import copy
import math
import numbers
from numbers import Integral, Real
import numpy as np
from scipy.sparse import issparse
from sklearn.utils import check_random_state
from sklearn.utils._param_validation import Interval, RealNotInt, StrOptions
from .._lib.sklearn.tree import DecisionTreeClassifier, _criterion
from .._lib.sklearn.tree import _tree as _sklearn_tree
from .._lib.sklearn.tree._criterion import BaseCriterion
from .._lib.sklearn.tree._tree import BestFirstTreeBuilder, DepthFirstTreeBuilder
from . import _oblique_splitter
from ._neighbors import SimMatrixMixin
from ._oblique_splitter import ObliqueSplitter
from ._oblique_tree import ObliqueTree
DTYPE = _sklearn_tree.DTYPE
DOUBLE = _sklearn_tree.DOUBLE
CRITERIA_CLF = {
"gini": _criterion.Gini,
"log_loss": _criterion.Entropy,
"entropy": _criterion.Entropy,
}
CRITERIA_REG = {
"squared_error": _criterion.MSE,
"friedman_mse": _criterion.FriedmanMSE,
"absolute_error": _criterion.MAE,
"poisson": _criterion.Poisson,
}
DENSE_SPLITTERS = {
"best": _oblique_splitter.MultiViewSplitter,
}
[docs]
class MultiViewDecisionTreeClassifier(SimMatrixMixin, DecisionTreeClassifier):
"""A multi-view axis-aligned decision tree classifier.
This is an experimental feature that applies an oblique decision tree to
multiple feature-sets concatenated across columns in ``X``.
Parameters
----------
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.
splitter : {"best"}, default="best"
The strategy used to choose the split at each node.
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 : array-like, int, float or {"auto", "sqrt", "log2"}, default=None
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
`int(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`.
If array-like, then `max_features` is the number of features to consider
for each feature set following the same logic as above, where
``n_features`` is the number of features in the respective feature set.
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.
Note: Compared to axis-aligned Random Forests, one can set
max_features to a number greater then ``n_features``.
random_state : int, RandomState instance or None, default=None
Controls the randomness of the estimator. The features are always
randomly permuted at each split, even if ``splitter`` is set to
``"best"``. When ``max_features < n_features``, the algorithm will
select ``max_features`` at random at each split before finding the best
split among them. But the best found split may vary across different
runs, even if ``max_features=n_features``. That is the case, if the
improvement of the criterion is identical for several splits and one
split has to be selected at random. To obtain a deterministic behaviour
during fitting, ``random_state`` has to be fixed to an integer.
See :term:`Glossary <random_state>` for details.
max_leaf_nodes : int, default=None
Grow a tree 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.
class_weight : dict, list of dict or "balanced", default=None
Weights associated with classes in the form ``{class_label: weight}``.
If None, 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))``
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.
feature_combinations : float, default=None
Not used.
ccp_alpha : non-negative float, default=0.0
Not used.
store_leaf_values : bool, default=False
Whether to store the leaf values.
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
Not used.
feature_set_ends : array-like of int of shape (n_feature_sets,), default=None
The indices of the end of each feature set. For example, if the first
feature set is the first 10 features, and the second feature set is the
next 20 features, then ``feature_set_ends = [10, 30]``. If ``None``,
then this will assume that there is only one feature set.
apply_max_features_per_feature_set : bool, default=False
Whether to apply sampling per feature set, where ``max_features`` is applied
to each feature-set. If ``False``, then sampling
is applied over the entire feature space.
Attributes
----------
classes_ : ndarray of shape (n_classes,) or list of ndarray
The classes labels (single output problem),
or a list of arrays of class labels (multi-output problem).
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 [4]_.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
max_features_ : int
The inferred value of max_features.
n_classes_ : int or list of int
The number of classes (for single output problems),
or a list containing the number of classes for each
output (for multi-output problems).
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.
tree_ : Tree instance
The underlying Tree object. Please refer to
``help(sklearn.tree._tree.Tree)`` for
attributes of Tree object.
feature_combinations_ : float
The number of feature combinations on average taken to fit the tree.
feature_set_ends_ : array-like of int of shape (n_feature_sets,)
The indices of the end of each feature set.
n_feature_sets_ : int
The number of feature sets.
max_features_per_set_ : array-like of int of shape (n_feature_sets,)
The number of features to sample per feature set. If ``None``, then
``max_features`` is applied to the entire feature space.
See Also
--------
sklearn.tree.DecisionTreeClassifier : An axis-aligned decision tree classifier.
