1713 lines
70 KiB
Python
1713 lines
70 KiB
Python
"""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 abc import ABCMeta
|
|
from abc import abstractmethod
|
|
import warnings
|
|
|
|
from ._base import BaseEnsemble
|
|
from ..base import ClassifierMixin
|
|
from ..base import RegressorMixin
|
|
from ..base import BaseEstimator
|
|
from ..base import is_classifier
|
|
from ..utils import deprecated
|
|
|
|
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.sparse import csc_matrix
|
|
from scipy.sparse import csr_matrix
|
|
from scipy.sparse import issparse
|
|
|
|
from time import time
|
|
from ..model_selection import train_test_split
|
|
from ..tree import DecisionTreeRegressor
|
|
from ..tree._tree import DTYPE, DOUBLE
|
|
from . import _gb_losses
|
|
|
|
from ..utils import check_random_state
|
|
from ..utils import check_array
|
|
from ..utils import column_or_1d
|
|
from ..utils.validation import check_is_fitted, _check_sample_weight
|
|
from ..utils.multiclass import check_classification_targets
|
|
from ..exceptions import NotFittedError
|
|
from ..utils.validation import _deprecate_positional_args
|
|
|
|
|
|
class VerboseReporter:
|
|
"""Reports verbose output to stdout.
|
|
|
|
Parameters
|
|
----------
|
|
verbose : int
|
|
Verbosity level. 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):
|
|
"""Initialize reporter
|
|
|
|
Parameters
|
|
----------
|
|
est : Estimator
|
|
The estimator
|
|
|
|
begin_at_stage : int, default=0
|
|
stage at which to begin reporting
|
|
"""
|
|
# 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.
|
|
|
|
Parameters
|
|
----------
|
|
j : int
|
|
The new iteration.
|
|
est : Estimator
|
|
The estimator.
|
|
"""
|
|
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(BaseEnsemble, metaclass=ABCMeta):
|
|
"""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, ccp_alpha,
|
|
random_state, alpha=0.9, verbose=0, max_leaf_nodes=None,
|
|
warm_start=False, validation_fraction=0.1,
|
|
n_iter_no_change=None, tol=1e-4):
|
|
|
|
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.ccp_alpha = ccp_alpha
|
|
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.validation_fraction = validation_fraction
|
|
self.n_iter_no_change = n_iter_no_change
|
|
self.tol = tol
|
|
|
|
@abstractmethod
|
|
def _validate_y(self, y, sample_weight=None):
|
|
"""Called by fit to validate y."""
|
|
|
|
def _fit_stage(self, i, X, y, raw_predictions, sample_weight, sample_mask,
|
|
random_state, X_csc=None, X_csr=None):
|
|
"""Fit another stage of ``_n_classes`` trees to the boosting model."""
|
|
|
|
assert sample_mask.dtype == bool
|
|
loss = self.loss_
|
|
original_y = y
|
|
|
|
# Need to pass a copy of raw_predictions to negative_gradient()
|
|
# because raw_predictions is partially updated at the end of the loop
|
|
# in update_terminal_regions(), and gradients need to be evaluated at
|
|
# iteration i - 1.
|
|
raw_predictions_copy = raw_predictions.copy()
|
|
|
|
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, raw_predictions_copy, 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,
|
|
ccp_alpha=self.ccp_alpha)
|
|
|
|
if self.subsample < 1.0:
|
|
# no inplace multiplication!
|
|
sample_weight = sample_weight * sample_mask.astype(np.float64)
|
|
|
|
X = X_csr if X_csr is not None else X
|
|
tree.fit(X, residual, sample_weight=sample_weight,
|
|
check_input=False)
|
|
|
|
# update tree leaves
|
|
loss.update_terminal_regions(
|
|
tree.tree_, X, y, residual, raw_predictions, sample_weight,
|
|
sample_mask, learning_rate=self.learning_rate, k=k)
|
|
|
|
# add tree to ensemble
|
|
self.estimators_[i, k] = tree
|
|
|
|
return raw_predictions
|
|
|
|
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 _gb_losses.LOSS_FUNCTIONS):
|
|
raise ValueError("Loss '{0:s}' not supported. ".format(self.loss))
|
|
|
|
if self.loss == 'deviance':
|
|
loss_class = (_gb_losses.MultinomialDeviance
|
|
if len(self.classes_) > 2
|
|
else _gb_losses.BinomialDeviance)
|
|
else:
|
|
loss_class = _gb_losses.LOSS_FUNCTIONS[self.loss]
|
|
|
|
if is_classifier(self):
|
|
self.loss_ = loss_class(self.n_classes_)
|
|
elif self.loss in ("huber", "quantile"):
|
|
self.loss_ = loss_class(self.alpha)
|
|
else:
|
|
self.loss_ = loss_class()
|
|
|
|
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:
|
|
# init must be an estimator or 'zero'
|
|
if isinstance(self.init, BaseEstimator):
|
|
self.loss_.check_init_estimator(self.init)
|
|
elif not (isinstance(self.init, str) and self.init == 'zero'):
|
|
raise ValueError(
|
|
"The init parameter must be an estimator or 'zero'. "
|
|
"Got init={}".format(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, str):
|
|
if self.max_features == "auto":
|
|
if is_classifier(self):
|
|
max_features = max(1, int(np.sqrt(self.n_features_)))
|
|
else:
|
|
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):
|
|
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
|
|
|
|
if not isinstance(self.n_iter_no_change,
|
|
(numbers.Integral, type(None))):
|
|
raise ValueError("n_iter_no_change should either be None or an "
|
|
"integer. %r was passed"
|
|
% self.n_iter_no_change)
|
|
|
|
def _init_state(self):
|
|
"""Initialize model state and allocate model state data structures. """
|
|
|
|
self.init_ = self.init
|
|
if self.init_ is None:
|
|
self.init_ = self.loss_.init_estimator()
|
|
|
|
self.estimators_ = np.empty((self.n_estimators, self.loss_.K),
|
|
dtype=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=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_
|
|
if hasattr(self, '_rng'):
|
|
del self._rng
|
|
|
|
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_ = np.resize(self.estimators_,
|
|
(total_n_estimators, self.loss_.K))
|
|
self.train_score_ = np.resize(self.train_score_, 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_ = np.resize(self.oob_improvement_,
|
|
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)
|
|
|
|
@abstractmethod
|
|
def _warn_mae_for_criterion(self):
|
|
pass
|
|
|
|
def fit(self, X, y, sample_weight=None, monitor=None):
|
|
"""Fit the gradient boosting model.
|
|
|
|
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``.
|
|
|
|
y : array-like of shape (n_samples,)
|
|
Target values (strings or integers in classification, real numbers
|
|
in regression)
|
|
For classification, labels must correspond to classes.
|
|
|
|
sample_weight : array-like of shape (n_samples,), default=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, default=None
|
|
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
|
|
"""
|
|
if self.criterion == 'mae':
|
|
# TODO: This should raise an error from 1.1
|
|
self._warn_mae_for_criterion()
|
|
|
|
# if not warmstart - clear the estimator state
|
|
if not self.warm_start:
|
|
self._clear_state()
|
|
|
|
# Check input
|
|
# Since check_array converts both X and y to the same dtype, but the
|
|
# trees use different types for X and y, checking them separately.
|
|
|
|
X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],
|
|
dtype=DTYPE, multi_output=True)
|
|
n_samples, self.n_features_ = X.shape
|
|
|
|
sample_weight_is_none = sample_weight is None
|
|
|
|
sample_weight = _check_sample_weight(sample_weight, X)
|
|
|
|
y = column_or_1d(y, warn=True)
|
|
|
|
if is_classifier(self):
|
|
y = self._validate_y(y, sample_weight)
|
|
else:
|
|
y = self._validate_y(y)
|
|
|
|
if self.n_iter_no_change is not None:
|
|
stratify = y if is_classifier(self) else None
|
|
X, X_val, y, y_val, sample_weight, sample_weight_val = (
|
|
train_test_split(X, y, sample_weight,
|
|
random_state=self.random_state,
|
|
test_size=self.validation_fraction,
|
|
stratify=stratify))
|
|
if is_classifier(self):
|
|
if self._n_classes != np.unique(y).shape[0]:
|
|
# We choose to error here. The problem is that the init
|
|
# estimator would be trained on y, which has some missing
|
|
# classes now, so its predictions would not have the
|
|
# correct shape.
|
|
raise ValueError(
|
|
'The training data after the early stopping split '
|
|
'is missing some classes. Try using another random '
|
|
'seed.'
