# -------------------------------------------------------------------------- # ⚠️ WARNING - AUTO-GENERATED CODE - DO NOT EDIT ⚠️ # ⚙️ Generated by 'python -m opgen' # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. # -------------------------------------------------------------------------- # pylint: disable=W0221,W0222,R0901,W0237 # mypy: disable-error-code=override # ruff: noqa: N801 # -------------------------------------------------------------------------- from __future__ import annotations from typing import Optional, Sequence, Tuple, TypeVar, Union from onnx import TensorProto from onnx.defs import get_schema from typing_extensions import TypeAlias from onnxscript.onnx_opset._impl.opset_ai_onnx_ml2 import Opset_ai_onnx_ml2 from onnxscript.onnx_types import DOUBLE, FLOAT, INT32, INT64, STRING from onnxscript.values import Op, Opset class Opset_ai_onnx_ml3(Opset_ai_onnx_ml2): def __new__(cls): return Opset.__new__(cls, "ai.onnx.ml", 3) T1_TreeEnsembleClassifier = TypeVar( "T1_TreeEnsembleClassifier", DOUBLE, FLOAT, INT32, INT64 ) T2_TreeEnsembleClassifier: TypeAlias = Union[INT64, STRING] def TreeEnsembleClassifier( self, X: T1_TreeEnsembleClassifier, *, base_values: Optional[Sequence[float]] = None, base_values_as_tensor: Optional[TensorProto] = None, class_ids: Optional[Sequence[int]] = None, class_nodeids: Optional[Sequence[int]] = None, class_treeids: Optional[Sequence[int]] = None, class_weights: Optional[Sequence[float]] = None, class_weights_as_tensor: Optional[TensorProto] = None, classlabels_int64s: Optional[Sequence[int]] = None, classlabels_strings: Optional[Sequence[str]] = None, nodes_falsenodeids: Optional[Sequence[int]] = None, nodes_featureids: Optional[Sequence[int]] = None, nodes_hitrates: Optional[Sequence[float]] = None, nodes_hitrates_as_tensor: Optional[TensorProto] = None, nodes_missing_value_tracks_true: Optional[Sequence[int]] = None, nodes_modes: Optional[Sequence[str]] = None, nodes_nodeids: Optional[Sequence[int]] = None, nodes_treeids: Optional[Sequence[int]] = None, nodes_truenodeids: Optional[Sequence[int]] = None, nodes_values: Optional[Sequence[float]] = None, nodes_values_as_tensor: Optional[TensorProto] = None, post_transform: str = "NONE", ) -> Tuple[T2_TreeEnsembleClassifier, FLOAT]: r"""[🌐 ai.onnx.ml::TreeEnsembleClassifier(3)](https://onnx.ai/onnx/operators/onnx_aionnxml_TreeEnsembleClassifier.html#treeensembleclassifier-3 "Online Documentation") 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. 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. Args: X: Input of shape [N,F] base_values: 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) base_values_as_tensor: 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) class_ids: The index of the class list that each weight is for. class_nodeids: node id that this weight is for. class_treeids: The id of the tree that this node is in. class_weights: The weight for the class in class_id. class_weights_as_tensor: The weight for the class in class_id. classlabels_int64s: Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined. classlabels_strings: Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined. nodes_falsenodeids: Child node if expression is false. nodes_featureids: Feature id for each node. nodes_hitrates: Popularity of each node, used for performance and may be omitted. nodes_hitrates_as_tensor: Popularity of each node, used for performance and may be omitted. nodes_missing_value_tracks_true: For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' 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: 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: Node id for each node. Ids may restart at zero for each tree, but it not required to. nodes_treeids: Tree id for each node. nodes_truenodeids: Child node if expression is true. nodes_values: Thresholds to do the splitting on for each node. nodes_values_as_tensor: Thresholds to do the splitting on for each node. post_transform: Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' """ schema = get_schema("TreeEnsembleClassifier", 3, "ai.onnx.ml") op = Op(self, "TreeEnsembleClassifier", schema) return