"""
Forest of trees-based ensemble methods.
Those methods include random forests and extremely randomized trees.
The module structure is the following:
- The ``BaseForest`` base class implements a common ``fit`` method for all
the estimators in the module. The ``fit`` method of the base ``Forest``
class calls the ``fit`` method of each sub-estimator on random samples
(with replacement, a.k.a. bootstrap) of the training set.
The init of the sub-estimator is further delegated to the
``BaseEnsemble`` constructor.
- The ``ForestClassifier`` and ``ForestRegressor`` base classes further
implement the prediction logic by computing an average of the predicted
outcomes of the sub-estimators.
- The ``RandomForestClassifier`` and ``RandomForestRegressor`` derived
classes provide the user with concrete implementations of
the forest ensemble method using classical, deterministic
``DecisionTreeClassifier`` and ``DecisionTreeRegressor`` as
sub-estimator implementations.
- The ``ExtraTreesClassifier`` and ``ExtraTreesRegressor`` derived
classes provide the user with concrete implementations of the
forest ensemble method using the extremely randomized trees
``ExtraTreeClassifier`` and ``ExtraTreeRegressor`` as
sub-estimator implementations.
Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Joly Arnaud <arnaud.v.joly@gmail.com>
# Fares Hedayati <fares.hedayati@gmail.com>
#
# License: BSD 3 clause
import threading
from abc import ABCMeta, abstractmethod
from numbers import Integral, Real
from time import time
from warnings import catch_warnings, simplefilter, warn
import numpy as np
from scipy.sparse import hstack as sparse_hstack
from scipy.sparse import issparse
from sklearn.base import (
ClassifierMixin,
MultiOutputMixin,
RegressorMixin,
TransformerMixin,
_fit_context,
is_classifier,
)
from sklearn.ensemble._base import BaseEnsemble, _partition_estimators
from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper
from sklearn.exceptions import DataConversionWarning
from sklearn.metrics import accuracy_score, r2_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import check_random_state, compute_sample_weight
from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
from sklearn.utils._param_validation import Interval, RealNotInt, StrOptions
from sklearn.utils._tags import _safe_tags
from sklearn.utils.multiclass import (
_check_partial_fit_first_call,
check_classification_targets,
type_of_target,
)
from sklearn.utils.parallel import Parallel, delayed
from sklearn.utils.validation import (
_check_feature_names_in,
_check_sample_weight,
_num_samples,
check_is_fitted,
)
from ..tree import (
BaseDecisionTree,
DecisionTreeClassifier,
DecisionTreeRegressor,
ExtraTreeClassifier,
ExtraTreeRegressor,
)
from ..tree._tree import DOUBLE, DTYPE
__all__ = [
"RandomForestClassifier",
"RandomForestRegressor",
"ExtraTreesClassifier",
"ExtraTreesRegressor",
"RandomTreesEmbedding",
]
MAX_INT = np.iinfo(np.int32).max
def _get_n_samples_bootstrap(n_samples, max_samples):
"""
Get the number of samples in a bootstrap sample.
The expected total number of unique samples in a bootstrap sample is
required to be at most ``n_samples - 1``.
This is equivalent to the expected number of out-of-bag samples being at
least 1.
Parameters
----------
n_samples : int
Number of samples in the dataset.
max_samples : int or float
The maximum number of samples to draw from the total available:
- if float, this indicates a fraction of the total;
- if int, this indicates the exact number of samples;
- if None, this indicates the total number of samples.
Returns
-------
n_samples_bootstrap : int
The total number of samples to draw for the bootstrap sample.
"""
if max_samples is None:
return n_samples
if isinstance(max_samples, Integral):
expected_oob_samples = (1 - np.exp(-max_samples / n_samples)) * n_samples
if expected_oob_samples >= n_samples - 1:
raise ValueError(
"The expected number of unique samples in the bootstrap sample"
f" must be at most {n_samples - 1}. It is: {expected_oob_samples}"
)
return max_samples
if isinstance(max_samples, Real):
expected_oob_samples = (1 - np.exp(-max_samples)) * n_samples
if expected_oob_samples >= n_samples - 1:
raise ValueError(
"The expected number of unique samples in the bootstrap sample"
f" must be at most {n_samples - 1}. It is: {expected_oob_samples}"
)
return max(round(n_samples * max_samples), 1)
def _generate_sample_indices(random_state, n_samples, n_samples_bootstrap):
"""
Private function used to _parallel_build_trees function."""
random_instance = check_random_state(random_state)
sample_indices = random_instance.randint(
0, n_samples, n_samples_bootstrap, dtype=np.int32
)
return sample_indices
def _generate_unsampled_indices(random_state, n_samples, n_samples_bootstrap):
"""
Private function used to forest._set_oob_score function."""
sample_indices = _generate_sample_indices(
random_state, n_samples, n_samples_bootstrap
)
sample_counts = np.bincount(sample_indices, minlength=n_samples)
unsampled_mask = sample_counts == 0
indices_range = np.arange(n_samples)
unsampled_indices = indices_range[unsampled_mask]
return unsampled_indices
def _parallel_build_trees(
tree,
bootstrap,
X,
y,
sample_weight,
tree_idx,
n_trees,
verbose=0,
class_weight=None,
n_samples_bootstrap=None,
missing_values_in_feature_mask=None,
classes=None,
):
"""
Private function used to fit a single tree in parallel."""
if verbose > 1:
print("building tree %d of %d" % (tree_idx + 1, n_trees))
if bootstrap:
n_samples = X.shape[0]
if sample_weight is None:
curr_sample_weight = np.ones((n_samples,), dtype=np.float64)
else:
curr_sample_weight = sample_weight.copy()
indices = _generate_sample_indices(
tree.random_state, n_samples, n_samples_bootstrap
)
sample_counts = np.bincount(indices, minlength=n_samples)
curr_sample_weight *= sample_counts
if class_weight == "subsample":
with catch_warnings():
simplefilter("ignore", DeprecationWarning)
curr_sample_weight *= compute_sample_weight("auto", y, indices=indices)
elif class_weight == "balanced_subsample":
curr_sample_weight *= compute_sample_weight("balanced", y, indices=indices)
tree._fit(
X,
y,
sample_weight=curr_sample_weight,
check_input=False,
missing_values_in_feature_mask=missing_values_in_feature_mask,
classes=classes,
)
else:
tree._fit(
X,
y,
sample_weight=sample_weight,
check_input=False,
missing_values_in_feature_mask=missing_values_in_feature_mask,
classes=classes,
)
return tree
def _parallel_update_trees(
tree,
bootstrap,
X,
y,
sample_weight,
tree_idx,
n_trees,
verbose=0,
class_weight=None,
n_samples_bootstrap=None,
classes=None,
):
"""
Private function used to fit a single tree in parallel."""
if verbose > 1:
print("Updating tree %d of %d" % (tree_idx + 1, n_trees))
if bootstrap:
n_samples = X.shape[0]
indices = _generate_sample_indices(
tree.random_state, n_samples, n_samples_bootstrap
)
tree.partial_fit(
X[indices, :],
y[indices],
sample_weight=sample_weight,
check_input=False,
classes=classes,
)
else:
tree.partial_fit(
X,
y,
sample_weight=sample_weight,
check_input=False,
classes=classes,
)
return tree
class BaseForest(MultiOutputMixin, BaseEnsemble, metaclass=ABCMeta):
"""
Base class for forests of trees.
Warning: This class should not be used directly. Use derived classes
instead.
