TreeEnsembleClassifier - version 1#

This page documents version 1 of operator TreeEnsembleClassifier. See TreeEnsembleClassifier for the latest version (since version 5).

  • Domain: ai.onnx.ml

  • Since version: 1

Tree Ensemble classifier. Returns the top class for each of N inputs. The attributes named ‘nodes_X’ form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes. Similarly, all fields prefixed with class_ are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index. One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list.

Inputs

  • X (T1): Input of shape [N,F]

Outputs

  • Y (T2): N, Top class for each point

  • Z (tensor(float)): The class score for each class, for each point, a tensor of shape [N,E].

Attributes

  • base_values (float[]): Base values for classification, added to final class score.

  • class_ids (int[]): The index of the class list that each weight is for.

  • class_nodeids (int[]): node id that this weight is for.

  • class_treeids (int[]): The id of the tree that this node is in.

  • class_weights (float[]): The weight for the class in class_id.

  • classlabels_int64s (int[]): Class labels if using integer labels. One and only one of the ‘classlabels*’ attributes must be defined.

  • classlabels_strings (string[]): Class labels if using string labels. One and only one of the ‘classlabels*’ attributes must be defined.

  • nodes_falsenodeids (int[]): Child node if expression is false.

  • nodes_featureids (int[]): Feature id for each node.

  • nodes_hitrates (float[]): Popularity of each node, used for performance and may be omitted.

  • nodes_missing_value_tracks_true (int[]): For each node, define what to do in the presence of a missing value.

  • nodes_modes (string[]): The node kind, that is, the comparison to make at the node.

  • nodes_nodeids (int[]): Node id for each node. Ids may restart at zero for each tree.

  • nodes_treeids (int[]): Tree id for each node.

  • nodes_truenodeids (int[]): Child node if expression is true.

  • nodes_values (float[]): Thresholds to do the splitting on for each node.

  • post_transform (string): Indicates the transform to apply to the score.

Type Constraints

  • T1: The input type must be a tensor of a numeric type. Allowed types: tensor(double), tensor(float), tensor(int32), tensor(int64).

  • T2: The output type will be a tensor of strings or integers, depending on which of the classlabels* attributes is used. Allowed types: tensor(int64), tensor(string).

Examples#

test_cc_treeensembleclassifier_int64_binary

Node:
  ai.onnx.ml.TreeEnsembleClassifier(x) -> (y, z)
  Attributes:
    nodes_treeids = [0, 0, 0]
    nodes_nodeids = [0, 1, 2]
    nodes_featureids = [0, 0, 0]
    nodes_values = [0.5, 0.0, 0.0]
    nodes_modes = ['BRANCH_LEQ', 'LEAF', 'LEAF']
    nodes_truenodeids = [1, 0, 0]
    nodes_falsenodeids = [2, 0, 0]
    class_treeids = [0, 0]
    class_nodeids = [1, 2]
    class_ids = [0, 1]
    class_weights = [1.0, 1.0]
    classlabels_int64s = [0, 1]
    post_transform = "NONE"

Inputs:
  x: shape=(2, 1), dtype=float32
    [[0.],
     [1.]]

Outputs:
  y: shape=(2,), dtype=int64
    [0, 1]
  z: shape=(2, 2), dtype=float32
    [[1., 0.],
     [0., 1.]]
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