import threading
from collections.abc import Callable
from numbers import Integral, Real
from typing import Any, ClassVar, Literal, Self
import numpy as np
from joblib import Parallel, delayed
from scipy.sparse import csr_matrix, issparse
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble._base import _partition_estimators
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils._param_validation import StrOptions
from sklearn.utils.validation import check_is_fitted, check_random_state
from ..._common import Parameter
from ..._types import FloatArrayLike, FloatNDArray, IntArrayLike, IntNDArray, ParameterConstraint
from ...metrics import Metric, expected_cost_loss
from ...utils._sklearn_compat import validate_data # type: ignore[attr-defined]
from ..csclassifier import CostSensitiveClassifier
from ._impurity import CostImpurity, EntropyCostImpurity, GiniCostImpurity
RF_PARAM_CONSTRAINTS = RandomForestClassifier._parameter_constraints.copy()
RF_PARAM_CONSTRAINTS.pop('criterion')
[docs]
class CSForestClassifier(CostSensitiveClassifier):
"""
Cost-sensitive random forest classifier.
A forest of cost-sensitive decision trees.
.. seealso::
:class:`~empulse.models.CSTreeClassifier` : Cost-sensitive decision tree classifier.
:class:`~empulse.models.CSLogitClassifier` : Cost-sensitive logistic regression classifier.
:class:`~empulse.models.CSBoostClassifier` : Cost-sensitive gradient boosting classifier.
Parameters
----------
n_estimators : int, default=100
The number of trees in the forest.
tp_cost : float or array-like, shape=(n_samples,), default=0.0
Cost of true positives. If ``float``, then all true positives have the same cost.
If array-like, then it is the cost of each true positive classification.
Is overwritten if another `tp_cost` is passed to the ``fit`` method.
.. note::
It is not recommended to pass instance-dependent costs to the ``__init__`` method.
Instead, pass them to the ``fit`` method.
fp_cost : float or array-like, shape=(n_samples,), default=0.0
Cost of false positives. If ``float``, then all false positives have the same cost.
If array-like, then it is the cost of each false positive classification.
Is overwritten if another `fp_cost` is passed to the ``fit`` method.
.. note::
It is not recommended to pass instance-dependent costs to the ``__init__`` method.
Instead, pass them to the ``fit`` method.
tn_cost : float or array-like, shape=(n_samples,), default=0.0
Cost of true negatives. If ``float``, then all true negatives have the same cost.
If array-like, then it is the cost of each true negative classification.
Is overwritten if another `tn_cost` is passed to the ``fit`` method.
.. note::
It is not recommended to pass instance-dependent costs to the ``__init__`` method.
Instead, pass them to the ``fit`` method.
fn_cost : float or array-like, shape=(n_samples,), default=0.0
Cost of false negatives. If ``float``, then all false negatives have the same cost.
If array-like, then it is the cost of each false negative classification.
Is overwritten if another `fn_cost` is passed to the ``fit`` method.
.. note::
It is not recommended to pass instance-dependent costs to the ``__init__`` method.
Instead, pass them to the ``fit`` method.
loss : Metric or None, default=None
The metric to measure the quality of a split.
If None, the cost impurity is used.
criterion : {"cost", "gini", "log_loss" or "entropy"}, default="cost"
The function to measure the quality of a split.
How the measure to estimate the quality of a split is weighted.
- If ``"cost"``: The metric is used normally, without extra weighting.
- If ``"gini"``: The Gini impurity is used to weight the metric.
- If ``"log_loss"`` or ``"entropy"``: The Shannon information gain is used to weight the metric.
combination : {"majority_voting", "weighted_voting"}, default="majority_voting"
How to combine the predictions of the individual models.
- "majority_voting": the majority vote of the models.
- "weighted_voting": the models are weighted by their oob score
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.
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`.
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.
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:`tree_ensemble_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. See
:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
for an example of such pruning.
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]`.
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>`.
Attributes
----------
estimator_ : :class:`~sklearn.tree.RandomForestClassifier`
The underlying RandomForestClassifier 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.
