Ranger Tree Regressor¶
- class skranger.tree.RangerTreeRegressor(*, verbose=False, mtry=0, importance='none', min_node_size=0, max_depth=0, replace=True, sample_fraction=None, keep_inbag=False, inbag=None, split_rule='variance', num_random_splits=1, alpha=0.5, minprop=0.1, split_select_weights=None, always_split_features=None, categorical_features=None, respect_categorical_features=None, scale_permutation_importance=False, local_importance=False, regularization_factor=None, regularization_usedepth=False, holdout=False, oob_error=False, save_memory=False, seed=42)[source]¶
Ranger Tree Regression implementation for sci-kit learn.
Provides a sklearn regressor interface to the Ranger C++ library using Cython.
- Parameters
verbose (bool) – Enable ranger’s verbose logging
mtry (int/callable) – The number of features to split on each node. When a callable is passed, the function must accept a single parameter which is the number of features passed, and return some value between 1 and the number of features.
importance (str) – One of one of
none
,impurity
,impurity_corrected
,permutation
.min_node_size (int) – The minimal node size.
max_depth (int) – The maximal tree depth; 0 means unlimited.
replace (bool) – Sample with replacement.
sample_fraction (float/list) – The fraction of observations to sample. The default is 1 when sampling with replacement, and 0.632 otherwise. This can be a list of class specific values.
keep_inbag (bool) – If true, save how often observations are in-bag in each tree. These will be stored in the
ranger_forest_
attribute under the key"inbag_counts"
.inbag (list) – A list of size
n_estimators
, containing inbag counts for each observation. Can be used for stratified sampling.split_rule (str) – One of
variance
,extratrees
,maxstat
,beta
; defaultvariance
.num_random_splits (int) – The number of random splits to consider for the
extratrees
splitrule.alpha (float) – Significance threshold to allow splitting for the
maxstat
split rule.minprop (float) – Lower quantile of covariate distribution to be considered for splitting for
maxstat
split rule.respect_categorical_features (str) – One of
ignore
,order
,partition
. The default ispartition
for theextratrees
splitrule, otherwise the default isignore
.scale_permutation_importance (bool) – For
permutation
importance, scale permutation importance by standard error as in (Breiman 2001).local_importance (bool) – For
permutation
importance, calculate and return local importance values as (Breiman 2001).regularization_factor (list) – A vector of regularization factors for the features.
regularization_usedepth (bool) – Whether to consider depth in regularization.
holdout (bool) – Hold-out all samples with case weight 0 and use these for feature importance and prediction error.
oob_error (bool) – Whether to calculate out-of-bag prediction error.
save_memory (bool) – Save memory at the cost of speed growing trees.
seed (int) – Random seed value.
- Variables
n_features_in_ (int) – The number of features (columns) from the fit input
X
.feature_names_ (list) – Names for the features of the fit input
X
.ranger_forest_ (dict) – The returned result object from calling C++ ranger.
mtry_ (int) – The mtry value as determined if
mtry
is callable, otherwise it is the same asmtry
.sample_fraction_ (float) – The sample fraction determined by input validation
regularization_factor_ (list) – The regularization factors determined by input validation.
unordered_features_ (list) – The unordered feature names determined by input validation.
split_rule_ (int) – The split rule integer corresponding to ranger enum
SplitRule
.use_regularization_factor_ (bool) – Input validation determined bool for using regularization factor input parameter.
respect_categorical_features_ (str) – Input validation determined string respecting categorical features.
importance_mode_ (int) – The importance mode integer corresponding to ranger enum
ImportanceMode
.feature_importances_ (ndarray) – The variable importances from ranger.
- apply(X)¶
Calculate the index of the leaf for each sample. :param array2d X: training input features
- property criterion¶
Compatibility alias for split rule.
- decision_path(X)¶
Calculate the decision path through the tree for each sample. :param array2d X: training input features
- fit(X, y, sample_weight=None, class_weights=None, split_select_weights=None, always_split_features=None, categorical_features=None)[source]¶
Fit the ranger random forest using training data.
- Parameters
X (array2d) – training input features
y (array1d) – training input targets
sample_weight (array1d) – optional weights for input samples
split_select_weights (list) – Vector of weights between 0 and 1 of probabilities to select features for splitting. Can be a single vector or a vector of vectors with one vector per tree.
always_split_features (list) – Features which should always be selected for splitting. A list of column index values.
categorical_features (list) – A list of column index values which should be considered categorical, or unordered.
- classmethod from_forest(forest: RangerForestRegressor, idx: int)[source]¶
Extract a tree from a forest.
- Parameters
forest (RangerForestClassifier) – A trained RangerForestClassifier instance
idx (int) – The tree index from the forest to extract.
- get_depth()¶
Calculate the maximum depth of the tree.
- get_n_leaves()¶
Calculate the number of leaves of the tree.
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- predict(X)[source]¶
Predict regression target for X.
- Parameters
X (array2d) – prediction input features
- score(X, y, sample_weight=None)¶
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares
((y_true - y_pred)** 2).sum()
and \(v\) is the total sum of squares((y_true - y_true.mean()) ** 2).sum()
. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters
X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
- Returns
score – \(R^2\) of
self.predict(X)
wrt. y.- Return type
float
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance