1847 lines
70 KiB
Python
1847 lines
70 KiB
Python
"""Gradient Boosted Regression Trees.
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This module contains methods for fitting gradient boosted regression trees for
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both classification and regression.
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The module structure is the following:
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- The ``BaseGradientBoosting`` base class implements a common ``fit`` method
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for all the estimators in the module. Regression and classification
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only differ in the concrete ``LossFunction`` used.
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- ``GradientBoostingClassifier`` implements gradient boosting for
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classification problems.
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- ``GradientBoostingRegressor`` implements gradient boosting for
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regression problems.
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"""
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# Authors: Peter Prettenhofer, Scott White, Gilles Louppe, Emanuele Olivetti,
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# Arnaud Joly, Jacob Schreiber
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# License: BSD 3 clause
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from abc import ABCMeta
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from abc import abstractmethod
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from numbers import Integral, Real
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import warnings
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from ._base import BaseEnsemble
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from ..base import ClassifierMixin, RegressorMixin
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from ..base import is_classifier
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from ..utils import deprecated
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from ._gradient_boosting import predict_stages
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from ._gradient_boosting import predict_stage
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from ._gradient_boosting import _random_sample_mask
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import numpy as np
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from scipy.sparse import csc_matrix
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from scipy.sparse import csr_matrix
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from scipy.sparse import issparse
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from time import time
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from ..model_selection import train_test_split
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from ..tree import DecisionTreeRegressor
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from ..tree._tree import DTYPE, DOUBLE
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from . import _gb_losses
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from ..utils import check_array, check_random_state, column_or_1d
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from ..utils._param_validation import HasMethods, Interval, StrOptions
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from ..utils.validation import check_is_fitted, _check_sample_weight
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from ..utils.multiclass import check_classification_targets
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from ..exceptions import NotFittedError
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class VerboseReporter:
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"""Reports verbose output to stdout.
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Parameters
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----------
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verbose : int
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Verbosity level. If ``verbose==1`` output is printed once in a while
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(when iteration mod verbose_mod is zero).; if larger than 1 then output
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is printed for each update.
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"""
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def __init__(self, verbose):
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self.verbose = verbose
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def init(self, est, begin_at_stage=0):
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"""Initialize reporter
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Parameters
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----------
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est : Estimator
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The estimator
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begin_at_stage : int, default=0
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stage at which to begin reporting
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"""
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# header fields and line format str
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header_fields = ["Iter", "Train Loss"]
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verbose_fmt = ["{iter:>10d}", "{train_score:>16.4f}"]
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# do oob?
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if est.subsample < 1:
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header_fields.append("OOB Improve")
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verbose_fmt.append("{oob_impr:>16.4f}")
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header_fields.append("Remaining Time")
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verbose_fmt.append("{remaining_time:>16s}")
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# print the header line
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print(("%10s " + "%16s " * (len(header_fields) - 1)) % tuple(header_fields))
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self.verbose_fmt = " ".join(verbose_fmt)
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# plot verbose info each time i % verbose_mod == 0
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self.verbose_mod = 1
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self.start_time = time()
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self.begin_at_stage = begin_at_stage
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def update(self, j, est):
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"""Update reporter with new iteration.
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Parameters
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----------
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j : int
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The new iteration.
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est : Estimator
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The estimator.
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"""
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do_oob = est.subsample < 1
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# we need to take into account if we fit additional estimators.
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i = j - self.begin_at_stage # iteration relative to the start iter
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if (i + 1) % self.verbose_mod == 0:
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oob_impr = est.oob_improvement_[j] if do_oob else 0
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remaining_time = (
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(est.n_estimators - (j + 1)) * (time() - self.start_time) / float(i + 1)
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)
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if remaining_time > 60:
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remaining_time = "{0:.2f}m".format(remaining_time / 60.0)
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else:
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remaining_time = "{0:.2f}s".format(remaining_time)
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print(
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self.verbose_fmt.format(
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iter=j + 1,
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train_score=est.train_score_[j],
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oob_impr=oob_impr,
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remaining_time=remaining_time,
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)
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)
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if self.verbose == 1 and ((i + 1) // (self.verbose_mod * 10) > 0):
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# adjust verbose frequency (powers of 10)
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self.verbose_mod *= 10
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class BaseGradientBoosting(BaseEnsemble, metaclass=ABCMeta):
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"""Abstract base class for Gradient Boosting."""
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_parameter_constraints: dict = {
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**DecisionTreeRegressor._parameter_constraints,
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"learning_rate": [Interval(Real, 0.0, None, closed="left")],
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"n_estimators": [Interval(Integral, 1, None, closed="left")],
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"criterion": [StrOptions({"friedman_mse", "squared_error"})],
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"subsample": [Interval(Real, 0.0, 1.0, closed="right")],
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"verbose": ["verbose"],
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"warm_start": ["boolean"],
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"validation_fraction": [Interval(Real, 0.0, 1.0, closed="neither")],
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"n_iter_no_change": [Interval(Integral, 1, None, closed="left"), None],
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"tol": [Interval(Real, 0.0, None, closed="left")],
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}
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_parameter_constraints.pop("splitter")
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@abstractmethod
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def __init__(
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self,
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*,
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loss,
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learning_rate,
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n_estimators,
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criterion,
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min_samples_split,
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min_samples_leaf,
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min_weight_fraction_leaf,
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max_depth,
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min_impurity_decrease,
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init,
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subsample,
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max_features,
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ccp_alpha,
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random_state,
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alpha=0.9,
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verbose=0,
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max_leaf_nodes=None,
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warm_start=False,
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validation_fraction=0.1,
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n_iter_no_change=None,
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tol=1e-4,
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):
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self.n_estimators = n_estimators
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self.learning_rate = learning_rate
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self.loss = loss
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self.criterion = criterion
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self.min_samples_split = min_samples_split
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self.min_samples_leaf = min_samples_leaf
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self.min_weight_fraction_leaf = min_weight_fraction_leaf
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self.subsample = subsample
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self.max_features = max_features
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self.max_depth = max_depth
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self.min_impurity_decrease = min_impurity_decrease
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self.ccp_alpha = ccp_alpha
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self.init = init
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self.random_state = random_state
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self.alpha = alpha
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self.verbose = verbose
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self.max_leaf_nodes = max_leaf_nodes
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self.warm_start = warm_start
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self.validation_fraction = validation_fraction
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self.n_iter_no_change = n_iter_no_change
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self.tol = tol
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@abstractmethod
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def _validate_y(self, y, sample_weight=None):
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"""Called by fit to validate y."""