op( *self._prepare_inputs(schema, X), base_values=base_values, base_values_as_tensor=base_values_as_tensor, class_ids=class_ids, class_nodeids=class_nodeids, class_treeids=class_treeids, class_weights=class_weights, class_weights_as_tensor=class_weights_as_tensor, classlabels_int64s=classlabels_int64s, classlabels_strings=classlabels_strings, nodes_falsenodeids=nodes_falsenodeids, nodes_featureids=nodes_featureids, nodes_hitrates=nodes_hitrates, nodes_hitrates_as_tensor=nodes_hitrates_as_tensor, nodes_missing_value_tracks_true=nodes_missing_value_tracks_true, nodes_modes=nodes_modes, nodes_nodeids=nodes_nodeids, nodes_treeids=nodes_treeids, nodes_truenodeids=nodes_truenodeids, nodes_values=nodes_values, nodes_values_as_tensor=nodes_values_as_tensor, post_transform=post_transform, ) T_TreeEnsembleRegressor = TypeVar("T_TreeEnsembleRegressor", DOUBLE, FLOAT, INT32, INT64) def TreeEnsembleRegressor( self, X: T_TreeEnsembleRegressor, *, aggregate_function: str = "SUM", base_values: Optional[Sequence[float]] = None, base_values_as_tensor: Optional[TensorProto] = None, n_targets: Optional[int] = None, nodes_falsenodeids: Optional[Sequence[int]] = None, nodes_featureids: Optional[Sequence[int]] = None, nodes_hitrates: Optional[Sequence[float]] = None, nodes_hitrates_as_tensor: Optional[TensorProto] = None, nodes_missing_value_tracks_true: Optional[Sequence[int]] = None, nodes_modes: Optional[Sequence[str]] = None, nodes_nodeids: Optional[Sequence[int]] = None, nodes_treeids: Optional[Sequence[int]] = None, nodes_truenodeids: Optional[Sequence[int]] = None, nodes_values: Optional[Sequence[float]] = None, nodes_values_as_tensor: Optional[TensorProto] = None, post_transform: str = "NONE", target_ids: Optional[Sequence[int]] = None, target_nodeids: Optional[Sequence[int]] = None, target_treeids: Optional[Sequence[int]] = None, target_weights: Optional[Sequence[float]] = None, target_weights_as_tensor: Optional[TensorProto] = None, ) -> FLOAT: r"""[🌐 ai.onnx.ml::TreeEnsembleRegressor(3)](https://onnx.ai/onnx/operators/onnx_aionnxml_TreeEnsembleRegressor.html#treeensembleregressor-3 "Online Documentation") 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 Args: X: Input of shape [N,F] aggregate_function: Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.' base_values: 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: 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: The total number of targets. nodes_falsenodeids: Child node if expression is false nodes_featureids: Feature id for each node. nodes_hitrates: Popularity of each node, used for performance and may be omitted. nodes_hitrates_as_tensor: Popularity of each node, used for performance and may be omitted. nodes_missing_value_tracks_true: 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: 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: Node id for each node. Node ids must restart at zero for each tree and increase sequentially. nodes_treeids: Tree id for each node. nodes_truenodeids: Child node if expression is true nodes_values: Thresholds to do the splitting on for each node. nodes_values_as_tensor: Thresholds to do the splitting on for each node. post_transform: Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' target_ids: The index of the target that each weight is for target_nodeids: The node id of each weight target_treeids: The id of the tree that each node is in. target_weights: The weight for each target target_weights_as_tensor: The weight for each target """ schema = get_schema("TreeEnsembleRegressor", 3, "ai.onnx.ml") op = Op(self, "TreeEnsembleRegressor", schema) return op( *self._prepare_inputs(schema, X), aggregate_function=aggregate_function, base_values=base_values, base_values_as_tensor=base_values_as_tensor, n_targets=n_targets, nodes_falsenodeids=nodes_falsenodeids, nodes_featureids=nodes_featureids, nodes_hitrates=nodes_hitrates, nodes_hitrates_as_tensor=nodes_hitrates_as_tensor, nodes_missing_value_tracks_true=nodes_missing_value_tracks_true, nodes_modes=nodes_modes, nodes_nodeids=nodes_nodeids, nodes_treeids=nodes_treeids, nodes_truenodeids=nodes_truenodeids, nodes_values=nodes_values, nodes_values_as_tensor=nodes_values_as_tensor, post_transform=post_transform, target_ids=target_ids, target_nodeids=target_nodeids, target_treeids=target_treeids, target_weights=target_weights, target_weights_as_tensor=target_weights_as_tensor, )