import numpy as np
from joblib import Parallel, delayed
from sklearn.base import _fit_context
from .._lib.sklearn.ensemble._forest import _parallel_build_trees
from ..ensemble._honest_forest import HonestForestClassifier
[docs]
class PermutationHonestForestClassifier(HonestForestClassifier):
"""
A forest classifier with a permutation over the dataset.
Parameters
----------
n_estimators : int, default=100
The number of trees in the forest.
criterion : {"gini", "entropy"}, default="gini"
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "entropy" for the information gain.
Note: this parameter is tree-specific.
splitter : {"best", "random"}, default="best"
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split.
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.
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
`round(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
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.
When bootstrap is True, each tree bootstrap samples the dataset, and then
the unique indices are split in half, where one half is used to train
the structure of the tree and one half is used to train the leaves of the tree.
The remaining sample indices are considered "out of bag".
oob_score : bool, default=False
Whether to use out-of-bag samples to estimate the generalization score.
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 `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:`the Glossary <warm_start>`.
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.
max_samples : int or float, default=None
If bootstrap is True, the number of samples to draw from X
to train each base tree estimator.
- If None (default), then draw `X.shape[0]` samples.
- If int, then draw `max_samples` samples.
- If float, then draw `max_samples * X.shape[0]` samples.
honest_prior : {"ignore", "uniform", "empirical"}, default="empirical"
Method for dealing with empty leaves during evaluation of a test
sample. If "ignore", the tree is ignored. If "uniform", the prior tree
posterior is 1/(number of classes). If "empirical", the prior tree
posterior is the relative class frequency in the voting subsample.
If all trees are ignored, the empirical estimate is returned.
honest_fraction : float, default=0.5
Fraction of training samples used for estimates in the trees. The
remaining samples will be used to learn the tree structure. A larger
fraction creates shallower trees with lower variance estimates.
tree_estimator : object, default=None
Type of decision tree classifier to use. By default `None`, which
defaults to `treeple.tree.DecisionTreeClassifier`. Note
that one MUST use trees imported from the `treeple.tree`
API namespace rather than from `sklearn.tree`.
stratify : bool
Whether or not to stratify sample when considering structure and leaf indices.
By default False.
permute_per_tree : bool
Whether or not to permute the dataset per tree. By default False.
**tree_estimator_params : dict
Parameters to pass to the underlying base tree estimators.
These must be parameters for ``tree_estimator``.
Attributes
----------
estimator : treeple.tree.HonestTreeClassifier
The child estimator template used to create the collection of fitted
sub-estimators.
estimators_ : list of treeple.tree.HonestTreeClassifier
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_ : int
The number of features when ``fit`` is performed.
n_features_in_ : int
Number of features seen during :term:`fit`.
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.
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.
honest_decision_function_ : ndarray of shape (n_samples, n_classes) or \
(n_samples, n_classes, n_outputs)
Decision function computed on each sample, including only the trees
for which it was in the honest subsample. It is possible that a sample
is never in the honest subset in which case `honest_decision_function_`
might contain NaN.
structure_indices_ : list of lists, shape=(n_estimators, n_structure)
Indices of training samples used to learn the structure.
honest_indices_ : list of lists, shape=(n_estimators, n_honest)
Indices of training samples used to learn leaf estimates.
oob_samples_ : list of lists, shape=(n_estimators, n_samples_bootstrap)
The indices of training samples that are "out-of-bag". Only used
if ``bootstrap=True``.
permutation_indices_ : list of lists, shape=(n_estimators, n_samples)
The indices of the permutation used to fit each tree. I.e.
which samples were shuffled.
covariate_index_ : list of (n_features,) or None
The index of the dataset to shuffle per tree. Will have up to
``n_features`` elements. By default None, which will shuffle all features.
"""
def __init__(
self,
n_estimators=100,
criterion="gini",
splitter="best",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0,
max_features="sqrt",
max_leaf_nodes=None,
min_impurity_decrease=0,
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None,
ccp_alpha=0,
max_samples=None,
honest_prior="empirical",
honest_fraction=0.5,
tree_estimator=None,
stratify=False,
permute_per_tree=False,
**tree_estimator_params,
):
super().__init__(
n_estimators,
criterion,
splitter,
max_depth,
min_samples_split,
min_samples_leaf,
min_weight_fraction_leaf,
max_features,
max_leaf_nodes,
min_impurity_decrease,
bootstrap,
oob_score,
n_jobs,
random_state,
verbose,
warm_start,
class_weight,
ccp_alpha,
max_samples,
honest_prior,
honest_fraction,
tree_estimator,
stratify,
**tree_estimator_params,
)
self.permute_per_tree = permute_per_tree
[docs]
@_fit_context(prefer_skip_nested_validation=True)
def fit(self, X, y, sample_weight=None, classes=None, covariate_index=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.
covariate_index : list
The indices of the dataset features (i.e. columns) to shuffle per tree.
Will have up to ``n_features`` elements. By default None, which will
shuffle all features.
Returns
-------
self : object
Fitted estimator.
"""
if covariate_index is None:
covariate_index = np.arange(X.shape[1], dtype=np.intp)
if not isinstance(covariate_index, (list, tuple, np.ndarray)):
raise RuntimeError("covariate_index must be an iterable of integer indices")
else:
if not all(isinstance(idx, (np.integer, int)) for idx in covariate_index):
raise RuntimeError("Not all covariate_index are integer indices")
if len(covariate_index) > X.shape[1]:
raise ValueError(
"The length of the covariate index must not be greater than the number of features."
)
self.covariate_index_ = covariate_index
self = super().fit(X, y, sample_weight, classes)
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.
if self.permute_per_tree:
# TODO: refactor to make this a more robust implementation
# XXX: this does not currently allow permuting individual covariates.
permutation_arr_per_tree = [
random_state.choice(self._n_samples, size=self._n_samples, replace=False)
for _ in range(self.n_estimators)
]
if sample_weight is None:
sample_weight = np.ones((self._n_samples,))
# fitted array of what indices were used to fit each tree
self.permutation_indices_ = permutation_arr_per_tree
trees = Parallel(
n_jobs=self.n_jobs,
verbose=self.verbose,
prefer="threads",
)(
delayed(_parallel_build_trees)(
t,
self.bootstrap,
X,
y[perm_idx],
sample_weight[perm_idx],
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, perm_idx) in enumerate(
zip(
trees,
permutation_arr_per_tree,
)
)
)
else:
perm_idx = np.array(
random_state.choice(self._n_samples, size=(self._n_samples, 1), replace=False)
)
X[:, self.covariate_index_] = X[perm_idx, self.covariate_index_]
self.permutation_indices_ = perm_idx
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)
)
self.estimators_.extend(trees)