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def _fit_stage(
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self,
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i,
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X,
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y,
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raw_predictions,
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sample_weight,
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sample_mask,
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random_state,
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X_csc=None,
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X_csr=None,
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):
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"""Fit another stage of ``_n_classes`` trees to the boosting model."""
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assert sample_mask.dtype == bool
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loss = self._loss
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original_y = y
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# Need to pass a copy of raw_predictions to negative_gradient()
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# because raw_predictions is partially updated at the end of the loop
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# in update_terminal_regions(), and gradients need to be evaluated at
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# iteration i - 1.
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raw_predictions_copy = raw_predictions.copy()
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for k in range(loss.K):
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if loss.is_multi_class:
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y = np.array(original_y == k, dtype=np.float64)
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residual = loss.negative_gradient(
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y, raw_predictions_copy, k=k, sample_weight=sample_weight
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)
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# induce regression tree on residuals
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tree = DecisionTreeRegressor(
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criterion=self.criterion,
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splitter="best",
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max_depth=self.max_depth,
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min_samples_split=self.min_samples_split,
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min_samples_leaf=self.min_samples_leaf,
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min_weight_fraction_leaf=self.min_weight_fraction_leaf,
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min_impurity_decrease=self.min_impurity_decrease,
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max_features=self.max_features,
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max_leaf_nodes=self.max_leaf_nodes,
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random_state=random_state,
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ccp_alpha=self.ccp_alpha,
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)
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if self.subsample < 1.0:
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# no inplace multiplication!
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sample_weight = sample_weight * sample_mask.astype(np.float64)
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X = X_csr if X_csr is not None else X
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tree.fit(X, residual, sample_weight=sample_weight, check_input=False)
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# update tree leaves
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loss.update_terminal_regions(
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tree.tree_,
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X,
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y,
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residual,
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raw_predictions,
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sample_weight,
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sample_mask,
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learning_rate=self.learning_rate,
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k=k,
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)
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# add tree to ensemble
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self.estimators_[i, k] = tree
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return raw_predictions
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def _check_params(self):
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# TODO(1.3): Remove
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if self.loss == "deviance":
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warnings.warn(
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"The loss parameter name 'deviance' was deprecated in v1.1 and will be "
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"removed in version 1.3. Use the new parameter name 'log_loss' which "
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"is equivalent.",
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FutureWarning,
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)
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loss_class = (
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_gb_losses.MultinomialDeviance
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if len(self.classes_) > 2
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else _gb_losses.BinomialDeviance
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)
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elif self.loss == "log_loss":
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loss_class = (
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_gb_losses.MultinomialDeviance
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if len(self.classes_) > 2
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else _gb_losses.BinomialDeviance
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)
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else:
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loss_class = _gb_losses.LOSS_FUNCTIONS[self.loss]
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if is_classifier(self):
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self._loss = loss_class(self.n_classes_)
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elif self.loss in ("huber", "quantile"):
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self._loss = loss_class(self.alpha)
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else:
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self._loss = loss_class()
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if isinstance(self.max_features, str):
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if self.max_features == "auto":
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if is_classifier(self):
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max_features = max(1, int(np.sqrt(self.n_features_in_)))
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else:
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max_features = self.n_features_in_
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elif self.max_features == "sqrt":
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max_features = max(1, int(np.sqrt(self.n_features_in_)))
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else: # self.max_features == "log2"
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max_features = max(1, int(np.log2(self.n_features_in_)))
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elif self.max_features is None:
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max_features = self.n_features_in_
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elif isinstance(self.max_features, Integral):
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max_features = self.max_features
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else: # float
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max_features = max(1, int(self.max_features * self.n_features_in_))
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self.max_features_ = max_features
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def _init_state(self):
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"""Initialize model state and allocate model state data structures."""
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self.init_ = self.init
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if self.init_ is None:
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self.init_ = self._loss.init_estimator()
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self.estimators_ = np.empty((self.n_estimators, self._loss.K), dtype=object)
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self.train_score_ = np.zeros((self.n_estimators,), dtype=np.float64)
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# do oob?
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if self.subsample < 1.0:
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self.oob_improvement_ = np.zeros((self.n_estimators), dtype=np.float64)
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def _clear_state(self):
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"""Clear the state of the gradient boosting model."""
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if hasattr(self, "estimators_"):
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self.estimators_ = np.empty((0, 0), dtype=object)
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if hasattr(self, "train_score_"):
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del self.train_score_
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if hasattr(self, "oob_improvement_"):
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del self.oob_improvement_
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if hasattr(self, "init_"):
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del self.init_
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if hasattr(self, "_rng"):
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del self._rng
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def _resize_state(self):
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"""Add additional ``n_estimators`` entries to all attributes."""
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# self.n_estimators is the number of additional est to fit
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total_n_estimators = self.n_estimators
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if total_n_estimators < self.estimators_.shape[0]:
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raise ValueError(
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"resize with smaller n_estimators %d < %d"
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% (total_n_estimators, self.estimators_[0])
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)
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self.estimators_ = np.resize(
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self.estimators_, (total_n_estimators, self._loss.K)
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)
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self.train_score_ = np.resize(self.train_score_, total_n_estimators)
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if self.subsample < 1 or hasattr(self, "oob_improvement_"):
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# if do oob resize arrays or create new if not available
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if hasattr(self, "oob_improvement_"):
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self.oob_improvement_ = np.resize(
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self.oob_improvement_, total_n_estimators
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)
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else:
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self.oob_improvement_ = np.zeros(
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(total_n_estimators,), dtype=np.float64
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)
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def _is_initialized(self):
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return len(getattr(self, "estimators_", [])) > 0
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def _check_initialized(self):
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"""Check that the estimator is initialized, raising an error if not."""
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check_is_fitted(self)
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def fit(self, X, y, sample_weight=None, monitor=None):
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"""Fit the gradient boosting model.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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The input samples. Internally, it will be converted to
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``dtype=np.float32`` and if a sparse matrix is provided
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to a sparse ``csr_matrix``.
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y : array-like of shape (n_samples,)
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Target values (strings or integers in classification, real numbers
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in regression)
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For classification, labels must correspond to classes.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights. If None, then samples are equally weighted. Splits
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that would create child nodes with net zero or negative weight are
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ignored while searching for a split in each node. In the case of
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classification, splits are also ignored if they would result in any
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single class carrying a negative weight in either child node.
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monitor : callable, default=None
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The monitor is called after each iteration with the current
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iteration, a reference to the estimator and the local variables of
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``_fit_stages`` as keyword arguments ``callable(i, self,
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locals())``. If the callable returns ``True`` the fitting procedure
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is stopped. The monitor can be used for various things such as
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computing held-out estimates, early stopping, model introspect, and
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snapshoting.
