Functions
std::unique_ptr< treelite::Model >	LoadRandomForestRegressor (int n_estimators, int n_features, int n_targets, std::int64_t const node_count, std::int64_t const children_left, std::int64_t const children_right, std::int64_t const feature, double const threshold, double const value, std::int64_t const n_node_samples, double const weighted_n_node_samples, double const *impurity)
	Load a scikit-learn RandomForestRegressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note that this function can also be used to load an ensemble of extremely randomized trees (sklearn.ensemble.ExtraTreesRegressor). More...

std::unique_ptr< treelite::Model >	LoadIsolationForest (int n_estimators, int n_features, std::int64_t const node_count, std::int64_t const children_left, std::int64_t const children_right, std::int64_t const feature, double const threshold, double const value, std::int64_t const n_node_samples, double const weighted_n_node_samples, double const *impurity, double ratio_c)
	Load a scikit-learn IsolationForest model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. More...

std::unique_ptr< treelite::Model >	LoadRandomForestClassifier (int n_estimators, int n_features, int n_targets, int32_t const n_classes, std::int64_t const node_count, std::int64_t const children_left, std::int64_t const children_right, std::int64_t const feature, double const threshold, double const value, std::int64_t const n_node_samples, double const weighted_n_node_samples, double const impurity)
	Load a scikit-learn RandomForestClassifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note that this function can also be used to load an ensemble of extremely randomized trees (sklearn.ensemble.ExtraTreesClassifier). More...

std::unique_ptr< treelite::Model >	LoadGradientBoostingRegressor (int n_iter, int n_features, std::int64_t const node_count, std::int64_t const children_left, std::int64_t const children_right, std::int64_t const feature, double const threshold, double const value, std::int64_t const n_node_samples, double const weighted_n_node_samples, double const impurity, double const baseline_prediction)
	Load a scikit-learn GradientBoostingRegressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note: GradientBoostingRegressor does not support multiple targets (outputs). More...

std::unique_ptr< treelite::Model >	LoadGradientBoostingClassifier (int n_iter, int n_features, int n_classes, std::int64_t const node_count, std::int64_t const children_left, std::int64_t const children_right, std::int64_t const feature, double const threshold, double const value, std::int64_t const n_node_samples, double const weighted_n_node_samples, double const impurity, double const baseline_prediction)
	Load a scikit-learn GradientBoostingClassifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note: GradientBoostingClassifier does not support multiple targets (outputs). More...

std::unique_ptr< treelite::Model >	LoadHistGradientBoostingRegressor (int n_iter, int n_features, std::int64_t const node_count, void const nodes, int expected_sizeof_node_struct, std::uint32_t n_categorical_splits, std::uint32_t const raw_left_cat_bitsets, std::uint32_t const known_cat_bitsets, std::uint32_t const known_cat_bitsets_offset_map, std::int32_t const features_map, std::int64_t const *categories_map, double const base_scores)
	Load a scikit-learn HistGradientBoostingRegressor model from a collection of arrays. Note: HistGradientBoostingRegressor does not support multiple targets (outputs). More...

std::unique_ptr< treelite::Model >	LoadHistGradientBoostingClassifier (int n_iter, int n_features, int n_classes, int64_t const node_count, void const nodes, int expected_sizeof_node_struct, std::uint32_t n_categorical_splits, std::uint32_t const raw_left_cat_bitsets, std::uint32_t const known_cat_bitsets, std::uint32_t const known_cat_bitsets_offset_map, std::int32_t const features_map, std::int64_t const *categories_map, double const base_scores)
	Load a scikit-learn HistGradientBoostingClassifier model from a collection of arrays. Note: HistGradientBoostingClassifier does not support multiple targets (outputs). More...

Function Documentation

◆ LoadGradientBoostingClassifier()

std::unique_ptr<treelite::Model> treelite::model_loader::sklearn::LoadGradientBoostingClassifier	(	int	n_iter,
		int	n_features,
		int	n_classes,
		std::int64_t const *	node_count,
		std::int64_t const **	children_left,
		std::int64_t const **	children_right,
		std::int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		std::int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity,
		double const *	baseline_prediction
	)

Load a scikit-learn GradientBoostingClassifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note: GradientBoostingClassifier does not support multiple targets (outputs).

