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3RNN/Lib/site-packages/sklearn/ensemble/_gb.py

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2024-05-26 19:49:15 +02:00
"""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
import math
import warnings
from abc import ABCMeta, abstractmethod
from numbers import Integral, Real
from time import time
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix, issparse
from .._loss.loss import (
_LOSSES,
AbsoluteError,
ExponentialLoss,
HalfBinomialLoss,
HalfMultinomialLoss,
HalfSquaredError,
HuberLoss,
PinballLoss,
)
from ..base import ClassifierMixin, RegressorMixin, _fit_context, is_classifier
from ..dummy import DummyClassifier, DummyRegressor
from ..exceptions import NotFittedError
from ..model_selection import train_test_split
from ..preprocessing import LabelEncoder
from ..tree import DecisionTreeRegressor
from ..tree._tree import DOUBLE, DTYPE, TREE_LEAF
from ..utils import check_array, check_random_state, column_or_1d
from ..utils._param_validation import HasMethods, Interval, StrOptions
from ..utils.multiclass import check_classification_targets
from ..utils.stats import _weighted_percentile
from ..utils.validation import _check_sample_weight, check_is_fitted
from ._base import BaseEnsemble
from ._gradient_boosting import _random_sample_mask, predict_stage, predict_stages
_LOSSES = _LOSSES.copy()
_LOSSES.update(
{
"quantile": PinballLoss,
"huber": HuberLoss,
}
)
def _safe_divide(numerator, denominator):
"""Prevents overflow and division by zero."""
# This is used for classifiers where the denominator might become zero exatly.
# For instance for log loss, HalfBinomialLoss, if proba=0 or proba=1 exactly, then
# denominator = hessian = 0, and we should set the node value in the line search to
# zero as there is no improvement of the loss possible.
# For numerical safety, we do this already for extremely tiny values.
if abs(denominator) < 1e-150:
return 0.0
else:
# Cast to Python float to trigger Python errors, e.g. ZeroDivisionError,
# without relying on `np.errstate` that is not supported by Pyodide.
result = float(numerator) / float(denominator)
# Cast to Python float to trigger a ZeroDivisionError without relying
# on `np.errstate` that is not supported by Pyodide.
result = float(numerator) / float(denominator)
if math.isinf(result):
warnings.warn("overflow encountered in _safe_divide", RuntimeWarning)
return result
def _init_raw_predictions(X, estimator, loss, use_predict_proba):
"""Return the initial raw predictions.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
The data array.
estimator : object
The estimator to use to compute the predictions.
loss : BaseLoss
An instance of a loss function class.
use_predict_proba : bool
Whether estimator.predict_proba is used instead of estimator.predict.
Returns
-------
raw_predictions : ndarray of shape (n_samples, K)
The initial raw predictions. K is equal to 1 for binary
classification and regression, and equal to the number of classes
for multiclass classification. ``raw_predictions`` is casted
into float64.
"""
# TODO: Use loss.fit_intercept_only where appropriate instead of
# DummyRegressor which is the default given by the `init` parameter,
# see also _init_state.
if use_predict_proba:
# Our parameter validation, set via _fit_context and _parameter_constraints
# already guarantees that estimator has a predict_proba method.
predictions = estimator.predict_proba(X)
if not loss.is_multiclass:
predictions = predictions[:, 1] # probability of positive class
eps = np.finfo(np.float32).eps # FIXME: This is quite large!
predictions = np.clip(predictions, eps, 1 - eps, dtype=np.float64)
else:
predictions = estimator.predict(X).astype(np.float64)
if predictions.ndim == 1:
return loss.link.link(predictions).reshape(-1, 1)
else:
return loss.link.link(predictions)
def _update_terminal_regions(
loss,
tree,
X,
y,
neg_gradient,
raw_prediction,
sample_weight,
sample_mask,
learning_rate=0.1,
k=0,
):
"""Update the leaf values to be predicted by the tree and raw_prediction.
