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gradient_boosting.py
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gradient_boosting.py
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"""Fast Gradient Boosting decision trees for classification and regression."""
# Author: Nicolas Hug
from abc import ABC, abstractmethod
import numpy as np
from timeit import default_timer as time
from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
from sklearn.utils import check_X_y, check_random_state, check_array
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.multiclass import check_classification_targets
from sklearn.metrics import check_scoring
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from ._gradient_boosting import _update_raw_predictions
from .types import Y_DTYPE, X_DTYPE, X_BINNED_DTYPE
from .binning import _BinMapper
from .grower import TreeGrower
from .loss import _LOSSES
class BaseHistGradientBoosting(BaseEstimator, ABC):
"""Base class for histogram-based gradient boosting estimators."""
@abstractmethod
def __init__(self, loss, learning_rate, max_iter, max_leaf_nodes,
max_depth, min_samples_leaf, l2_regularization, max_bins,
scoring, validation_fraction, n_iter_no_change, tol, verbose,
random_state):
self.loss = loss
self.learning_rate = learning_rate
self.max_iter = max_iter
self.max_leaf_nodes = max_leaf_nodes
self.max_depth = max_depth
self.min_samples_leaf = min_samples_leaf
self.l2_regularization = l2_regularization
self.max_bins = max_bins
self.n_iter_no_change = n_iter_no_change
self.validation_fraction = validation_fraction
self.scoring = scoring
self.tol = tol
self.verbose = verbose
self.random_state = random_state
def _validate_parameters(self):
"""Validate parameters passed to __init__.
The parameters that are directly passed to the grower are checked in
TreeGrower."""
if self.loss not in self._VALID_LOSSES:
raise ValueError(
"Loss {} is not supported for {}. Accepted losses: "
"{}.".format(self.loss, self.__class__.__name__,
', '.join(self._VALID_LOSSES)))
if self.learning_rate <= 0:
raise ValueError('learning_rate={} must '
'be strictly positive'.format(self.learning_rate))
if self.max_iter < 1:
raise ValueError('max_iter={} must not be smaller '
'than 1.'.format(self.max_iter))
if self.n_iter_no_change is not None and self.n_iter_no_change < 0:
raise ValueError('n_iter_no_change={} must be '
'positive.'.format(self.n_iter_no_change))
if (self.validation_fraction is not None and
self.validation_fraction <= 0):
raise ValueError(
'validation_fraction={} must be strictly '
'positive, or None.'.format(self.validation_fraction))
if self.tol is not None and self.tol < 0:
raise ValueError('tol={} '
'must not be smaller than 0.'.format(self.tol))
def fit(self, X, y):
"""Fit the gradient boosting model.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
The input samples.
y : array-like, shape=(n_samples,)
Target values.
Returns
-------
self : object
"""
fit_start_time = time()
acc_find_split_time = 0. # time spent finding the best splits
acc_apply_split_time = 0. # time spent splitting nodes
acc_compute_hist_time = 0. # time spent computing histograms
# time spent predicting X for gradient and hessians update
acc_prediction_time = 0.
X, y = check_X_y(X, y, dtype=[X_DTYPE])
y = self._encode_y(y)
rng = check_random_state(self.random_state)
self._validate_parameters()
self.n_features_ = X.shape[1] # used for validation in predict()
# we need this stateful variable to tell raw_predict() that it was
# called from fit() (this current method), and that the data it has
# received is pre-binned.
# predicting is faster on pre-binned data, so we want early stopping
# predictions to be made on pre-binned data. Unfortunately the scorer_
# can only call predict() or predict_proba(), not raw_predict(), and
# there's no way to tell the scorer that it needs to predict binned
# data.
self._in_fit = True
# bin the data
if self.verbose:
print("Binning {:.3f} GB of data: ".format(X.nbytes / 1e9), end="",
flush=True)
tic = time()
self.bin_mapper_ = _BinMapper(max_bins=self.max_bins, random_state=rng)
X_binned = self.bin_mapper_.fit_transform(X)
toc = time()
if self.verbose:
duration = toc - tic
print("{:.3f} s".format(duration))
self.loss_ = self._get_loss()
self.do_early_stopping_ = (self.n_iter_no_change is not None and
self.n_iter_no_change > 0)
# create validation data if needed
self._use_validation_data = self.validation_fraction is not None
if self.do_early_stopping_ and self._use_validation_data:
# stratify for classification
stratify = y if hasattr(self.loss_, 'predict_proba') else None
X_binned_train, X_binned_val, y_train, y_val = train_test_split(
X_binned, y, test_size=self.validation_fraction,
stratify=stratify, random_state=rng)
