PIEModel.fit#

PIEModel.fit(X, y=None, *, method='mcmc', progressbar=None, random_seed=None, sample_kwargs=None, **kwargs)[source]#

Fit a model using the data passed as a parameter.

Thin wrapper around ModelFitter.fit() supporting the X/y data convention. Sets attrs to inference data of the model.

Parameters:

Xarray_like | array, shape (n_obs, n_features): The training input samples. If scikit-learn is available, array-like, otherwise array.
yarray_like | array, shape (n_obs,): The target values (real numbers). If scikit-learn is available, array-like, otherwise array.
methodstr: Method used to fit the model. One of "mcmc", "map", "demz", "advi" or "fullrank_advi". See ModelFitter.fit().
progressbarbool, optional: Specifies whether the fit progress bar should be displayed. Defaults to True.
random_seedOptional[RandomState]: Provides sampler with initial random seed for obtaining reproducible samples.
sample_kwargsdict, optional: Only used by the variational methods; forwarded to Approximation.sample.
**kwargsAny: Custom sampler settings can be provided in form of keyword arguments.

Returns:

Examples

model = MyModel()
idata = model.fit(X, y)
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...