=shape, dtype=dtype, order=order) if self.constant_hessian: # If the hessians are constant, we consider them equal to 1. # - This is correct for HalfSquaredError # - For AbsoluteError, hessians are actually 0, but they are # always ignored anyway. hessian = np.ones(shape=(1,), dtype=dtype) else: hessian = np.empty(shape=shape, dtype=dtype, order=order) return gradient, hessian # Note: Naturally, we would inherit in the following order # class HalfSquaredError(IdentityLink, CyHalfSquaredError, BaseLoss) # But because of https://github.com/cython/cython/issues/4350 we # set BaseLoss as the last one. This, of course, changes the MRO. class HalfSquaredError(BaseLoss): """Half 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. """ def __init__(self, sample_weight=None): super().__init__(closs=CyHalfSquaredError(), link=IdentityLink()) self.constant_hessian = sample_weight is None class AbsoluteError(BaseLoss): """Absolute 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| Note that the exact hessian = 0 almost everywhere (except at one point, therefore differentiable = False). Optimization routines like in HGBT, however, need a hessian > 0. Therefore, we assign 1. """ differentiable = False need_update_leaves_values = True def __init__(self, sample_weight=None): super().__init__(closs=CyAbsoluteError(), link=IdentityLink()) self.approx_hessian = True self.constant_hessian = sample_weight is None def fit_intercept_only(self, y_true, sample_weight=None): """Compute raw_prediction of an intercept-only model. This is the weighted median of the target, i.e. over the samples axis=0. """ if sample_weight is None: return np.median(y_true, axis=0) else: return _weighted_percentile(y_true, sample_weight, 50) class PinballLoss(BaseLoss): """Quantile 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(). Note that the exact hessian = 0 almost everywhere (except at one point, therefore differentiable = False). Optimization routines like in HGBT, however, need a hessian > 0. Therefore, we assign 1. Additional Attributes --------------------- quantile : float The quantile level of the quantile to be estimated. Must be in range (0, 1). """ differentiable = False need_update_leaves_values = True def __init__(self, sample_weight=None, quantile=0.5): check_scalar( quantile, "quantile", target_type=numbers.Real, min_val=0, max_val=1, include_boundaries="neither", ) super().__init__( closs=CyPinballLoss(quantile=float(quantile)), link=IdentityLink(), ) self.approx_hessian = True self.constant_hessian = sample_weight is None def fit_intercept_only(self, y_true, sample_weight=None): """Compute raw_prediction of an intercept-only model. This is the weighted median of the target, i.e. over the samples axis=0. """ if sample_weight is None: return np.percentile(y_true, 100 * self.closs.quantile, axis=0) else: return _weighted_percentile( y_true, sample_weight, 100 * self.closs.quantile ) class HuberLoss(BaseLoss): """Huber 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 Huber loss is defined as:: loss(x_i) = 1/2 * abserr**2 if abserr <= delta delta * (abserr - delta/2) if abserr > delta abserr = |y_true_i - raw_prediction_i| delta = quantile(abserr, self.quantile) Note: HuberLoss(quantile=1) equals HalfSquaredError and HuberLoss(quantile=0) equals delta * (AbsoluteError() - delta/2). Additional Attributes --------------------- quantile : float The quantile level which defines the breaking point `delta` to distinguish between absolute error and squared error. Must be in range (0, 1). Reference --------- .. [1] Friedman, J.H. (2001). :doi:`Greedy function approximation: A gradient boosting machine <10.1214/aos/1013203451>`. Annals of Statistics, 29, 1189-1232. """ differentiable = False need_update_leaves_values = True def __init__(self, sample_weight=None, quantile=0.9, delta=0.5): check_scalar( quantile, "quantile", target_type=numbers.Real, min_val=0, max_val=1, include_boundaries="neither", ) self.quantile = quantile # This is better stored outside of Cython. super().