"""
_parameter_constraints: dict = {
"n_estimators": [Interval(Integral, 1, None, closed="left")],
"bootstrap": ["boolean"],
"oob_score": ["boolean", callable],
"n_jobs": [Integral, None],
"random_state": ["random_state"],
"verbose": ["verbose"],
"warm_start": ["boolean"],
"max_samples": [
None,
Interval(RealNotInt, 0.0, 1.0, closed="right"),
Interval(Integral, 1, None, closed="left"),
],
"max_bins": [
None,
Interval(Integral, 1, None, closed="left"),
],
"store_leaf_values": ["boolean"],
}
@abstractmethod
def __init__(
self,
estimator,
n_estimators=100,
*,
estimator_params=tuple(),
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None,
max_samples=None,
max_bins=None,
store_leaf_values=False,
):
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
estimator_params=estimator_params,
)
self.bootstrap = bootstrap
self.oob_score = oob_score
self.n_jobs = n_jobs
self.random_state = random_state
self.verbose = verbose
self.warm_start = warm_start
self.class_weight = class_weight
self.max_samples = max_samples
self.max_bins = max_bins
self.store_leaf_values = store_leaf_values
def apply(self, X):
"""
Apply trees in the forest to X, return leaf indices.
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 into a sparse ``csr_matrix``.
Returns
-------
X_leaves : ndarray of shape (n_samples, n_estimators)
For each datapoint x in X and for each tree in the forest,
return the index of the leaf x ends up in.
"""
X = self._validate_X_predict(X)
# if we trained a binning tree, then we should re-bin the data
# XXX: this is inefficient and should be improved to be in line with what
# the Histogram Gradient Boosting Tree does, where the binning thresholds
# are passed into the tree itself, thus allowing us to set the node feature
# value thresholds within the tree itself.
if self.max_bins is not None:
X = self._bin_data(X, is_training_data=False).astype(DTYPE)
results = Parallel(
n_jobs=self.n_jobs,
verbose=self.verbose,
prefer="threads",
)(delayed(tree.apply)(X, check_input=False) for tree in self.estimators_)
return np.array(results).T
def decision_path(self, X):
"""
Return the decision path in the forest.
.. versionadded:: 0.18
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 into a sparse ``csr_matrix``.
Returns
-------
indicator : sparse matrix of shape (n_samples, n_nodes)
Return a node indicator matrix where non zero elements indicates
that the samples goes through the nodes. The matrix is of CSR
format.
n_nodes_ptr : ndarray of shape (n_estimators + 1,)
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
gives the indicator value for the i-th estimator.
"""
X = self._validate_X_predict(X)
indicators = Parallel(
n_jobs=self.n_jobs,
verbose=self.verbose,
prefer="threads",
)(
delayed(tree.decision_path)(X, check_input=False)
for tree in self.estimators_
)
n_nodes = [0]
n_nodes.extend([i.shape[1] for i in indicators])
n_nodes_ptr = np.array(n_nodes).cumsum()
return sparse_hstack(indicators).tocsr(), n_nodes_ptr
@_fit_context(prefer_skip_nested_validation=True)
def fit(self, X, y, sample_weight=None, classes=None):
"""
Build a forest of trees from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, its dtype will be converted
to ``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in
regression).
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.
classes : array-like of shape (n_classes,), default=None
List of all the classes that can possibly appear in the y vector.
Returns
-------
self : object
Fitted estimator.
"""
# Validate or convert input data
if issparse(y):
raise ValueError("sparse multilabel-indicator for y is not supported.")
X, y = self._validate_data(
X,
y,
multi_output=True,
accept_sparse="csc",
dtype=DTYPE,
force_all_finite=False,
)
# _compute_missing_values_in_feature_mask checks if X has missing values and
# will raise an error if the underlying tree base estimator can't handle missing
# values. Only the criterion is required to determine if the tree supports
# missing values.
estimator = type(self.estimator)(criterion=self.criterion)
missing_values_in_feature_mask = (
estimator._compute_missing_values_in_feature_mask(
X, estimator_name=self.__class__.__name__
)
)
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X)
if issparse(X):
# Pre-sort indices to avoid that each individual tree of the
# ensemble sorts the indices.
X.sort_indices()
y = np.atleast_1d(y)
if y.ndim == 2 and y.shape[1] == 1:
warn(
(
"A column-vector y was passed when a 1d array was"
" expected. Please change the shape of y to "
"(n_samples,), for example using ravel()."
),
DataConversionWarning,
stacklevel=2,
)
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
if self.criterion == "poisson":
if np.any(y < 0):
raise ValueError(
"Some value(s) of y are negative which is "
"not allowed for Poisson regression."
)
if np.sum(y) <= 0:
raise ValueError(
"Sum of y is not strictly positive which "
"is necessary for Poisson regression."
)
self._n_samples, self.n_outputs_ = y.shape
y, expanded_class_weight = self._validate_y_class_weight(y, classes=classes)
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
if expanded_class_weight is not None:
if sample_weight is not None:
sample_weight = sample_weight * expanded_class_weight
else:
sample_weight = expanded_class_weight
if not self.bootstrap and self.max_samples is not None:
raise ValueError(
"`max_sample` cannot be set if `bootstrap=False`. "
"Either switch to `bootstrap=True` or set "
"`max_sample=None`."
)
elif self.bootstrap:
n_samples_bootstrap = _get_n_samples_bootstrap(
n_samples=X.shape[0], max_samples=self.max_samples
)
else:
n_samples_bootstrap = None
self._n_samples_bootstrap = n_samples_bootstrap
self._validate_estimator()
if not self.bootstrap and self.oob_score:
raise ValueError("Out of bag estimation only available if bootstrap=True")
random_state = check_random_state(self.random_state)
if not self.warm_start or not hasattr(self, "estimators_"):
# Free allocated memory, if any
self.estimators_ = []
n_more_estimators = self.n_estimators - len(self.estimators_)
if self.max_bins is not None:
# `_openmp_effective_n_threads` is used to take cgroups CPU quotes
# into account when determine the maximum number of threads to use.
n_threads = _openmp_effective_n_threads()
# Bin the data
# For ease of use of the API, the user-facing GBDT classes accept the
# parameter max_bins, which doesn't take into account the bin for
# missing values (which is always allocated). However, since max_bins
# isn't the true maximal number of bins, all other private classes
# (binmapper, histbuilder...) accept n_bins instead, which is the
# actual total number of bins. Everywhere in the code, the
# convention is that n_bins == max_bins + 1
n_bins = self.max_bins + 1 # + 1 for missing values
self._bin_mapper = _BinMapper(
n_bins=n_bins,
# is_categorical=self.is_categorical_,
known_categories=None,
random_state=random_state,
n_threads=n_threads,
)
# XXX: in order for this to work with the underlying tree submodule's Cython
# code, we need to convert this into the original data's DTYPE because
# the Cython code assumes that `DTYPE` is used.
# The proper implementation will be a lot more complicated and should be
# tackled once scikit-learn has finalized their inclusion of missing data
# and categorical support for decision trees
X = self._bin_data(X, is_training_data=True) # .astype(DTYPE)
else:
self._bin_mapper = None
if n_more_estimators < 0:
raise ValueError(
"n_estimators=%d must be larger or equal to "
"len(estimators_)=%d when warm_start==True"
% (self.n_estimators, len(self.estimators_))
)
elif n_more_estimators == 0:
warn(
"Warm-start fitting without increasing n_estimators does not "
"fit new trees."
)
else:
if self.warm_start and len(self.estimators_) > 0:
# We draw from the random state to get the random state we
# would have got if we hadn't used a warm_start.
random_state.randint(MAX_INT, size=len(self.estimators_))
# construct the trees in parallel
self._construct_trees(
X,
y,
sample_weight,
random_state,
n_samples_bootstrap,
missing_values_in_feature_mask,
classes,
n_more_estimators,
)
if self.oob_score and (
n_more_estimators > 0 or not hasattr(self, "oob_score_")
):
y_type = type_of_target(y)
if y_type == "unknown" or (
self._estimator_type == "classifier"
and y_type == "multiclass-multioutput"
):
# FIXME: we could consider to support multiclass-multioutput if
# we introduce or reuse a constructor parameter (e.g.
# oob_score) allowing our user to pass a callable defining the
# scoring strategy on OOB sample.
raise ValueError(
"The type of target cannot be used to compute OOB "
f"estimates. Got {y_type} while only the following are "
"supported: continuous, continuous-multioutput, binary, "
"multiclass, multilabel-indicator."
