ai.onnx.ml - TreeEnsembleRegressor

TreeEnsembleRegressor - 5 (ai.onnx.ml)

Version

This version of the operator has been deprecated since version 5 of domain ai.onnx.ml.

Summary

This operator is DEPRECATED. Please use TreeEnsemble instead which provides the same functionality.
Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF

Attributes

  • aggregate_function - STRING (default is 'SUM'):

    Defines how to aggregate leaf values within a target.
    One of ‘AVERAGE,’ ‘SUM,’ ‘MIN,’ ‘MAX.’

  • base_values - FLOATS :

    Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)

  • base_values_as_tensor - TENSOR :

    Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)

  • n_targets - INT :

    The total number of targets.

  • nodes_falsenodeids - INTS :

    Child node if expression is false

  • nodes_featureids - INTS :

    Feature id for each node.

  • nodes_hitrates - FLOATS :

    Popularity of each node, used for performance and may be omitted.

  • nodes_hitrates_as_tensor - TENSOR :

    Popularity of each node, used for performance and may be omitted.

  • nodes_missing_value_tracks_true - INTS :

    For each node, define what to do in the presence of a NaN: use the ‘true’ (if the attribute value is 1) or ‘false’ (if the attribute value is 0) branch based on the value in this array.
    This attribute may be left undefined and the default value is false (0) for all nodes.

  • nodes_modes - STRINGS :

    The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
    One of ‘BRANCH_LEQ’, ‘BRANCH_LT’, ‘BRANCH_GTE’, ‘BRANCH_GT’, ‘BRANCH_EQ’, ‘BRANCH_NEQ’, ‘LEAF’

  • nodes_nodeids - INTS :

    Node id for each node. Node ids must restart at zero for each tree and increase sequentially.

  • nodes_treeids - INTS :

    Tree id for each node.

  • nodes_truenodeids - INTS :

    Child node if expression is true

  • nodes_values - FLOATS :

    Thresholds to do the splitting on for each node.

  • nodes_values_as_tensor - TENSOR :

    Thresholds to do the splitting on for each node.

  • post_transform - STRING (default is 'NONE'):

    Indicates the transform to apply to the score.
    One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’

  • target_ids - INTS :

    The index of the target that each weight is for

  • target_nodeids - INTS :

    The node id of each weight

  • target_treeids - INTS :

    The id of the tree that each node is in.

  • target_weights - FLOATS :

    The weight for each target

  • target_weights_as_tensor - TENSOR :

    The weight for each target

Inputs

  • X (heterogeneous) - T:

    Input of shape [N,F]

Outputs

  • Y (heterogeneous) - tensor(float):

    N classes

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ):

    The input type must be a tensor of a numeric type.

TreeEnsembleRegressor - 3 (ai.onnx.ml)

Version

This version of the operator has been available since version 3 of domain ai.onnx.ml.

Summary

Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF

Attributes

  • aggregate_function - STRING (default is 'SUM'):

    Defines how to aggregate leaf values within a target.
    One of ‘AVERAGE,’ ‘SUM,’ ‘MIN,’ ‘MAX.’

  • base_values - FLOATS :

    Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)

  • base_values_as_tensor - TENSOR :

    Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)

  • n_targets - INT :

    The total number of targets.

  • nodes_falsenodeids - INTS :

    Child node if expression is false

  • nodes_featureids - INTS :

    Feature id for each node.

  • nodes_hitrates - FLOATS :

    Popularity of each node, used for performance and may be omitted.

  • nodes_hitrates_as_tensor - TENSOR :

    Popularity of each node, used for performance and may be omitted.

  • nodes_missing_value_tracks_true - INTS :

    For each node, define what to do in the presence of a NaN: use the ‘true’ (if the attribute value is 1) or ‘false’ (if the attribute value is 0) branch based on the value in this array.
    This attribute may be left undefined and the default value is false (0) for all nodes.

  • nodes_modes - STRINGS :

    The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
    One of ‘BRANCH_LEQ’, ‘BRANCH_LT’, ‘BRANCH_GTE’, ‘BRANCH_GT’, ‘BRANCH_EQ’, ‘BRANCH_NEQ’, ‘LEAF’

  • nodes_nodeids - INTS :

    Node id for each node. Node ids must restart at zero for each tree and increase sequentially.

  • nodes_treeids - INTS :

    Tree id for each node.

  • nodes_truenodeids - INTS :

    Child node if expression is true

  • nodes_values - FLOATS :

    Thresholds to do the splitting on for each node.

  • nodes_values_as_tensor - TENSOR :

    Thresholds to do the splitting on for each node.

  • post_transform - STRING (default is 'NONE'):

    Indicates the transform to apply to the score.
    One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’

  • target_ids - INTS :

    The index of the target that each weight is for

  • target_nodeids - INTS :

    The node id of each weight

  • target_treeids - INTS :

    The id of the tree that each node is in.

  • target_weights - FLOATS :

    The weight for each target

  • target_weights_as_tensor - TENSOR :

    The weight for each target

Inputs

  • X (heterogeneous) - T:

    Input of shape [N,F]

Outputs

  • Y (heterogeneous) - tensor(float):

    N classes

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ):

    The input type must be a tensor of a numeric type.

TreeEnsembleRegressor - 1 (ai.onnx.ml)

Version

This version of the operator has been available since version 1 of domain ai.onnx.ml.

Summary

Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF

Attributes

  • aggregate_function - STRING (default is 'SUM'):

    Defines how to aggregate leaf values within a target.
    One of ‘AVERAGE,’ ‘SUM,’ ‘MIN,’ ‘MAX.’

  • base_values - FLOATS :

    Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)

  • n_targets - INT :

    The total number of targets.

  • nodes_falsenodeids - INTS :

    Child node if expression is false

  • nodes_featureids - INTS :

    Feature id for each node.

  • nodes_hitrates - FLOATS :

    Popularity of each node, used for performance and may be omitted.

  • nodes_missing_value_tracks_true - INTS :

    For each node, define what to do in the presence of a NaN: use the ‘true’ (if the attribute value is 1) or ‘false’ (if the attribute value is 0) branch based on the value in this array.
    This attribute may be left undefined and the default value is false (0) for all nodes.

  • nodes_modes - STRINGS :

    The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
    One of ‘BRANCH_LEQ’, ‘BRANCH_LT’, ‘BRANCH_GTE’, ‘BRANCH_GT’, ‘BRANCH_EQ’, ‘BRANCH_NEQ’, ‘LEAF’

  • nodes_nodeids - INTS :

    Node id for each node. Node ids must restart at zero for each tree and increase sequentially.

  • nodes_treeids - INTS :

    Tree id for each node.

  • nodes_truenodeids - INTS :

    Child node if expression is true

  • nodes_values - FLOATS :

    Thresholds to do the splitting on for each node.

  • post_transform - STRING (default is 'NONE'):

    Indicates the transform to apply to the score.
    One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’

  • target_ids - INTS :

    The index of the target that each weight is for

  • target_nodeids - INTS :

    The node id of each weight

  • target_treeids - INTS :

    The id of the tree that each node is in.

  • target_weights - FLOATS :

    The weight for each target

Inputs

  • X (heterogeneous) - T:

    Input of shape [N,F]

Outputs

  • Y (heterogeneous) - tensor(float):

    N classes

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ):

    The input type must be a tensor of a numeric type.