__init__( closs=CyHuberLoss(delta=float(delta)), link=IdentityLink(), ) self.approx_hessian = True self.constant_hessian = False def fit_intercept_only(self, y_true, sample_weight=None): """Compute raw_prediction of an intercept-only model. This is the weighted median of the target, i.e. over the samples axis=0. """ # See formula before algo 4 in Friedman (2001), but we apply it to y_true, # not to the residual y_true - raw_prediction. An estimator like # HistGradientBoostingRegressor might then call it on the residual, e.g. # fit_intercept_only(y_true - raw_prediction). if sample_weight is None: median = np.percentile(y_true, 50, axis=0) else: median = _weighted_percentile(y_true, sample_weight, 50) diff = y_true - median term = np.sign(diff) * np.minimum(self.closs.delta, np.abs(diff)) return median + np.average(term, weights=sample_weight) class HalfPoissonLoss(BaseLoss): """Half 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`. """ def __init__(self, sample_weight=None): super().__init__(closs=CyHalfPoissonLoss(), link=LogLink()) self.interval_y_true = Interval(0, np.inf, True, False) def constant_to_optimal_zero(self, y_true, sample_weight=None): term = xlogy(y_true, y_true) - y_true if sample_weight is not None: term *= sample_weight return term class HalfGammaLoss(BaseLoss): """Half 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`. """ def __init__(self, sample_weight=None): super().__init__(closs=CyHalfGammaLoss(), link=LogLink()) self.interval_y_true = Interval(0, np.inf, False, False) def constant_to_optimal_zero(self, y_true, sample_weight=None): term = -np.log(y_true) - 1 if sample_weight is not None: term *= sample_weight return term class HalfTweedieLoss(BaseLoss): """Half 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. """ def __init__(self, sample_weight=None, power=1.5): super().__init__( closs=CyHalfTweedieLoss(power=float(power)), link=LogLink(), ) if self.closs.power <= 0: self.interval_y_true = Interval(-np.inf, np.inf, False, False) elif self.closs.power < 2: self.interval_y_true = Interval(0, np.inf, True, False) else: self.interval_y_true = Interval(0, np.inf, False, False) def constant_to_optimal_zero(self, y_true, sample_weight=None): if self.closs.power == 0: return HalfSquaredError().constant_to_optimal_zero( y_true=y_true, sample_weight=sample_weight ) elif self.closs.power == 1: return HalfPoissonLoss().constant_to_optimal_zero( y_true=y_true, sample_weight=sample_weight ) elif self.closs.power == 2: return HalfGammaLoss().constant_to_optimal_zero( y_true=y_true, sample_weight=sample_weight ) else: p = self.closs.power term = np.power(np.maximum(y_true, 0), 2 - p) / (1 - p) / (2 - p) if sample_weight is not None: term *= sample_weight return term class HalfTweedieLossIdentity(BaseLoss): """Half 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. """ def __init__(self, sample_weight=None, power=1.5): super().__init__( closs=CyHalfTweedieLossIdentity(power=float(power)), link=IdentityLink(), ) if self.closs.power <= 0: self.interval_y_true = Interval(-np.inf, np.inf, False, False) elif self.closs.power < 2: self.interval_y_true = Interval(0, np.inf, True, False) else: self.interval_y_true = Interval(0, np.inf, False, False) if self.closs.power == 0: self.interval_y_pred = Interval(-np.inf, np.inf, False, False) else: self.interval_y_pred = Interval(0, np.inf, False, False) class HalfBinomialLoss(BaseLoss): """Half 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. More details: Inserting the predicted probability y_pred = expit(raw_prediction) in the loss gives the well known:: loss(x_i) = - y_true_i * log(y_pred_i) - (1 - y_true_i) * log(1 - y_pred_i) """ def __init__(self, sample_weight=None): super().__init__( closs=CyHalfBinomialLoss(), link=LogitLink(), n_classes=2, ) self.interval_y_true = Interval(0, 1, True, True) def constant_to_optimal_zero(self, y_true, sample_weight=None): # This is non-zero only if y_true is neither 0 nor 1. term = xlogy(y_true, y_true) + xlogy(1 - y_true, 1 - y_true) if sample_weight is not None: term *= sample_weight return term def predict_proba(self, raw_prediction): """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. """ # Be graceful to shape (n_samples, 1) -> (n_samples,) if raw_prediction.ndim == 2 and raw_prediction.shape[1] == 1: raw_prediction = raw_prediction.squeeze(1) proba = np.empty((raw_prediction.shape[0], 2), dtype=raw_prediction.dtype) proba[:, 1] = self.link.inverse(raw_prediction) proba[:, 0] = 1 - proba[:, 1] return proba class HalfMultinomialLoss(BaseLoss): """Categorical 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>` """ is_multiclass = True def __init__(self, sample_weight=None, n_classes=3): super().