)
if callable(self.oob_score):
self._set_oob_score_and_attributes(
X, y, scoring_function=self.oob_score
)
else:
self._set_oob_score_and_attributes(X, y)
# Decapsulate classes_ attributes
if hasattr(self, "classes_") and self.n_outputs_ == 1:
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
return self
def _construct_trees(
self,
X,
y,
sample_weight,
random_state,
n_samples_bootstrap,
missing_values_in_feature_mask,
classes,
n_more_estimators,
):
trees = [
self._make_estimator(append=False, random_state=random_state)
for i in range(n_more_estimators)
]
# Parallel loop: we prefer the threading backend as the Cython code
# for fitting the trees is internally releasing the Python GIL
# making threading more efficient than multiprocessing in
# that case. However, for joblib 0.12+ we respect any
# parallel_backend contexts set at a higher level,
# since correctness does not rely on using threads.
trees = Parallel(
n_jobs=self.n_jobs,
verbose=self.verbose,
prefer="threads",
)(
delayed(_parallel_build_trees)(
t,
self.bootstrap,
X,
y,
sample_weight,
i,
len(trees),
verbose=self.verbose,
class_weight=self.class_weight,
n_samples_bootstrap=n_samples_bootstrap,
missing_values_in_feature_mask=missing_values_in_feature_mask,
classes=classes,
)
for i, t in enumerate(trees)
)
# Collect newly grown trees
self.estimators_.extend(trees)
@abstractmethod
def _set_oob_score_and_attributes(self, X, y, scoring_function=None):
"""Compute and set the OOB score and attributes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The target matrix.
scoring_function : callable, default=None
Scoring function for OOB score. Default depends on whether
this is a regression (R2 score) or classification problem
(accuracy score).
"""
def _compute_oob_predictions(self, X, y):
"""Compute and set the OOB score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The target matrix.
Returns
-------
oob_pred : ndarray of shape (n_samples, n_classes, n_outputs) or \
(n_samples, 1, n_outputs)
The OOB predictions.
"""
# Prediction requires X to be in CSR format
if issparse(X):
X = X.tocsr()
n_samples = y.shape[0]
n_outputs = self.n_outputs_
if is_classifier(self) and hasattr(self, "n_classes_"):
# n_classes_ is a ndarray at this stage
# all the supported type of target will have the same number of
# classes in all outputs
oob_pred_shape = (n_samples, self.n_classes_[0], n_outputs)
else:
# for regression, n_classes_ does not exist and we create an empty
# axis to be consistent with the classification case and make
# the array operations compatible with the 2 settings
oob_pred_shape = (n_samples, 1, n_outputs)
oob_pred = np.zeros(shape=oob_pred_shape, dtype=np.float64)
n_oob_pred = np.zeros((n_samples, n_outputs), dtype=np.int64)
n_samples_bootstrap = _get_n_samples_bootstrap(
n_samples,
self.max_samples,
)
for estimator in self.estimators_:
unsampled_indices = _generate_unsampled_indices(
estimator.random_state,
n_samples,
n_samples_bootstrap,
)
y_pred = self._get_oob_predictions(estimator, X[unsampled_indices, :])
oob_pred[unsampled_indices, ...] += y_pred
n_oob_pred[unsampled_indices, :] += 1
for k in range(n_outputs):
if (n_oob_pred == 0).any():
warn(
(
"Some inputs do not have OOB scores. This probably means "
"too few trees were used to compute any reliable OOB "
"estimates."
),
UserWarning,
)
n_oob_pred[n_oob_pred == 0] = 1
oob_pred[..., k] /= n_oob_pred[..., [k]]
return oob_pred
def _validate_y_class_weight(self, y, classes=None):
# Default implementation
return y, None
def _validate_X_predict(self, X):
"""
Validate X whenever one tries to predict, apply, predict_proba."""
check_is_fitted(self)
if self.estimators_[0]._support_missing_values(X):
force_all_finite = "allow-nan"
else:
force_all_finite = True
X = self._validate_data(
X,
dtype=DTYPE,
accept_sparse="csr",
reset=False,
force_all_finite=force_all_finite,
)
if issparse(X) and (X.indices.dtype != np.intc or X.indptr.dtype != np.intc):
raise ValueError("No support for np.int64 index based sparse matrices")
return X
@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.
"""
check_is_fitted(self)
all_importances = Parallel(n_jobs=self.n_jobs, prefer="threads")(
delayed(getattr)(tree, "feature_importances_")
for tree in self.estimators_
if tree.tree_.node_count > 1
)
if not all_importances:
return np.zeros(self.n_features_in_, dtype=np.float64)
all_importances = np.mean(all_importances, axis=0, dtype=np.float64)
return all_importances / np.sum(all_importances)
def _bin_data(self, X, is_training_data):
"""Bin data X.
If is_training_data, then fit the _bin_mapper attribute.
Else, the binned data is converted to a C-contiguous array.
"""
description = "training" if is_training_data else "validation"
if self.verbose:
print(
"Binning {:.3f} GB of {} data: ".format(X.nbytes / 1e9, description),
end="",
flush=True,
)
tic = time()
if is_training_data:
X_binned = self._bin_mapper.fit_transform(X) # F-aligned array
else:
X_binned = self._bin_mapper.transform(X) # F-aligned array
# We convert the array to C-contiguous since predicting is faster
# with this layout (training is faster on F-arrays though)
X_binned = np.ascontiguousarray(X_binned)
toc = time()
if self.verbose:
duration = toc - tic
print("{:.3f} s".format(duration))
return X_binned
def predict_quantiles(self, X, quantiles=0.5, method="nearest"):
"""Predict class or regression value for X at given quantiles.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data.
quantiles : float, optional
The quantiles at which to evaluate, by default 0.5 (median).
method : str, optional
The method to interpolate, by default 'linear'. Can be any keyword
argument accepted by :func:`~np.quantile`.
Returns
-------
y : ndarray of shape (n_samples, n_quantiles, [n_outputs])
The predicted values. The ``n_outputs`` dimension is present only
for multi-output regressors.
"""
if not self.store_leaf_values:
raise RuntimeError(
"Quantile prediction is not available when store_leaf_values=False"
)
check_is_fitted(self)
# Check data
X = self._validate_X_predict(X)
if not isinstance(quantiles, (np.ndarray, list)):
quantiles = np.array([quantiles])
# if we trained a binning tree, then we should re-bin the data
# XXX: this is inefficient and should be improved to be in line with what
# the Histogram Gradient Boosting Tree does, where the binning thresholds
# are passed into the tree itself, thus allowing us to set the node feature
# value thresholds within the tree itself.
if self.max_bins is not None:
X = self._bin_data(X, is_training_data=False).astype(DTYPE)
# Assign chunk of trees to jobs
# n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# avoid storing the output of every estimator by summing them here
if self.n_outputs_ > 1:
y_hat = np.zeros(
(X.shape[0], len(quantiles), self.n_outputs_), dtype=np.float64
)
else:
y_hat = np.zeros((X.shape[0], len(quantiles)), dtype=np.float64)
# get (n_samples, n_estimators) indicator of leaf nodes
X_leaves = self.apply(X)
# we now want to aggregate all leaf samples across all trees for each sample
for idx in range(X.shape[0]):
# get leaf nodes for this sample
leaf_nodes = X_leaves[idx, :]
# (n_total_leaf_samples, n_outputs)
leaf_node_samples = np.vstack(
[
est.tree_.leaf_nodes_samples[leaf_nodes[jdx]]
for jdx, est in enumerate(self.estimators_)
]
)
# get quantiles across all leaf node samples
try:
y_hat[idx, ...] = np.quantile(
leaf_node_samples, quantiles, axis=0, method=method
)
except TypeError:
y_hat[idx, ...] = np.quantile(
leaf_node_samples, quantiles, axis=0, interpolation=method
)
if is_classifier(self):
if self.n_outputs_ == 1:
for i in range(len(quantiles)):
class_pred_per_sample = y_hat[idx, i, :].squeeze().astype(int)
y_hat[idx, ...] = self.classes_.take(
class_pred_per_sample, axis=0
)
else:
for k in range(self.n_outputs_):
for i in range(len(quantiles)):
class_pred_per_sample = (
y_hat[idx, i, k].squeeze().astype(int)
)
y_hat[idx, i, k] = self.classes_[k].take(
class_pred_per_sample, axis=0
)
return y_hat
def get_leaf_node_samples(self, X):
"""For each datapoint x in X, get the training samples in the leaf node.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Dataset to apply the forest to.
