ModelBuilder

class pymc_experimental.model_builder.ModelBuilder(model_config: Dict | None = None, sampler_config: Dict | None = None)

ModelBuilder can be used to provide an easy-to-use API (similar to scikit-learn) for models and help with deployment.

__init__(model_config: Dict | None = None, sampler_config: Dict | None = None)

Initializes model configuration and sampler configuration for the model

Parameters:

• data (Dictionary, optional) – It is the data we need to train the model on.

• model_config (Dictionary, optional) – dictionary of parameters that initialise model configuration. Class-default defined by the user default_model_config method.

• sampler_config (Dictionary, optional) – dictionary of parameters that initialise sampler configuration. Class-default defined by the user default_sampler_config method.

Examples

>>> class MyModel(ModelBuilder):
>>>     ...
>>> model = MyModel(model_config, sampler_config)

Methods

__init__([model_config, sampler_config])	Initializes model configuration and sampler configuration for the model
build_model(X, y, **kwargs)	Creates an instance of pm.Model based on provided data and model_config, and attaches it to self.
fit(X[, y, progressbar, predictor_names, ...])	Fit a model using the data passed as a parameter.
get_default_model_config()	Returns a class default config dict for model builder if no model_config is provided on class initialization Useful for understanding structure of required model_config to allow its customization by users .
get_default_sampler_config(self)	Returns a class default sampler dict for model builder if no sampler_config is provided on class initialization Useful for understanding structure of required sampler_config to allow its customization by users .
get_params([deep])	Get all the model parameters needed to instantiate a copy of the model, not including training data.
load(fname)	Creates a ModelBuilder instance from a file, Loads inference data for the model.
predict(X_pred[, extend_idata])	Uses model to predict on unseen data and return point prediction of all the samples.
predict_posterior(X_pred[, extend_idata, ...])	Generate posterior predictive samples on unseen data.
predict_proba(X_pred[, extend_idata, combined])	Alias for predict_posterior, for consistency with scikit-learn probabilistic estimators.
sample_model(**kwargs)	Sample from the PyMC model.
sample_posterior_predictive(X_pred, ...)	Sample from the model's posterior predictive distribution.
sample_prior_predictive(X_pred[, y_pred, ...])	Sample from the model's prior predictive distribution.
save(fname)	Save the model's inference data to a file.
set_idata_attrs([idata])	Set attributes on an InferenceData object.
set_params(**params)	Set all the model parameters needed to instantiate the model, not including training data.

Attributes

id	Generate a unique hash value for the model.
output_var	Returns the name of the output variable of the model.
version