pyreal.transformers.NestedFrameToNumpy3d#

class pyreal.transformers.NestedFrameToNumpy3d(model=True, interpret=False, algorithm=None, require_values=False)[source]#

Convert sktime nested DataFrame format into NumPy ndarray with shape (n_instances, n_variables, n_timepoints).

__init__(model=True, interpret=False, algorithm=None, require_values=False)#

Set this Transformer’s flags.

Parameters:

  • model (Boolean) – If True, this transformer is required by the model-ready feature space. It will be run any time a model prediction is needed

  • interpret (Boolean) – If True, this transformer makes the data more human-interpretable

  • algorithm (Boolean) – If True, this transformer is required for the explanation algorithm. If algorithm is False, but model is True, this transformer will be applied only when making model predictions during the explanation algorithm. Cannot be True if if the model flag is False

  • require_values (Boolean) – If True, this transformer must be given values alongside the explanation in order to work for local explanations (for global explanations, this parameter is ignored)

Methods

__init__([model, interpret, algorithm, ...])

Set this Transformer's flags.

data_transform(x)

param x:

Input sktime nested DataFrame

fit(x, **params)

Fit this transformer to data

fit_transform(x, **fit_params)

Fits this transformer to data and then transforms the same data

inverse_data_transform(x_new)

Wrapper for inverse_data_transform.

inverse_transform(x_new)

Transforms data x_new from new feature space back into the original feature space.

inverse_transform_explanation(explanation)

Transforms the explanation from the second feature space handled by this transformer to the first.

inverse_transform_explanation_additive_feature_contribution(...)

Inverse transforms additive feature contribution explanations

inverse_transform_explanation_additive_feature_importance(...)

Inverse transforms additive feature importance explanations

inverse_transform_explanation_decision_tree(...)

Inverse transforms decision-tree explanations

inverse_transform_explanation_example(...)

Inverse transforms example-based explanations

inverse_transform_explanation_feature_based(...)

Inverse transforms feature-based explanations

inverse_transform_explanation_feature_contribution(...)

Inverse transforms feature contribution explanations

inverse_transform_explanation_feature_importance(...)

Inverse transforms feature importance explanations

inverse_transform_explanation_similar_example(...)

Inverse transforms similar-example-based explanations

set_flags([model, interpret, algorithm])

transform(x)

Wrapper for data_transform.

transform_explanation(explanation)

Transforms the explanation from the first feature space handled by this transformer to the second.

transform_explanation_additive_feature_contribution(...)

Transforms additive feature contribution explanations

transform_explanation_additive_feature_importance(...)

Transforms additive feature importance explanations

transform_explanation_decision_tree(explanation)

Inverse transforms feature-based explanations

transform_explanation_example(explanation)

Transforms example-based explanations

transform_explanation_feature_based(explanation)

Transforms feature-based explanations

transform_explanation_feature_contribution(...)

Transforms feature contribution explanations

transform_explanation_feature_importance(...)

Transforms feature importance explanations

transform_explanation_similar_example(...)

Transforms example-based explanations