Returns
-------
leaf_node_samples : a list of array-like
Each sample is represented by the indices of the training samples that
reached the leaf node. The ``n_leaf_node_samples`` may vary between
samples, since the number of samples that fall in a leaf node is
variable. Each array-like has shape (n_leaf_node_samples, n_outputs).
"""
if not self.store_leaf_values:
raise RuntimeError(
"Leaf node samples are not available when store_leaf_values=False"
)
check_is_fitted(self)
# Check data
X = self._validate_X_predict(X)
# if we trained a binning tree, then we should re-bin the data
# XXX: this is inefficient and should be improved to be in line with what
# the Histogram Gradient Boosting Tree does, where the binning thresholds
# are passed into the tree itself, thus allowing us to set the node feature
# value thresholds within the tree itself.
if self.max_bins is not None:
X = self._bin_data(X, is_training_data=False).astype(DTYPE)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# avoid storing the output of every estimator by summing them here
result = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
delayed(_accumulate_leaf_nodes_samples)(e.get_leaf_node_samples, X)
for e in self.estimators_
)
leaf_nodes_samples = result[0]
for result_ in result[1:]:
for i, node_samples in enumerate(result_):
leaf_nodes_samples[i] = np.vstack((leaf_nodes_samples[i], node_samples))
return leaf_nodes_samples
def _get_estimators_indices(self):
# Get drawn indices along both sample and feature axes
for tree in self.estimators_:
if not self.bootstrap:
yield np.arange(self._n_samples, dtype=np.int32)
else:
# tree.random_state is actually an immutable integer seed rather
# than a mutable RandomState instance, so it's safe to use it
# repeatedly when calling this property.
seed = tree.random_state
# Operations accessing random_state must be performed identically
# to those in `_parallel_build_trees()`
yield _generate_sample_indices(
seed, self._n_samples, self._n_samples_bootstrap
)
@property
def estimators_samples_(self):
"""The subset of drawn samples for each base estimator.
Returns a dynamically generated list of indices identifying
the samples used for fitting each member of the ensemble, i.e.,
the in-bag samples.
Note: the list is re-created at each call to the property in order
to reduce the object memory footprint by not storing the sampling
data. Thus fetching the property may be slower than expected.
"""
return [sample_indices for sample_indices in self._get_estimators_indices()]
def _more_tags(self):
# Only the criterion is required to determine if the tree supports
# missing values
estimator = type(self.estimator)(criterion=self.criterion)
return {"allow_nan": _safe_tags(estimator, key="allow_nan")}
def _accumulate_prediction(predict, X, out, lock):
"""
This is a utility function for joblib's Parallel.
It can't go locally in ForestClassifier or ForestRegressor, because joblib
complains that it cannot pickle it when placed there.
"""
prediction = predict(X, check_input=False)
with lock:
if len(out) == 1:
out[0] += prediction
else:
for i in range(len(out)):
out[i] += prediction[i]
def _accumulate_leaf_nodes_samples(func, X):
"""
This is a utility function for joblib's Parallel.
It can't go locally in ForestClassifier or ForestRegressor, because joblib
complains that it cannot pickle it when placed there.
"""
leaf_nodes_samples = func(X, check_input=False)
return leaf_nodes_samples
class ForestClassifier(ClassifierMixin, BaseForest, metaclass=ABCMeta):
"""
Base class for forest of trees-based classifiers.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(
self,
estimator,
n_estimators=100,
*,
estimator_params=tuple(),
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None,
max_samples=None,
max_bins=None,
store_leaf_values=False,
):
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
estimator_params=estimator_params,
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
class_weight=class_weight,
max_samples=max_samples,
max_bins=max_bins,
store_leaf_values=store_leaf_values,
)
@staticmethod
def _get_oob_predictions(tree, X):
"""Compute the OOB predictions for an individual tree.
Parameters
----------
tree : DecisionTreeClassifier object
A single decision tree classifier.
X : ndarray of shape (n_samples, n_features)
The OOB samples.
Returns
-------
y_pred : ndarray of shape (n_samples, n_classes, n_outputs)
The OOB associated predictions.
"""
y_pred = tree.predict_proba(X, check_input=False)
y_pred = np.asarray(y_pred)
if y_pred.ndim == 2:
# binary and multiclass
y_pred = y_pred[..., np.newaxis]
else:
# Roll the first `n_outputs` axis to the last axis. We will reshape
# from a shape of (n_outputs, n_samples, n_classes) to a shape of
# (n_samples, n_classes, n_outputs).
y_pred = np.rollaxis(y_pred, axis=0, start=3)
return y_pred
def _set_oob_score_and_attributes(self, X, y, scoring_function=None):
"""Compute and set the OOB score and attributes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The target matrix.
scoring_function : callable, default=None
Scoring function for OOB score. Defaults to `accuracy_score`.
"""
self.oob_decision_function_ = super()._compute_oob_predictions(X, y)
if self.oob_decision_function_.shape[-1] == 1:
# drop the n_outputs axis if there is a single output
self.oob_decision_function_ = self.oob_decision_function_.squeeze(axis=-1)
if scoring_function is None:
scoring_function = accuracy_score
self.oob_score_ = scoring_function(
y, np.argmax(self.oob_decision_function_, axis=1)
)
def _validate_y_class_weight(self, y, classes=None):
check_classification_targets(y)
y = np.copy(y)
expanded_class_weight = None
if self.class_weight is not None:
y_original = np.copy(y)
self.classes_ = []
self.n_classes_ = []
y_store_unique_indices = np.zeros(y.shape, dtype=int)
if classes is not None:
classes = np.atleast_1d(classes)
if classes.ndim == 1:
classes = np.array([classes])
for k in classes:
self.classes_.append(np.array(k))
self.n_classes_.append(np.array(k).shape[0])
for i in range(y.shape[0]):
for j in range(self.n_outputs_):
y_store_unique_indices[i, j] = np.where(
self.classes_[j] == y[i, j]
)[0][0]
else:
for k in range(self.n_outputs_):
classes_k, y_store_unique_indices[:, k] = np.unique(
y[:, k], return_inverse=True
)
self.classes_.append(classes_k)
self.n_classes_.append(classes_k.shape[0])
y = y_store_unique_indices
if self.class_weight is not None:
valid_presets = ("balanced", "balanced_subsample")
if isinstance(self.class_weight, str):
if self.class_weight not in valid_presets:
raise ValueError(
"Valid presets for class_weight include "
'"balanced" and "balanced_subsample".'
'Given "%s".'
% self.class_weight
)
if self.warm_start:
warn(
'class_weight presets "balanced" or '
'"balanced_subsample" are '
"not recommended for warm_start if the fitted data "
"differs from the full dataset. In order to use "
'"balanced" weights, use compute_class_weight '
'("balanced", classes, y). In place of y you can use '
"a large enough sample of the full training set "
"target to properly estimate the class frequency "
"distributions. Pass the resulting weights as the "
"class_weight parameter."