"""
tree_type = "oblique"
_parameter_constraints = {
**DecisionTreeClassifier._parameter_constraints,
"feature_combinations": [
Interval(Real, 1.0, None, closed="left"),
None,
],
"feature_set_ends": ["array-like", None],
"apply_max_features_per_feature_set": ["boolean"],
}
_parameter_constraints.pop("max_features")
_parameter_constraints["max_features"] = [
Interval(Integral, 1, None, closed="left"),
Interval(RealNotInt, 0.0, 1.0, closed="right"),
StrOptions({"sqrt", "log2"}),
"array-like",
None,
]
def __init__(
self,
*,
criterion="gini",
splitter="best",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features=None,
random_state=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
class_weight=None,
feature_combinations=None,
ccp_alpha=0.0,
store_leaf_values=False,
monotonic_cst=None,
feature_set_ends=None,
apply_max_features_per_feature_set=False,
):
super().__init__(
criterion=criterion,
splitter=splitter,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
class_weight=class_weight,
random_state=random_state,
min_impurity_decrease=min_impurity_decrease,
ccp_alpha=ccp_alpha,
store_leaf_values=store_leaf_values,
monotonic_cst=monotonic_cst,
)
self.feature_combinations = feature_combinations
self.feature_set_ends = feature_set_ends
self.apply_max_features_per_feature_set = apply_max_features_per_feature_set
self._max_features_arr = None
def _build_tree(
self,
X,
y,
sample_weight,
missing_values_in_feature_mask,
min_samples_leaf,
min_weight_leaf,
max_leaf_nodes,
min_samples_split,
max_depth,
random_state,
):
"""Build the actual tree.
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.
min_samples_leaf : int or float
The minimum number of samples required to be at a leaf node.
min_weight_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights.
max_leaf_nodes : int, default=None
Grow a tree with ``max_leaf_nodes`` in best-first fashion.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node.
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.
random_state : int, RandomState instance or None, default=None
Controls the randomness of the estimator.
"""
monotonic_cst = None
self.monotonic_cst_ = monotonic_cst
_, n_features = X.shape
self.feature_combinations_ = 1
# Build tree
criterion = self.criterion
if not isinstance(criterion, BaseCriterion):
criterion = CRITERIA_CLF[self.criterion](self.n_outputs_, self.n_classes_)
else:
# Make a deepcopy in case the criterion has mutable attributes that
# might be shared and modified concurrently during parallel fitting
criterion = copy.deepcopy(criterion)
if self.feature_set_ends is None:
self.feature_set_ends_ = np.asarray([n_features], dtype=np.intp)
else:
self.feature_set_ends_ = np.atleast_1d(self.feature_set_ends).astype(np.intp)
self.n_feature_sets_ = len(self.feature_set_ends_)
if self.feature_set_ends_[-1] != n_features:
raise ValueError(
f"The last feature set end must be equal to the number of features, "
f"{n_features}, but got {self.feature_set_ends_[-1]}."
)
splitter = self.splitter
if issparse(X):
raise ValueError(
"Sparse input is not supported for oblique trees. "
"Please convert your data to a dense array."
)
else:
SPLITTERS = DENSE_SPLITTERS
if isinstance(self._max_features_arr, (Integral, Real, str, type(None))):
max_features_arr_ = [self._max_features_arr] * self.n_feature_sets_
stratify_mtry_per_view = self.apply_max_features_per_feature_set
else:
if not isinstance(self._max_features_arr, (list, np.ndarray)):
raise ValueError(
f"max_features must be an array-like, int, float, str, or None; "
f"got {type(self._max_features_arr)}"
)
if len(self._max_features_arr) != self.n_feature_sets_:
raise ValueError(
f"max_features must be an array-like of length {self.n_feature_sets_}; "
f"got {len(self.max_features)}"
)
max_features_arr_ = self._max_features_arr
stratify_mtry_per_view = True
self.n_features_in_set_ = []
if stratify_mtry_per_view:
# XXX: experimental
# we can replace max_features_ here based on whether or not uniform logic over
# feature sets
max_features_per_set = []
n_features_in_prev = 0
for idx in range(self.n_feature_sets_):
max_features = max_features_arr_[idx]
n_features_in_ = self.feature_set_ends_[idx] - n_features_in_prev
n_features_in_prev += n_features_in_
self.n_features_in_set_.append(n_features_in_)
if isinstance(max_features, str):
if max_features == "sqrt":
max_features = max(1, math.ceil(np.sqrt(n_features_in_)))
elif max_features == "log2":
max_features = max(1, math.ceil(np.log2(n_features_in_)))
elif max_features is None:
max_features = n_features_in_
elif isinstance(max_features, numbers.Integral):
max_features = max_features
else: # float
if max_features > 0.0:
max_features = max(1, math.ceil(max_features * n_features_in_))
else:
max_features = 0
if max_features > n_features_in_:
raise ValueError(
f"max_features must be less than or equal to "
f"the number of features in feature set {idx}: {n_features_in_}, but "
f"max_features = {max_features} when applying sampling"
f"per feature set."
)
max_features_per_set.append(max_features)
self.max_features_ = np.sum(max_features_per_set)
if self.max_features_ > n_features:
raise ValueError(
"max_features is greater than the number of features: "
f"{max_features} > {n_features}."
"This should not be possible. Please submit a bug report."