Parameters

n_iter	Number of boosting iterations
n_features	Number of features in the training data
n_classes	Number of classes in the target variable
node_count	node_count[i] stores the number of nodes in the i-th tree
children_left	children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
children_right	children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
feature	feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
threshold	threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
value	value[i][k] stores the leaf output of node k of the i-th tree. This is only defined if node k is a leaf node.
n_node_samples	n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree.
weighted_n_node_samples	weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree.
impurity	impurity[i][k] stores the impurity measure (gini, entropy etc) associated with node k of the i-th tree.
baseline_prediction	Baseline predictions for outputs. At prediction, margin scores will be adjusted by this amount before applying the post-processing (link) function. Required shape: (n_classes,)

Returns: Loaded model

◆ LoadGradientBoostingRegressor()

std::unique_ptr<treelite::Model> treelite::model_loader::sklearn::LoadGradientBoostingRegressor	(	int	n_iter,
		int	n_features,
		std::int64_t const *	node_count,
		std::int64_t const **	children_left,
		std::int64_t const **	children_right,
		std::int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		std::int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity,
		double const *	baseline_prediction
	)

Load a scikit-learn GradientBoostingRegressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note: GradientBoostingRegressor does not support multiple targets (outputs).

Parameters

n_iter	Number of boosting iterations
n_features	Number of features in the training data
node_count	node_count[i] stores the number of nodes in the i-th tree
children_left	children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
children_right	children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
feature	feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
threshold	threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
value	value[i][k] stores the leaf output of node k of the i-th tree. This is only defined if node k is a leaf node.
n_node_samples	n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree.
weighted_n_node_samples	weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree.
impurity	impurity[i][k] stores the impurity measure (gini, entropy etc) associated with node k of the i-th tree.
baseline_prediction	Baseline predictions for outputs. At prediction, margin scores will be adjusted by this amount before applying the post-processing (link) function. Required shape: (1,)

Returns: Loaded model

◆ LoadHistGradientBoostingClassifier()

std::unique_ptr<treelite::Model> treelite::model_loader::sklearn::LoadHistGradientBoostingClassifier	(	int	n_iter,
		int	n_features,
		int	n_classes,
		int64_t const *	node_count,
		void const **	nodes,
		int	expected_sizeof_node_struct,
		std::uint32_t	n_categorical_splits,
		std::uint32_t const **	raw_left_cat_bitsets,
		std::uint32_t const *	known_cat_bitsets,
		std::uint32_t const *	known_cat_bitsets_offset_map,
		std::int32_t const *	features_map,
		std::int64_t const **	categories_map,
		double const *	base_scores
	)

Load a scikit-learn HistGradientBoostingClassifier model from a collection of arrays. Note: HistGradientBoostingClassifier does not support multiple targets (outputs).

Parameters

n_iter	Number of boosting iterations
n_features	Number of features in the training data
n_classes	Number of classes in the target variable
node_count	node_count[i] stores the number of nodes in the i-th tree
nodes	nodes[i][k] stores the k-th node of the i-th tree.
expected_sizeof_node_struct	Expected size of Node struct, in bytes
n_categorical_splits	n_categorical_splits[i] stores the number of categorical splits in the i-th tree.
raw_left_cat_bitsets	raw_left_cat_bitsets[i][k] stores the bitmaps for node k of tree i. The bitmaps are used to represent categorical tests. Shape of raw_left_cat_bitsets[i]: (n_categorical_splits, 8)
known_cat_bitsets	Bitsets representing the list of known categories per categorical feature. Shape: (n_categorical_features, 8)
known_cat_bitsets_offset_map	Map from an original feature index to the corresponding index in the known_cat_bitsets array. Shape: (n_features,)
features_map	Mapping to re-order features. This is needed because HistGradientBoosting estimator internally re-orders features using ColumnTransformer so that the categorical features come before the numerical features.
categories_map	Mapping to transform categorical features. This is needed because HistGradientBoosting estimator embeds an OrdinalEncoder. categories_map[i] represents the mapping for i-th categorical feature.
base_scores	Baseline predictions for outputs. At prediction, margin scores will be adjusted by this amount before applying the post-processing (link) function. Required shape: (1,) for binary classification; (n_classes,) for multi-class classification

Returns: Loaded model

◆ LoadHistGradientBoostingRegressor()

std::unique_ptr<treelite::Model> treelite::model_loader::sklearn::LoadHistGradientBoostingRegressor	(	int	n_iter,
		int	n_features,
		std::int64_t const *	node_count,
		void const **	nodes,
		int	expected_sizeof_node_struct,
		std::uint32_t	n_categorical_splits,
		std::uint32_t const **	raw_left_cat_bitsets,
		std::uint32_t const *	known_cat_bitsets,
		std::uint32_t const *	known_cat_bitsets_offset_map,
		std::int32_t const *	features_map,
		std::int64_t const **	categories_map,
		double const *	base_scores
	)

Load a scikit-learn HistGradientBoostingRegressor model from a collection of arrays. Note: HistGradientBoostingRegressor does not support multiple targets (outputs).