References
----------
.. [1] Correa Bahnsen, A., Aouada, D., & Ottersten, B.
`"Ensemble of Example-Dependent Cost-Sensitive Decision Trees" <http://arxiv.org/abs/1505.04637>`__,
2015, http://arxiv.org/abs/1505.04637.
"""
_parameter_constraints: ClassVar[ParameterConstraint] = {
**CostSensitiveClassifier._parameter_constraints,
'criterion': [StrOptions({'cost', 'log_loss', 'gini', 'entropy'}), Metric],
'combination': [
StrOptions({'majority_voting', 'weighted_voting'}),
],
**RF_PARAM_CONSTRAINTS,
}
def __init__(
self,
n_estimators: int = 100,
*,
tp_cost: FloatArrayLike | float = 0.0,
tn_cost: FloatArrayLike | float = 0.0,
fn_cost: FloatArrayLike | float = 0.0,
fp_cost: FloatArrayLike | float = 0.0,
loss: Metric | None = None,
criterion: Literal['cost', 'gini', 'entropy', 'log_loss'] = 'cost',
combination: Literal['majority_voting', 'weighted_voting'] = 'majority_voting',
max_depth: int | None = None,
min_samples_split: float = 2,
min_samples_leaf: float = 1,
min_weight_fraction_leaf: float = 0.0,
max_features: Literal['sqrt', 'log2'] | float = 'sqrt',
max_leaf_nodes: int | None = None,
min_impurity_decrease: float = 0.0,
bootstrap: bool = True,
oob_score: bool | Callable[[Any, Any], float] = False,
n_jobs: int | None = None,
random_state: int | np.random.RandomState | None = None,
verbose: bool | int = 0,
warm_start: bool = False,
class_weight: dict[int, float] | Literal['balanced'] | None = None,
ccp_alpha: float = 0.0,
max_samples: float | None = None,
monotonic_cst: IntArrayLike | None = None,
):
self.n_estimators = n_estimators
self.criterion = criterion
self.combination = combination
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.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.ccp_alpha = ccp_alpha
self.max_samples = max_samples
self.monotonic_cst = monotonic_cst
super().__init__(tp_cost=tp_cost, tn_cost=tn_cost, fp_cost=fp_cost, fn_cost=fn_cost, loss=loss)
@property
def estimators_(self) -> list[DecisionTreeClassifier]:
"""The collection of fitted sub-estimators."""
check_is_fitted(self)
estimators: list[DecisionTreeClassifier] = self.estimator_.estimators_
return estimators
@property
def n_classes_(self) -> int | list[int]:
"""The number of classes seen during :term:`fit`."""
check_is_fitted(self)
n_classes: int | list[int] = self.estimator_.n_classes_
return n_classes
@property
def feature_importances_(self) -> FloatNDArray:
"""The impurity-based feature importances."""
check_is_fitted(self)
importances: FloatNDArray = self.estimator_.feature_importances_
return importances
@property
def oob_score_(self) -> float:
"""Score of the training dataset obtained using an out-of-bag estimate."""
check_is_fitted(self)
oob_score: float = self.estimator_.oob_score_
return oob_score
@property
def oob_decision_function_(self) -> FloatNDArray:
"""Decision function computed with out-of-bag estimate on the training set."""
check_is_fitted(self)
oob_decision_function: FloatNDArray = self.estimator_.oob_decision_function_
return oob_decision_function
@property
def estimators_samples_(self) -> list[IntNDArray]:
"""The subset of drawn samples (i.e., the in-bag samples) for each base estimator."""
check_is_fitted(self)
estimators_samples: list[IntNDArray] = self.estimator_.estimators_samples_
return estimators_samples
def _fit(
self,
X: FloatNDArray,
y: IntNDArray,
loss: Metric,
**loss_params: Any,
) -> Self:
"""
Build an example-dependent cost-sensitive decision tree from the training set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input samples.
y : array-like of shape (n_samples,)
Ground truth (correct) labels.
loss : Metric
Loss to be optimized.
loss_params : dict
Additional keyword arguments to pass to the loss function if using a custom loss function.
