import typing as t
import numpy as np
from sklearn.base import RegressorMixin
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
if t.TYPE_CHECKING: # pragma: no cover
from skgrf.ensemble.quantile_regressor import GRFForestQuantileRegressor
[docs]class GRFTreeQuantileRegressor(BaseGRFTree, RegressorMixin):
r"""GRF Tree Quantile Regression implementation for sci-kit learn.
Provides a sklearn tree quantile regressor interface to the GRF C++ library using
Cython.
.. warning::
Because the training dataset is required for prediction, the training dataset
is recorded onto the estimator instance. This means that serializing this
estimator will result in a file at least as large as the serialized training
dataset.
:param list(float) quantiles: A list of quantiles on which to predict.
:param bool regression_splitting: Use regression splits instead of splitting
specially for quantiles.
: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 array2d train\_: The ``X,y`` concatenated train matrix passed to grf.
:ivar str criterion: The criterion used for splitting: ``gini``
"""
def __init__(
self,
quantiles=None,
regression_splitting=False,
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,
seed=42,
):
self.quantiles = quantiles
self.regression_splitting = regression_splitting
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: "GRFForestQuantileRegressor", idx: int):
"""Extract a tree from a forest.
:param GRFForestQuantileRegressor forest: A trained GRFQuantileRegressor 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(
quantiles=forest.quantiles,
regression_splitting=forest.regression_splitting,
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.samples_per_cluster_ = forest.samples_per_cluster_
instance.mtry_ = forest.mtry_
instance.sample_weight_index_ = forest.sample_weight_index_
# data
instance.train_ = forest.train_
return instance
[docs] def fit(self, X, y, cluster=None):
"""Fit the grf tree quantile regressor using training data.
:param array2d X: training input features
:param array1d y: training input targets
:param array1d cluster: optional cluster assignments for input samples
"""
if self.quantiles is None:
raise ValueError("quantiles must be set")
X, y = self._validate_data(X, y, force_all_finite="allow-nan")
self._check_num_samples(X)
self._check_n_features(X, reset=True)
self._check_sample_fraction()
self._check_alpha()
cluster = self._check_cluster(X=X, cluster=cluster)
self.samples_per_cluster_ = self._check_equalize_cluster_weights(
cluster=cluster, sample_weight=None
)
self.mtry_ = self._check_mtry(X=X)
train_matrix = self._create_train_matrices(X, y)
self.train_ = train_matrix
self.grf_forest_ = grf.quantile_train(
self.quantiles,
self.regression_splitting,
np.asfortranarray(train_matrix.astype("float64")),
self.outcome_index_,
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_,
False, # compute_oob_predictions,
1, # num_threads
self.seed,
)
self._ensure_ptr()
sample_weight = np.ones(len(X))
self._set_node_values(y, sample_weight)
self._set_n_classes()
return self
[docs] def predict(self, X):
"""Predict quantile regression target(s) for X.
:param array2d X: prediction input features
"""
return np.atleast_1d(np.squeeze(np.array(self._predict(X)["predictions"])))
def _predict(self, X):
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.quantile_predict(
self.grf_forest_cpp_,
self.quantiles,
np.asfortranarray(self.train_.astype("float64")),
self.outcome_index_,
np.asfortranarray(X.astype("float64")), # test_matrix
1, # num_threads
)
return result
def _more_tags(self):
return {
"requires_y": True,
"poor_score": True,
"allow_nan": True,
}