)
if self.class_weight != "balanced_subsample" or not self.bootstrap:
if self.class_weight == "balanced_subsample":
class_weight = "balanced"
else:
class_weight = self.class_weight
expanded_class_weight = compute_sample_weight(class_weight, y_original)
return y, expanded_class_weight
def partial_fit(self, X, y, sample_weight=None, classes=None):
"""Update a decision tree classifier from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
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. Splits are also
ignored if they would result in any single class carrying a
negative weight in either child node.
classes : array-like of shape (n_classes,), default=None
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to partial_fit, can be omitted
in subsequent calls.
Returns
-------
self : object
Returns the instance itself.
"""
self._validate_params()
# validate input parameters
first_call = _check_partial_fit_first_call(self, classes=classes)
# Fit if no tree exists yet
if first_call:
self.fit(
X,
y,
sample_weight=sample_weight,
classes=classes,
)
return self
X, y = self._validate_data(
X,
y,
multi_output=True,
accept_sparse="csc",
dtype=DTYPE,
force_all_finite=False,
reset=first_call,
)
if issparse(y):
raise ValueError("sparse multilabel-indicator for y is not supported.")
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X)
if issparse(X):
# Pre-sort indices to avoid that each individual tree of the
# ensemble sorts the indices.
X.sort_indices()
y = np.atleast_1d(y)
if y.ndim == 2 and y.shape[1] == 1:
warn(
(
"A column-vector y was passed when a 1d array was"
" expected. Please change the shape of y to "
"(n_samples,), for example using ravel()."
),
DataConversionWarning,
stacklevel=2,
)
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
if self.criterion == "poisson":
if np.any(y < 0):
raise ValueError(
"Some value(s) of y are negative which is "
"not allowed for Poisson regression."
)
if np.sum(y) <= 0:
raise ValueError(
"Sum of y is not strictly positive which "
"is necessary for Poisson regression."
)
self.n_outputs_ = y.shape[1]
classes = self.classes_
if self.n_outputs_ == 1:
classes = [classes]
y, expanded_class_weight = self._validate_y_class_weight(y, classes)
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
if expanded_class_weight is not None:
if sample_weight is not None:
sample_weight = sample_weight * expanded_class_weight
else:
sample_weight = expanded_class_weight
if not self.bootstrap and self.max_samples is not None:
raise ValueError(
"`max_sample` cannot be set if `bootstrap=False`. "
"Either switch to `bootstrap=True` or set "
"`max_sample=None`."
)
elif self.bootstrap:
n_samples_bootstrap = _get_n_samples_bootstrap(
n_samples=X.shape[0], max_samples=self.max_samples
)
else:
n_samples_bootstrap = None
self._validate_estimator()
if not self.bootstrap and self.oob_score:
raise ValueError("Out of bag estimation only available if bootstrap=True")
random_state = check_random_state(self.random_state)
if self.max_bins is not None:
# `_openmp_effective_n_threads` is used to take cgroups CPU quotes
# into account when determine the maximum number of threads to use.
n_threads = _openmp_effective_n_threads()
# Bin the data
# For ease of use of the API, the user-facing GBDT classes accept the
# parameter max_bins, which doesn't take into account the bin for
# missing values (which is always allocated). However, since max_bins
# isn't the true maximal number of bins, all other private classes
# (binmapper, histbuilder...) accept n_bins instead, which is the
# actual total number of bins. Everywhere in the code, the
# convention is that n_bins == max_bins + 1
n_bins = self.max_bins + 1 # + 1 for missing values
self._bin_mapper = _BinMapper(
n_bins=n_bins,
# is_categorical=self.is_categorical_,
known_categories=None,
random_state=random_state,
n_threads=n_threads,
)
# XXX: in order for this to work with the underlying tree submodule's Cython
# code, we need to convert this into the original data's DTYPE because
# the Cython code assumes that `DTYPE` is used.
# The proper implementation will be a lot more complicated and should be
# tackled once scikit-learn has finalized their inclusion of missing data
# and categorical support for decision trees
X = self._bin_data(X, is_training_data=True) # .astype(DTYPE)
else:
self._bin_mapper = None
# We draw from the random state to get the random state we
# would have got if we hadn't used a warm_start.
random_state.randint(MAX_INT, size=len(self.estimators_))
# Parallel loop: we prefer the threading backend as the Cython code
# for fitting the trees is internally releasing the Python GIL
# making threading more efficient than multiprocessing in
# that case. However, for joblib 0.12+ we respect any
# parallel_backend contexts set at a higher level,
# since correctness does not rely on using threads.
Parallel(
n_jobs=self.n_jobs,
verbose=self.verbose,
prefer="threads",
)(
delayed(_parallel_update_trees)(
t,
self.bootstrap,
X,
y,
sample_weight,
i,
len(self.estimators_),
verbose=self.verbose,
class_weight=self.class_weight,
n_samples_bootstrap=n_samples_bootstrap,
classes=classes[0],
)
for i, t in enumerate(self.estimators_)
)
if self.oob_score:
y_type = type_of_target(y)
if y_type in ("multiclass-multioutput", "unknown"):
# FIXME: we could consider to support multiclass-multioutput if
# we introduce or reuse a constructor parameter (e.g.
# oob_score) allowing our user to pass a callable defining the
# scoring strategy on OOB sample.
raise ValueError(
"The type of target cannot be used to compute OOB "
f"estimates. Got {y_type} while only the following are "
"supported: continuous, continuous-multioutput, binary, "
"multiclass, multilabel-indicator."
)
if callable(self.oob_score):
self._set_oob_score_and_attributes(
X, y, scoring_function=self.oob_score
)
else:
self._set_oob_score_and_attributes(X, y)
# Decapsulate classes_ attributes
if hasattr(self, "classes_") and self.n_outputs_ == 1:
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
return self
def predict(self, X):
"""
Predict class for X.
The predicted class of an input sample is a vote by the trees in
the forest, weighted by their probability estimates. That is,
the predicted class is the one with highest mean probability
estimate across the trees.
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 into a sparse ``csr_matrix``.
Returns
-------
y : ndarray of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes.
"""
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return self.classes_.take(np.argmax(proba, axis=1), axis=0)
else:
n_samples = proba[0].shape[0]
# all dtypes should be the same, so just take the first
class_type = self.classes_[0].dtype
predictions = np.empty((n_samples, self.n_outputs_), dtype=class_type)
for k in range(self.n_outputs_):
predictions[:, k] = self.classes_[k].take(
np.argmax(proba[k], axis=1), axis=0
)
return predictions
def predict_proba(self, X):
"""
Predict class probabilities for X.
The predicted class probabilities of an input sample are computed as
the mean predicted class probabilities of the trees in the forest.
The class probability of a single tree is the fraction of samples of
the same class in a leaf.
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 into a sparse ``csr_matrix``.
Returns
-------
p : ndarray of shape (n_samples, n_classes), or a list of such arrays
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
check_is_fitted(self)
# Check data
X = self._validate_X_predict(X)
# if we trained a binning tree, then we should re-bin the data
# XXX: this is inefficient and should be improved to be in line with what
# the Histogram Gradient Boosting Tree does, where the binning thresholds
# are passed into the tree itself, thus allowing us to set the node feature
# value thresholds within the tree itself.
if self.max_bins is not None:
X = self._bin_data(X, is_training_data=False).astype(DTYPE)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# avoid storing the output of every estimator by summing them here
all_proba = [
np.zeros((X.shape[0], j), dtype=np.float64)
for j in np.atleast_1d(self.n_classes_)
]
lock = threading.Lock()
Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")(
delayed(_accumulate_prediction)(e.predict_proba, X, all_proba, lock)
for e in self.estimators_
)
for proba in all_proba:
proba /= len(self.estimators_)
if len(all_proba) == 1:
return all_proba[0]
else:
return all_proba
def predict_log_proba(self, X):
"""
Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the trees in the
forest.
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 into a sparse ``csr_matrix``.