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Returns
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-------
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self : object
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Fitted estimator.
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"""
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self._validate_params()
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if not self.warm_start:
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self._clear_state()
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# Check input
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# Since check_array converts both X and y to the same dtype, but the
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# trees use different types for X and y, checking them separately.
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X, y = self._validate_data(
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X, y, accept_sparse=["csr", "csc", "coo"], dtype=DTYPE, multi_output=True
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)
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sample_weight_is_none = sample_weight is None
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sample_weight = _check_sample_weight(sample_weight, X)
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y = column_or_1d(y, warn=True)
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if is_classifier(self):
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y = self._validate_y(y, sample_weight)
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else:
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y = self._validate_y(y)
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self._check_params()
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if self.n_iter_no_change is not None:
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stratify = y if is_classifier(self) else None
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X, X_val, y, y_val, sample_weight, sample_weight_val = train_test_split(
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X,
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y,
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sample_weight,
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random_state=self.random_state,
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test_size=self.validation_fraction,
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stratify=stratify,
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)
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if is_classifier(self):
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if self._n_classes != np.unique(y).shape[0]:
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# We choose to error here. The problem is that the init
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# estimator would be trained on y, which has some missing
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# classes now, so its predictions would not have the
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# correct shape.
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raise ValueError(
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"The training data after the early stopping split "
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"is missing some classes. Try using another random "
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"seed."
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)
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else:
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X_val = y_val = sample_weight_val = None
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if not self._is_initialized():
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# init state
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self._init_state()
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# fit initial model and initialize raw predictions
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if self.init_ == "zero":
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raw_predictions = np.zeros(
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shape=(X.shape[0], self._loss.K), dtype=np.float64
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)
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else:
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# XXX clean this once we have a support_sample_weight tag
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if sample_weight_is_none:
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self.init_.fit(X, y)
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else:
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msg = (
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"The initial estimator {} does not support sample "
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"weights.".format(self.init_.__class__.__name__)
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)
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try:
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self.init_.fit(X, y, sample_weight=sample_weight)
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except TypeError as e:
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if "unexpected keyword argument 'sample_weight'" in str(e):
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# regular estimator without SW support
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raise ValueError(msg) from e
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else: # regular estimator whose input checking failed
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raise
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except ValueError as e:
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if (
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"pass parameters to specific steps of "
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"your pipeline using the "
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"stepname__parameter"
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in str(e)
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): # pipeline
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raise ValueError(msg) from e
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else: # regular estimator whose input checking failed
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raise
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raw_predictions = self._loss.get_init_raw_predictions(X, self.init_)
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begin_at_stage = 0
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# The rng state must be preserved if warm_start is True
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self._rng = check_random_state(self.random_state)
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|
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]
|
|
# The requirements of _raw_predict
|
|
# are more constrained than fit. It accepts only CSR
|
|
# matrices. Finite values have already been checked in _validate_data.
|
|
X = check_array(
|
|
X,
|
|
dtype=DTYPE,
|
|
order="C",
|
|
accept_sparse="csr",
|
|
force_all_finite=False,
|
|
)
|
|
raw_predictions = self._raw_predict(X)
|
|
self._resize_state()
|
|
|
|
# fit the boosting stages
|
|
n_stages = self._fit_stages(
|
|
X,
|
|
y,
|
|
raw_predictions,
|
|
sample_weight,
|
|
self._rng,
|
|
X_val,
|
|
y_val,
|
|
sample_weight_val,
|
|
begin_at_stage,
|
|
monitor,
|
|
)
|
|
|
|
# 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]
|
|
|
|
self.n_estimators_ = n_stages
|
|
return self
|
|
|
|
def _fit_stages(
|
|
self,
|
|
X,
|
|
y,
|
|
raw_predictions,
|
|
sample_weight,
|
|
random_state,
|
|
X_val,
|
|
y_val,
|
|
sample_weight_val,
|
|
begin_at_stage=0,
|
|
monitor=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=bool)
|
|
n_inbag = max(1, int(self.subsample * n_samples))
|
|
loss_ = self._loss
|
|
|
|
if self.verbose:
|
|
verbose_reporter = VerboseReporter(verbose=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
|
|
|
|
if self.n_iter_no_change is not None:
|
|
loss_history = np.full(self.n_iter_no_change, np.inf)
|
|
# We create a generator to get the predictions for X_val after
|
|
# the addition of each successive stage
|
|
y_val_pred_iter = self._staged_raw_predict(X_val, check_input=False)
|
|
|
|
# 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],
|
|
raw_predictions[~sample_mask],
|
|
sample_weight[~sample_mask],
|
|
)
|
|
|
|
# fit next stage of trees
|
|
raw_predictions = self._fit_stage(
|
|
i,
|
|
X,
|
|
y,
|
|
raw_predictions,
|
|
sample_weight,
|
|
sample_mask,
|
|
random_state,
|
|
X_csc,
|
|
X_csr,
|
|
)
|
|
|
|
# track deviance (= loss)
|
|
if do_oob:
|
|
self.train_score_[i] = loss_(
|
|
y[sample_mask],
|
|
raw_predictions[sample_mask],
|
|
sample_weight[sample_mask],
|
|
)
|
|
self.oob_improvement_[i] = old_oob_score - loss_(
|
|
y[~sample_mask],
|
|
raw_predictions[~sample_mask],
|
|
sample_weight[~sample_mask],
|
|
)
|
|
else:
|
|
# no need to fancy index w/ no subsampling
|
|
self.train_score_[i] = loss_(y, raw_predictions, 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
|
|
|
|
# We also provide an early stopping based on the score from
|
|
# validation set (X_val, y_val), if n_iter_no_change is set
|
|
if self.n_iter_no_change is not None:
|
|
# By calling next(y_val_pred_iter), we get the predictions
|
|
# for X_val after the addition of the current stage
|
|
validation_loss = loss_(y_val, next(y_val_pred_iter), sample_weight_val)
|
|
|
|
# Require validation_score to be better (less) than at least
|
|
# one of the last n_iter_no_change evaluations
|
|
if np.any(validation_loss + self.tol < loss_history):
|
|
loss_history[i % len(loss_history)] = validation_loss
|
|
else:
|
|
break
|
|
|
|
return i + 1
|
|
|
|
def _make_estimator(self, append=True):
|
|
# we don't need _make_estimator
|
|
raise NotImplementedError()
|
|
|
|
def _raw_predict_init(self, X):
|
|
"""Check input and compute raw predictions of the init estimator."""
|
|
self._check_initialized()
|
|
X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True)
|
|
if self.init_ == "zero":
|
|
raw_predictions = np.zeros(
|
|
shape=(X.shape[0], self._loss.K), dtype=np.float64
|
|
)
|
|
else:
|
|
raw_predictions = self._loss.get_init_raw_predictions(X, self.init_).astype(
|
|
np.float64
|
|
)
|
|
return raw_predictions
|
|
|
|
def _raw_predict(self, X):
|
|
"""Return the sum of the trees raw predictions (+ init estimator)."""
|
|
raw_predictions = self._raw_predict_init(X)
|
|
predict_stages(self.estimators_, X, self.learning_rate, raw_predictions)
|
|
return raw_predictions
|
|
|
|
def _staged_raw_predict(self, X, check_input=True):
|
|
"""Compute raw predictions of ``X`` for each iteration.
|
|
|
|
This method allows monitoring (i.e. determine error on testing set)
|
|
after each stage.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of 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``.
|
|
|
|
check_input : bool, default=True
|
|
If False, the input arrays X will not be checked.
|
|
|
|
Returns
|
|
-------
|
|
raw_predictions : generator of ndarray of shape (n_samples, k)
|
|
The raw predictions of the input samples. The order of the
|
|
classes corresponds to that in the attribute :term:`classes_`.
|
|
Regression and binary classification are special cases with
|
|
``k == 1``, otherwise ``k==n_classes``.
|
|
"""
|
|
if check_input:
|
|
X = self._validate_data(
|
|
X, dtype=DTYPE, order="C", accept_sparse="csr", reset=False
|
|
)
|
|
raw_predictions = self._raw_predict_init(X)
|
|
for i in range(self.estimators_.shape[0]):
|
|
predict_stage(self.estimators_, i, X, self.learning_rate, raw_predictions)
|
|
yield raw_predictions.copy()
|
|
|
|
@property
|
|
def feature_importances_(self):
|
|
"""The impurity-based feature importances.
|
|
|
|
The higher, the more important the feature.
|
|
The importance of a feature is computed as the (normalized)
|
|
total reduction of the criterion brought by that feature. It is also
|
|
known as the Gini importance.
|
|
|
|
Warning: impurity-based feature importances can be misleading for
|
|
high cardinality features (many unique values). See
|
|
:func:`sklearn.inspection.permutation_importance` as an alternative.
|
|
|
|
Returns
|
|
-------
|
|
feature_importances_ : ndarray of shape (n_features,)
|
|
The values of this array sum to 1, unless all trees are single node
|
|
trees consisting of only the root node, in which case it will be an
|
|
array of zeros.
|
|
"""
|
|
self._check_initialized()
|
|
|
|
relevant_trees = [
|
|
tree
|
|
for stage in self.estimators_
|
|
for tree in stage
|
|
if tree.tree_.node_count > 1
|
|
]
|
|
if not relevant_trees:
|
|
# degenerate case where all trees have only one node
|
|
return np.zeros(shape=self.n_features_in_, dtype=np.float64)
|
|
|
|
relevant_feature_importances = [
|
|
tree.tree_.compute_feature_importances(normalize=False)
|
|
for tree in relevant_trees
|
|
]
|
|
avg_feature_importances = np.mean(
|
|
relevant_feature_importances, axis=0, dtype=np.float64
|
|
)
|
|
return avg_feature_importances / np.sum(avg_feature_importances)
|
|
|
|
def _compute_partial_dependence_recursion(self, grid, target_features):
|
|
"""Fast partial dependence computation.
|
|
|
|
Parameters
|
|
----------
|
|
grid : ndarray of shape (n_samples, n_target_features)
|
|
The grid points on which the partial dependence should be
|
|
evaluated.
|
|
target_features : ndarray of shape (n_target_features,)
|
|
The set of target features for which the partial dependence
|
|
should be evaluated.
|
|
|
|
Returns
|
|
-------
|
|
averaged_predictions : ndarray of shape \
|
|
(n_trees_per_iteration, n_samples)
|
|
The value of the partial dependence function on each grid point.
|
|
"""
|
|
if self.init is not None:
|
|
warnings.warn(
|
|
"Using recursion method with a non-constant init predictor "
|
|
"will lead to incorrect partial dependence values. "
|
|
"Got init=%s."
|
|
% self.init,
|
|
UserWarning,
|
|
)
|
|
grid = np.asarray(grid, dtype=DTYPE, order="C")
|
|
n_estimators, n_trees_per_stage = self.estimators_.shape
|
|
averaged_predictions = np.zeros(
|
|
(n_trees_per_stage, grid.shape[0]), dtype=np.float64, order="C"
|
|
)
|
|
for stage in range(n_estimators):
|
|
for k in range(n_trees_per_stage):
|
|
tree = self.estimators_[stage, k].tree_
|
|
tree.compute_partial_dependence(
|
|
grid, target_features, averaged_predictions[k]
|
|
)
|
|
averaged_predictions *= self.learning_rate
|
|
|
|
return averaged_predictions
|
|
|
|
def apply(self, X):
|
|
"""Apply trees in the ensemble to X, return leaf indices.
|
|
|
|
.. versionadded:: 0.17
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of 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 of 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
|
|
|
|
# TODO(1.3): Remove
|
|
# mypy error: Decorated property not supported
|
|
@deprecated( # type: ignore
|
|
"Attribute `loss_` was deprecated in version 1.1 and will be removed in 1.3."
|
|
)
|
|
@property
|
|
def loss_(self):
|
|
return self._loss
|
|
|
|
|
|
class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
|
|
"""Gradient Boosting for classification.
|
|
|
|
This algorithm 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 loss function, e.g. binary or multiclass log loss. Binary
|
|
classification is a special case where only a single regression tree is
|
|
induced.
|
|
|
|
:class:`sklearn.ensemble.HistGradientBoostingClassifier` is a much faster
|
|
variant of this algorithm for intermediate datasets (`n_samples >= 10_000`).
|
|
|
|
Read more in the :ref:`User Guide <gradient_boosting>`.
|
|
|
|
Parameters
|
|
----------
|
|
loss : {'log_loss', 'deviance', 'exponential'}, default='log_loss'
|
|
The loss function to be optimized. 'log_loss' refers to binomial and
|
|
multinomial deviance, the same as used in logistic regression.
|
|
It is a good choice for classification with probabilistic outputs.
|
|
For loss 'exponential', gradient boosting recovers the AdaBoost algorithm.
|
|
|
|
.. deprecated:: 1.1
|
|
The loss 'deviance' was deprecated in v1.1 and will be removed in
|
|
version 1.3. Use `loss='log_loss'` which is equivalent.
|
|
|
|
learning_rate : float, 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.
|
|
Values must be in the range `[0.0, inf)`.
|
|
|
|
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.
|
|
Values must be in the range `[1, inf)`.
|
|
|
|
subsample : float, 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.
|
|
Values must be in the range `(0.0, 1.0]`.
|
|
|
|
criterion : {'friedman_mse', 'squared_error'}, 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, 'squared_error' for mean squared 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 or float, default=2
|
|
The minimum number of samples required to split an internal node:
|
|
|
|
- If int, values must be in the range `[2, inf)`.
|
|
- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`
|
|
will be `ceil(min_samples_split * n_samples)`.
|
|
|
|
.. versionchanged:: 0.18
|
|
Added float values for fractions.
|
|
|
|
min_samples_leaf : int or float, default=1
|
|
The minimum number of samples required to be at a leaf node.
|
|
A split point at any depth will only be considered if it leaves at
|
|
least ``min_samples_leaf`` training samples in each of the left and
|
|
right branches. This may have the effect of smoothing the model,
|
|
especially in regression.
|
|
|
|
- If int, values must be in the range `[1, inf)`.
|
|
- If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`
|
|
will be `ceil(min_samples_leaf * n_samples)`.
|
|
|
|
.. versionchanged:: 0.18
|
|
Added float values for fractions.
|
|
|
|
min_weight_fraction_leaf : float, default=0.0
|
|
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.
|
|
Values must be in the range `[0.0, 0.5]`.
|
|
|
|
max_depth : int or None, 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. If None, then nodes are expanded until
|
|
all leaves are pure or until all leaves contain less than
|
|
min_samples_split samples.
|
|
If int, values must be in the range `[1, inf)`.
|
|
|
|
min_impurity_decrease : float, default=0.0
|
|
A node will be split if this split induces a decrease of the impurity
|
|
greater than or equal to this value.
|
|
Values must be in the range `[0.0, inf)`.
|
|
|
|
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 : estimator or 'zero', default=None
|
|
An estimator object that is used to compute the initial predictions.
|
|
``init`` has to provide :meth:`fit` and :meth:`predict_proba`. If
|
|
'zero', the initial raw predictions are set to zero. By default, a
|
|
``DummyEstimator`` predicting the classes priors is used.
|
|
|
|
random_state : int, RandomState instance or None, default=None
|
|
Controls the random seed given to each Tree estimator at each
|
|
boosting iteration.
|
|
In addition, it controls the random permutation of the features at
|
|
each split (see Notes for more details).
|
|
It also controls the random splitting of the training data to obtain a
|
|
validation set if `n_iter_no_change` is not None.
|
|
Pass an int for reproducible output across multiple function calls.
|
|
See :term:`Glossary <random_state>`.
|
|
|
|
max_features : {'auto', 'sqrt', 'log2'}, int or float, default=None
|
|
The number of features to consider when looking for the best split:
|
|
|
|
- If int, values must be in the range `[1, inf)`.
|
|
- If float, values must be in the range `(0.0, 1.0]` and the features
|
|
considered at each split will be `max(1, int(max_features * n_features_in_))`.
|
|
- 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.
|
|
|
|
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.
|
|
Values must be in the range `[0, inf)`.
|
|
|
|
max_leaf_nodes : int, default=None
|
|
Grow trees with ``max_leaf_nodes`` in best-first fashion.
|
|
Best nodes are defined as relative reduction in impurity.
|
|
Values must be in the range `[2, inf)`.
|
|
If `None`, then unlimited number of leaf nodes.
|
|
|
|
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. See :term:`the Glossary <warm_start>`.
|
|
|
|
validation_fraction : float, default=0.1
|
|
The proportion of training data to set aside as validation set for
|
|
early stopping. Values must be in the range `(0.0, 1.0)`.
|
|
Only used if ``n_iter_no_change`` is set to an integer.
|
|
|
|
.. versionadded:: 0.20
|
|
|
|
n_iter_no_change : int, default=None
|
|
``n_iter_no_change`` is used to decide if early stopping will be used
|
|
to terminate training when validation score is not improving. By
|
|
default it is set to None to disable early stopping. If set to a
|
|
number, it will set aside ``validation_fraction`` size of the training
|
|
data as validation and terminate training when validation score is not
|
|
improving in all of the previous ``n_iter_no_change`` numbers of
|
|
iterations. The split is stratified.
|
|
Values must be in the range `[1, inf)`.
|
|
|
|
.. versionadded:: 0.20
|
|
|
|
tol : float, default=1e-4
|
|
Tolerance for the early stopping. When the loss is not improving
|
|
by at least tol for ``n_iter_no_change`` iterations (if set to a
|
|
number), the training stops.
|
|
Values must be in the range `[0.0, inf)`.
|
|
|
|
.. versionadded:: 0.20
|
|
|
|
ccp_alpha : non-negative float, default=0.0
|
|
Complexity parameter used for Minimal Cost-Complexity Pruning. The
|
|
subtree with the largest cost complexity that is smaller than
|
|
``ccp_alpha`` will be chosen. By default, no pruning is performed.
|
|
Values must be in the range `[0.0, inf)`.
|
|
See :ref:`minimal_cost_complexity_pruning` for details.
|
|
|
|
.. versionadded:: 0.22
|
|
|
|
Attributes
|
|
----------
|
|
n_estimators_ : int
|
|
The number of estimators as selected by early stopping (if
|
|
``n_iter_no_change`` is specified). Otherwise it is set to
|
|
``n_estimators``.
|
|
|
|
.. versionadded:: 0.20
|
|
|
|
feature_importances_ : ndarray of shape (n_features,)
|
|
The impurity-based feature importances.
|
|
The higher, the more important the feature.
|
|
The importance of a feature is computed as the (normalized)
|
|
total reduction of the criterion brought by that feature. It is also
|
|
known as the Gini importance.
|
|
|
|
Warning: impurity-based feature importances can be misleading for
|
|
high cardinality features (many unique values). See
|
|
:func:`sklearn.inspection.permutation_importance` as an alternative.
|
|
|
|
oob_improvement_ : ndarray of 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.
|
|
Only available if ``subsample < 1.0``
|
|
|
|
train_score_ : ndarray of 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.
|
|
|
|
.. deprecated:: 1.1
|
|
Attribute `loss_` was deprecated in version 1.1 and will be
|
|
removed in 1.3.
|
|
|
|
init_ : estimator
|
|
The estimator that provides the initial predictions.
|
|
Set via the ``init`` argument or ``loss.init_estimator``.
|
|
|
|
estimators_ : ndarray of DecisionTreeRegressor of \
|
|
shape (n_estimators, ``loss_.K``)
|
|
The collection of fitted sub-estimators. ``loss_.K`` is 1 for binary
|
|
classification, otherwise n_classes.
|
|
|
|
classes_ : ndarray of shape (n_classes,)
|
|
The classes labels.
|
|
|
|
n_features_in_ : int
|
|
Number of features seen during :term:`fit`.
|
|
|
|
.. versionadded:: 0.24
|
|
|
|
feature_names_in_ : ndarray of shape (`n_features_in_`,)
|
|
Names of features seen during :term:`fit`. Defined only when `X`
|
|
has feature names that are all strings.
|
|
|
|
.. versionadded:: 1.0
|
|
|
|
n_classes_ : int
|
|
The number of classes.
|
|
|
|
max_features_ : int
|
|
The inferred value of max_features.
|
|
|
|
See Also
|
|
--------
|
|
HistGradientBoostingClassifier : Histogram-based Gradient Boosting
|
|
Classification Tree.
|
|
sklearn.tree.DecisionTreeClassifier : A decision tree classifier.
|
|
RandomForestClassifier : A meta-estimator that fits a number of decision
|
|
tree classifiers on various sub-samples of the dataset and uses
|
|
averaging to improve the predictive accuracy and control over-fitting.
|
|
AdaBoostClassifier : A meta-estimator that begins by fitting a classifier
|
|
on the original dataset and then fits additional copies of the
|
|
classifier on the same dataset where the weights of incorrectly
|
|
classified instances are adjusted such that subsequent classifiers
|
|
focus more on difficult cases.
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
Examples
|
|
--------
|
|
The following example shows how to fit a gradient boosting classifier with
|
|
100 decision stumps as weak learners.
|
|
|
|
>>> from sklearn.datasets import make_hastie_10_2
|
|
>>> from sklearn.ensemble import GradientBoostingClassifier
|
|
|
|
>>> X, y = make_hastie_10_2(random_state=0)
|
|
>>> X_train, X_test = X[:2000], X[2000:]
|
|
>>> y_train, y_test = y[:2000], y[2000:]
|
|
|
|
>>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,
|
|
... max_depth=1, random_state=0).fit(X_train, y_train)
|
|
>>> clf.score(X_test, y_test)
|
|
0.913...
|
|
"""
|
|
|
|
# TODO(1.3): remove "deviance"
|
|
_parameter_constraints: dict = {
|
|
**BaseGradientBoosting._parameter_constraints,
|
|
"loss": [
|
|
StrOptions({"log_loss", "deviance", "exponential"}, deprecated={"deviance"})
|
|
],
|
|
"init": [StrOptions({"zero"}), None, HasMethods(["fit", "predict_proba"])],
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
loss="log_loss",
|
|
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=0.0,
|
|
max_depth=3,
|
|
min_impurity_decrease=0.0,
|
|
init=None,
|
|
random_state=None,
|
|
max_features=None,
|
|
verbose=0,
|
|
max_leaf_nodes=None,
|
|
warm_start=False,
|
|
validation_fraction=0.1,
|
|
n_iter_no_change=None,
|
|
tol=1e-4,
|
|
ccp_alpha=0.0,
|
|
):
|
|
|
|
super().__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,
|
|
warm_start=warm_start,
|
|
validation_fraction=validation_fraction,
|
|
n_iter_no_change=n_iter_no_change,
|
|
tol=tol,
|
|
ccp_alpha=ccp_alpha,
|
|
)
|
|
|
|
def _validate_y(self, y, sample_weight):
|
|
check_classification_targets(y)
|
|
self.classes_, y = np.unique(y, return_inverse=True)
|
|
n_trim_classes = np.count_nonzero(np.bincount(y, sample_weight))
|
|
if n_trim_classes < 2:
|
|
raise ValueError(
|
|
"y contains %d class after sample_weight "
|
|
"trimmed classes with zero weights, while a "
|
|
"minimum of 2 classes are required." % n_trim_classes
|
|
)
|
|
self._n_classes = len(self.classes_)
|
|
# expose n_classes_ attribute
|
|
self.n_classes_ = self._n_classes
|
|
return y
|
|
|
|
def decision_function(self, X):
|
|
"""Compute the decision function of ``X``.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of 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 : ndarray of shape (n_samples, n_classes) or (n_samples,)
|
|
The decision function of the input samples, which corresponds to
|
|
the raw values predicted from the trees of the ensemble . The
|
|
order of the classes corresponds to that in the attribute
|
|
:term:`classes_`. Regression and binary classification produce an
|
|
array of shape (n_samples,).
|
|
"""
|
|
X = self._validate_data(
|
|
X, dtype=DTYPE, order="C", accept_sparse="csr", reset=False
|
|
)
|
|
raw_predictions = self._raw_predict(X)
|
|
if raw_predictions.shape[1] == 1:
|
|
return raw_predictions.ravel()
|
|
return raw_predictions
|
|
|
|
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, sparse matrix} of 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``.
|
|
|
|
Yields
|
|
------
|
|
score : generator of ndarray of shape (n_samples, k)
|
|
The decision function of the input samples, which corresponds to
|
|
the raw values predicted from the trees of the ensemble . The
|
|
classes corresponds to that in the attribute :term:`classes_`.
|
|
Regression and binary classification are special cases with
|
|
``k == 1``, otherwise ``k==n_classes``.
|
|
"""
|
|
yield from self._staged_raw_predict(X)
|
|
|
|
def predict(self, X):
|
|
"""Predict class for X.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of 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 : ndarray of shape (n_samples,)
|
|
The predicted values.
|
|
"""
|
|
raw_predictions = self.decision_function(X)
|
|
encoded_labels = self._loss._raw_prediction_to_decision(raw_predictions)
|
|
return self.classes_.take(encoded_labels, 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, sparse matrix} of 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``.
|
|
|
|
Yields
|
|
------
|
|
y : generator of ndarray of shape (n_samples,)
|
|
The predicted value of the input samples.
|
|
"""
|
|
for raw_predictions in self._staged_raw_predict(X):
|
|
encoded_labels = self._loss._raw_prediction_to_decision(raw_predictions)
|
|
yield self.classes_.take(encoded_labels, axis=0)
|
|
|
|
def predict_proba(self, X):
|
|
"""Predict class probabilities for X.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of 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
|
|
-------
|
|
p : ndarray of shape (n_samples, n_classes)
|
|
The class probabilities of the input samples. The order of the
|
|
classes corresponds to that in the attribute :term:`classes_`.