# Predicting is faster of C-contiguous arrays, training is faster
# on Fortran arrays.
X_binned_val = np.ascontiguousarray(X_binned_val)
X_binned_train = np.asfortranarray(X_binned_train)
else:
X_binned_train, y_train = X_binned, y
X_binned_val, y_val = None, None
if self.verbose:
print("Fitting gradient boosted rounds:")
# initialize raw_predictions: those are the accumulated values
# predicted by the trees for the training data. raw_predictions has
# shape (n_trees_per_iteration, n_samples) where
# n_trees_per_iterations is n_classes in multiclass classification,
# else 1.
n_samples = X_binned_train.shape[0]
self._baseline_prediction = self.loss_.get_baseline_prediction(
y_train, self.n_trees_per_iteration_
)
raw_predictions = np.zeros(
shape=(self.n_trees_per_iteration_, n_samples),
dtype=self._baseline_prediction.dtype
)
raw_predictions += self._baseline_prediction
# initialize gradients and hessians (empty arrays).
# shape = (n_trees_per_iteration, n_samples).
gradients, hessians = self.loss_.init_gradients_and_hessians(
n_samples=n_samples,
prediction_dim=self.n_trees_per_iteration_
)
# predictors is a matrix (list of lists) of TreePredictor objects
# with shape (n_iter_, n_trees_per_iteration)
self._predictors = predictors = []
# Initialize structures and attributes related to early stopping
self.scorer_ = None # set if scoring != loss
raw_predictions_val = None # set if scoring == loss and use val
self.train_score_ = []
self.validation_score_ = []
if self.do_early_stopping_:
# populate train_score and validation_score with the predictions
# of the initial model (before the first tree)
if self.scoring == 'loss':
# we're going to compute scoring w.r.t the loss. As losses
# take raw predictions as input (unlike the scorers), we can
# optimize a bit and avoid repeating computing the predictions
# of the previous trees. We'll re-use raw_predictions (as it's
# needed for training anyway) for evaluating the training
# loss, and create raw_predictions_val for storing the
# raw predictions of the validation data.
if self._use_validation_data:
raw_predictions_val = np.zeros(
shape=(self.n_trees_per_iteration_,
X_binned_val.shape[0]),
dtype=self._baseline_prediction.dtype
)
raw_predictions_val += self._baseline_prediction
self._check_early_stopping_loss(raw_predictions, y_train,
raw_predictions_val, y_val)
else:
self.scorer_ = check_scoring(self, self.scoring)
# scorer_ is a callable with signature (est, X, y) and calls
# est.predict() or est.predict_proba() depending on its nature.
# Unfortunately, each call to scorer_() will compute
# the predictions of all the trees. So we use a subset of the
# training set to compute train scores.
subsample_size = 10000 # should we expose this parameter?
indices = np.arange(X_binned_train.shape[0])
if X_binned_train.shape[0] > subsample_size:
# TODO: not critical but stratify using resample()
indices = rng.choice(indices, subsample_size,
replace=False)
X_binned_small_train = X_binned_train[indices]
y_small_train = y_train[indices]
# Predicting is faster on C-contiguous arrays.
X_binned_small_train = np.ascontiguousarray(
X_binned_small_train)
self._check_early_stopping_scorer(
X_binned_small_train, y_small_train,
X_binned_val, y_val,
)
for iteration in range(self.max_iter):
if self.verbose:
iteration_start_time = time()
print("[{}/{}] ".format(iteration + 1, self.max_iter),
end='', flush=True)
# Update gradients and hessians, inplace
self.loss_.update_gradients_and_hessians(gradients, hessians,
y_train, raw_predictions)
# Append a list since there may be more than 1 predictor per iter
predictors.append([])
