"""Gradient Boosted Regression Trees This module contains methods for fitting gradient boosted regression trees for both classification and regression. The module structure is the following: - The ``BaseGradientBoosting`` base class implements a common ``fit`` method for all the estimators in the module. Regression and classification only differ in the concrete ``LossFunction`` used. - ``GradientBoostingClassifier`` implements gradient boosting for classification problems. - ``GradientBoostingRegressor`` implements gradient boosting for regression problems. """ # Authors: Peter Prettenhofer, Scott White, Gilles Louppe, Emanuele Olivetti, # Arnaud Joly, Jacob Schreiber # License: BSD 3 clause from __future__ import print_function from __future__ import division from abc import ABCMeta from abc import abstractmethod from .base import BaseEnsemble from ..base import ClassifierMixin from ..base import RegressorMixin from ..externals import six from ._gradient_boosting import predict_stages from ._gradient_boosting import predict_stage from ._gradient_boosting import _random_sample_mask import numbers import numpy as np from scipy import stats from scipy.sparse import csc_matrix from scipy.sparse import csr_matrix from scipy.sparse import issparse from scipy.special import expit from time import time from ..tree.tree import DecisionTreeRegressor from ..tree._tree import DTYPE from ..tree._tree import TREE_LEAF from ..utils import check_random_state from ..utils import check_array from ..utils import check_X_y from ..utils import column_or_1d from ..utils import check_consistent_length from ..utils import deprecated from ..utils.fixes import logsumexp from ..utils.stats import _weighted_percentile from ..utils.validation import check_is_fitted from ..utils.multiclass import check_classification_targets from ..exceptions import NotFittedError class QuantileEstimator(object): """An estimator predicting the alpha-quantile of the training targets.""" def __init__(self, alpha=0.9): if not 0 < alpha < 1.0: raise ValueError("`alpha` must be in (0, 1.0) but was %r" % alpha) self.alpha = alpha def fit(self, X, y, sample_weight=None): if sample_weight is None: self.quantile = stats.scoreatpercentile(y, self.alpha * 100.0) else: self.quantile = _weighted_percentile(y, sample_weight, self.alpha * 100.0) def predict(self, X): check_is_fitted(self, 'quantile') y = np.empty((X.shape[0], 1), dtype=np.float64) y.fill(self.quantile) return y class MeanEstimator(object): """An estimator predicting the mean of the training targets.""" def fit(self, X, y, sample_weight=None): if sample_weight is None: self.mean = np.mean(y) else: self.mean = np.average(y, weights=sample_weight) def predict(self, X): check_is_fitted(self, 'mean') y = np.empty((X.shape[0], 1), dtype=np.float64) y.fill(self.mean) return y class LogOddsEstimator(object): """An estimator predicting the log odds ratio.""" scale = 1.0 def fit(self, X, y, sample_weight=None): # pre-cond: pos, neg are encoded as 1, 0 if sample_weight is None: pos = np.sum(y) neg = y.shape[0] - pos else: pos = np.sum(sample_weight * y) neg = np.sum(sample_weight * (1 - y)) if neg == 0 or pos == 0: raise ValueError('y contains non binary labels.') self.prior = self.scale * np.log(pos / neg) def predict(self, X): check_is_fitted(self, 'prior') y = np.empty((X.shape[0], 1), dtype=np.float64) y.fill(self.prior) return y class ScaledLogOddsEstimator(LogOddsEstimator): """Log odds ratio scaled by 0.5 -- for exponential loss. """ scale = 0.5 class PriorProbabilityEstimator(object): """An estimator predicting the probability of each class in the training data. """ def fit(self, X, y, sample_weight=None): if sample_weight is None: sample_weight = np.ones_like(y, dtype=np.float64) class_counts = np.bincount(y, weights=sample_weight) self.priors = class_counts / class_counts.sum() def predict(self, X): check_is_fitted(self, 'priors') y = np.empty((X.shape[0], self.priors.shape[0]), dtype=np.float64) y[:] = self.priors return y class ZeroEstimator(object): """An estimator that simply predicts zero. """ def fit(self, X, y, sample_weight=None): if np.issubdtype(y.dtype, np.signedinteger): # classification self.n_classes = np.unique(y).shape[0] if self.n_classes == 2: self.n_classes = 1 else: # regression self.n_classes = 1 def predict(self, X): check_is_fitted(self, 'n_classes') y = np.empty((X.shape[0], self.n_classes), dtype=np.float64) y.fill(0.0) return y class LossFunction(six.with_metaclass(ABCMeta, object)): """Abstract base class for various loss functions. Attributes ---------- K : int The number of regression trees to be induced; 1 for regression and binary classification; ``n_classes`` for multi-class classification. """ is_multi_class = False def __init__(self, n_classes): self.K = n_classes def init_estimator(self): """Default ``init`` estimator for loss function. """ raise NotImplementedError() @abstractmethod def __call__(self, y, pred, sample_weight=None): """Compute the loss of prediction ``pred`` and ``y``. """ @abstractmethod def negative_gradient(self, y, y_pred, **kargs): """Compute the negative gradient. Parameters --------- y : np.ndarray, shape=(n,) The target labels. y_pred : np.ndarray, shape=(n,): The predictions. """ def update_terminal_regions(self, tree, X, y, residual, y_pred, sample_weight, sample_mask, learning_rate=1.0, k=0): """Update the terminal regions (=leaves) of the given tree and updates the current predictions of the model. Traverses tree and invokes template method `_update_terminal_region`. Parameters ---------- tree : tree.Tree The tree object. X : ndarray, shape=(n, m) The data array. y : ndarray, shape=(n,) The target labels. residual : ndarray, shape=(n,) The residuals (usually the negative gradient). y_pred : ndarray, shape=(n,) The predictions. sample_weight : ndarray, shape=(n,) The weight of each sample. sample_mask : ndarray, shape=(n,) The sample mask to be used. learning_rate : float, default=0.1 learning rate shrinks the contribution of each tree by ``learning_rate``. k : int, default 0 The index of the