|
|
|
|
Raises
|
|
------
|
|
AttributeError
|
|
If the ``loss`` does not support probabilities.
|
|
"""
|
|
raw_predictions = self.decision_function(X)
|
|
try:
|
|
return self._loss._raw_prediction_to_proba(raw_predictions)
|
|
except NotFittedError:
|
|
raise
|
|
except AttributeError as e:
|
|
raise AttributeError(
|
|
"loss=%r does not support predict_proba" % self.loss
|
|
) from e
|
|
|
|
def predict_log_proba(self, X):
|
|
"""Predict class log-probabilities for X.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of 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
|
|
-------
|
|
p : ndarray of shape (n_samples, n_classes)
|
|
The class log-probabilities of the input samples. The order of the
|
|
classes corresponds to that in the attribute :term:`classes_`.
|
|
|
|
Raises
|
|
------
|
|
AttributeError
|
|
If the ``loss`` does not support probabilities.
|
|
"""
|
|
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, sparse matrix} of 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``.
|
|
|
|
Yields
|
|
------
|
|
y : generator of ndarray of shape (n_samples,)
|
|
The predicted value of the input samples.
|
|
"""
|
|
try:
|
|
for raw_predictions in self._staged_raw_predict(X):
|
|
yield self._loss._raw_prediction_to_proba(raw_predictions)
|
|
except NotFittedError:
|
|
raise
|
|
except AttributeError as e:
|
|
raise AttributeError(
|
|
"loss=%r does not support predict_proba" % self.loss
|
|
) from e
|
|
|
|
|
|
class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
|
|
"""Gradient Boosting for regression.
|
|
|
|
This estimator 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.
|
|
|
|
:class:`sklearn.ensemble.HistGradientBoostingRegressor` is a much faster
|
|
variant of this algorithm for intermediate datasets (`n_samples >= 10_000`).
|
|
|
|
Read more in the :ref:`User Guide <gradient_boosting>`.
|
|
|
|
Parameters
|
|
----------
|
|
loss : {'squared_error', 'absolute_error', 'huber', 'quantile'}, \
|
|
default='squared_error'
|
|
Loss function to be optimized. 'squared_error' refers to the squared
|
|
error for regression. 'absolute_error' refers to the absolute error of
|
|
regression and is a robust loss function. 'huber' is a
|
|
combination of the two. 'quantile' allows quantile regression (use
|
|
`alpha` to specify the quantile).
|
|
|
|
learning_rate : float, 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.
|
|
Values must be in the range `[0.0, inf)`.
|
|
|
|
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.
|
|
Values must be in the range `[1, inf)`.
|
|
|
|
subsample : float, 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.
|
|
Values must be in the range `(0.0, 1.0]`.
|
|
|
|
criterion : {'friedman_mse', 'squared_error'}, 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, "squared_error" for mean squared 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 or float, default=2
|
|
The minimum number of samples required to split an internal node:
|
|
|
|
- If int, values must be in the range `[2, inf)`.
|
|
- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`
|
|
will be `ceil(min_samples_split * n_samples)`.
|
|
|
|
.. versionchanged:: 0.18
|
|
Added float values for fractions.
|
|
|
|
min_samples_leaf : int or float, default=1
|
|
The minimum number of samples required to be at a leaf node.
|
|
A split point at any depth will only be considered if it leaves at
|
|
least ``min_samples_leaf`` training samples in each of the left and
|
|
right branches. This may have the effect of smoothing the model,
|
|
especially in regression.
|
|
|
|
- If int, values must be in the range `[1, inf)`.
|
|
- If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`
|
|
will be `ceil(min_samples_leaf * n_samples)`.
|
|
|
|
.. versionchanged:: 0.18
|
|
Added float values for fractions.
|
|
|
|
min_weight_fraction_leaf : float, default=0.0
|
|
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.
|
|
Values must be in the range `[0.0, 0.5]`.
|
|
|
|
max_depth : int or None, 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. If None, then nodes are expanded until
|
|
all leaves are pure or until all leaves contain less than
|
|
min_samples_split samples.
|
|
If int, values must be in the range `[1, inf)`.
|
|
|
|
min_impurity_decrease : float, default=0.0
|
|
A node will be split if this split induces a decrease of the impurity
|
|
greater than or equal to this value.
|
|
Values must be in the range `[0.0, inf)`.
|
|
|
|
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 : estimator or 'zero', default=None
|
|
An estimator object that is used to compute the initial predictions.
|
|
``init`` has to provide :term:`fit` and :term:`predict`. If 'zero', the
|
|
initial raw predictions are set to zero. By default a
|
|
``DummyEstimator`` is used, predicting either the average target value
|
|
(for loss='squared_error'), or a quantile for the other losses.
|
|
|
|
random_state : int, RandomState instance or None, default=None
|
|
Controls the random seed given to each Tree estimator at each
|
|
boosting iteration.
|
|
In addition, it controls the random permutation of the features at
|
|
each split (see Notes for more details).
|
|
It also controls the random splitting of the training data to obtain a
|
|
validation set if `n_iter_no_change` is not None.
|
|
Pass an int for reproducible output across multiple function calls.
|
|
See :term:`Glossary <random_state>`.
|
|
|
|
max_features : {'auto', 'sqrt', 'log2'}, int or float, default=None
|
|
The number of features to consider when looking for the best split:
|
|
|
|
- If int, values must be in the range `[1, inf)`.
|
|
- If float, values must be in the range `(0.0, 1.0]` and the features
|
|
considered at each split will be `max(1, int(max_features * n_features_in_))`.
|
|
- 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.
|
|
|
|
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'``.
|
|
Values must be in the range `(0.0, 1.0)`.
|
|
|
|
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.
|
|
Values must be in the range `[0, inf)`.
|
|
|
|
max_leaf_nodes : int, default=None
|
|
Grow trees with ``max_leaf_nodes`` in best-first fashion.
|
|
Best nodes are defined as relative reduction in impurity.
|
|
Values must be in the range `[2, inf)`.
|
|
If None, then unlimited number of leaf nodes.
|
|
|
|
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. See :term:`the Glossary <warm_start>`.
|
|
|
|
validation_fraction : float, default=0.1
|
|
The proportion of training data to set aside as validation set for
|
|
early stopping. Values must be in the range `(0.0, 1.0)`.
|
|
Only used if ``n_iter_no_change`` is set to an integer.