Returns
-------
p : ndarray of shape (n_samples, n_classes), or a list of such arrays
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return np.log(proba)
else:
for k in range(self.n_outputs_):
proba[k] = np.log(proba[k])
return proba
def _more_tags(self):
return {"multilabel": True}
class ForestRegressor(RegressorMixin, BaseForest, metaclass=ABCMeta):
"""
Base class for forest of trees-based regressors.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(
self,
estimator,
n_estimators=100,
*,
estimator_params=tuple(),
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
max_samples=None,
max_bins=None,
store_leaf_values=False,
):
super().__init__(
estimator,
n_estimators=n_estimators,
estimator_params=estimator_params,
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
max_samples=max_samples,
max_bins=max_bins,
store_leaf_values=store_leaf_values,
)
def predict(self, X):
"""
Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the trees in the forest.
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 into a sparse ``csr_matrix``.
Returns
-------
y : ndarray of shape (n_samples,) or (n_samples, n_outputs)
The predicted values.
"""
check_is_fitted(self)
# Check data
X = self._validate_X_predict(X)
# if we trained a binning tree, then we should re-bin the data
# XXX: this is inefficient and should be improved to be in line with what
# the Histogram Gradient Boosting Tree does, where the binning thresholds
# are passed into the tree itself, thus allowing us to set the node feature
# value thresholds within the tree itself.
if self.max_bins is not None:
X = self._bin_data(X, is_training_data=False).astype(DTYPE)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# avoid storing the output of every estimator by summing them here
if self.n_outputs_ > 1:
y_hat = np.zeros((X.shape[0], self.n_outputs_), dtype=np.float64)
else:
y_hat = np.zeros((X.shape[0]), dtype=np.float64)
# Parallel loop
lock = threading.Lock()
Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")(
delayed(_accumulate_prediction)(e.predict, X, [y_hat], lock)
for e in self.estimators_
)
y_hat /= len(self.estimators_)
return y_hat
@staticmethod
def _get_oob_predictions(tree, X):
"""Compute the OOB predictions for an individual tree.
Parameters
----------
tree : DecisionTreeRegressor object
A single decision tree regressor.
X : ndarray of shape (n_samples, n_features)
The OOB samples.
Returns
-------
y_pred : ndarray of shape (n_samples, 1, n_outputs)
The OOB associated predictions.
"""
y_pred = tree.predict(X, check_input=False)
if y_pred.ndim == 1:
# single output regression
y_pred = y_pred[:, np.newaxis, np.newaxis]
else:
# multioutput regression
y_pred = y_pred[:, np.newaxis, :]
return y_pred
def _set_oob_score_and_attributes(self, X, y, scoring_function=None):
"""Compute and set the OOB score and attributes.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
y : ndarray of shape (n_samples, n_outputs)
The target matrix.
scoring_function : callable, default=None
Scoring function for OOB score. Defaults to `r2_score`.
"""
self.oob_prediction_ = super()._compute_oob_predictions(X, y).squeeze(axis=1)
if self.oob_prediction_.shape[-1] == 1:
# drop the n_outputs axis if there is a single output
self.oob_prediction_ = self.oob_prediction_.squeeze(axis=-1)
if scoring_function is None:
scoring_function = r2_score
self.oob_score_ = scoring_function(y, self.oob_prediction_)
def _compute_partial_dependence_recursion(self, grid, target_features):
"""Fast partial dependence computation.
Parameters
----------
grid : ndarray of shape (n_samples, n_target_features), dtype=DTYPE
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_samples,)
The value of the partial dependence function on each grid point.
"""
grid = np.asarray(grid, dtype=DTYPE, order="C")
target_features = np.asarray(target_features, dtype=np.intp, order="C")
averaged_predictions = np.zeros(
shape=grid.shape[0], dtype=np.float64, order="C"
)
for tree in self.estimators_:
# Note: we don't sum in parallel because the GIL isn't released in
# the fast method.
tree.tree_.compute_partial_dependence(
grid, target_features, averaged_predictions
)
# Average over the forest
averaged_predictions /= len(self.estimators_)
return averaged_predictions
def _more_tags(self):
return {"multilabel": True}
[docs]
class RandomForestClassifier(ForestClassifier):
"""
A random forest classifier.
A random forest is 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.
Trees in the forest use the best split strategy, i.e. equivalent to passing
`splitter="best"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.
The sub-sample size is controlled with the `max_samples` parameter if
`bootstrap=True` (default), otherwise the whole dataset is used to build
each tree.
For a comparison between tree-based ensemble models see the example
:ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : int, default=100
The number of trees in the forest.
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.
criterion : {"gini", "entropy", "log_loss"}, default="gini"
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "log_loss" and "entropy" both for the
Shannon information gain, see :ref:`tree_mathematical_formulation`.
Note: This parameter is tree-specific.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : {"sqrt", "log2", None}, int or float, default="sqrt"
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`max(1, int(max_features * n_features_in_))` features are considered at each
split.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
.. versionchanged:: 1.1
The default of `max_features` changed from `"auto"` to `"sqrt"`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
bootstrap : bool, default=True
Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree.
oob_score : bool or callable, default=False
Whether to use out-of-bag samples to estimate the generalization score.
By default, :func:`~sklearn.metrics.accuracy_score` is used.
Provide a callable with signature `metric(y_true, y_pred)` to use a
custom metric. Only available if `bootstrap=True`.
n_jobs : int, default=None
The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details.
random_state : int, RandomState instance or None, default=None
Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``).
See :term:`Glossary <random_state>` for details.
verbose : int, default=0
Controls the verbosity when fitting and predicting.
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 fit a whole
new forest. See :term:`Glossary <warm_start>` and
:ref:`gradient_boosting_warm_start` for details.
class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
default=None
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}].
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
The "balanced_subsample" mode is the same as "balanced" except that
weights are computed based on the bootstrap sample for every tree
grown.
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
max_samples : int or float, default=None
If bootstrap is True, the number of samples to draw from X
to train each base estimator.
- If None (default), then draw `X.shape[0]` samples.
- If int, then draw `max_samples` samples.
- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,
`max_samples` should be in the interval `(0.0, 1.0]`.
.. versionadded:: 0.22
max_bins : int, default=255
The maximum number of bins to use for non-missing values.
**This is an experimental feature**.
store_leaf_values : bool, default=False
Whether to store the leaf values in the ``get_leaf_node_samples`` function.
**This is an experimental feature**.
monotonic_cst : array-like of int of shape (n_features), default=None
Indicates the monotonicity constraint to enforce on each feature.
- 1: monotonic increase
- 0: no constraint
- -1: monotonic decrease
If monotonic_cst is None, no constraints are applied.
Monotonicity constraints are not supported for:
- multiclass classifications (i.e. when `n_classes > 2`),
- multioutput classifications (i.e. when `n_outputs_ > 1`),
- classifications trained on data with missing values.
The constraints hold over the probability of the positive class.
Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.
.. versionadded:: 1.4
Attributes
----------
estimator_ : :class:`~sklearn.tree.DecisionTreeClassifier`
The child estimator template used to create the collection of fitted
sub-estimators.
.. versionadded:: 1.2
`base_estimator_` was renamed to `estimator_`.
estimators_ : list of DecisionTreeClassifier
The collection of fitted sub-estimators.
classes_ : ndarray of shape (n_classes,) or a list of such arrays
The classes labels (single output problem), or a list of arrays of
class labels (multi-output problem).
n_classes_ : int or list
The number of classes (single output problem), or a list containing the
number of classes for each output (multi-output problem).
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_outputs_ : int
The number of outputs when ``fit`` is performed.
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_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
This attribute exists only when ``oob_score`` is True.
oob_decision_function_ : ndarray of shape (n_samples, n_classes) or \
(n_samples, n_classes, n_outputs)
Decision function computed with out-of-bag estimate on the training
set. If n_estimators is small it might be possible that a data point
was never left out during the bootstrap. In this case,
`oob_decision_function_` might contain NaN. This attribute exists
only when ``oob_score`` is True.
estimators_samples_ : list of arrays
The subset of drawn samples (i.e., the in-bag samples) for each base
estimator. Each subset is defined by an array of the indices selected.