The current raw predictions of the model (of this stage) are updated.
Additionally, the terminal regions (=leaves) of the given tree are updated as well.
This corresponds to the line search step in "Greedy Function Approximation" by
Friedman, Algorithm 1 step 5.
Update equals:
argmin_{x} loss(y_true, raw_prediction_old + x * tree.value)
For non-trivial cases like the Binomial loss, the update has no closed formula and
is an approximation, again, see the Friedman paper.
Also note that the update formula for the SquaredError is the identity. Therefore,
in this case, the leaf values don't need an update and only the raw_predictions are
updated (with the learning rate included).
Parameters
----------
loss : BaseLoss
tree : tree.Tree
The tree object.
X : ndarray of shape (n_samples, n_features)
The data array.
y : ndarray of shape (n_samples,)
The target labels.
neg_gradient : ndarray of shape (n_samples,)
The negative gradient.
raw_prediction : ndarray of shape (n_samples, n_trees_per_iteration)
The raw predictions (i.e. values from the tree leaves) of the
tree ensemble at iteration ``i - 1``.
sample_weight : ndarray of shape (n_samples,)
The weight of each sample.
sample_mask : ndarray of shape (n_samples,)
The sample mask to be used.
learning_rate : float, default=0.1
Learning rate shrinks the contribution of each tree by
``learning_rate``.
k : int, default=0
The index of the estimator being updated.
"""
# compute leaf for each sample in ``X``.
terminal_regions = tree.apply(X)
if not isinstance(loss, HalfSquaredError):
# mask all which are not in sample mask.
masked_terminal_regions = terminal_regions.copy()
masked_terminal_regions[~sample_mask] = -1
if isinstance(loss, HalfBinomialLoss):
def compute_update(y_, indices, neg_gradient, raw_prediction, k):
# Make a single Newton-Raphson step, see "Additive Logistic Regression:
# A Statistical View of Boosting" FHT00 and note that we use a slightly
# different version (factor 2) of "F" with proba=expit(raw_prediction).
# Our node estimate is given by:
# sum(w * (y - prob)) / sum(w * prob * (1 - prob))
# we take advantage that: y - prob = neg_gradient
neg_g = neg_gradient.take(indices, axis=0)
prob = y_ - neg_g
# numerator = negative gradient = y - prob
numerator = np.average(neg_g, weights=sw)
# denominator = hessian = prob * (1 - prob)
denominator = np.average(prob * (1 - prob), weights=sw)
return _safe_divide(numerator, denominator)
elif isinstance(loss, HalfMultinomialLoss):
def compute_update(y_, indices, neg_gradient, raw_prediction, k):
# we take advantage that: y - prob = neg_gradient
neg_g = neg_gradient.take(indices, axis=0)