# Build `n_trees_per_iteration` trees.
for k in range(self.n_trees_per_iteration_):
grower = TreeGrower(
X_binned_train, gradients[k, :], hessians[k, :],
max_bins=self.max_bins,
actual_n_bins=self.bin_mapper_.actual_n_bins_,
max_leaf_nodes=self.max_leaf_nodes,
max_depth=self.max_depth,
min_samples_leaf=self.min_samples_leaf,
l2_regularization=self.l2_regularization,
shrinkage=self.learning_rate)
grower.grow()
acc_apply_split_time += grower.total_apply_split_time
acc_find_split_time += grower.total_find_split_time
acc_compute_hist_time += grower.total_compute_hist_time
predictor = grower.make_predictor(
bin_thresholds=self.bin_mapper_.bin_thresholds_
)
predictors[-1].append(predictor)
# Update raw_predictions with the predictions of the newly
# created tree.
tic_pred = time()
_update_raw_predictions(raw_predictions[k, :], grower)
toc_pred = time()
acc_prediction_time += toc_pred - tic_pred
should_early_stop = False
if self.do_early_stopping_:
if self.scoring == 'loss':
# Update raw_predictions_val with the newest tree(s)
if self._use_validation_data:
for k, pred in enumerate(self._predictors[-1]):
raw_predictions_val[k, :] += (
pred.predict_binned(X_binned_val))
should_early_stop = self._check_early_stopping_loss(
raw_predictions, y_train,
raw_predictions_val, y_val
)
else:
should_early_stop = self._check_early_stopping_scorer(
X_binned_small_train, y_small_train,
X_binned_val, y_val,
)
if self.verbose:
self._print_iteration_stats(iteration_start_time)
# maybe we could also early stop if all the trees are stumps?
if should_early_stop:
break
if self.verbose:
duration = time() - fit_start_time
n_total_leaves = sum(
predictor.get_n_leaf_nodes()
for predictors_at_ith_iteration in self._predictors
for predictor in predictors_at_ith_iteration
)
n_predictors = sum(
len(predictors_at_ith_iteration)
for predictors_at_ith_iteration in self._predictors)
print("Fit {} trees in {:.3f} s, ({} total leaves)".format(
n_predictors, duration, n_total_leaves))
print("{:<32} {:.3f}s".format('Time spent computing histograms:',
acc_compute_hist_time))
print("{:<32} {:.3f}s".format('Time spent finding best splits:',
acc_find_split_time))
print("{:<32} {:.3f}s".format('Time spent applying splits:',
acc_apply_split_time))
print("{:<32} {:.3f}s".format('Time spent predicting:',
acc_prediction_time))
self.train_score_ = np.asarray(self.train_score_)
self.validation_score_ = np.asarray(self.validation_score_)
del self._in_fit # hard delete so we're sure it can't be used anymore
return self
def _check_early_stopping_scorer(self, X_binned_small_train, y_small_train,
X_binned_val, y_val):
"""Check if fitting should be early-stopped based on scorer.
Scores are computed on validation data or on training data.
"""
self.train_score_.append(
self.scorer_(self, X_binned_small_train, y_small_train)
)
if self._use_validation_data:
self.validation_score_.append(
self.scorer_(self, X_binned_val, y_val)
)
return self._should_stop(self.validation_score_)
else:
return self._should_stop(self.train_score_)
def _check_early_stopping_loss(self,
raw_predictions,
y_train,
raw_predictions_val,
y_val):
"""Check if fitting should be early-stopped based on loss.
Scores are computed on validation data or on training data.
"""
self.train_score_.append(
-self.loss_(y_train, raw_predictions)
)
if self._use_validation_data:
self.validation_score_.append(
-self.loss_(y_val, raw_predictions_val)
)
return self._should_stop(self.validation_score_)
else:
return self._should_stop(self.train_score_)
def _should_stop(self, scores):
"""
Return True (do early stopping) if the last n scores aren't better
than the (n-1)th-to-last score, up to some tolerance.
