import typing as t
import numpy as np
from sklearn.base import ClassifierMixin
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import check_array
from sklearn.utils.validation import check_is_fitted
from skgrf import grf
from skgrf.tree.base import BaseGRFTree
from skgrf.utils.validation import check_sample_weight
if t.TYPE_CHECKING: # pragma: no cover
from skgrf.ensemble.classifier import GRFForestClassifier
[docs]class GRFTreeClassifier(BaseGRFTree, ClassifierMixin):
r"""GRF Tree Classification implementation for sci-kit learn.
Provides a sklearn tree classifier interface to the GRF C++ library using Cython.
:param bool equalize_cluster_weights: Weight the samples such that clusters have
equally weight. If ``False``, larger clusters will have more weight. If
``True``, the number of samples drawn from each cluster is equal to the size of
the smallest cluster. If ``True``, sample weights should not be passed on
fitting.
:param float sample_fraction: Fraction of samples used in each tree.
:param int mtry: The number of features to split on each node. The default is
``sqrt(p) + 20`` where ``p`` is the number of features.
:param int min_node_size: The minimum number of observations in each tree leaf.
:param bool honesty: Use honest splitting (subsample splitting).
:param float honesty_fraction: The fraction of data used for subsample splitting.
:param bool honesty_prune_leaves: Prune estimation sample tree such that no leaves
are empty. If ``False``, trees with empty leaves are skipped.
:param float alpha: The maximum imbalance of a split.
:param float imbalance_penalty: Penalty applied to imbalanced splits.
:param int seed: Random seed value.
:ivar int n_features_in\_: The number of features (columns) from the fit input
``X``.
:ivar dict grf_forest\_: The returned result object from calling C++ grf.
:ivar int mtry\_: The ``mtry`` value determined by validation.
:ivar int outcome_index\_: The index of the grf train matrix holding the outcomes.
:ivar list samples_per_cluster\_: The number of samples to train per cluster.
:ivar list clusters\_: The cluster labels determined from the fit input ``cluster``.
:ivar int n_clusters\_: The number of unique cluster labels from the fit input
``cluster``.
:ivar list classes\_: The class labels determined from the fit input ``y``.
:ivar int n_classes\_: The number of unique class labels from the fit input
``y``.
:ivar str criterion: The criterion used for splitting: ``gini``
"""
def __init__(
self,
equalize_cluster_weights=False,
sample_fraction=0.5,
mtry=None,
min_node_size=5,
honesty=True,
honesty_fraction=0.5,
honesty_prune_leaves=True,
alpha=0.05,
imbalance_penalty=0.0,
seed=42,
):
self.equalize_cluster_weights = equalize_cluster_weights
self.sample_fraction = sample_fraction
self.mtry = mtry
self.min_node_size = min_node_size
self.honesty = honesty
self.honesty_fraction = honesty_fraction
self.honesty_prune_leaves = honesty_prune_leaves
self.alpha = alpha
self.imbalance_penalty = imbalance_penalty
self.seed = seed
@property
def criterion(self):
return "gini"
[docs] @classmethod
def from_forest(cls, forest: "GRFForestClassifier", idx: int):
"""Extract a tree from a forest.
:param GRFForestClassifier forest: A trained GRFClassifier instance
:param int idx: The tree index from the forest to extract.
"""