|
|
)
|
|
else:
|
|
X_val = y_val = sample_weight_val = None
|
|
|
|
self._check_params()
|
|
|
|
if not self._is_initialized():
|
|
# init state
|
|
self._init_state()
|
|
|
|
# fit initial model and initialize raw predictions
|
|
if self.init_ == 'zero':
|
|
raw_predictions = np.zeros(shape=(X.shape[0], self.loss_.K),
|
|
dtype=np.float64)
|
|
else:
|
|
# XXX clean this once we have a support_sample_weight tag
|
|
if sample_weight_is_none:
|
|
self.init_.fit(X, y)
|
|
else:
|
|
msg = ("The initial estimator {} does not support sample "
|
|
"weights.".format(self.init_.__class__.__name__))
|
|
try:
|
|
self.init_.fit(X, y, sample_weight=sample_weight)
|
|
except TypeError as e:
|
|
# regular estimator without SW support
|
|
raise ValueError(msg) from e
|
|
except ValueError as e:
|
|
if "pass parameters to specific steps of "\
|
|
"your pipeline using the "\
|
|
"stepname__parameter" in str(e): # pipeline
|
|
raise ValueError(msg) from e
|
|
else: # regular estimator whose input checking failed
|
|
raise
|
|
|
|
raw_predictions = \
|
|
self.loss_.get_init_raw_predictions(X, self.init_)
|
|
|
|
begin_at_stage = 0
|
|
|
|
# The rng state must be preserved if warm_start is True
|
|
self._rng = check_random_state(self.random_state)
|
|
|
|
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 _decision_function (called in two lines
|
|
# below) are more constrained than fit. It accepts only CSR
|
|
# matrices.
|
|
X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')
|
|
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)
|
|
|
|
# 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 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]))
|
|
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):
|
|
"""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``.
|
|
|
|
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``.
|
|
"""
|
|
X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')
|
|
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_, 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
|
|
|
|
|
|
class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
|
|
"""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 <gradient_boosting>`.
|
|
|
|
Parameters
|
|
----------
|
|
loss : {'deviance', 'exponential'}, default='deviance'
|
|
The 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, 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.
|
|
|
|
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.
|
|
|
|
criterion : {'friedman_mse', 'mse', 'mae'}, 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
|
|
.. deprecated:: 0.24
|
|
`criterion='mae'` is deprecated and will be removed in version
|
|
1.1 (renaming of 0.26). Use `criterion='friedman_mse'` or `'mse'`
|
|
instead, as trees should use a least-square criterion in
|
|
Gradient Boosting.
|
|
|
|
min_samples_split : int or float, 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 fraction and
|
|
`ceil(min_samples_split * n_samples)` are the minimum
|
|
number of samples for each split.
|
|
|
|
.. 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, then consider `min_samples_leaf` as the minimum number.
|
|
- If float, then `min_samples_leaf` is a fraction and
|
|
`ceil(min_samples_leaf * n_samples)` are the minimum
|
|
number of samples for each node.
|
|
|
|
.. 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.
|
|
|
|
max_depth : int, default=3
|
|
The 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.
|
|
|
|
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.
|
|
|
|
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
|
|
|
|
min_impurity_split : float, default=None
|
|
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. The default value of
|
|
``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
|
|
will be removed in 1.0 (renaming of 0.25).
|
|
Use ``min_impurity_decrease`` instead.
|
|
|
|
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 spliting 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, then consider `max_features` features at each split.
|
|
- If float, then `max_features` is a fraction 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.
|
|
|
|
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.
|
|
|
|
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.
|
|
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. Must be between 0 and 1.
|
|
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.
|
|
|
|
.. 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.
|
|
|
|
.. 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. 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.
|
|
|
|
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_ : int
|
|
The number of data features.
|
|
|
|
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...
|
|
"""
|
|
|
|
_SUPPORTED_LOSS = ('deviance', 'exponential')
|
|
|
|
@_deprecate_positional_args
|
|
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,
|
|
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,
|
|
min_impurity_split=min_impurity_split,
|
|
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 _warn_mae_for_criterion(self):
|
|
# TODO: This should raise an error from 1.1
|
|
warnings.warn("criterion='mae' was deprecated in version 0.24 and "
|
|
"will be removed in version 1.1 (renaming of 0.26). Use "
|
|
"criterion='friedman_mse' or 'mse' instead, as trees "
|
|
"should use a least-square criterion in Gradient "
|
|
"Boosting.", FutureWarning)
|
|
|
|
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 = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')
|
|
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``.