|
|
|
|
.. versionadded:: 0.20
|
|
|
|
n_iter_no_change : int, default=None
|
|
``n_iter_no_change`` is used to decide if early stopping will be used
|
|
to terminate training when validation score is not improving. By
|
|
default it is set to None to disable early stopping. If set to a
|
|
number, it will set aside ``validation_fraction`` size of the training
|
|
data as validation and terminate training when validation score is not
|
|
improving in all of the previous ``n_iter_no_change`` numbers of
|
|
iterations.
|
|
Values must be in the range `[1, inf)`.
|
|
|
|
.. versionadded:: 0.20
|
|
|
|
tol : float, default=1e-4
|
|
Tolerance for the early stopping. When the loss is not improving
|
|
by at least tol for ``n_iter_no_change`` iterations (if set to a
|
|
number), the training stops.
|
|
Values must be in the range `[0.0, inf)`.
|
|
|
|
.. versionadded:: 0.20
|
|
|
|
ccp_alpha : non-negative float, default=0.0
|
|
Complexity parameter used for Minimal Cost-Complexity Pruning. The
|
|
subtree with the largest cost complexity that is smaller than
|
|
``ccp_alpha`` will be chosen. By default, no pruning is performed.
|
|
Values must be in the range `[0.0, inf)`.
|
|
See :ref:`minimal_cost_complexity_pruning` for details.
|
|
|
|
.. versionadded:: 0.22
|
|
|
|
Attributes
|
|
----------
|
|
feature_importances_ : ndarray of shape (n_features,)
|
|
The impurity-based feature importances.
|
|
The higher, the more important the feature.
|
|
The importance of a feature is computed as the (normalized)
|
|
total reduction of the criterion brought by that feature. It is also
|
|
known as the Gini importance.
|
|
|
|
Warning: impurity-based feature importances can be misleading for
|
|
high cardinality features (many unique values). See
|
|
:func:`sklearn.inspection.permutation_importance` as an alternative.
|
|
|
|
oob_improvement_ : ndarray of 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.
|
|
Only available if ``subsample < 1.0``
|
|
|
|
train_score_ : ndarray of 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.
|
|
|
|
.. deprecated:: 1.1
|
|
Attribute `loss_` was deprecated in version 1.1 and will be
|
|
removed in 1.3.
|
|
|
|
init_ : estimator
|
|
The estimator that provides the initial predictions.
|
|
Set via the ``init`` argument or ``loss.init_estimator``.
|
|
|
|
estimators_ : ndarray of DecisionTreeRegressor of shape (n_estimators, 1)
|
|
The collection of fitted sub-estimators.
|
|
|
|
n_estimators_ : int
|
|
The number of estimators as selected by early stopping (if
|
|
``n_iter_no_change`` is specified). Otherwise it is set to
|
|
``n_estimators``.
|
|
|
|
n_features_in_ : int
|
|
Number of features seen during :term:`fit`.
|
|
|
|
.. versionadded:: 0.24
|
|
|
|
feature_names_in_ : ndarray of shape (`n_features_in_`,)
|
|
Names of features seen during :term:`fit`. Defined only when `X`
|
|
has feature names that are all strings.
|
|
|
|
.. versionadded:: 1.0
|
|
|
|
max_features_ : int
|
|
The inferred value of max_features.
|
|
|
|
See Also
|
|
--------
|
|
HistGradientBoostingRegressor : Histogram-based Gradient Boosting
|
|
Classification Tree.
|
|
sklearn.tree.DecisionTreeRegressor : A decision tree regressor.
|
|
sklearn.ensemble.RandomForestRegressor : A random forest regressor.
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.datasets import make_regression
|
|
>>> from sklearn.ensemble import GradientBoostingRegressor
|
|
>>> from sklearn.model_selection import train_test_split
|
|
>>> X, y = make_regression(random_state=0)
|
|
>>> X_train, X_test, y_train, y_test = train_test_split(
|
|
... X, y, random_state=0)
|
|
>>> reg = GradientBoostingRegressor(random_state=0)
|
|
>>> reg.fit(X_train, y_train)
|
|
GradientBoostingRegressor(random_state=0)
|
|
>>> reg.predict(X_test[1:2])
|
|
array([-61...])
|
|
>>> reg.score(X_test, y_test)
|
|
0.4...
|
|
"""
|
|
|
|
_parameter_constraints: dict = {
|
|
**BaseGradientBoosting._parameter_constraints,
|
|
"loss": [StrOptions({"squared_error", "absolute_error", "huber", "quantile"})],
|
|
"init": [StrOptions({"zero"}), None, HasMethods(["fit", "predict"])],
|
|
"alpha": [Interval(Real, 0.0, 1.0, closed="neither")],
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
loss="squared_error",
|
|
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=0.0,
|
|
max_depth=3,
|
|
min_impurity_decrease=0.0,
|
|
init=None,
|
|
random_state=None,
|
|
max_features=None,
|
|
alpha=0.9,
|
|
verbose=0,
|
|
max_leaf_nodes=None,
|
|
warm_start=False,
|
|
validation_fraction=0.1,
|
|
n_iter_no_change=None,
|
|
tol=1e-4,
|
|
ccp_alpha=0.0,
|
|
):
|
|
|
|
super().__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,
|
|
random_state=random_state,
|
|
alpha=alpha,
|
|
verbose=verbose,
|
|
max_leaf_nodes=max_leaf_nodes,
|
|
warm_start=warm_start,
|
|
validation_fraction=validation_fraction,
|
|
n_iter_no_change=n_iter_no_change,
|
|
tol=tol,
|
|
ccp_alpha=ccp_alpha,
|
|
)
|
|
|
|
def _validate_y(self, y, sample_weight=None):
|
|
if y.dtype.kind == "O":
|
|
y = y.astype(DOUBLE)
|
|
return y
|
|
|
|
def predict(self, X):
|
|
"""Predict regression target for X.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of 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 : ndarray of shape (n_samples,)
|
|
The predicted values.
|
|
"""
|
|
X = self._validate_data(
|
|
X, dtype=DTYPE, order="C", accept_sparse="csr", reset=False
|
|
)
|
|
# In regression we can directly return the raw value from the trees.
|
|
return self._raw_predict(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, sparse matrix} of 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``.
|
|
|
|
Yields
|
|
------
|
|
y : generator of ndarray of shape (n_samples,)
|
|
The predicted value of the input samples.
|
|
"""
|
|
for raw_predictions in self._staged_raw_predict(X):
|
|
yield raw_predictions.ravel()
|
|
|
|
def apply(self, X):
|
|
"""Apply trees in the ensemble to X, return leaf indices.
|
|
|
|
.. versionadded:: 0.17
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of 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 of 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().apply(X)
|
|
leaves = leaves.reshape(X.shape[0], self.estimators_.shape[0])
|
|
return leaves
|