Returns
-------
self : object
Returns self.
"""
if isinstance(self.loss, Metric):
fp_cost, fn_cost, tp_cost, tn_cost = self.loss._evaluate_costs(**loss_params)
else:
tp_cost, tn_cost, fn_cost, fp_cost = self._check_costs(
tp_cost=loss_params.get('tp_cost', Parameter.UNCHANGED),
tn_cost=loss_params.get('tn_cost', Parameter.UNCHANGED),
fn_cost=loss_params.get('fn_cost', Parameter.UNCHANGED),
fp_cost=loss_params.get('fp_cost', Parameter.UNCHANGED),
)
n_samples = X.shape[0]
for name, cost in zip(
['tp_cost', 'tn_cost', 'fn_cost', 'fp_cost'], [tp_cost, tn_cost, fn_cost, fp_cost], strict=True
):
if isinstance(cost, np.ndarray) and cost.shape[0] != n_samples:
raise ValueError(f'{name} has shape {cost.shape}, but should have shape ({n_samples},)')
if self.criterion == 'cost':
self.criterion_ = CostImpurity(
n_outputs=1,
n_classes=np.array([2], dtype=np.intp),
)
elif self.criterion == 'gini':
self.criterion_ = GiniCostImpurity(
n_outputs=1,
n_classes=np.array([2], dtype=np.intp),
)
elif self.criterion in {'entropy', 'log_loss'}:
self.criterion_ = EntropyCostImpurity(
n_outputs=1,
n_classes=np.array([2], dtype=np.intp),
)
else:
raise ValueError(f'Unknown criterion: {self.criterion}')
self.criterion_.set_costs(
tp_cost=tp_cost if not isinstance(tp_cost, np.ndarray) else 0.0,
tn_cost=tn_cost if not isinstance(tn_cost, np.ndarray) else 0.0,
fp_cost=fp_cost if not isinstance(fp_cost, np.ndarray) else 0.0,
fn_cost=fn_cost if not isinstance(fn_cost, np.ndarray) else 0.0,
)
self.criterion_.set_array_costs(
tp_cost=tp_cost.reshape(-1).astype(np.float64)
if isinstance(tp_cost, np.ndarray)
else np.array([], dtype=np.float64),
tn_cost=tn_cost.reshape(-1).astype(np.float64)
if isinstance(tn_cost, np.ndarray)
else np.array([], dtype=np.float64),
fp_cost=fp_cost.reshape(-1).astype(np.float64)
if isinstance(fp_cost, np.ndarray)
else np.array([], dtype=np.float64),
fn_cost=fn_cost.reshape(-1).astype(np.float64)
if isinstance(fn_cost, np.ndarray)
else np.array([], dtype=np.float64),
n_samples=n_samples,
)
self.estimator_ = RandomForestClassifier(
n_estimators=self.n_estimators,
criterion=self.criterion_,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
max_features=self.max_features,
max_leaf_nodes=self.max_leaf_nodes,
min_impurity_decrease=self.min_impurity_decrease,
bootstrap=self.bootstrap,
oob_score=self.oob_score,
n_jobs=self.n_jobs,
random_state=self.random_state,
verbose=self.verbose,
warm_start=self.warm_start,
class_weight=self.class_weight,
ccp_alpha=self.ccp_alpha,
max_samples=self.max_samples,
monotonic_cst=self.monotonic_cst,
)
self.estimator_.fit(X, y)
if self.combination == 'weighted_voting':
if not self.bootstrap:
raise ValueError('Weighted voting is only available when bootstrap=True.')
if self.loss is None:
self.estimator_weights_ = self._get_oob_weights(
X,
y,
tp_cost=tp_cost,
tn_cost=tn_cost,
fn_cost=fn_cost,
fp_cost=fp_cost,
check_input=False,
)
else:
self.estimator_weights_ = self._get_oob_weights(X, y, **loss_params)
return self
[docs]
def predict_proba(self, X: FloatArrayLike) -> FloatNDArray:
"""
Predict class probabilities of the input samples X.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
prob : array of shape = [n_samples, 2]
The class probabilities of the input samples.