# Even though we have a tree object, we keep the exact same dictionary structure
# that exists in the forests, so that we can reuse the Cython entrypoints.
# We also copy over some instance attributes from the trained forest.
# params
instance = cls(
equalize_cluster_weights=forest.equalize_cluster_weights,
sample_fraction=forest.sample_fraction,
mtry=forest.mtry,
min_node_size=forest.min_node_size,
honesty=forest.honesty,
honesty_fraction=forest.honesty_fraction,
honesty_prune_leaves=forest.honesty_prune_leaves,
alpha=forest.alpha,
imbalance_penalty=forest.imbalance_penalty,
seed=forest.seed,
)
# forest
grf_forest = {}
for k, v in forest.grf_forest_.items():
if isinstance(v, list):
grf_forest[k] = [forest.grf_forest_[k][idx]]
else:
grf_forest[k] = v
grf_forest["num_trees"] = 1
instance.grf_forest_ = grf_forest
instance._ensure_ptr()
# vars
instance.outcome_index_ = forest.outcome_index_
instance.n_features_in_ = forest.n_features_in_
instance.clusters_ = forest.clusters_
instance.n_clusters_ = forest.n_clusters_
instance.classes_ = forest.classes_
instance.n_classes_ = forest.n_classes_
instance.samples_per_cluster_ = forest.samples_per_cluster_
instance.mtry_ = forest.mtry_
instance.sample_weight_index_ = forest.sample_weight_index_
return instance
[docs] def fit(
self, X, y, sample_weight=None, cluster=None, compute_oob_predictions=False
):
"""Fit the grf forest using training data.
:param array2d X: training input features
:param array1d y: training input targets
:param array1d sample_weight: optional weights for input samples
:param array1d cluster: optional cluster assignments for input samples
"""
X, y = self._validate_data(X, y, force_all_finite="allow-nan")
check_classification_targets(y)
self._check_num_samples(X)
self._check_n_features(X, reset=True)
self._check_sample_fraction(oob=compute_oob_predictions)
self._check_alpha()
sample_weight, use_sample_weight = check_sample_weight(sample_weight, X)
cluster_ = self._check_cluster(X=X, cluster=cluster)
self.samples_per_cluster_ = self._check_equalize_cluster_weights(
cluster=cluster_, sample_weight=sample_weight
)
self.mtry_ = self._check_mtry(X=X)
# Map classes to indices
y = np.copy(y)
self.classes_, y = np.unique(y, return_inverse=True)
self.n_classes_ = len(self.classes_)
train_matrix = self._create_train_matrices(
X=X, y=y, sample_weight=sample_weight
)
self.grf_forest_ = grf.probability_train(
np.asfortranarray(train_matrix.astype("float64")),
self.outcome_index_,
self.sample_weight_index_,
use_sample_weight,
self.n_classes_,
self.mtry_,
1, # num_trees
self.min_node_size,
self.sample_fraction,
self.honesty,
self.honesty_fraction,
self.honesty_prune_leaves,
1, # ci_group_size
self.alpha,
self.imbalance_penalty,
cluster_,
self.samples_per_cluster_,
compute_oob_predictions,
1, # num_threads
self.seed,
)
self._ensure_ptr()
sample_weight = sample_weight if sample_weight is not None else np.ones(len(X))
self._set_node_values(y, sample_weight)
self._set_n_classes()
return self
[docs] def predict(self, X):
"""Predict classes from X.
:param array2d X: prediction input features
"""
probas = self.predict_proba(X)
return self.classes_.take(np.argmax(probas, axis=1), axis=0)
[docs] def predict_proba(self, X):
"""Predict probabilities for classes from X.
:param array2d X: prediction input features
"""
result = self._predict(X)
predictions = np.atleast_2d(np.array(result["predictions"]))
return predictions
[docs] def predict_log_proba(self, X):
"""Predict log probabilities for classes from X.
:param array2d X: prediction input features
"""
proba = self.predict_proba(X)
return np.log(proba)
def _predict(self, X, estimate_variance=False):
check_is_fitted(self)
X = check_array(X, force_all_finite="allow-nan")
self._check_n_features(X, reset=False)
self._ensure_ptr()
result = grf.probability_predict(
self.grf_forest_cpp_,
np.asfortranarray([[]]), # train_matrix
self.outcome_index_,
self.n_classes_,
np.asfortranarray(X.astype("float64")), # test_matrix
1, # num_threads
estimate_variance,
)
return result
def _more_tags(self):
return {
"_xfail_checks": {
"check_sample_weights_invariance": "zero sample_weight is not equivalent to removing samples",
},
"allow_nan": True,
}