pyreal.transformers.Numpy3dToMultiIndexFrame#

class pyreal.transformers.Numpy3dToMultiIndexFrame(var_names=None, timestamps=None, **kwargs)[source]#

Convert 3D NumPy array to Pyreal time-series format.

__init__(var_names=None, timestamps=None, **kwargs)[source]#

Initializes the converter.

Parameters:

  • var_names (None or list-like with length of n_variables) – Optional list of names to use for the variables

  • timestamps (None or list-like with length of n_timepoints) – Optional time series index of returned DataFrame

Methods

__init__([var_names, timestamps])

Initializes the converter.

data_transform(x)

Converts input data into a DataFrame with MultiIndex column

fit(x)

Check if the input data is a 3D NumPy array and create a MultiIndex object.

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