.. versionadded:: 1.4
See Also
--------
sklearn.tree.DecisionTreeClassifier : A decision tree classifier.
sklearn.ensemble.ExtraTreesClassifier : Ensemble of extremely randomized
tree classifiers.
sklearn.ensemble.HistGradientBoostingClassifier : A Histogram-based Gradient
Boosting Classification Tree, very fast for big datasets (n_samples >=
10_000).
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
The features are always randomly permuted at each split. Therefore,
the best found split may vary, even with the same training data,
``max_features=n_features`` and ``bootstrap=False``, 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
----------
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
Examples
--------
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, n_features=4,
... n_informative=2, n_redundant=0,
... random_state=0, shuffle=False)
>>> clf = RandomForestClassifier(max_depth=2, random_state=0)
>>> clf.fit(X, y)
RandomForestClassifier(...)
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]
"""
_parameter_constraints: dict = {
**ForestClassifier._parameter_constraints,
**DecisionTreeClassifier._parameter_constraints,
"class_weight": [
StrOptions({"balanced_subsample", "balanced"}),
dict,
list,
None,
],
}
_parameter_constraints.pop("splitter")
def __init__(
self,
n_estimators=100,
*,
criterion="gini",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features="sqrt",
max_leaf_nodes=None,
min_impurity_decrease=0.0,
bootstrap=True,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None,
ccp_alpha=0.0,
max_samples=None,
max_bins=None,
store_leaf_values=False,
monotonic_cst=None,
):
super().__init__(
estimator=DecisionTreeClassifier(),
n_estimators=n_estimators,
estimator_params=(
"criterion",
"max_depth",
"min_samples_split",
"min_samples_leaf",
"min_weight_fraction_leaf",
"max_features",
"max_leaf_nodes",
"min_impurity_decrease",
"random_state",
"ccp_alpha",
"store_leaf_values",
"monotonic_cst",
),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
class_weight=class_weight,
max_samples=max_samples,
max_bins=max_bins,
store_leaf_values=store_leaf_values,
)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.monotonic_cst = monotonic_cst
self.ccp_alpha = ccp_alpha
[docs]
class RandomForestRegressor(ForestRegressor):
"""
A random forest regressor.
A random forest is a meta estimator that fits a number of decision tree
regressors on various sub-samples of the dataset and uses averaging to
improve the predictive accuracy and control over-fitting.
Trees in the forest use the best split strategy, i.e. equivalent to passing
`splitter="best"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.
The sub-sample size is controlled with the `max_samples` parameter if
`bootstrap=True` (default), otherwise the whole dataset is used to build
each tree.
For a comparison between tree-based ensemble models see the example
:ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : int, default=100
The number of trees in the forest.
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.
criterion : {"squared_error", "absolute_error", "friedman_mse", "poisson"}, \
default="squared_error"
The function to measure the quality of a split. Supported criteria
are "squared_error" for the mean squared error, which is equal to
variance reduction as feature selection criterion and minimizes the L2
loss using the mean of each terminal node, "friedman_mse", which uses
mean squared error with Friedman's improvement score for potential
splits, "absolute_error" for the mean absolute error, which minimizes
the L1 loss using the median of each terminal node, and "poisson" which
uses reduction in Poisson deviance to find splits.
Training using "absolute_error" is significantly slower
than when using "squared_error".
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
.. versionadded:: 1.0
Poisson criterion.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : {"sqrt", "log2", None}, int or float, default=1.0
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`max(1, int(max_features * n_features_in_))` features are considered at each
split.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None or 1.0, then `max_features=n_features`.
.. note::
The default of 1.0 is equivalent to bagged trees and more
randomness can be achieved by setting smaller values, e.g. 0.3.
.. versionchanged:: 1.1
The default of `max_features` changed from `"auto"` to 1.0.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
bootstrap : bool, default=True
Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree.
oob_score : bool or callable, default=False
Whether to use out-of-bag samples to estimate the generalization score.
By default, :func:`~sklearn.metrics.r2_score` is used.
Provide a callable with signature `metric(y_true, y_pred)` to use a
custom metric. Only available if `bootstrap=True`.
n_jobs : int, default=None
The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details.
random_state : int, RandomState instance or None, default=None
Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``).
See :term:`Glossary <random_state>` for details.
verbose : int, default=0
Controls the verbosity when fitting and predicting.
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 fit a whole
new forest. See :term:`Glossary <warm_start>` and
:ref:`gradient_boosting_warm_start` for details.
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
max_samples : int or float, default=None
If bootstrap is True, the number of samples to draw from X
to train each base estimator.
- If None (default), then draw `X.shape[0]` samples.
- If int, then draw `max_samples` samples.
- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,
`max_samples` should be in the interval `(0.0, 1.0]`.
.. versionadded:: 0.22
max_bins : int, default=255
The maximum number of bins to use for non-missing values. Used for
speeding up training time.
**This is an experimental feature**.
store_leaf_values : bool, default=False
Whether to store the leaf values in the ``get_leaf_node_samples`` function.
**This is an experimental feature**.
monotonic_cst : array-like of int of shape (n_features), default=None
Indicates the monotonicity constraint to enforce on each feature.
- 1: monotonically increasing
- 0: no constraint
- -1: monotonically decreasing
If monotonic_cst is None, no constraints are applied.
Monotonicity constraints are not supported for:
- multioutput regressions (i.e. when `n_outputs_ > 1`),
- regressions trained on data with missing values.
Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.
.. versionadded:: 1.4
Attributes
----------
estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor`
The child estimator template used to create the collection of fitted
sub-estimators.
.. versionadded:: 1.2
`base_estimator_` was renamed to `estimator_`.
estimators_ : list of DecisionTreeRegressor
The collection of fitted sub-estimators.
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.
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_outputs_ : int
The number of outputs when ``fit`` is performed.
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
This attribute exists only when ``oob_score`` is True.
oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs)
Prediction computed with out-of-bag estimate on the training set.
This attribute exists only when ``oob_score`` is True.
estimators_samples_ : list of arrays
The subset of drawn samples (i.e., the in-bag samples) for each base
estimator. Each subset is defined by an array of the indices selected.
.. versionadded:: 1.4
See Also
--------
sklearn.tree.DecisionTreeRegressor : A decision tree regressor.
sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized
tree regressors.
sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient
Boosting Regression Tree, very fast for big datasets (n_samples >=
10_000).
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
The features are always randomly permuted at each split. Therefore,
the best found split may vary, even with the same training data,
``max_features=n_features`` and ``bootstrap=False``, 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.
The default value ``max_features=1.0`` uses ``n_features``
rather than ``n_features / 3``. The latter was originally suggested in
[1], whereas the former was more recently justified empirically in [2].
References
----------
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
.. [2] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized
trees", Machine Learning, 63(1), 3-42, 2006.
Examples
--------
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, n_informative=2,
... random_state=0, shuffle=False)
>>> regr = RandomForestRegressor(max_depth=2, random_state=0)
>>> regr.fit(X, y)
RandomForestRegressor(...)
>>> print(regr.predict([[0, 0, 0, 0]]))
[-8.32987858]
"""
_parameter_constraints: dict = {
**ForestRegressor._parameter_constraints,
**DecisionTreeRegressor._parameter_constraints,
}
_parameter_constraints.pop("splitter")
def __init__(
self,
n_estimators=100,
*,
criterion="squared_error",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features=1.0,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
bootstrap=True,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
ccp_alpha=0.0,
max_samples=None,
max_bins=None,
store_leaf_values=False,
monotonic_cst=None,
):
super().__init__(
estimator=DecisionTreeRegressor(),
n_estimators=n_estimators,
estimator_params=(
"criterion",
"max_depth",
"min_samples_split",
"min_samples_leaf",
"min_weight_fraction_leaf",
"max_features",
"max_leaf_nodes",
"min_impurity_decrease",
"random_state",
"ccp_alpha",
"store_leaf_values",
"monotonic_cst",
),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
max_samples=max_samples,
max_bins=max_bins,
store_leaf_values=store_leaf_values,
)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.ccp_alpha = ccp_alpha
self.monotonic_cst = monotonic_cst
class RandomTreesEmbedding(TransformerMixin, BaseForest):
"""
An ensemble of totally random trees.