prob = y_ - neg_g
K = loss.n_classes
# numerator = negative gradient * (k - 1) / k
# Note: The factor (k - 1)/k appears in the original papers "Greedy
# Function Approximation" by Friedman and "Additive Logistic
# Regression" by Friedman, Hastie, Tibshirani. This factor is, however,
# wrong or at least arbitrary as it directly multiplies the
# learning_rate. We keep it for backward compatibility.
numerator = np.average(neg_g, weights=sw)
numerator *= (K - 1) / K
# denominator = (diagonal) hessian = prob * (1 - prob)
denominator = np.average(prob * (1 - prob), weights=sw)
return _safe_divide(numerator, denominator)
elif isinstance(loss, ExponentialLoss):
def compute_update(y_, indices, neg_gradient, raw_prediction, k):
neg_g = neg_gradient.take(indices, axis=0)
# numerator = negative gradient = y * exp(-raw) - (1-y) * exp(raw)
numerator = np.average(neg_g, weights=sw)
# denominator = hessian = y * exp(-raw) + (1-y) * exp(raw)
# if y=0: hessian = exp(raw) = -neg_g
# y=1: hessian = exp(-raw) = neg_g
hessian = neg_g.copy()
hessian[y_ == 0] *= -1
denominator = np.average(hessian, weights=sw)
return _safe_divide(numerator, denominator)
else:
def compute_update(y_, indices, neg_gradient, raw_prediction, k):
return loss.fit_intercept_only(
y_true=y_ - raw_prediction[indices, k],
sample_weight=sw,
)
# update each leaf (= perform line search)
for leaf in np.nonzero(tree.children_left == TREE_LEAF)[0]:
indices = np.nonzero(masked_terminal_regions == leaf)[
0
] # of terminal regions
y_ = y.take(indices, axis=0)
sw = None if sample_weight is None else sample_weight[indices]
update = compute_update(y_, indices, neg_gradient, raw_prediction, k)
# TODO: Multiply here by learning rate instead of everywhere else.
tree.value[leaf, 0, 0] = update
# update predictions (both in-bag and out-of-bag)
raw_prediction[:, k] += learning_rate * tree.value[:, 0, 0].take(
terminal_regions, axis=0
)
def set_huber_delta(loss, y_true, raw_prediction, sample_weight=None):
"""Calculate and set self.closs.delta based on self.quantile."""
abserr = np.abs(y_true - raw_prediction.squeeze())
# sample_weight is always a ndarray, never None.
delta = _weighted_percentile(abserr, sample_weight, 100 * loss.quantile)
loss.closs.delta = float(delta)
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."""
_parameter_constraints: dict = {
**DecisionTreeRegressor._parameter_constraints,
"learning_rate": [Interval(Real, 0.0, None, closed="left")],
"n_estimators": [Interval(Integral, 1, None, closed="left")],
"criterion": [StrOptions({"friedman_mse", "squared_error"})],
"subsample": [Interval(Real, 0.0, 1.0, closed="right")],
"verbose": ["verbose"],
"warm_start": ["boolean"],
"validation_fraction": [Interval(Real, 0.0, 1.0, closed="neither")],
"n_iter_no_change": [Interval(Integral, 1, None, closed="left"), None],
"tol": [Interval(Real, 0.0, None, closed="left")],
}
_parameter_constraints.pop("splitter")
_parameter_constraints.pop("monotonic_cst")
@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,
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.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 _encode_y(self, y=None, sample_weight=None):
"""Called by fit to validate and encode y."""
@abstractmethod
def _get_loss(self, sample_weight):
"""Get loss object from sklearn._loss.loss."""
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_trees_per_iteration_`` trees."""
original_y = y
if isinstance(self._loss, HuberLoss):
set_huber_delta(
loss=self._loss,
y_true=y,
raw_prediction=raw_predictions,
sample_weight=sample_weight,
)
# TODO: Without oob, i.e. with self.subsample = 1.0, we could call
# self._loss.loss_gradient and use it to set train_score_.
# But note that train_score_[i] is the score AFTER fitting the i-th tree.
# Note: We need the negative gradient!
neg_gradient = -self._loss.gradient(
y_true=y,
raw_prediction=raw_predictions,
sample_weight=None, # We pass sample_weights to the tree directly.
)
# 2-d views of shape (n_samples, n_trees_per_iteration_) or (n_samples, 1)
# on neg_gradient to simplify the loop over n_trees_per_iteration_.
if neg_gradient.ndim == 1:
neg_g_view = neg_gradient.reshape((-1, 1))
else:
neg_g_view = neg_gradient
for k in range(self.n_trees_per_iteration_):
if self._loss.is_multiclass:
y = np.array(original_y == k, dtype=np.float64)
# induce regression tree on the negative gradient
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,
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_csc if X_csc is not None else X
tree.fit(
X, neg_g_view[:, k], sample_weight=sample_weight, check_input=False
)
# update tree leaves
X_for_tree_update = X_csr if X_csr is not None else X
_update_terminal_regions(
self._loss,
tree.tree_,
X_for_tree_update,
y,
neg_g_view[:, k],
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 _set_max_features(self):
"""Set self.max_features_."""
