Functions
int	TreeliteLoadSKLearnRandomForestRegressor (int n_estimators, int n_features, int n_targets, int64_t const node_count, int64_t const children_left, int64_t const children_right, int64_t const feature, double const threshold, double const value, int64_t const n_node_samples, double const weighted_n_node_samples, double const impurity, TreeliteModelHandle out)
	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...

int	TreeliteLoadSKLearnIsolationForest (int n_estimators, int n_features, int64_t const node_count, int64_t const children_left, int64_t const children_right, int64_t const feature, double const threshold, double const value, int64_t const n_node_samples, double const weighted_n_node_samples, double const impurity, double ratio_c, TreeliteModelHandle out)
	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...

int	TreeliteLoadSKLearnRandomForestClassifier (int n_estimators, int n_features, int n_targets, int32_t const n_classes, int64_t const node_count, int64_t const children_left, int64_t const children_right, int64_t const feature, double const threshold, double const value, int64_t const n_node_samples, double const weighted_n_node_samples, double const impurity, TreeliteModelHandle *out)
	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...

int	TreeliteLoadSKLearnGradientBoostingRegressor (int n_iter, int n_features, int64_t const node_count, int64_t const children_left, int64_t const children_right, int64_t const feature, double const threshold, double const value, int64_t const n_node_samples, double const weighted_n_node_samples, double const impurity, double const base_scores, TreeliteModelHandle *out)
	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...

int	TreeliteLoadSKLearnGradientBoostingClassifier (int n_iter, int n_features, int n_classes, int64_t const node_count, int64_t const children_left, int64_t const children_right, int64_t const feature, double const threshold, double const value, int64_t const n_node_samples, double const weighted_n_node_samples, double const impurity, double const base_scores, TreeliteModelHandle *out)
	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...

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

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

Detailed Description

Function Documentation

◆ TreeliteLoadSKLearnGradientBoostingClassifier()

int TreeliteLoadSKLearnGradientBoostingClassifier	(	int	n_iter,
		int	n_features,
		int	n_classes,
		int64_t const *	node_count,
		int64_t const **	children_left,
		int64_t const **	children_right,
		int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity,
		double const *	base_scores,
		TreeliteModelHandle *	out
	)

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.
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: (n_classes,)
out	Loaded model

Returns: 0 for success, -1 for failure

◆ TreeliteLoadSKLearnGradientBoostingRegressor()

int TreeliteLoadSKLearnGradientBoostingRegressor	(	int	n_iter,
		int	n_features,
		int64_t const *	node_count,
		int64_t const **	children_left,
		int64_t const **	children_right,
		int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity,
		double const *	base_scores,
		TreeliteModelHandle *	out
	)

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.
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,)
out	Loaded model

Returns: 0 for success, -1 for failure

◆ TreeliteLoadSKLearnHistGradientBoostingClassifier()

int TreeliteLoadSKLearnHistGradientBoostingClassifier	(	int	n_iter,
		int	n_features,
		int	n_classes,
		int64_t const *	node_count,
		void const **	nodes,
		int	expected_sizeof_node_struct,
		uint32_t	n_categorical_splits,
		uint32_t const **	raw_left_cat_bitsets,
		uint32_t const *	known_cat_bitsets,
		uint32_t const *	known_cat_bitsets_offset_map,
		int32_t const *	features_map,
		int64_t const **	categories_map,
		double const *	base_scores,
		TreeliteModelHandle *	out
	)

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
out	Loaded model

Returns: 0 for success, -1 for failure

◆ TreeliteLoadSKLearnHistGradientBoostingRegressor()

int TreeliteLoadSKLearnHistGradientBoostingRegressor	(	int	n_iter,
		int	n_features,
		int64_t const *	node_count,
		void const **	nodes,
		int	expected_sizeof_node_struct,
		uint32_t	n_categorical_splits,
		uint32_t const **	raw_left_cat_bitsets,
		uint32_t const *	known_cat_bitsets,
		uint32_t const *	known_cat_bitsets_offset_map,
		int32_t const *	features_map,
		int64_t const **	categories_map,
		double const *	base_scores,
		TreeliteModelHandle *	out
	)

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,)
out	Loaded model

Returns: 0 for success, -1 for failure

◆ TreeliteLoadSKLearnIsolationForest()

int TreeliteLoadSKLearnIsolationForest	(	int	n_estimators,
		int	n_features,
		int64_t const *	node_count,
		int64_t const **	children_left,
		int64_t const **	children_right,
		int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity,
		double	ratio_c,
		TreeliteModelHandle *	out
	)

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.
out	Loaded model

Returns: 0 for success, -1 for failure

◆ TreeliteLoadSKLearnRandomForestClassifier()

int TreeliteLoadSKLearnRandomForestClassifier	(	int	n_estimators,
		int	n_features,
		int	n_targets,
		int32_t const *	n_classes,
		int64_t const *	node_count,
		int64_t const **	children_left,
		int64_t const **	children_right,
		int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity,
		TreeliteModelHandle *	out
	)

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.
out	Loaded model

Returns: 0 for success, -1 for failure

◆ TreeliteLoadSKLearnRandomForestRegressor()

int TreeliteLoadSKLearnRandomForestRegressor	(	int	n_estimators,
		int	n_features,
		int	n_targets,
		int64_t const *	node_count,
		int64_t const **	children_left,
		int64_t const **	children_right,
		int64_t const **	feature,
		double const **	threshold,
		double const **	value,
		int64_t const **	n_node_samples,
		double const **	weighted_n_node_samples,
		double const **	impurity,
		TreeliteModelHandle *	out
	)

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.
out	Loaded model

Returns: 0 for success, -1 for failure

Functions

Detailed Description

Function Documentation

◆ TreeliteLoadSKLearnGradientBoostingClassifier()

◆ TreeliteLoadSKLearnGradientBoostingRegressor()

◆ TreeliteLoadSKLearnHistGradientBoostingClassifier()

◆ TreeliteLoadSKLearnHistGradientBoostingRegressor()

◆ TreeliteLoadSKLearnIsolationForest()

◆ TreeliteLoadSKLearnRandomForestClassifier()

◆ TreeliteLoadSKLearnRandomForestRegressor()