"""
reference_position = self.n_iter_no_change + 1
if len(scores) < reference_position:
return False
# A higher score is always better. Higher tol means that it will be
# harder for subsequent iteration to be considered an improvement upon
# the reference score, and therefore it is more likely to early stop
# because of the lack of significant improvement.
tol = 0 if self.tol is None else self.tol
reference_score = scores[-reference_position] + tol
recent_scores = scores[-reference_position + 1:]
recent_improvements = [score > reference_score
for score in recent_scores]
return not any(recent_improvements)
def _print_iteration_stats(self, iteration_start_time):
"""Print info about the current fitting iteration."""
log_msg = ''
predictors_of_ith_iteration = [
predictors_list for predictors_list in self._predictors[-1]
if predictors_list
]
n_trees = len(predictors_of_ith_iteration)
max_depth = max(predictor.get_max_depth()
for predictor in predictors_of_ith_iteration)
n_leaves = sum(predictor.get_n_leaf_nodes()
for predictor in predictors_of_ith_iteration)
if n_trees == 1:
log_msg += ("{} tree, {} leaves, ".format(n_trees, n_leaves))
else:
log_msg += ("{} trees, {} leaves ".format(n_trees, n_leaves))
log_msg += ("({} on avg), ".format(int(n_leaves / n_trees)))
log_msg += "max depth = {}, ".format(max_depth)
if self.do_early_stopping_:
if self.scoring == 'loss':
factor = -1 # score_ arrays contain the negative loss
name = 'loss'
else:
factor = 1
name = 'score'
log_msg += "train {}: {:.5f}, ".format(name, factor *
self.train_score_[-1])
if self._use_validation_data:
log_msg += "val {}: {:.5f}, ".format(
name, factor * self.validation_score_[-1])
iteration_time = time() - iteration_start_time
log_msg += "in {:0.3f}s".format(iteration_time)
print(log_msg)
def _raw_predict(self, X):
"""Return the sum of the leaves values over all predictors.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
The input samples.
Returns
-------
raw_predictions : array, shape (n_samples * n_trees_per_iteration,)
The raw predicted values.
"""
X = check_array(X, dtype=[X_DTYPE, X_BINNED_DTYPE])
check_is_fitted(self, '_predictors')
if X.shape[1] != self.n_features_:
raise ValueError(
'X has {} features but this estimator was trained with '
'{} features.'.format(X.shape[1], self.n_features_)
)
is_binned = getattr(self, '_in_fit', False)
n_samples = X.shape[0]
raw_predictions = np.zeros(
shape=(self.n_trees_per_iteration_, n_samples),
dtype=self._baseline_prediction.dtype
)
raw_predictions += self._baseline_prediction
for predictors_of_ith_iteration in self._predictors:
for k, predictor in enumerate(predictors_of_ith_iteration):
predict = (predictor.predict_binned if is_binned
else predictor.predict)
raw_predictions[k, :] += predict(X)
return raw_predictions
@abstractmethod
def _get_loss(self):
pass
@abstractmethod
def _encode_y(self, y=None):
pass
@property
def n_iter_(self):
check_is_fitted(self, '_predictors')
return len(self._predictors)
class HistGradientBoostingRegressor(BaseHistGradientBoosting, RegressorMixin):
"""Histogram-based Gradient Boosting Regression Tree.
This estimator is much faster than
:class:`GradientBoostingRegressor<sklearn.ensemble.GradientBoostingRegressor>`
for big datasets (n_samples >= 10 000). The input data ``X`` is pre-binned
into integer-valued bins, which considerably reduces the number of
splitting points to consider, and allows the algorithm to leverage
integer-based data structures. For small sample sizes,
:class:`GradientBoostingRegressor<sklearn.ensemble.GradientBoostingRegressor>`
might be preferred since binning may lead to split points that are too
approximate in this setting.
This implementation is inspired by
`LightGBM <https://github.com/Microsoft/LightGBM>`_.