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_in_)))
else:
max_features = self.n_features_in_
elif self.max_features == "sqrt":
max_features = max(1, int(np.sqrt(self.n_features_in_)))
else: # self.max_features == "log2"
max_features = max(1, int(np.log2(self.n_features_in_)))
elif self.max_features is None:
max_features = self.n_features_in_
elif isinstance(self.max_features, Integral):
max_features = self.max_features
else: # float
max_features = max(1, int(self.max_features * self.n_features_in_))
self.max_features_ = max_features
def _init_state(self):
"""Initialize model state and allocate model state data structures."""
self.init_ = self.init
if self.init_ is None:
if is_classifier(self):
self.init_ = DummyClassifier(strategy="prior")
elif isinstance(self._loss, (AbsoluteError, HuberLoss)):
self.init_ = DummyRegressor(strategy="quantile", quantile=0.5)
elif isinstance(self._loss, PinballLoss):
self.init_ = DummyRegressor(strategy="quantile", quantile=self.alpha)
else:
self.init_ = DummyRegressor(strategy="mean")
self.estimators_ = np.empty(
(self.n_estimators, self.n_trees_per_iteration_), 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)
self.oob_scores_ = np.zeros((self.n_estimators), dtype=np.float64)
self.oob_score_ = np.nan
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, "oob_scores_"):
del self.oob_scores_
if hasattr(self, "oob_score_"):
del self.oob_score_
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.n_trees_per_iteration_)
)
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
)
self.oob_scores_ = np.resize(self.oob_scores_, total_n_estimators)
self.oob_score_ = np.nan
else:
self.oob_improvement_ = np.zeros(
(total_n_estimators,), dtype=np.float64
)
self.oob_scores_ = np.zeros((total_n_estimators,), dtype=np.float64)
self.oob_score_ = np.nan
def _is_fitted(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)
@_fit_context(
# GradientBoosting*.init is not validated yet
prefer_skip_nested_validation=False
)
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
snapshotting.
Returns
-------
self : object
Fitted estimator.
"""
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
)
sample_weight_is_none = sample_weight is None
sample_weight = _check_sample_weight(sample_weight, X)
if sample_weight_is_none:
y = self._encode_y(y=y, sample_weight=None)
else:
y = self._encode_y(y=y, sample_weight=sample_weight)
y = column_or_1d(y, warn=True) # TODO: Is this still required?
self._set_max_features()
# self.loss is guaranteed to be a string
self._loss = self._get_loss(sample_weight=sample_weight)
if self.n_iter_no_change is not None:
stratify = y if is_classifier(self) else None
(
X_train,
X_val,
y_train,
y_val,
sample_weight_train,
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_train).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_train, y_train, sample_weight_train = X, y, sample_weight
X_val = y_val = sample_weight_val = None
n_samples = X_train.shape[0]
# First time calling fit.
if not self._is_fitted():
# init state
self._init_state()
# fit initial model and initialize raw predictions
if self.init_ == "zero":
raw_predictions = np.zeros(
shape=(n_samples, self.n_trees_per_iteration_),
dtype=np.float64,
)
else:
# XXX clean this once we have a support_sample_weight tag
if sample_weight_is_none:
self.init_.fit(X_train, y_train)
else:
msg = (
"The initial estimator {} does not support sample "
"weights.".format(self.init_.__class__.__name__)
)
try:
self.init_.fit(
X_train, y_train, sample_weight=sample_weight_train
)
except TypeError as e:
if "unexpected keyword argument 'sample_weight'" in str(e):
# regular estimator without SW support
raise ValueError(msg) from e
else: # regular estimator whose input checking failed
raise
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 = _init_raw_predictions(
X_train, self.init_, self._loss, is_classifier(self)
)
begin_at_stage = 0
# The rng state must be preserved if warm_start is True
self._rng = check_random_state(self.random_state)
# warm start: this is not the first time fit was called
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_train = check_array(
X_train,
dtype=DTYPE,
order="C",
accept_sparse="csr",
force_all_finite=False,
)
raw_predictions = self._raw_predict(X_train)
self._resize_state()
# fit the boosting stages
n_stages = self._fit_stages(
X_train,
y_train,
raw_predictions,
sample_weight_train,
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_"):
# OOB scores were computed
self.oob_improvement_ = self.oob_improvement_[:n_stages]
self.oob_scores_ = self.oob_scores_[:n_stages]
self.oob_score_ = self.oob_scores_[-1]
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))
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)