"""
check_is_fitted(self)
X: FloatNDArray = validate_data(self, X, reset=False)
if self.combination == 'weighted_voting':
y_proba: FloatNDArray = self._predict_weighted_proba(X)
else:
y_proba = self.estimator_.predict_proba(X)
return y_proba
[docs]
def predict_log_proba(self, X: FloatArrayLike) -> FloatNDArray:
"""
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_`.
"""
check_is_fitted(self)
y_proba = self.predict_proba(X)
return np.log(y_proba)
[docs]
def apply(self, X: FloatArrayLike) -> IntNDArray:
"""
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.
"""
check_is_fitted(self)
X_leaves: IntNDArray = self.estimator_.apply(X)
return X_leaves
[docs]
def decision_path(self, X: FloatArrayLike) -> tuple[csr_matrix, IntNDArray]:
"""
Return the decision path 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
-------
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.
"""
check_is_fitted(self)
indicator: csr_matrix
n_nodes_ptr: IntNDArray
indicator, n_nodes_ptr = self.estimator_.decision_path(X)
return indicator, n_nodes_ptr
def _get_oob_weights(self, X: FloatNDArray, y: IntNDArray, **kwargs: Any) -> FloatNDArray:
# Prediction requires X to be in CSR format
if issparse(X):
X = X.tocsr() # type: ignore[attr-defined]
X = X.astype(np.float32)
n_samples = y.shape[0]
estimator_weights = np.zeros(self.n_estimators, dtype=np.float64)
if self.max_samples is None:
n_samples_bootstrap = n_samples
if isinstance(self.max_samples, Integral):
if self.max_samples > n_samples:
msg = '`max_samples` must be <= n_samples={} but got value {}'
raise ValueError(msg.format(n_samples, self.max_samples))
n_samples_bootstrap = self.max_samples
if isinstance(self.max_samples, Real):
n_samples_bootstrap = max(round(n_samples * self.max_samples), 1)
weight_fn = self.loss if isinstance(self.loss, Metric) else expected_cost_loss
for i, estimator in enumerate(self.estimators_):
unsampled_indices = _generate_unsampled_indices(
estimator.random_state,
n_samples,
n_samples_bootstrap,
)
y_pred = self.estimator_._get_oob_predictions(estimator, X[unsampled_indices, :])
estimator_weights[i] += weight_fn(y[unsampled_indices], y_pred[:, 1, 0], **kwargs)
estimator_weights /= estimator_weights.sum()
return estimator_weights
def _predict_weighted_proba(self, X: FloatArrayLike) -> FloatNDArray:
X: FloatNDArray = self.estimator_._validate_X_predict(X)
# 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], self.n_classes_), dtype=np.float64) # type: ignore[arg-type, type-var]
lock = threading.Lock()
Parallel(n_jobs=n_jobs, verbose=self.verbose, require='sharedmem')(
delayed(_accumulate_weighted_prediction)(e.predict_proba, X, all_proba, weight, lock)
for e, weight in zip(self.estimators_, self.estimator_weights_, strict=True)
)
return all_proba
def _generate_unsampled_indices(random_state: int, n_samples: int, n_samples_bootstrap: int) -> IntNDArray:
"""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: IntNDArray = indices_range[unsampled_mask]
return unsampled_indices
def _generate_sample_indices(random_state: int, n_samples: int, n_samples_bootstrap: int) -> IntNDArray:
"""Private function used to _parallel_build_trees function."""
random_instance = check_random_state(random_state)
sample_indices: IntNDArray = random_instance.randint(0, n_samples, n_samples_bootstrap, dtype=np.int32)
return sample_indices
def _accumulate_weighted_prediction(
predict: Callable[..., FloatNDArray],
X: FloatArrayLike,
out: FloatNDArray,
weight: float,
lock: threading.Lock,
) -> None:
"""Calculate the weighted prediction."""
prediction = predict(X, check_input=False)
with lock:
out += prediction * weight # type: ignore[misc]