.. note::
This estimator is still **experimental** for now: the predictions
and the API might change without any deprecation cycle. To use it,
you need to explicitly import ``enable_hist_gradient_boosting``::
>>> # explicitly require this experimental feature
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
>>> # now you can import normally from ensemble
>>> from sklearn.ensemble import HistGradientBoostingClassifier
Parameters
----------
loss : {'least_squares'}, optional (default='least_squares')
The loss function to use in the boosting process. Note that the
"least squares" loss actually implements an "half least squares loss"
to simplify the computation of the gradient.
learning_rate : float, optional (default=0.1)
The learning rate, also known as *shrinkage*. This is used as a
multiplicative factor for the leaves values. Use ``1`` for no
shrinkage.
max_iter : int, optional (default=100)
The maximum number of iterations of the boosting process, i.e. the
maximum number of trees.
max_leaf_nodes : int or None, optional (default=31)
The maximum number of leaves for each tree. Must be strictly greater
than 1. If None, there is no maximum limit.
max_depth : int or None, optional (default=None)
The maximum depth of each tree. The depth of a tree is the number of
nodes to go from the root to the deepest leaf. Must be strictly greater
than 1. Depth isn't constrained by default.
min_samples_leaf : int, optional (default=20)
The minimum number of samples per leaf. For small datasets with less
than a few hundred samples, it is recommended to lower this value
since only very shallow trees would be built.
l2_regularization : float, optional (default=0)
The L2 regularization parameter. Use ``0`` for no regularization
(default).
max_bins : int, optional (default=256)
The maximum number of bins to use. Before training, each feature of
the input array ``X`` is binned into at most ``max_bins`` bins, which
allows for a much faster training stage. Features with a small
number of unique values may use less than ``max_bins`` bins. Must be no
larger than 256.
scoring : str or callable or None, optional (default=None)
Scoring parameter to use for early stopping. It can be a single
string (see :ref:`scoring_parameter`) or a callable (see
:ref:`scoring`). If None, the estimator's default scorer is used. If
``scoring='loss'``, early stopping is checked w.r.t the loss value.
Only used if ``n_iter_no_change`` is not None.
validation_fraction : int or float or None, optional (default=0.1)
Proportion (or absolute size) of training data to set aside as
validation data for early stopping. If None, early stopping is done on
the training data. Only used if ``n_iter_no_change`` is not None.
n_iter_no_change : int or None, optional (default=None)
Used to determine when to "early stop". The fitting process is
stopped when none of the last ``n_iter_no_change`` scores are better
than the ``n_iter_no_change - 1``th-to-last one, up to some
tolerance. If None or 0, no early-stopping is done.
tol : float or None, optional (default=1e-7)
The absolute tolerance to use when comparing scores during early
stopping. The higher the tolerance, the more likely we are to early
stop: higher tolerance means that it will be harder for subsequent
iterations to be considered an improvement upon the reference score.
verbose: int, optional (default=0)
The verbosity level. If not zero, print some information about the
fitting process.
random_state : int, np.random.RandomStateInstance or None, \
optional (default=None)
Pseudo-random number generator to control the subsampling in the
binning process, and the train/validation data split if early stopping
is enabled. See :term:`random_state`.
Attributes
----------
n_iter_ : int
The number of iterations as selected by early stopping (if
n_iter_no_change is not None). Otherwise it corresponds to max_iter.
n_trees_per_iteration_ : int
The number of tree that are built at each iteration. For regressors,
this is always 1.
train_score_ : ndarray, shape (max_iter + 1,)
The scores at each iteration on the training data. The first entry
is the score of the ensemble before the first iteration. Scores are
computed according to the ``scoring`` parameter. If ``scoring`` is
not 'loss', scores are computed on a subset of at most 10 000
samples. Empty if no early stopping.
validation_score_ : ndarray, shape (max_iter + 1,)
The scores at each iteration on the held-out validation data. The
first entry is the score of the ensemble before the first iteration.
Scores are computed according to the ``scoring`` parameter. Empty if
no early stopping or if ``validation_fraction`` is None.
Examples
--------
>>> # To use this experimental feature, we need to explicitly ask for it:
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
>>> from sklearn.ensemble import HistGradientBoostingRegressor
>>> from sklearn.datasets import load_boston
>>> X, y = load_boston(return_X_y=True)
>>> est = HistGradientBoostingRegressor().fit(X, y)
>>> est.score(X, y)
0.98...