estimator being updated. """ # compute leaf for each sample in ``X``. terminal_regions = tree.apply(X) # mask all which are not in sample mask. masked_terminal_regions = terminal_regions.copy() masked_terminal_regions[~sample_mask] = -1 # update each leaf (= perform line search) for leaf in np.where(tree.children_left == TREE_LEAF)[0]: self._update_terminal_region(tree, masked_terminal_regions, leaf, X, y, residual, y_pred[:, k], sample_weight) # update predictions (both in-bag and out-of-bag) y_pred[:, k] += (learning_rate * tree.value[:, 0, 0].take(terminal_regions, axis=0)) @abstractmethod def _update_terminal_region(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight): """Template method for updating terminal regions (=leaves). """ class RegressionLossFunction(six.with_metaclass(ABCMeta, LossFunction)): """Base class for regression loss functions. """ def __init__(self, n_classes): if n_classes != 1: raise ValueError("``n_classes`` must be 1 for regression but " "was %r" % n_classes) super(RegressionLossFunction, self).__init__(n_classes) class LeastSquaresError(RegressionLossFunction): """Loss function for least squares (LS) estimation. Terminal regions need not to be updated for least squares. """ def init_estimator(self): return MeanEstimator() def __call__(self, y, pred, sample_weight=None): if sample_weight is None: return np.mean((y - pred.ravel()) ** 2.0) else: return (1.0 / sample_weight.sum() * np.sum(sample_weight * ((y - pred.ravel()) ** 2.0))) def negative_gradient(self, y, pred, **kargs): return y - pred.ravel() def update_terminal_regions(self, tree, X, y, residual, y_pred, sample_weight, sample_mask, learning_rate=1.0, k=0): """Least squares does not need to update terminal regions. But it has to update the predictions. """ # update predictions y_pred[:, k] += learning_rate * tree.predict(X).ravel() def _update_terminal_region(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight): pass class LeastAbsoluteError(RegressionLossFunction): """Loss function for least absolute deviation (LAD) regression. """ def init_estimator(self): return QuantileEstimator(alpha=0.5) def __call__(self, y, pred, sample_weight=None): if sample_weight is None: return np.abs(y - pred.ravel()).mean() else: return (1.0 / sample_weight.sum() * np.sum(sample_weight * np.abs(y - pred.ravel()))) def negative_gradient(self, y, pred, **kargs): """1.0 if y - pred > 0.0 else -1.0""" pred = pred.ravel() return 2.0 * (y - pred > 0.0) - 1.0 def _update_terminal_region(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight): """LAD updates terminal regions to median estimates. """ terminal_region = np.where(terminal_regions == leaf)[0] sample_weight = sample_weight.take(terminal_region, axis=0) diff = y.take(terminal_region, axis=0) - pred.take(terminal_region, axis=0) tree.value[leaf, 0, 0] = _weighted_percentile(diff, sample_weight, percentile=50) class HuberLossFunction(RegressionLossFunction): """Huber loss function for robust regression. M-Regression proposed in Friedman 2001. References ---------- J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001. """ def __init__(self, n_classes, alpha=0.9): super(HuberLossFunction, self).__init__(n_classes) self.alpha = alpha self.gamma = None def init_estimator(self): return QuantileEstimator(alpha=0.5) def __call__(self, y, pred, sample_weight=None): pred = pred.ravel() diff = y - pred gamma = self.gamma if gamma is None: if sample_weight is None: gamma = stats.scoreatpercentile(np.abs(diff), self.alpha * 100) else: gamma = _weighted_percentile(np.abs(diff), sample_weight, self.alpha * 100) gamma_mask = np.abs(diff) <= gamma if sample_weight is None: sq_loss = np.sum(0.5 * diff[gamma_mask] ** 2.0) lin_loss = np.sum(gamma * (np.abs(diff[~gamma_mask]) - gamma / 2.0)) loss = (sq_loss + lin_loss) / y.shape[0] else: sq_loss = np.sum(0.5 * sample_weight[gamma_mask] * diff[gamma_mask] ** 2.0) lin_loss = np.sum(gamma * sample_weight[~gamma_mask] * (np.abs(diff[~gamma_mask]) - gamma / 2.0)) loss = (sq_loss + lin_loss) / sample_weight.sum() return loss def negative_gradient(self, y, pred, sample_weight=None, **kargs): pred = pred.ravel() diff = y - pred if sample_weight is None: gamma = stats.scoreatpercentile(np.abs(diff), self.alpha * 100) else: gamma = _weighted_percentile(np.abs(diff), sample_weight, self.alpha * 100) gamma_mask = np.abs(diff) <= gamma residual = np.zeros((y.shape[0],), dtype=np.float64) residual[gamma_mask] = diff[gamma_mask] residual[~gamma_mask] = gamma * np.sign(diff[~gamma_mask]) self.gamma = gamma return residual def _update_terminal_region(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight): terminal_region = np.where(terminal_regions == leaf)[0] sample_weight = sample_weight.take(terminal_region, axis=0) gamma = self.gamma diff = (y.take(terminal_region, axis=0) - pred.take(terminal_region, axis=0)) median = _weighted_percentile(diff, sample_weight, percentile=50) diff_minus_median = diff - median tree.value[leaf, 0] = median + np.mean( np.sign(diff_minus_median) * np.minimum(np.abs(diff_minus_median), gamma)) class QuantileLossFunction(RegressionLossFunction): """Loss function for quantile regression. Quantile regression allows to estimate the percentiles of the conditional distribution of the target. """ def __init__(self, n_classes, alpha=0.9): super(QuantileLossFunction, self).__init__(n_classes) assert 0 < alpha < 1.0 self.alpha = alpha self.percentile = alpha * 100.0 def init_estimator(self): return QuantileEstimator(self.alpha) def __call__(self, y, pred, sample_weight=None): pred = pred.ravel() diff = y - pred alpha = self.alpha mask = y > pred if sample_weight is None: loss = (alpha * diff[mask].sum() - (1.0 - alpha) * diff[~mask].sum()) / y.shape[0] else: loss = ((alpha * np.sum(sample_weight[mask] * diff[mask]) - (1.0 - alpha) * np.sum(sample_weight[~mask] * diff[~mask])) / sample_weight.sum()) return loss def negative_gradient(self, y, pred, **kargs): alpha = self.alpha pred = pred.ravel() mask = y > pred return (alpha * mask) - ((1.0 - alpha) * ~mask) def _update_terminal_region(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight): terminal_region = np.where(terminal_regions == leaf)[0] diff = (y.take(terminal_region, axis=0) - pred.take(terminal_region, axis=0)) sample_weight = sample_weight.take(terminal_region, axis=0) val = _weighted_percentile(diff, sample_weight, self.percentile) tree.value[leaf, 0] = val class ClassificationLossFunction(six.with_metaclass(ABCMeta, LossFunction)): """Base class for classification loss functions. """ def _score_to_proba(self, score): """Template method to convert scores to probabilities. the does not support probabilities raises AttributeError. """ raise TypeError('%s does not support predict_proba' % type(self).