# Older versions of GBT had its own loss functions. With the new common
# private loss function submodule _loss, we often are a factor of 2
# away from the old version. Here we keep backward compatibility for
# oob_scores_ and oob_improvement_, even if the old way is quite
# inconsistent (sometimes the gradient is half the gradient, sometimes
# not).
if isinstance(
self._loss,
(
HalfSquaredError,
HalfBinomialLoss,
),
):
factor = 2
else:
factor = 1
# 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)
y_oob_masked = y[~sample_mask]
sample_weight_oob_masked = sample_weight[~sample_mask]
if i == 0: # store the initial loss to compute the OOB score
initial_loss = factor * self._loss(
y_true=y_oob_masked,
raw_prediction=raw_predictions[~sample_mask],
sample_weight=sample_weight_oob_masked,
)
# fit next stage of trees
raw_predictions = self._fit_stage(
i,
X,
y,
raw_predictions,
sample_weight,
sample_mask,
random_state,
X_csc=X_csc,
X_csr=X_csr,
)
# track loss
if do_oob:
self.train_score_[i] = factor * self._loss(
y_true=y[sample_mask],
raw_prediction=raw_predictions[sample_mask],
sample_weight=sample_weight[sample_mask],
)
self.oob_scores_[i] = factor * self._loss(
y_true=y_oob_masked,
raw_prediction=raw_predictions[~sample_mask],
sample_weight=sample_weight_oob_masked,
)
previous_loss = initial_loss if i == 0 else self.oob_scores_[i - 1]
self.oob_improvement_[i] = previous_loss - self.oob_scores_[i]
self.oob_score_ = self.oob_scores_[-1]
else:
# no need to fancy index w/ no subsampling
self.train_score_[i] = factor * self._loss(
y_true=y,
raw_prediction=raw_predictions,
sample_weight=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 = factor * self._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.n_trees_per_iteration_), dtype=np.float64
)
else:
raw_predictions = _init_raw_predictions(
X, self.init_, self._loss, is_classifier(self)
)
return raw_predictions
def _raw_predict(self, X):
"""Return the sum of the trees raw predictions (+ init estimator)."""
check_is_fitted(self)
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), dtype=np.float32
The grid points on which the partial dependence should be
evaluated.
target_features : ndarray of shape (n_target_features,), dtype=np.intp
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"
)
target_features = np.asarray(target_features, dtype=np.intp, 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.
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', '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.
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_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 : {'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 '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)`.
See
:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.
.. 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
n_trees_per_iteration_ : int
The number of trees that are built at each iteration. For binary classifiers,
this is always 1.
.. versionadded:: 1.4.0
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 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``.
oob_scores_ : ndarray of shape (n_estimators,)
The full history of the loss values on the out-of-bag
samples. Only available if `subsample < 1.0`.
.. versionadded:: 1.3
oob_score_ : float
The last value of the loss on the out-of-bag samples. It is
the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`.
.. versionadded:: 1.3
train_score_ : ndarray of shape (n_estimators,)
The i-th score ``train_score_[i]`` is the loss of the
model at iteration ``i`` on the in-bag sample.
If ``subsample == 1`` this is the loss on the training data.
init_ : estimator
The estimator that provides the initial predictions. Set via the ``init``
argument.
estimators_ : ndarray of DecisionTreeRegressor of \
shape (n_estimators, ``n_trees_per_iteration_``)
The collection of fitted sub-estimators. ``n_trees_per_iteration_`` 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...