Parameters

n_iter	Number of boosting iterations
n_features	Number of features in the training data
node_count	node_count[i] stores the number of nodes in the i-th tree
nodes	nodes[i][k] stores the k-th node of the i-th tree.
expected_sizeof_node_struct	Expected size of Node struct, in bytes
n_categorical_splits	n_categorical_splits[i] stores the number of categorical splits in the i-th tree.
raw_left_cat_bitsets	raw_left_cat_bitsets[i][k] stores the bitmaps for node k of tree i. The bitmaps are used to represent categorical tests. Shape of raw_left_cat_bitsets[i]: (n_categorical_splits, 8)
known_cat_bitsets	Bitsets representing the list of known categories per categorical feature. Shape: (n_categorical_features, 8)
known_cat_bitsets_offset_map	Map from an original feature index to the corresponding index in the known_cat_bitsets array. Shape: (n_features,)
features_map	Mapping to re-order features. This is needed because HistGradientBoosting estimator internally re-orders features using ColumnTransformer so that the categorical features come before the numerical features.
categories_map	Mapping to transform categorical features. This is needed because HistGradientBoosting estimator embeds an OrdinalEncoder. categories_map[i] represents the mapping for i-th categorical feature.
base_scores	Baseline predictions for outputs. At prediction, margin scores will be adjusted by this amount before applying the post-processing (link) function. Required shape: (1,)

Returns: Loaded model

◆ LoadIsolationForest()

std::unique_ptr<treelite::Model> treelite::model_loader::sklearn::LoadIsolationForest	(	int	n_estimators,
		int	n_features,
		std::int64_t const *	node_count,
		std::int64_t const **	children_left,
		std::int64_t const **	children_right,
		std::int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		std::int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity,
		double	ratio_c
	)

Load a scikit-learn IsolationForest model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail.

Parameters

n_estimators	Number of trees in the isolation forest
n_features	Number of features in the training data
node_count	node_count[i] stores the number of nodes in the i-th tree
children_left	children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
children_right	children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
feature	feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
threshold	threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
value	value[i][k] stores the expected isolation depth of node k of the i-th tree. This is only defined if node k is a leaf node.
n_node_samples	n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree.
weighted_n_node_samples	weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree.
impurity	Not used, but must be passed as array of arrays for each tree and node.
ratio_c	Standardizing constant to use for calculation of the anomaly score.

Returns: Loaded model

◆ LoadRandomForestClassifier()

std::unique_ptr<treelite::Model> treelite::model_loader::sklearn::LoadRandomForestClassifier	(	int	n_estimators,
		int	n_features,
		int	n_targets,
		int32_t const *	n_classes,
		std::int64_t const *	node_count,
		std::int64_t const **	children_left,
		std::int64_t const **	children_right,
		std::int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		std::int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity
	)

Load a scikit-learn RandomForestClassifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note that this function can also be used to load an ensemble of extremely randomized trees (sklearn.ensemble.ExtraTreesClassifier).

Parameters

n_estimators	Number of trees in the random forest
n_features	Number of features in the training data
n_targets	Number of targets (outputs)
n_classes	n_classes[i] stores the number of classes in the i-th target
node_count	node_count[i] stores the number of nodes in the i-th tree
children_left	children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
children_right	children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
feature	feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
threshold	threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
value	value[i][k] stores the leaf output of node k of the i-th tree. This is only defined if node k is a leaf node.
n_node_samples	n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree.
weighted_n_node_samples	weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree.
impurity	impurity[i][k] stores the impurity measure (gini, entropy etc) associated with node k of the i-th tree.

Returns: Loaded model

◆ LoadRandomForestRegressor()

std::unique_ptr<treelite::Model> treelite::model_loader::sklearn::LoadRandomForestRegressor	(	int	n_estimators,
		int	n_features,
		int	n_targets,
		std::int64_t const *	node_count,
		std::int64_t const **	children_left,
		std::int64_t const **	children_right,
		std::int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		std::int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity
	)

Load a scikit-learn RandomForestRegressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note that this function can also be used to load an ensemble of extremely randomized trees (sklearn.ensemble.ExtraTreesRegressor).

Parameters

n_estimators	Number of trees in the random forest
n_features	Number of features in the training data
n_targets	Number of targets (outputs)
node_count	node_count[i] stores the number of nodes in the i-th tree
children_left	children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
children_right	children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
feature	feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
threshold	threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node.
value	value[i][k] stores the leaf output of node k of the i-th tree. This is only defined if node k is a leaf node.
n_node_samples	n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree.
weighted_n_node_samples	weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree.
impurity	impurity[i][k] stores the impurity measure (gini, entropy etc) associated with node k of the i-th tree.

Returns: Loaded model

Functions

Function Documentation

◆ LoadGradientBoostingClassifier()

◆ LoadGradientBoostingRegressor()

◆ LoadHistGradientBoostingClassifier()

◆ LoadHistGradientBoostingRegressor()

◆ LoadIsolationForest()

◆ LoadRandomForestClassifier()

◆ LoadRandomForestRegressor()