__name__) @abstractmethod def _score_to_decision(self, score): """Template method to convert scores to decisions. Returns int arrays. """ class BinomialDeviance(ClassificationLossFunction): """Binomial deviance loss function for binary classification. Binary classification is a special case; here, we only need to fit one tree instead of ``n_classes`` trees. """ def __init__(self, n_classes): if n_classes != 2: raise ValueError("{0:s} requires 2 classes.".format( self.__class__.__name__)) # we only need to fit one tree for binary clf. super(BinomialDeviance, self).__init__(1) def init_estimator(self): return LogOddsEstimator() def __call__(self, y, pred, sample_weight=None): """Compute the deviance (= 2 * negative log-likelihood). """ # logaddexp(0, v) == log(1.0 + exp(v)) pred = pred.ravel() if sample_weight is None: return -2.0 * np.mean((y * pred) - np.logaddexp(0.0, pred)) else: return (-2.0 / sample_weight.sum() * np.sum(sample_weight * ((y * pred) - np.logaddexp(0.0, pred)))) def negative_gradient(self, y, pred, **kargs): """Compute the residual (= negative gradient). """ return y - expit(pred.ravel()) def _update_terminal_region(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight): """Make a single Newton-Raphson step. our node estimate is given by: sum(w * (y - prob)) / sum(w * prob * (1 - prob)) we take advantage that: y - prob = residual """ terminal_region = np.where(terminal_regions == leaf)[0] residual = residual.take(terminal_region, axis=0) y = y.take(terminal_region, axis=0) sample_weight = sample_weight.take(terminal_region, axis=0) numerator = np.sum(sample_weight * residual) denominator = np.sum(sample_weight * (y - residual) * (1 - y + residual)) # prevents overflow and division by zero if abs(denominator) < 1e-150: tree.value[leaf, 0, 0] = 0.0 else: tree.value[leaf, 0, 0] = numerator / denominator def _score_to_proba(self, score): proba = np.ones((score.shape[0], 2), dtype=np.float64) proba[:, 1] = expit(score.ravel()) proba[:, 0] -= proba[:, 1] return proba def _score_to_decision(self, score): proba = self._score_to_proba(score) return np.argmax(proba, axis=1) class MultinomialDeviance(ClassificationLossFunction): """Multinomial deviance loss function for multi-class classification. For multi-class classification we need to fit ``n_classes`` trees at each stage. """ is_multi_class = True def __init__(self, n_classes): if n_classes < 3: raise ValueError("{0:s} requires more than 2 classes.".format( self.__class__.__name__)) super(MultinomialDeviance, self).__init__(n_classes) def init_estimator(self): return PriorProbabilityEstimator() def __call__(self, y, pred, sample_weight=None): # create one-hot label encoding Y = np.zeros((y.shape[0], self.K), dtype=np.float64) for k in range(self.K): Y[:, k] = y == k if sample_weight is None: return np.sum(-1 * (Y * pred).sum(axis=1) + logsumexp(pred, axis=1)) else: return np.sum(-1 * sample_weight * (Y * pred).sum(axis=1) + logsumexp(pred, axis=1)) def negative_gradient(self, y, pred, k=0, **kwargs): """Compute negative gradient for the ``k``-th class. """ return y - np.nan_to_num(np.exp(pred[:, k] - logsumexp(pred, axis=1))) def _update_terminal_region(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight): """Make a single Newton-Raphson step. """ terminal_region = np.where(terminal_regions == leaf)[0] residual = residual.take(terminal_region, axis=0) y = y.take(terminal_region, axis=0) sample_weight = sample_weight.take(terminal_region, axis=0) numerator = np.sum(sample_weight * residual) numerator *= (self.K - 1) / self.K denominator = np.sum(sample_weight * (y - residual) * (1.0 - y + residual)) # prevents overflow and division by zero if abs(denominator) < 1e-150: tree.value[leaf, 0, 0] = 0.0 else: tree.value[leaf, 0, 0] = numerator / denominator def _score_to_proba(self, score): return np.nan_to_num( np.exp(score - (logsumexp(score, axis=1)[:, np.newaxis]))) def _score_to_decision(self, score): proba = self._score_to_proba(score) return np.argmax(proba, axis=1) class ExponentialLoss(ClassificationLossFunction): """Exponential loss function for binary classification. Same loss as AdaBoost. References ---------- Greg Ridgeway, Generalized Boosted Models: A guide to the gbm package, 2007 """ def __init__(self, n_classes): if n_classes != 2: raise ValueError("{0:s} requires 2 classes.".format( self.__class__.__name__)) # we only need to fit one tree for binary clf. super(ExponentialLoss, self).__init__(1) def init_estimator(self): return ScaledLogOddsEstimator() def __call__(self, y, pred, sample_weight=None): pred = pred.ravel() if sample_weight is None: return np.mean(np.exp(-(2. * y - 1.) * pred)) else: return (1.0 / sample_weight.sum() * np.sum(sample_weight * np.exp(-(2 * y - 1) * pred))) def negative_gradient(self, y, pred, **kargs): y_ = -(2. * y - 1.) return y_ * np.exp(y_ * pred.ravel()) def _update_terminal_region(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight): terminal_region = np.where(terminal_regions == leaf)[0] pred = pred.take(terminal_region, axis=0) y = y.take(terminal_region, axis=0) sample_weight = sample_weight.take(terminal_region, axis=0) y_ = 2. * y - 1. numerator = np.sum(y_ * sample_weight * np.exp(-y_ * pred)) denominator = np.sum(sample_weight * np.exp(-y_ * pred)) # prevents overflow and division by zero if abs(denominator) < 1e-150: tree.value[leaf, 0, 0] = 0.0 else: tree.value[leaf, 0, 0] = numerator / denominator def _score_to_proba(self, score): proba = np.ones((score.shape[0], 2), dtype=np.float64) proba[:, 1] = expit(2.0 * score.ravel()) proba[:, 0] -= proba[:, 1] return proba def _score_to_decision(self, score): return (score.ravel() >= 0.0).astype(np.int) LOSS_FUNCTIONS = {'ls': LeastSquaresError, 'lad': LeastAbsoluteError, 'huber': HuberLossFunction, 'quantile': QuantileLossFunction, 'deviance': None, # for both, multinomial and binomial 'exponential': ExponentialLoss, } INIT_ESTIMATORS = {'zero': ZeroEstimator} class VerboseReporter(object): """Reports verbose output to stdout. If ``verbose==1`` output is printed once in a while (when iteration mod verbose_mod is zero).; if larger than 1 then output is printed for each update. """ def __init__(self, verbose): self.verbose = verbose def init(self, est, begin_at_stage=0): # header fields and line format str header_fields = ['Iter', 'Train Loss'] verbose_fmt = ['{iter:>10d}', '{train_score:>16.4f}'] # do oob? if est.subsample < 1: header_fields.append('OOB Improve') verbose_fmt.append('{oob_impr:>16.4f}') header_fields.append('Remaining Time') verbose_fmt.append('{remaining_time:>16s}') # print the header line print(('%10s ' + '%16s ' * (len(header_fields) - 1)) % tuple(header_fields)) self.verbose_fmt = ' '.join(verbose_fmt) # plot verbose info each time i % verbose_mod == 0 self.verbose_mod = 1 self.start_time = time() self.begin_at_stage = begin_at_stage def update(self, j, est): """Update reporter with new iteration. """ do_oob = est.subsample < 1 # we need to take into account if we fit additional estimators. i = j - self.begin_at_stage # iteration relative to the start iter if (i + 1) % self.verbose_mod == 0: oob_impr = est.oob_improvement_[j] if do_oob else 0 remaining_time = ((est.n_estimators - (j + 1)) * (time() - self.start_time) / float(i + 1)) if remaining_time > 60: remaining_time = '{0:.2f}m'.format(remaining_time / 60.0) else: remaining_time = '{0:.2f}s'.format(remaining_time) print(self.verbose_fmt.format(iter=j + 1, train_score=est.train_score_[j], oob_impr=oob_impr, remaining_time=remaining_time)) if self.verbose == 1 and ((i + 1) // (self.verbose_mod * 10) > 0): # adjust verbose frequency (powers of 10) self.verbose_mod *= 10 class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble)): """Abstract base class for Gradient Boosting. """ @abstractmethod def __init__(self, loss, learning_rate, n_estimators, criterion, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_depth, min_impurity_decrease, min_impurity_split, init, subsample, max_features, random_state, alpha=0.9, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto'): self.n_estimators = n_estimators self.learning_rate = learning_rate self.loss = loss self.criterion = criterion self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.subsample = subsample self.max_features = max_features self.max_depth = max_depth self.min_impurity_decrease = min_impurity_decrease self.min_impurity_split = min_impurity_split self.init = init self.random_state = random_state self.alpha = alpha self.verbose = verbose self.max_leaf_nodes = max_leaf_nodes self.warm_start = warm_start self.presort = presort def _fit_stage(self, i, X, y, y_pred, sample_weight, sample_mask, random_state, X_idx_sorted, X_csc=None, X_csr=None): """Fit another stage of ``n_classes_`` trees to the boosting model. """ assert sample_mask.dtype == np.bool loss = self.loss_ original_y = y for k in range(loss.K): if loss.is_multi_class: y = np.array(original_y == k, dtype=np.float64) residual = loss.negative_gradient(y, y_pred, k=k, sample_weight=sample_weight) # induce regression tree on residuals tree = DecisionTreeRegressor( criterion=self.criterion, splitter='best', max_depth=self.max_depth, min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, min_weight_fraction_leaf=self.min_weight_fraction_leaf, min_impurity_decrease=self.min_impurity_decrease, min_impurity_split=self.min_impurity_split, max_features=self.max_features, max_leaf_nodes=self.max_leaf_nodes, random_state=random_state, presort=self.presort) if self.subsample < 1.0: # no inplace multiplication! sample_weight = sample_weight * sample_mask.astype(np.float64) if X_csc is not None: tree.fit(X_csc, residual, sample_weight=sample_weight, check_input=False, X_idx_sorted=X_idx_sorted) else: tree.fit(X, residual, sample_weight=sample_weight, check_input=False, X_idx_sorted=X_idx_sorted) # update tree leaves if X_csr is not None: loss.update_terminal_regions(tree.tree_, X_csr, y, residual, y_pred, sample_weight, sample_mask, self.learning_rate, k=k) else: loss.update_terminal_regions(tree.tree_, X, y, residual, y_pred, sample_weight, sample_mask, self.learning_rate, k=k) # add tree to ensemble self.estimators_[i, k] = tree return y_pred def _check_params(self): """Check validity of parameters and raise ValueError if not valid. """ if self.n_estimators <= 0: raise ValueError("n_estimators must be greater than 0 but " "was %r" % self.n_estimators) if self.learning_rate <= 0.0: raise ValueError("learning_rate must be greater than 0 but " "was %r" % self.learning_rate) if (self.loss not in self._SUPPORTED_LOSS or self.loss not in LOSS_FUNCTIONS): raise ValueError("Loss '{0:s}' not supported. ".format(self.loss)) if self.loss == 'deviance': loss_class = (MultinomialDeviance if len(self.classes_) > 2 else BinomialDeviance) else: loss_class = LOSS_FUNCTIONS[self.loss] if self.loss in ('huber', 'quantile'): self.loss_ = loss_class(self.n_classes_, self.alpha) else: self.loss_ = loss_class(self.n_classes_) if not (0.0 < self.subsample <= 1.0): raise ValueError("subsample must be in (0,1] but " "was %r" % self.subsample) if self.init is not None: if isinstance(self.init, six.string_types): if self.init not in INIT_ESTIMATORS: raise ValueError('init="%s" is not supported' % self.init) else: if (not hasattr(self.init, 'fit') or not hasattr(self.init, 'predict')): raise ValueError("init=%r must be valid BaseEstimator " "and support both fit and " "predict" % self.init) if not (0.0 < self.alpha < 1.0): raise ValueError("alpha must be in (0.0, 1.0) but " "was %r" % self.alpha) if isinstance(self.max_features, six.string_types): if self.max_features == "auto": # if is_classification if self.n_classes_ > 1: max_features = max(1, int(np.sqrt(self.n_features_))) else: # is regression max_features = self.n_features_ elif self.max_features == "sqrt": max_features = max(1, int(np.sqrt(self.n_features_))) elif self.max_features == "log2": max_features = max(1, int(np.log2(self.n_features_))) else: raise ValueError("Invalid value for max_features: %r. " "Allowed string values are 'auto', 'sqrt' " "or 'log2'." % self.max_features) elif self.max_features is None: max_features = self.n_features_ elif isinstance(self.max_features, (numbers.Integral, np.integer)): max_features = self.max_features else: # float if 0. < self.max_features <= 1.: max_features = max(int(self.max_features * self.n_features_), 1) else: raise ValueError("max_features must be in (0, n_features]") self.max_features_ = max_features def _init_state(self): """Initialize model state and allocate model state data structures. """ if self.init is None: self.init_ = self.loss_.init_estimator() elif isinstance(self.init, six.string_types): self.init_ = INIT_ESTIMATORS[self.init]() else: self.init_ = self.init self.estimators_ = np.empty((self.n_estimators, self.loss_.K), dtype=np.object) self.train_score_ = np.zeros((self.n_estimators,), dtype=np.float64) # do oob? if self.subsample < 1.0: self.oob_improvement_ = np.zeros((self.n_estimators), dtype=np.float64) def _clear_state(self): """Clear the state of the gradient boosting model. """ if hasattr(self, 'estimators_'): self.estimators_ = np.empty((0, 0), dtype=np.object) if hasattr(self, 'train_score_'): del self.train_score_ if hasattr(self, 'oob_improvement_'): del self.oob_improvement_ if hasattr(self, 'init_'): del self.init_ def _resize_state(self): """Add additional ``n_estimators`` entries to all attributes. """ # self.n_estimators is the number of additional est to fit total_n_estimators = self.n_estimators if total_n_estimators < self.estimators_.shape[0]: raise ValueError('resize with smaller n_estimators %d < %d' % (total_n_estimators, self.estimators_[0])) self.estimators_.resize((total_n_estimators, self.loss_.K)) self.train_score_.resize(total_n_estimators) if (self.subsample < 1 or hasattr(self, 'oob_improvement_')): # if do oob resize arrays or create new if not available if hasattr(self, 'oob_improvement_'): self.oob_improvement_.resize(total_n_estimators) else: self.oob_improvement_ = np.zeros((total_n_estimators,), dtype=np.float64) def _is_initialized(self): return len(getattr(self, 'estimators_', [])) > 0 def _check_initialized(self): """Check that the estimator is initialized, raising an error if not.""" check_is_fitted(self, 'estimators_') @property @deprecated("Attribute n_features was deprecated in version 0.19 and " "will be removed in 0.21.") def n_features(self): return self.n_features_ def fit(self, X, y, sample_weight=None, monitor=None): """Fit the gradient boosting model. Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values (integers in classification, real numbers in regression) For classification, labels must correspond to classes. sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node. monitor : callable, optional The monitor is called after each iteration with the current iteration, a reference to the estimator and the local variables of ``_fit_stages`` as keyword arguments ``callable(i, self, locals())``. If the callable returns ``True`` the fitting procedure is stopped. The monitor can be used for various things such as computing held-out estimates, early stopping, model introspect, and snapshoting. Returns ------- self : object Returns self. """ # if not warmstart - clear the estimator state if not self.warm_start: self._clear_state() # Check input X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE) n_samples, self.n_features_ = X.shape if sample_weight is None: sample_weight = np.ones(n_samples, dtype=np.float32) else: sample_weight = column_or_1d(sample_weight, warn=True) check_consistent_length(X, y, sample_weight) y = self._validate_y(y) random_state = check_random_state(self.random_state) self._check_params() if not self._is_initialized(): # init state self._init_state() # fit initial model - FIXME make sample_weight optional self.init_.fit(X, y, sample_weight) # init predictions y_pred = self.init_.predict(X) begin_at_stage = 0 else: # add more estimators to fitted model # invariant: warm_start = True if self.n_estimators < self.estimators_.shape[0]: raise ValueError('n_estimators=%d must be larger or equal to ' 'estimators_.shape[0]=%d when ' 'warm_start==True' % (self.n_estimators, self.estimators_.shape[0])) begin_at_stage = self.estimators_.shape[0] y_pred = self._decision_function(X) self._resize_state() X_idx_sorted = None presort = self.presort # Allow presort to be 'auto', which means True if the dataset is dense, # otherwise it will be False. if presort == 'auto' and issparse(X): presort = False elif presort == 'auto': presort = True if presort == True: if issparse(X): raise ValueError("Presorting is not supported for sparse matrices.") else: X_idx_sorted = np.asfortranarray(np.argsort(X, axis=0), dtype=np.int32) # fit the boosting stages n_stages = self._fit_stages(X, y, y_pred, sample_weight, random_state, begin_at_stage, monitor, X_idx_sorted) # change shape of arrays after fit (early-stopping or additional ests) if n_stages != self.estimators_.shape[0]: self.estimators_ = self.estimators_[:n_stages] self.train_score_ = self.train_score_[:n_stages] if hasattr(self, 'oob_improvement_'): self.oob_improvement_ = self.oob_improvement_[:n_stages] return self def _fit_stages(self, X, y, y_pred, sample_weight, random_state, begin_at_stage=0, monitor=None, X_idx_sorted=None): """Iteratively fits the stages. For each stage it computes the progress (OOB, train score) and delegates to ``_fit_stage``. Returns the number of stages fit; might differ from ``n_estimators`` due to early stopping. """ n_samples = X.shape[0] do_oob = self.subsample < 1.0 sample_mask = np.ones((n_samples, ), dtype=np.bool) n_inbag = max(1, int(self.subsample * n_samples)) loss_ = self.loss_ # Set min_weight_leaf from min_weight_fraction_leaf if self.min_weight_fraction_leaf != 0. and sample_weight is not None: min_weight_leaf = (self.min_weight_fraction_leaf * np.sum(sample_weight)) else: min_weight_leaf = 0. if self.verbose: verbose_reporter = VerboseReporter(self.verbose) verbose_reporter.init(self, begin_at_stage) X_csc = csc_matrix(X) if issparse(X) else None X_csr = csr_matrix(X) if issparse(X) else None # perform boosting iterations i = begin_at_stage for i in range(begin_at_stage, self.n_estimators): # subsampling if do_oob: sample_mask = _random_sample_mask(n_samples, n_inbag, random_state) # OOB score before adding this stage old_oob_score = loss_(y[~sample_mask], y_pred[~sample_mask], sample_weight[~sample_mask]) # fit next stage of trees y_pred = self._fit_stage(i, X, y, y_pred, sample_weight, sample_mask, random_state, X_idx_sorted, X_csc, X_csr) # track deviance (= loss) if do_oob: self.train_score_[i] = loss_(y[sample_mask], y_pred[sample_mask], sample_weight[sample_mask]) self.oob_improvement_[i] = ( old_oob_score - loss_(y[~sample_mask], y_pred[~sample_mask], sample_weight[~sample_mask])) else: # no need to fancy index w/ no subsampling self.train_score_[i] = loss_(y, y_pred, sample_weight) if self.verbose > 0: verbose_reporter.update(i, self) if monitor is not None: early_stopping = monitor(i, self, locals()) if early_stopping: break return i + 1 def _make_estimator(self, append=True): # we don't need _make_estimator raise NotImplementedError() def _init_decision_function(self, X): """Check input and compute prediction of ``init``. """ self._check_initialized() X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True) if X.shape[1] != self.n_features_: raise ValueError("X.shape[1] should be {0:d}, not {1:d}.".format( self.n_features_, X.shape[1])) score = self.init_.predict(X).astype(np.float64) return score def _decision_function(self, X): # for use in inner loop, not raveling the output in single-class case, # not doing input validation. score = self._init_decision_function(X) predict_stages(self.estimators_, X, self.learning_rate, score) return score def _staged_decision_function(self, X): """Compute decision function of ``X`` for each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- score : generator of array, shape = [n_samples, k] The decision function of the input samples. The order of the classes corresponds to that in the attribute `classes_`. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. """ X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr') score = self._init_decision_function(X) for i in range(self.estimators_.shape[0]): predict_stage(self.estimators_, i, X, self.learning_rate, score) yield score.copy() @property def feature_importances_(self): """Return the feature importances (the higher, the more important the feature). Returns ------- feature_importances_ : array, shape = [n_features] """ self._check_initialized() total_sum = np.zeros((self.n_features_, ), dtype=np.float64) for stage in self.estimators_: stage_sum = sum(tree.feature_importances_ for tree in stage) / len(stage) total_sum += stage_sum importances = total_sum / len(self.estimators_) return importances def _validate_y(self, y): self.n_classes_ = 1 if y.dtype.kind == 'O': y = y.astype(np.float64) # Default implementation return y def apply(self, X): """Apply trees in the ensemble to X, return leaf indices. .. versionadded:: 0.17 Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted to a sparse ``csr_matrix``. Returns ------- X_leaves : array_like, shape = [n_samples, n_estimators, n_classes] For each datapoint x in X and for each tree in the ensemble, return the index of the leaf x ends up in each estimator. In the case of binary classification n_classes is 1. """ self._check_initialized() X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True) # n_classes will be equal to 1 in the binary classification or the # regression case. n_estimators, n_classes = self.estimators_.shape leaves = np.zeros((X.shape[0], n_estimators, n_classes)) for i in range(n_estimators): for j in range(n_classes): estimator = self.estimators_[i, j] leaves[:, i, j] = estimator.apply(X, check_input=False) return leaves class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin): """Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage ``n_classes_`` regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced. Read more in the :ref:`User Guide `. Parameters ---------- loss : {'deviance', 'exponential'}, optional (default='deviance') loss function to be optimized. 'deviance' refers to deviance (= logistic regression) for classification with probabilistic outputs. For loss 'exponential' gradient boosting recovers the AdaBoost algorithm. learning_rate : float, optional (default=0.1) learning rate shrinks the contribution of each tree by `learning_rate`. There is a trade-off between learning_rate and n_estimators. n_estimators : int (default=100) The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance. max_depth : integer, optional (default=3) maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. criterion : string, optional (default="friedman_mse") The function to measure the quality of a split. Supported criteria are "friedman_mse" for the mean squared error with improvement score by Friedman, "mse" for mean squared error, and "mae" for the mean absolute error. The default value of "friedman_mse" is generally the best as it can provide a better approximation in some cases. .. versionadded:: 0.18 min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. subsample : float, optional (default=1.0) The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias. max_features : int, float, string or None, optional (default=None) The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a percentage and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=sqrt(n_features)`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_split : float, Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19 and will be removed in 0.21. Use ``min_impurity_decrease`` instead. min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 init : BaseEstimator, None, optional (default=None) An estimator object that is used to compute the initial predictions. ``init`` has to provide ``fit`` and ``predict``. If None it uses ``loss.init_estimator``. verbose : int, default: 0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. warm_start : bool, default: False When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. presort : bool or 'auto', optional (default='auto') Whether to presort the data to speed up the finding of best splits in fitting. Auto mode by default will use presorting on dense data and default to normal sorting on sparse data. Setting presort to true on sparse data will raise an error. .. versionadded:: 0.17 *presort* parameter. Attributes ---------- feature_importances_ : array, shape = [n_features] The feature importances (the higher, the more important the feature). oob_improvement_ : array, shape = [n_estimators] The improvement in loss (= deviance) on the out-of-bag samples relative to the previous iteration. ``oob_improvement_[0]`` is the improvement in loss of the first stage over the ``init`` estimator. train_score_ : array, shape = [n_estimators] The i-th score ``train_score_[i]`` is the deviance (= loss) of the model at iteration ``i`` on the in-bag sample. If ``subsample == 1`` this is the deviance on the training data. loss_ : LossFunction The concrete ``LossFunction`` object. init : BaseEstimator The estimator that provides the initial predictions. Set via the ``init`` argument or ``loss.init_estimator``. estimators_ : ndarray of DecisionTreeRegressor, shape = [n_estimators, ``loss_.K``] The collection of fitted sub-estimators. ``loss_.K`` is 1 for binary classification, otherwise n_classes. Notes ----- The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed. See also -------- sklearn.tree.DecisionTreeClassifier, RandomForestClassifier AdaBoostClassifier References ---------- J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001. J. Friedman, Stochastic Gradient Boosting, 1999 T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009. """ _SUPPORTED_LOSS = ('deviance', 'exponential') def __init__(self, loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_depth=3, min_impurity_decrease=0., min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto'): super(GradientBoostingClassifier, self).__init__( loss=loss, learning_rate=learning_rate, n_estimators=n_estimators, criterion=criterion, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_depth=max_depth, init=init, subsample=subsample, max_features=max_features, random_state=random_state, verbose=verbose, max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=min_impurity_decrease, min_impurity_split=min_impurity_split, warm_start=warm_start, presort=presort) def _validate_y(self, y): check_classification_targets(y) self.classes_, y = np.unique(y, return_inverse=True) self.n_classes_ = len(self.classes_) return y def decision_function(self, X): """Compute the decision function of ``X``. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- score : array, shape = [n_samples, n_classes] or [n_samples] The decision function of the input samples. The order of the classes corresponds to that in the attribute `classes_`. Regression and binary classification produce an array of shape [n_samples]. """ X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr') score = self._decision_function(X) if score.shape[1] == 1: return score.ravel() return score def staged_decision_function(self, X): """Compute decision function of ``X`` for each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- score : generator of array, shape = [n_samples, k] The decision function of the input samples. The order of the classes corresponds to that in the attribute `classes_`. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. """ for dec in self._staged_decision_function(X): # no yield from in Python2.X yield dec def predict(self, X): """Predict class for X. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- y : array of shape = [n_samples] The predicted values. """ score = self.decision_function(X) decisions = self.loss_._score_to_decision(score) return self.classes_.take(decisions, axis=0) def staged_predict(self, X): """Predict class at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- y : generator of array of shape = [n_samples] The predicted value of the input samples. """ for score in self._staged_decision_function(X): decisions = self.loss_._score_to_decision(score) yield self.classes_.take(decisions, axis=0) def predict_proba(self, X): """Predict class probabilities for X. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Raises ------ AttributeError If the ``loss`` does not support probabilities. Returns ------- p : array of shape = [n_samples] The class probabilities of the input samples. The order of the classes corresponds to that in the attribute `classes_`. """ score = self.decision_function(X) try: return self.loss_._score_to_proba(score) except NotFittedError: raise except AttributeError: raise AttributeError('loss=%r does not support predict_proba' % self.loss) def predict_log_proba(self, X): """Predict class log-probabilities for X. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Raises ------ AttributeError If the ``loss`` does not support probabilities. Returns ------- p : array of shape = [n_samples] The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute `classes_`. """ proba = self.predict_proba(X) return np.log(proba) def staged_predict_proba(self, X): """Predict class probabilities at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- y : generator of array of shape = [n_samples] The predicted value of the input samples. """ try: for score in self._staged_decision_function(X): yield self.loss_._score_to_proba(score) except NotFittedError: raise except AttributeError: raise AttributeError('loss=%r does not support predict_proba' % self.loss) class GradientBoostingRegressor(BaseGradientBoosting, RegressorMixin): """Gradient Boosting for regression. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function. Read more in the :ref:`User Guide `. Parameters ---------- loss : {'ls', 'lad', 'huber', 'quantile'}, optional (default='ls') loss function to be optimized. 'ls' refers to least squares regression. 'lad' (least absolute deviation) is a highly robust loss function solely based on order information of the input variables. 'huber' is a combination of the two. 'quantile' allows quantile regression (use `alpha` to specify the quantile). learning_rate : float, optional (default=0.1) learning rate shrinks the contribution of each tree by `learning_rate`. There is a trade-off between learning_rate and n_estimators. n_estimators : int (default=100) The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance. max_depth : integer, optional (default=3) maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. criterion : string, optional (default="friedman_mse") The function to measure the quality of a split. Supported criteria are "friedman_mse" for the mean squared error with improvement score by Friedman, "mse" for mean squared error, and "mae" for the mean absolute error. The default value of "friedman_mse" is generally the best as it can provide a better approximation in some cases. .. versionadded:: 0.18 min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. subsample : float, optional (default=1.0) The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias. max_features : int, float, string or None, optional (default=None) The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a percentage and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=n_features`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_split : float, Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19 and will be removed in 0.21. Use ``min_impurity_decrease`` instead. min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 alpha : float (default=0.9) The alpha-quantile of the huber loss function and the quantile loss function. Only if ``loss='huber'`` or ``loss='quantile'``. init : BaseEstimator, None, optional (default=None) An estimator object that is used to compute the initial predictions. ``init`` has to provide ``fit`` and ``predict``. If None it uses ``loss.init_estimator``. verbose : int, default: 0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. warm_start : bool, default: False When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. presort : bool or 'auto', optional (default='auto') Whether to presort the data to speed up the finding of best splits in fitting. Auto mode by default will use presorting on dense data and default to normal sorting on sparse data. Setting presort to true on sparse data will raise an error. .. versionadded:: 0.17 optional parameter *presort*. Attributes ---------- feature_importances_ : array, shape = [n_features] The feature importances (the higher, the more important the feature). oob_improvement_ : array, shape = [n_estimators] The improvement in loss (= deviance) on the out-of-bag samples relative to the previous iteration. ``oob_improvement_[0]`` is the improvement in loss of the first stage over the ``init`` estimator. train_score_ : array, shape = [n_estimators] The i-th score ``train_score_[i]`` is the deviance (= loss) of the model at iteration ``i`` on the in-bag sample. If ``subsample == 1`` this is the deviance on the training data. loss_ : LossFunction The concrete ``LossFunction`` object. init : BaseEstimator The estimator that provides the initial predictions. Set via the ``init`` argument or ``loss.init_estimator``. estimators_ : ndarray of DecisionTreeRegressor, shape = [n_estimators, 1] The collection of fitted sub-estimators. Notes ----- The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed. See also -------- DecisionTreeRegressor, RandomForestRegressor References ---------- J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001. J. Friedman, Stochastic Gradient Boosting, 1999 T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009. """ _SUPPORTED_LOSS = ('ls', 'lad', 'huber', 'quantile') def __init__(self, loss='ls', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_depth=3, min_impurity_decrease=0., min_impurity_split=None, init=None, random_state=None, max_features=None, alpha=0.9, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto'): super(GradientBoostingRegressor, self).__init__( loss=loss, learning_rate=learning_rate, n_estimators=n_estimators, criterion=criterion, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_depth=max_depth, init=init, subsample=subsample, max_features=max_features, min_impurity_decrease=min_impurity_decrease, min_impurity_split=min_impurity_split, random_state=random_state, alpha=alpha, verbose=verbose, max_leaf_nodes=max_leaf_nodes, warm_start=warm_start, presort=presort) def predict(self, X): """Predict regression target for X. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- y : array of shape = [n_samples] The predicted values. """ X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr') return self._decision_function(X).ravel() def staged_predict(self, X): """Predict regression target at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- y : generator of array of shape = [n_samples] The predicted value of the input samples. """ for y in self._staged_decision_function(X): yield y.ravel() def apply(self, X): """Apply trees in the ensemble to X, return leaf indices. .. versionadded:: 0.17 Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted to a sparse ``csr_matrix``. Returns ------- X_leaves : array_like, shape = [n_samples, n_estimators] For each datapoint x in X and for each tree in the ensemble, return the index of the leaf x ends up in each estimator. """ leaves = super(GradientBoostingRegressor, self).apply(X) leaves = leaves.reshape(X.shape[0], self.estimators_.shape[0]) return leaves