module onnxrt.ops_cpu.op_tree_ensemble_classifier_p_
#
Short summary#
module mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_
Implements runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_Classifier.cc in onnxruntime.
Classes#
class |
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Implements double runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_Classifier.cc … |
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Implements float runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_Classifier.cc … |
Properties#
property |
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See lpyort-TreeEnsembleClassifier. |
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Tells if the model handles missing values. |
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Tells if the model handles missing values. |
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See lpyort-TreeEnsembleClassifier. |
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Returns the mode for every node. |
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Returns the mode for every node. |
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Number of observations above which the computation is parallelized. |
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Number of observations above which the computation is parallelized. |
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Number of trees above which the computation is parallelized for one observation. |
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Number of trees above which the computation is parallelized for one observation. |
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See lpyort-TreeEnsembleClassifier. |
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Returns the roots indices. |
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Returns the roots indices. |
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Tells if all nodes applies the same rule for thresholds. |
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Tells if all nodes applies the same rule for thresholds. |
Documentation#
Implements runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_Classifier.cc in onnxruntime.
- class mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPDouble(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPDouble, arg0: int, arg1: int, arg2: int, arg3: bool, arg4: bool)#
Bases:
pybind11_object
Implements double runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_Classifier.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_classifier_p_.RuntimeTreeEnsembleClassifierPDouble, arg0: int, arg1: int, arg2: int, arg3: bool, arg4: bool) None #
- __sizeof__(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPDouble) int #
Returns the size of the object.
- property base_values_#
- compute(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPDouble, arg0: numpy.ndarray[numpy.float64]) tuple #
Computes the predictions for the random forest.
- compute_tree_outputs(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPDouble, arg0: numpy.ndarray[numpy.float64]) numpy.ndarray[numpy.float64] #
Computes every tree output.
- debug_threshold(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPDouble, 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_classifier_p_.RuntimeTreeEnsembleClassifierPDouble, arg0: numpy.ndarray[numpy.float64], arg1: numpy.ndarray[numpy.int64], arg2: numpy.ndarray[numpy.int64], arg3: numpy.ndarray[numpy.int64], arg4: numpy.ndarray[numpy.float64], arg5: numpy.ndarray[numpy.int64], arg6: List[str], arg7: numpy.ndarray[numpy.int64], arg8: numpy.ndarray[numpy.int64], arg9: numpy.ndarray[numpy.float64], arg10: numpy.ndarray[numpy.int64], arg11: List[str], arg12: numpy.ndarray[numpy.int64], arg13: numpy.ndarray[numpy.int64], arg14: numpy.ndarray[numpy.int64], arg15: numpy.ndarray[numpy.float64], arg16: str) None #
Initializes the runtime with the ONNX attributes in alphabetical order.
- property n_classes_#
- 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_classifier_p_.RuntimeTreeEnsembleClassifierPDouble) 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_#
- property roots_#
Returns the roots indices.
- runtime_options(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPDouble) 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_classifier_p_.RuntimeTreeEnsembleClassifierPFloat(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPFloat, arg0: int, arg1: int, arg2: int, arg3: bool, arg4: bool)#
Bases:
pybind11_object
Implements float runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_Classifier.cc in onnxruntime. Supports float 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_classifier_p_.RuntimeTreeEnsembleClassifierPFloat, arg0: int, arg1: int, arg2: int, arg3: bool, arg4: bool) None #
- __sizeof__(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPFloat) int #
Returns the size of the object.
- property base_values_#
See lpyort-TreeEnsembleClassifier.
- compute(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPFloat, arg0: numpy.ndarray[numpy.float32]) tuple #
Computes the predictions for the random forest.
- compute_tree_outputs(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPFloat, arg0: numpy.ndarray[numpy.float32]) numpy.ndarray[numpy.float32] #
Computes every tree output.
- debug_threshold(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPFloat, arg0: numpy.ndarray[numpy.float32]) 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_classifier_p_.RuntimeTreeEnsembleClassifierPFloat, arg0: numpy.ndarray[numpy.float32], arg1: numpy.ndarray[numpy.int64], arg2: numpy.ndarray[numpy.int64], arg3: numpy.ndarray[numpy.int64], arg4: numpy.ndarray[numpy.float32], arg5: numpy.ndarray[numpy.int64], arg6: List[str], arg7: numpy.ndarray[numpy.int64], arg8: numpy.ndarray[numpy.int64], arg9: numpy.ndarray[numpy.float32], arg10: numpy.ndarray[numpy.int64], arg11: List[str], arg12: numpy.ndarray[numpy.int64], arg13: numpy.ndarray[numpy.int64], arg14: numpy.ndarray[numpy.int64], arg15: numpy.ndarray[numpy.float32], arg16: str) None #
Initializes the runtime with the ONNX attributes in alphabetical order.
- property n_classes_#
See lpyort-TreeEnsembleClassifier.
- 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_classifier_p_.RuntimeTreeEnsembleClassifierPFloat) 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-TreeEnsembleClassifier.
- property roots_#
Returns the roots indices.
- runtime_options(self: mlprodict.onnxrt.ops_cpu.op_tree_ensemble_classifier_p_.RuntimeTreeEnsembleClassifierPFloat) str #
Returns indications about how the runtime was compiled.
- property same_mode_#
Tells if all nodes applies the same rule for thresholds.