"""
_VALID_LOSSES = ('least_squares',)
def __init__(self, loss='least_squares', learning_rate=0.1,
max_iter=100, max_leaf_nodes=31, max_depth=None,
min_samples_leaf=20, l2_regularization=0., max_bins=256,
scoring=None, validation_fraction=0.1, n_iter_no_change=None,
tol=1e-7, verbose=0, random_state=None):
super(HistGradientBoostingRegressor, self).__init__(
loss=loss, learning_rate=learning_rate, max_iter=max_iter,
max_leaf_nodes=max_leaf_nodes, max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
l2_regularization=l2_regularization, max_bins=max_bins,
scoring=scoring, validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, tol=tol, verbose=verbose,
random_state=random_state)
def predict(self, X):
"""Predict values for X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input samples.
Returns
-------
y : ndarray, shape (n_samples,)
The predicted values.
"""
# Return raw predictions after converting shape
# (n_samples, 1) to (n_samples,)
return self._raw_predict(X).ravel()
def _encode_y(self, y):
# Just convert y to the expected dtype
self.n_trees_per_iteration_ = 1
y = y.astype(Y_DTYPE, copy=False)
return y
def _get_loss(self):
return _LOSSES[self.loss]()
class HistGradientBoostingClassifier(BaseHistGradientBoosting,
ClassifierMixin):
"""Histogram-based Gradient Boosting Classification Tree.
This estimator is much faster than
:class:`GradientBoostingClassifier<sklearn.ensemble.GradientBoostingClassifier>`
for big datasets (n_samples >= 10 000). The input data ``X`` is pre-binned
into integer-valued bins, which considerably reduces the number of
splitting points to consider, and allows the algorithm to leverage
integer-based data structures. For small sample sizes,
:class:`GradientBoostingClassifier<sklearn.ensemble.GradientBoostingClassifier>`
might be preferred since binning may lead to split points that are too
approximate in this setting.
This implementation is inspired by
`LightGBM <https://github.com/Microsoft/LightGBM>`_.
.. note::
This estimator is still **experimental** for now: the predictions
and the API might change without any deprecation cycle. To use it,
you need to explicitly import ``enable_hist_gradient_boosting``::
>>> # explicitly require this experimental feature
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
>>> # now you can import normally from ensemble
>>> from sklearn.ensemble import HistGradientBoostingClassifier
Parameters
----------
loss : {'auto', 'binary_crossentropy', 'categorical_crossentropy'}, \
optional (default='auto')
The loss function to use in the boosting process. 'binary_crossentropy'
(also known as logistic loss) is used for binary classification and
generalizes to 'categorical_crossentropy' for multiclass
classification. 'auto' will automatically choose either loss depending
on the nature of the problem.
learning_rate : float, optional (default=1)
The learning rate, also known as *shrinkage*. This is used as a
multiplicative factor for the leaves values. Use ``1`` for no
shrinkage.
max_iter : int, optional (default=100)
The maximum number of iterations of the boosting process, i.e. the
maximum number of trees for binary classification. For multiclass
classification, `n_classes` trees per iteration are built.
max_leaf_nodes : int or None, optional (default=31)
The maximum number of leaves for each tree. Must be strictly greater
than 1. If None, there is no maximum limit.
max_depth : int or None, optional (default=None)
The maximum depth of each tree. The depth of a tree is the number of
nodes to go from the root to the deepest leaf. Must be strictly greater
than 1. Depth isn't constrained by default.
min_samples_leaf : int, optional (default=20)
The minimum number of samples per leaf. For small datasets with less
than a few hundred samples, it is recommended to lower this value
since only very shallow trees would be built.
l2_regularization : float, optional (default=0)
The L2 regularization parameter. Use 0 for no regularization.
max_bins : int, optional (default=256)
The maximum number of bins to use. Before training, each feature of
the input array ``X`` is binned into at most ``max_bins`` bins, which
allows for a much faster training stage. Features with a small
number of unique values may use less than ``max_bins`` bins. Must be no
larger than 256.
scoring : str or callable or None, optional (default=None)
Scoring parameter to use for early stopping. It can be a single
string (see :ref:`scoring_parameter`) or a callable (see
:ref:`scoring`). If None, the estimator's default scorer
is used. If ``scoring='loss'``, early stopping is checked
w.r.t the loss value. Only used if ``n_iter_no_change`` is not None.
validation_fraction : int or float or None, optional (default=0.1)
Proportion (or absolute size) of training data to set aside as
validation data for early stopping. If None, early stopping is done on
the training data.
