module `onnxrt.ops_cpu.op_tree_ensemble_regressor_p_`#

Short summary#

module mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_

Implements runtime for operator TreeEnsembleRegressor. The code is inspired from tree_ensemble_regressor.cc in onnxruntime.

source on GitHub

Classes#

class	truncated documentation
`RuntimeTreeEnsembleRegressorPDouble`	Implements double runtime for operator TreeEnsembleRegressor. The code is inspired from tree_ensemble_regressor.cc …
`RuntimeTreeEnsembleRegressorPFloat`	Implements float runtime for operator TreeEnsembleRegressor. The code is inspired from tree_ensemble_regressor.cc …

Properties#

property	truncated documentation
`base_values_`	See lpyort-TreeEnsembleRegressorDouble.
`base_values_`	See lpyort-TreeEnsembleRegressor.
`has_missing_tracks_`	Tells if the model handles missing values.
`has_missing_tracks_`	Tells if the model handles missing values.
`n_targets_`	See lpyort-TreeEnsembleRegressorDouble.
`n_targets_`	See lpyort-TreeEnsembleRegressor.
`nodes_modes_`	Returns the mode for every node.
`nodes_modes_`	Returns the mode for every node.
`omp_N_`	Number of observations above which the computation is parallelized.
`omp_N_`	Number of observations above which the computation is parallelized.
`omp_tree_`	Number of trees above which the computation is parallelized for one observation.
`omp_tree_`	Number of trees above which the computation is parallelized for one observation.
`post_transform_`	See lpyort-TreeEnsembleRegressorDouble.
`post_transform_`	See lpyort-TreeEnsembleRegressor.
`roots_`	Returns the roots indices.
`roots_`	Returns the roots indices.
`same_mode_`	Tells if all nodes applies the same rule for thresholds.
`same_mode_`	Tells if all nodes applies the same rule for thresholds.

Documentation#

Implements runtime for operator TreeEnsembleRegressor. The code is inspired from tree_ensemble_regressor.cc in onnxruntime.

class mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble, arg0: int, arg1: int, arg2: int, arg3: bool, arg4: bool)#

Bases: pybind11_object

Implements double runtime for operator TreeEnsembleRegressor. The code is inspired from tree_ensemble_regressor.cc in onnxruntime. Supports double only.

Parameters:

omp_tree – number of trees above which the runtime uses openmp to parallelize tree computation when the number of observations it 1
omp_N – number of observations above which the runtime uses openmp to parallelize the predictions
array_structure – (bool) different implementation for better performance
para_tree – (bool) parallelize the computation per tree instead of observations

__init__(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble, arg0: int, arg1: int, arg2: int, arg3: bool, arg4: bool) → None#

__sizeof__(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble) → int#: Returns the size of the object.

property base_values_#: See lpyort-TreeEnsembleRegressorDouble.

compute(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble, arg0: numpy.ndarray[numpy.float64]) → numpy.ndarray[numpy.float64]#: Computes the predictions for the random forest.

compute_tree_outputs(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble, arg0: numpy.ndarray[numpy.float64]) → numpy.ndarray[numpy.float64]#: Computes every tree output.

debug_threshold(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble, arg0: numpy.ndarray[numpy.float64]) → numpy.ndarray[numpy.int32]#: Checks every features against every features against every threshold. Returns a matrix of boolean.

property has_missing_tracks_#: Tells if the model handles missing values.

init(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble, arg0: str, arg1: numpy.ndarray[numpy.float64], arg2: int, arg3: numpy.ndarray[numpy.int64], arg4: numpy.ndarray[numpy.int64], arg5: numpy.ndarray[numpy.float64], arg6: numpy.ndarray[numpy.int64], arg7: List[str], arg8: numpy.ndarray[numpy.int64], arg9: numpy.ndarray[numpy.int64], arg10: numpy.ndarray[numpy.int64], arg11: numpy.ndarray[numpy.float64], arg12: str, arg13: numpy.ndarray[numpy.int64], arg14: numpy.ndarray[numpy.int64], arg15: numpy.ndarray[numpy.int64], arg16: numpy.ndarray[numpy.float64]) → None#: Initializes the runtime with the ONNX attributes in alphabetical order.

property n_targets_#: See lpyort-TreeEnsembleRegressorDouble.

property nodes_modes_#: Returns the mode for every node.

property omp_N_#: Number of observations above which the computation is parallelized.

omp_get_max_threads(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble) → int#: Returns omp_get_max_threads from openmp library.

property omp_tree_#: Number of trees above which the computation is parallelized for one observation.

property post_transform_#: See lpyort-TreeEnsembleRegressorDouble.

property roots_#: Returns the roots indices.

runtime_options(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPDouble) → str#: Returns indications about how the runtime was compiled.

property same_mode_#: Tells if all nodes applies the same rule for thresholds.

class mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPFloat(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_regressor_p_.RuntimeTreeEnsembleRegressorPFloat, arg0: int, arg1: int, arg2: int, arg3: bool, arg4: bool)#