|
|
|
|
Returns
|
|
-------
|
|
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``.
|
|
|
|
Returns
|
|
-------
|
|
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``.
|
|
|
|
Raises
|
|
------
|
|
AttributeError
|
|
If the ``loss`` does not support probabilities.
|
|
|
|
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_`.
|
|
"""
|
|
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``.
|
|
|
|
Raises
|
|
------
|
|
AttributeError
|
|
If the ``loss`` does not support probabilities.
|
|
|
|
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_`.
|
|
"""
|
|
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``.
|
|
|
|
Returns
|
|
-------
|
|
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.
|
|
|
|
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 <gradient_boosting>`.
|
|
|
|
Parameters
|
|
----------
|
|
loss : {'ls', 'lad', 'huber', 'quantile'}, 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, 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.
|
|
|
|
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.
|
|
|
|
criterion : {'friedman_mse', 'mse', 'mae'}, 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
|
|
.. deprecated:: 0.24
|
|
`criterion='mae'` is deprecated and will be removed in version
|
|
1.1 (renaming of 0.26). The correct way of minimizing the absolute
|
|
error is to use `loss='lad'` instead.
|
|
|
|
min_samples_split : int or float, 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 fraction and
|
|
`ceil(min_samples_split * n_samples)` are the minimum
|
|
number of samples for each split.
|
|
|
|
.. 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, then consider `min_samples_leaf` as the minimum number.
|
|
- If float, then `min_samples_leaf` is a fraction and
|
|
`ceil(min_samples_leaf * n_samples)` are the minimum
|
|
number of samples for each node.
|
|
|
|
.. 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.
|
|
|
|
max_depth : int, 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.
|
|
|
|
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.
|
|
|
|
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
|
|
|
|
min_impurity_split : float, default=None
|
|
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. The default value of
|
|
``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
|
|
will be removed in 1.0 (renaming of 0.25).
|
|
Use ``min_impurity_decrease`` instead.
|
|
|
|
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='ls'), 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 spliting 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, then consider `max_features` features at each split.
|
|
- If float, then `max_features` is a fraction 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.
|
|
|
|
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'``.
|
|
|
|
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.
|
|
|
|
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.
|
|
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. Must be between 0 and 1.
|
|
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.
|
|
|
|
.. 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.
|
|
|
|
.. 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. 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.
|
|
|
|
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_classes_ : int
|
|
The number of classes, set to 1 for regressors.
|
|
|
|
.. deprecated:: 0.24
|
|
Attribute ``n_classes_`` was deprecated in version 0.24 and
|
|
will be removed in 1.1 (renaming of 0.26).
|
|
|
|
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_ : int
|
|
The number of data features.
|
|
|
|
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.tree.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.
|
|
|
|
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...
|
|
|
|
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')
|
|
|
|
@_deprecate_positional_args
|
|
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, 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,
|
|
min_impurity_split=min_impurity_split,
|
|
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 _warn_mae_for_criterion(self):
|
|
# TODO: This should raise an error from 1.1
|
|
warnings.warn("criterion='mae' was deprecated in version 0.24 and "
|
|
"will be removed in version 1.1 (renaming of 0.26). The "
|
|
"correct way of minimizing the absolute error is to use "
|
|
" loss='lad' instead.", FutureWarning)
|
|
|
|
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 = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')
|
|
# 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``.
|
|
|
|
Returns
|
|
-------
|
|
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
|
|
|
|
# FIXME: to be removed in 1.1
|
|
# mypy error: Decorated property not supported
|
|
@deprecated("Attribute n_classes_ was deprecated " # type: ignore
|
|
"in version 0.24 and will be removed in 1.1 "
|
|
"(renaming of 0.26).")
|
|
@property
|
|
def n_classes_(self):
|
|
try:
|
|
check_is_fitted(self)
|
|
except NotFittedError as nfe:
|
|
raise AttributeError(
|
|
"{} object has no n_classes_ attribute."
|
|
.format(self.__class__.__name__)
|
|
) from nfe
|
|
return 1
|