PyMCModelAdapter#

class causalpy.experiments.model_adapter.PyMCModelAdapter[source]#

Adapter for PyMCModel backends.

Parameters:: model (PyMCModel) – Fitted or unfitted PyMC backend model.

Methods

`PyMCModelAdapter.coefficients`()	Return posterior coefficient draws in the canonical container.
`PyMCModelAdapter.fit`(X, y, *[, coords])	Fit the PyMC model.
`PyMCModelAdapter.predict`(X, *[, coords, ...])	Predict expected outcomes using the PyMC model.
`PyMCModelAdapter.print_coefficients`(labels)	Print model coefficients with labels.
`PyMCModelAdapter.require_idata`()	Return fitted inference-result DataTree or raise an explicit capability error.
`PyMCModelAdapter.score`(X, y, *[, coords])	Score predictions from the PyMC model.

Attributes

`idata`	Return the model's DataTree when fitted.
`is_bayesian`	Whether the backend is Bayesian (PyMC or pymc-forecast).
`is_ols`	Whether the backend is OLS/sklearn.
`kind`	Backend identifier.
`model`	The underlying PyMC model.
`supports_idata`	Whether the backend exposes an inference-result DataTree.

__init__(model)[source]#