n_iter_no_change : int or None, optional (default=None)
Used to determine when to "early stop". The fitting process is
stopped when none of the last ``n_iter_no_change`` scores are better
than the ``n_iter_no_change - 1``th-to-last one, up to some
tolerance. If None or 0, no early-stopping is done.
tol : float or None, optional (default=1e-7)
The absolute tolerance to use when comparing scores. The higher the
tolerance, the more likely we are to early stop: higher tolerance
means that it will be harder for subsequent iterations to be
considered an improvement upon the reference score.
verbose: int, optional (default=0)
The verbosity level. If not zero, print some information about the
fitting process.
random_state : int, np.random.RandomStateInstance or None, \
optional (default=None)
Pseudo-random number generator to control the subsampling in the
binning process, and the train/validation data split if early stopping
is enabled. See :term:`random_state`.
Attributes
----------
n_iter_ : int
The number of estimators as selected by early stopping (if
n_iter_no_change is not None). Otherwise it corresponds to max_iter.
n_trees_per_iteration_ : int
The number of tree that are built at each iteration. This is equal to 1
for binary classification, and to ``n_classes`` for multiclass
classification.
train_score_ : ndarray, shape (max_iter + 1,)
The scores at each iteration on the training data. The first entry
is the score of the ensemble before the first iteration. Scores are
computed according to the ``scoring`` parameter. If ``scoring`` is
not 'loss', scores are computed on a subset of at most 10 000
samples. Empty if no early stopping.
validation_score_ : ndarray, shape (max_iter + 1,)
The scores at each iteration on the held-out validation data. The
first entry is the score of the ensemble before the first iteration.
Scores are computed according to the ``scoring`` parameter. Empty if
no early stopping or if ``validation_fraction`` is None.
Examples
--------
>>> # To use this experimental feature, we need to explicitly ask for it:
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
>>> from sklearn.ensemble import HistGradientBoostingRegressor
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> clf = HistGradientBoostingClassifier().fit(X, y)
>>> clf.score(X, y)
1.0
"""
_VALID_LOSSES = ('binary_crossentropy', 'categorical_crossentropy',
'auto')
def __init__(self, loss='auto', learning_rate=0.1, max_iter=100,
max_leaf_nodes=31, max_depth=None, min_samples_leaf=20,
l2_regularization=0., max_bins=256, scoring=None,
validation_fraction=0.1, n_iter_no_change=None, tol=1e-7,
verbose=0, random_state=None):
super(HistGradientBoostingClassifier, self).__init__(
loss=loss, learning_rate=learning_rate, max_iter=max_iter,
max_leaf_nodes=max_leaf_nodes, max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
l2_regularization=l2_regularization, max_bins=max_bins,
scoring=scoring, validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, tol=tol, verbose=verbose,
random_state=random_state)
def predict(self, X):
"""Predict classes for X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input samples.
Returns
-------
y : ndarray, shape (n_samples,)
The predicted classes.
"""
# TODO: This could be done in parallel
encoded_classes = np.argmax(self.predict_proba(X), axis=1)
return self.classes_[encoded_classes]
def predict_proba(self, X):
"""Predict class probabilities for X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input samples.
Returns
-------
p : ndarray, shape (n_samples, n_classes)
The class probabilities of the input samples.
"""
raw_predictions = self._raw_predict(X)
return self.loss_.predict_proba(raw_predictions)
def decision_function(self, X):
"""Compute the decision function of X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input samples.
Returns
-------
decision : ndarray, shape (n_samples,) or \
(n_samples, n_trees_per_iteration)
The raw predicted values (i.e. the sum of the trees leaves) for
each sample. n_trees_per_iteration is equal to the number of
classes in multiclass classification.
"""
decision = self._raw_predict(X)
if decision.shape[0] == 1:
decision = decision.ravel()
return decision.T
def _encode_y(self, y):
# 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 = 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.astype(Y_DTYPE, copy=False)
return encoded_y
def _get_loss(self):
if self.loss == 'auto':
if self.n_trees_per_iteration_ == 1:
return _LOSSES['binary_crossentropy']()
else:
return _LOSSES['categorical_crossentropy']()
return _LOSSES[self.loss]()