__init__( closs=CyHalfMultinomialLoss(), link=MultinomialLogit(), n_classes=n_classes, ) self.interval_y_true = Interval(0, np.inf, True, False) self.interval_y_pred = Interval(0, 1, False, False) def in_y_true_range(self, y): """Return True if y is in the valid range of y_true. Parameters ---------- y : ndarray """ return self.interval_y_true.includes(y) and np.all(y.astype(int) == y) def fit_intercept_only(self, y_true, sample_weight=None): """Compute 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. """ out = np.zeros(self.n_classes, dtype=y_true.dtype) eps = np.finfo(y_true.dtype).eps for k in range(self.n_classes): out[k] = np.average(y_true == k, weights=sample_weight, axis=0) out[k] = np.clip(out[k], eps, 1 - eps) return self.link.link(out[None, :]).reshape(-1) def predict_proba(self, raw_prediction): """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. """ return self.link.inverse(raw_prediction) def gradient_proba( self, y_true, raw_prediction, sample_weight=None, gradient_out=None, proba_out=None, n_threads=1, ): """Compute 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. """ if gradient_out is None: if proba_out is None: gradient_out = np.empty_like(raw_prediction) proba_out = np.empty_like(raw_prediction) else: gradient_out = np.empty_like(proba_out) elif proba_out is None: proba_out = np.empty_like(gradient_out) self.closs.gradient_proba( y_true=y_true, raw_prediction=raw_prediction, sample_weight=sample_weight, gradient_out=gradient_out, proba_out=proba_out, n_threads=n_threads, ) return gradient_out, proba_out class ExponentialLoss(BaseLoss): """Exponential loss with (half) logit link, for binary classification. This is also know as boosting 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(2 * raw_prediction) For a given sample x_i, the exponential loss is defined as:: loss(x_i) = y_true_i * exp(-raw_pred_i)) + (1 - y_true_i) * exp(raw_pred_i) See: - J. Friedman, T. Hastie, R. Tibshirani. "Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)." Ann. Statist. 28 (2) 337 - 407, April 2000. https://doi.org/10.1214/aos/1016218223 - A. Buja, W. Stuetzle, Y. Shen. (2005). "Loss Functions for Binary Class Probability Estimation and Classification: Structure and Applications." Note that the formulation works for classification, y = {0, 1}, as well as "exponential logistic" regression, y = [0, 1]. Note that this is a proper scoring rule, but without it's canonical link. More details: Inserting the predicted probability y_pred = expit(2 * raw_prediction) in the loss gives:: loss(x_i) = y_true_i * sqrt((1 - y_pred_i) / y_pred_i) + (1 - y_true_i) * sqrt(y_pred_i / (1 - y_pred_i)) """ def __init__(self, sample_weight=None): super().__init__( closs=CyExponentialLoss(), link=HalfLogitLink(), n_classes=2, ) self.interval_y_true = Interval(0, 1, True, True) def constant_to_optimal_zero(self, y_true, sample_weight=None): # This is non-zero only if y_true is neither 0 nor 1. term = -2 * np.sqrt(y_true * (1 - y_true)) if sample_weight is not None: term *= sample_weight return term def predict_proba(self, raw_prediction): """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. """ # Be graceful to shape (n_samples, 1) -> (n_samples,) if raw_prediction.ndim == 2 and raw_prediction.shape[1] == 1: raw_prediction = raw_prediction.squeeze(1) proba = np.empty((raw_prediction.shape[0], 2), dtype=raw_prediction.dtype) proba[:, 1] = self.link.inverse(raw_prediction) proba[:, 0] = 1 - proba[:, 1] return proba _LOSSES = { "squared_error": HalfSquaredError, "absolute_error": AbsoluteError, "pinball_loss": PinballLoss, "huber_loss": HuberLoss, "poisson_loss": HalfPoissonLoss, "gamma_loss": HalfGammaLoss, "tweedie_loss": HalfTweedieLoss, "binomial_loss": HalfBinomialLoss, "multinomial_loss": HalfMultinomialLoss, "exponential_loss": ExponentialLoss, }