)
self.max_features_per_set_ = np.asarray(max_features_per_set, dtype=np.intp)
# the total number of features to sample per split
self.max_features_ = np.sum(self.max_features_per_set_)
else:
self.max_features_per_set_ = None
self.max_features = self._max_features_arr
if isinstance(self.max_features, str):
if self.max_features == "sqrt":
max_features = max(1, int(np.sqrt(self.n_features_in_)))
elif self.max_features == "log2":
max_features = max(1, int(np.log2(self.n_features_in_)))
elif self.max_features is None:
max_features = self.n_features_in_
elif isinstance(self.max_features, numbers.Integral):
max_features = self.max_features
else: # float
if self.max_features > 0.0:
max_features = max(1, int(self.max_features * self.n_features_in_))
else:
max_features = 0
self.max_features_ = max_features
if not isinstance(self.splitter, ObliqueSplitter):
splitter = SPLITTERS[self.splitter](
criterion,
self.max_features_,
min_samples_leaf,
min_weight_leaf,
random_state,
monotonic_cst,
self.feature_combinations_,
self.feature_set_ends_,
self.n_feature_sets_,
self.max_features_per_set_,
)
self.tree_ = ObliqueTree(self.n_features_in_, self.n_classes_, self.n_outputs_)
# Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
if max_leaf_nodes < 0:
builder = DepthFirstTreeBuilder(
splitter,
min_samples_split,
min_samples_leaf,
min_weight_leaf,
max_depth,
self.min_impurity_decrease,
)
else:
builder = BestFirstTreeBuilder(
splitter,
min_samples_split,
min_samples_leaf,
min_weight_leaf,
max_depth,
max_leaf_nodes,
self.min_impurity_decrease,
)
builder.build(self.tree_, X, y, sample_weight, None)
if self.n_outputs_ == 1:
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
def _update_tree(self, X, y, sample_weight):
# Update tree
max_leaf_nodes = -1 if self.max_leaf_nodes is None else self.max_leaf_nodes
min_samples_split = self.min_samples_split_
min_samples_leaf = self.min_samples_leaf_
min_weight_leaf = self.min_weight_leaf_
# set decision-tree model parameters
max_depth = np.iinfo(np.int32).max if self.max_depth is None else self.max_depth
monotonic_cst = self.monotonic_cst_
# Build tree
# Note: this reconstructs the builder with the same state it had during the
# initial fit. This is necessary because the builder is not saved as part
# of the class, and thus the state may be lost if pickled/unpickled.
criterion = self.criterion
if not isinstance(criterion, BaseCriterion):
criterion = CRITERIA_CLF[self.criterion](self.n_outputs_, self._n_classes_)
else:
# Make a deepcopy in case the criterion has mutable attributes that
# might be shared and modified concurrently during parallel fitting
criterion = copy.deepcopy(criterion)
random_state = check_random_state(self.random_state)
splitter = self.splitter
if issparse(X):
raise ValueError(
"Sparse input is not supported for oblique trees. "
"Please convert your data to a dense array."
)
else:
SPLITTERS = DENSE_SPLITTERS
if not isinstance(self.splitter, ObliqueSplitter):
splitter = SPLITTERS[self.splitter](
criterion,
self.max_features_,
min_samples_leaf,
min_weight_leaf,
random_state,
monotonic_cst,
self.feature_combinations_,
self.feature_set_ends_,
self.n_feature_sets_,
self.max_features_per_set_,
)
# Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
if max_leaf_nodes < 0:
builder = DepthFirstTreeBuilder(
splitter,
min_samples_split,
min_samples_leaf,
min_weight_leaf,
max_depth,
self.min_impurity_decrease,
self.store_leaf_values,
)
else:
builder = BestFirstTreeBuilder(
splitter,
min_samples_split,
min_samples_leaf,
min_weight_leaf,
max_depth,
max_leaf_nodes,
self.min_impurity_decrease,
self.store_leaf_values,
)
builder.initialize_node_queue(self.tree_, X, y, sample_weight)
builder.build(self.tree_, X, y, sample_weight)
self._prune_tree()
return self
[docs]
def fit(self, X, y, sample_weight=None, check_input=True, classes=None):
"""Build 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.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
classes : array-like of shape (n_classes,), default=None
List of all the classes that can possibly appear in the y vector.
Returns
-------
self : MultiViewDecisionTreeClassifier
Fitted estimator.
"""
return self._fit(
X, y, sample_weight=sample_weight, check_input=check_input, classes=classes
)
def _fit(
self,
X,
y,
sample_weight=None,
check_input=True,
missing_values_in_feature_mask=None,
classes=None,
):
# XXX: BaseDecisionTree does a check that requires max_features to not be a list/array-like
# so we need to temporarily set it to an acceptable value
# in the meantime, we will reset:
# - self.max_features_ to the original value
# - self.max_features_arr contains a possible array-like setting of max_features
self._max_features_arr = self.max_features
self.max_features = None
super()._fit(X, y, sample_weight, check_input, missing_values_in_feature_mask, classes)
self.max_features = self._max_features_arr
return self
@property
def _inheritable_fitted_attribute(self):
"""Define additional attributes to pass onto a parent meta tree-estimator.
Used for passing parameters to HonestTreeClassifier.
"""
return [
"max_features_",
"feature_combinations_",
"feature_set_ends_",
"n_feature_sets_",
"n_features_in_set_",
"max_features_per_set_",
]