pymoose.predictors.tree_ensemble module#

class pymoose.predictors.tree_ensemble.DecisionTreeRegressor(weights, children, split_conditions, split_indices)[source]#

Bases: pymoose.predictors.predictor.Predictor

aes_predictor_factory()[source]#
classmethod from_json(tree_json)[source]#
class pymoose.predictors.tree_ensemble.TreeEnsemble(trees, n_features, base_score, learning_rate)[source]#

Bases: pymoose.predictors.predictor.Predictor

abstract classmethod from_onnx(model_proto)[source]#
abstract post_transform(tree_scores, fixedpoint_dtype)[source]#
predictor_fn(x, fixedpoint_dtype)[source]#
class pymoose.predictors.tree_ensemble.TreeEnsembleClassifier(trees, n_features, n_classes, base_score, learning_rate, transform_output, tree_class_map)[source]#

Bases: pymoose.predictors.tree_ensemble.TreeEnsemble

Tree ensemble classification Predictor for GBTs and Random Forests.

This class can be used for binary, multiclass, or multilabel classification. Support for multiclass classification uses the one-vs-rest method.

Parameters

  • trees – Nested collection of :class:`~DecisionTreeRegressor`s.

  • n_features – Number of features expected for input data.

  • n_classes – Number of output classes.

  • base_score – The base score for the underlying tree ensemble model, similar to a bias/intercept term.

  • learning_rate – Learning rate parameter used to re-scale leaf weights in the model trees.

  • transform_output – Boolean determining whether a softmax should be applied to derive probabilities from the tree ensemble output.

  • tree_class_map – Dictionary mapping trees indices to class indices. Keeps track of which trees in trees correspond to each class in the one-vs-rest formulation of multiclass classification.

classmethod from_onnx(model_proto)[source]#

Construct a TreeEnsembleClassifier from a parsed ONNX model.

Parameters

model_proto – An ONNX ModelProto containing a TreeEnsembleClassifier node.

Returns

A TreeEnsembleClassifier built from the parameters and configuration of the given ONNX model.

Raises

ValueError if ONNX graph is missing expected nodes. –

post_transform(tree_scores, fixedpoint_dtype)[source]#
class pymoose.predictors.tree_ensemble.TreeEnsembleRegressor(trees, n_features, base_score, learning_rate)[source]#

Bases: pymoose.predictors.tree_ensemble.TreeEnsemble

Tree ensemble regression Predictor, accommodating both GBTs and Random Forests.

Parameters

  • trees – Nested collection of :class:`~DecisionTreeRegressor`s.

  • n_features – Number of features expected for input data.

  • base_score – The base score for the underlying tree ensemble model, similar to a bias/intercept term.

  • learning_rate – Learning rate parameter used to re-scale leaf weights in the model trees.

classmethod from_onnx(model_proto)[source]#

Construct a TreeEnsembleRegressor from a parsed ONNX model.

Parameters

model_proto – An ONNX ModelProto containing a TreeEnsembleRegressor node.

Returns

A TreeEnsembleRegressor built from the parameters and configuration of the given ONNX model.

Raises

ValueError if ONNX graph is missing expected nodes. –

post_transform(tree_scores, fixedpoint_dtype)[source]#