"""
_parameter_constraints: dict = {
**BaseGradientBoosting._parameter_constraints,
"loss": [StrOptions({"log_loss", "exponential"})],
"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 _encode_y(self, y, sample_weight):
# encode classes into 0 ... n_classes - 1 and sets attributes classes_
# and n_trees_per_iteration_
check_classification_targets(y)
label_encoder = LabelEncoder()
encoded_y_int = label_encoder.fit_transform(y)
self.classes_ = label_encoder.classes_
n_classes = self.classes_.shape[0]
# only 1 tree for binary classification. For multiclass classification,
# we build 1 tree per class.
self.n_trees_per_iteration_ = 1 if n_classes <= 2 else n_classes
encoded_y = encoded_y_int.astype(float, copy=False)
# From here on, it is additional to the HGBT case.
# expose n_classes_ attribute
self.n_classes_ = n_classes
if sample_weight is None:
n_trim_classes = n_classes
else:
n_trim_classes = np.count_nonzero(np.bincount(encoded_y_int, 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
)
return encoded_y
def _get_loss(self, sample_weight):
if self.loss == "log_loss":
if self.n_classes_ == 2:
return HalfBinomialLoss(sample_weight=sample_weight)
else:
return HalfMultinomialLoss(
sample_weight=sample_weight, n_classes=self.n_classes_
)
elif self.loss == "exponential":
if self.n_classes_ > 2:
raise ValueError(
f"loss='{self.loss}' is only suitable for a binary classification "
f"problem, you have n_classes={self.n_classes_}. "
"Please use loss='log_loss' instead."
)
else:
return ExponentialLoss(sample_weight=sample_weight)
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)
if raw_predictions.ndim == 1: # decision_function already squeezed it
encoded_classes = (raw_predictions >= 0).astype(int)
else:
encoded_classes = np.argmax(raw_predictions, axis=1)
return self.classes_[encoded_classes]
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.
"""
if self.n_classes_ == 2: # n_trees_per_iteration_ = 1
for raw_predictions in self._staged_raw_predict(X):
encoded_classes = (raw_predictions.squeeze() >= 0).astype(int)
yield self.classes_.take(encoded_classes, axis=0)
else:
for raw_predictions in self._staged_raw_predict(X):
encoded_classes = np.argmax(raw_predictions, axis=1)
yield self.classes_.take(encoded_classes, 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)
return self._loss.predict_proba(raw_predictions)
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.predict_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 : {'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 "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)`.
See
:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.
.. 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``.
n_trees_per_iteration_ : int
The number of trees that are built at each iteration. For regressors, this is
always 1.
.. versionadded:: 1.4.0
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 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``.
oob_scores_ : ndarray of shape (n_estimators,)
The full history of the loss values on the out-of-bag
samples. Only available if `subsample < 1.0`.
.. versionadded:: 1.3
oob_score_ : float
The last value of the loss on the out-of-bag samples. It is
the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`.
.. versionadded:: 1.3
train_score_ : ndarray of shape (n_estimators,)
The i-th score ``train_score_[i]`` is the loss of the
model at iteration ``i`` on the in-bag sample.
If ``subsample == 1`` this is the loss on the training data.
init_ : estimator
The estimator that provides the initial predictions. Set via the ``init``
argument.
estimators_ : ndarray of DecisionTreeRegressor of shape (n_estimators, 1)
The collection of fitted sub-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 _encode_y(self, y=None, sample_weight=None):
# Just convert y to the expected dtype
self.n_trees_per_iteration_ = 1
y = y.astype(DOUBLE, copy=False)
return y
def _get_loss(self, sample_weight):
if self.loss in ("quantile", "huber"):
return _LOSSES[self.loss](sample_weight=sample_weight, quantile=self.alpha)
else:
return _LOSSES[self.loss](sample_weight=sample_weight)
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
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