An unsupervised transformation of a dataset to a high-dimensional
sparse representation. A datapoint is coded according to which leaf of
each tree it is sorted into. Using a one-hot encoding of the leaves,
this leads to a binary coding with as many ones as there are trees in
the forest.
The dimensionality of the resulting representation is
``n_out <= n_estimators * max_leaf_nodes``. If ``max_leaf_nodes == None``,
the number of leaf nodes is at most ``n_estimators * 2 ** max_depth``.
Read more in the :ref:`User Guide <random_trees_embedding>`.
Parameters
----------
n_estimators : int, default=100
Number of trees in the forest.
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.
max_depth : int, default=5
The maximum depth of each tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` is the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` is the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
sparse_output : bool, default=True
Whether or not to return a sparse CSR matrix, as default behavior,
or to return a dense array compatible with dense pipeline operators.
n_jobs : int, default=None
The number of jobs to run in parallel. :meth:`fit`, :meth:`transform`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details.
random_state : int, RandomState instance or None, default=None
Controls the generation of the random `y` used to fit the trees
and the draw of the splits for each feature at the trees' nodes.
See :term:`Glossary <random_state>` for details.
verbose : int, default=0
Controls the verbosity when fitting and predicting.
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 fit a whole
new forest. See :term:`Glossary <warm_start>` and
:ref:`gradient_boosting_warm_start` for details.
store_leaf_values : bool, default=False
Whether to store the leaf values in the ``get_leaf_node_samples`` function.
Attributes
----------
estimator_ : :class:`~sklearn.tree.ExtraTreeRegressor` instance
The child estimator template used to create the collection of fitted
sub-estimators.
.. versionadded:: 1.2
`base_estimator_` was renamed to `estimator_`.
estimators_ : list of :class:`~sklearn.tree.ExtraTreeRegressor` instances
The collection of fitted sub-estimators.
feature_importances_ : ndarray of shape (n_features,)
The feature importances (the higher, the more important the feature).
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_outputs_ : int
The number of outputs when ``fit`` is performed.
one_hot_encoder_ : OneHotEncoder instance
One-hot encoder used to create the sparse embedding.
estimators_samples_ : list of arrays
The subset of drawn samples (i.e., the in-bag samples) for each base
estimator. Each subset is defined by an array of the indices selected.
.. versionadded:: 1.4
See Also
--------
ExtraTreesClassifier : An extra-trees classifier.
ExtraTreesRegressor : An extra-trees regressor.
RandomForestClassifier : A random forest classifier.
RandomForestRegressor : A random forest regressor.
sklearn.tree.ExtraTreeClassifier: An extremely randomized
tree classifier.
sklearn.tree.ExtraTreeRegressor : An extremely randomized
tree regressor.
References
----------
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
Machine Learning, 63(1), 3-42, 2006.
.. [2] Moosmann, F. and Triggs, B. and Jurie, F. "Fast discriminative
visual codebooks using randomized clustering forests"
NIPS 2007
Examples
--------
>>> from sklearn.ensemble import RandomTreesEmbedding
>>> X = [[0,0], [1,0], [0,1], [-1,0], [0,-1]]
>>> random_trees = RandomTreesEmbedding(
... n_estimators=5, random_state=0, max_depth=1).fit(X)
>>> X_sparse_embedding = random_trees.transform(X)
>>> X_sparse_embedding.toarray()
array([[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
[0., 1., 0., 1., 0., 1., 0., 1., 0., 1.],
[1., 0., 1., 0., 1., 0., 1., 0., 1., 0.],
[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.]])
"""
_parameter_constraints: dict = {
"n_estimators": [Interval(Integral, 1, None, closed="left")],
"n_jobs": [Integral, None],
"verbose": ["verbose"],
"warm_start": ["boolean"],
**BaseDecisionTree._parameter_constraints,
"sparse_output": ["boolean"],
}
for param in ("max_features", "ccp_alpha", "splitter", "monotonic_cst"):
_parameter_constraints.pop(param)
criterion = "squared_error"
max_features = 1
def __init__(
self,
n_estimators=100,
*,
max_depth=5,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
sparse_output=True,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
store_leaf_values=False,
):
super().__init__(
estimator=ExtraTreeRegressor(),
n_estimators=n_estimators,
estimator_params=(
"criterion",
"max_depth",
"min_samples_split",
"min_samples_leaf",
"min_weight_fraction_leaf",
"max_features",
"max_leaf_nodes",
"min_impurity_decrease",
"random_state",
"store_leaf_values",
),
bootstrap=False,
oob_score=False,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
max_samples=None,
store_leaf_values=store_leaf_values,
)
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.sparse_output = sparse_output
def _set_oob_score_and_attributes(self, X, y, scoring_function=None):
raise NotImplementedError("OOB score not supported by tree embedding")
def fit(self, X, y=None, sample_weight=None, classes=None):
"""
Fit estimator.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csc_matrix`` for maximum efficiency.
y : Ignored
Not used, present for API consistency by convention.
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.
classes : array-like of shape (n_classes,), default=None
List of all the classes that can possibly appear in the y vector.
Returns
-------
self : object
Returns the instance itself.
"""
# Parameters are validated in fit_transform
self.fit_transform(X, y, sample_weight=sample_weight, classes=classes)
return self
@_fit_context(prefer_skip_nested_validation=True)
def fit_transform(self, X, y=None, sample_weight=None, classes=None):
"""
Fit estimator and transform dataset.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data used to build forests. Use ``dtype=np.float32`` for
maximum efficiency.
y : Ignored
Not used, present for API consistency by convention.
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.
classes : array-like of shape (n_classes,), default=None
List of all the classes that can possibly appear in the y vector.
Returns
-------
X_transformed : sparse matrix of shape (n_samples, n_out)
Transformed dataset.
"""
rnd = check_random_state(self.random_state)
y = rnd.uniform(size=_num_samples(X))
super().fit(X, y, sample_weight=sample_weight, classes=classes)
self.one_hot_encoder_ = OneHotEncoder(sparse_output=self.sparse_output)
output = self.one_hot_encoder_.fit_transform(self.apply(X))
self._n_features_out = output.shape[1]
return output
def get_feature_names_out(self, input_features=None):
"""Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Only used to validate feature names with the names seen in :meth:`fit`.
Returns
-------
feature_names_out : ndarray of str objects
Transformed feature names, in the format of
`randomtreesembedding_{tree}_{leaf}`, where `tree` is the tree used
to generate the leaf and `leaf` is the index of a leaf node
in that tree. Note that the node indexing scheme is used to
index both nodes with children (split nodes) and leaf nodes.
Only the latter can be present as output features.
As a consequence, there are missing indices in the output
feature names.
"""
check_is_fitted(self, "_n_features_out")
_check_feature_names_in(
self, input_features=input_features, generate_names=False
)
feature_names = [
f"randomtreesembedding_{tree}_{leaf}"
for tree in range(self.n_estimators)
for leaf in self.one_hot_encoder_.categories_[tree]
]
return np.asarray(feature_names, dtype=object)
def transform(self, X):
"""
Transform dataset.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data to be transformed. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csr_matrix`` for maximum efficiency.
Returns
-------
X_transformed : sparse matrix of shape (n_samples, n_out)
Transformed dataset.
"""
check_is_fitted(self)
return self.one_hot_encoder_.transform(self.apply(X))