stanbkt.models.predict_posterior#

stanbkt.models.predict_posterior(model, data, column_mapping=None, *, smoothed=False, backend='stan', output='draws', quantiles=None, stan_output=None, n_cores=1)#
Overloads:

  • model (BKTModelBase), data (pd.DataFrame), column_mapping (_ColumnMappingInput), smoothed (bool), backend (Literal[‘stan’, ‘numba’]), output (Literal[‘stan’]), quantiles (Optional[list[float]]), stan_output (Optional[dict[str, csp.CmdStanGQ]]), n_cores (int) → dict[str, csp.CmdStanGQ]

  • model (BKTModelBase), data (pd.DataFrame), column_mapping (_ColumnMappingInput), smoothed (bool), backend (Literal[‘stan’, ‘numba’]), output (Literal[‘draws’]), quantiles (Optional[list[float]]), stan_output (Optional[dict[str, csp.CmdStanGQ]]), n_cores (int) → dict[str, pd.DataFrame]

  • model (BKTModelBase), data (pd.DataFrame), column_mapping (_ColumnMappingInput), smoothed (bool), backend (Literal[‘stan’, ‘numba’]), output (Literal[‘summary’]), quantiles (Optional[list[float]]), stan_output (Optional[dict[str, csp.CmdStanGQ]]), n_cores (int) → pd.DataFrame

Parameters:

  • model (BKTModelBase)

  • data (pd.DataFrame)

  • column_mapping (_ColumnMappingInput)

  • smoothed (bool)

  • backend (Literal['stan', 'numba'])

  • output (Literal['stan', 'draws', 'summary'])

  • quantiles (Optional[list[float]])

  • stan_output (Optional[dict[str, csp.CmdStanGQ]])

  • n_cores (int)

Return type:

dict[str, csp.CmdStanGQ] | dict[str, pd.DataFrame] | pd.DataFrame

Public wrapper for posterior prediction workflows.

Parameters:

  • model (BKTModelBase) – Fitted model instance.

  • data (DataFrame) – Interaction data.

  • column_mapping (Union[Mapping[ColumnNames, str], Mapping[str, str], Mapping[ColumnNames | str, str], None]) – Mapping from expected column names to source data names.

  • smoothed (bool) – Whether to use smoothed posterior hidden-state prediction models.

  • backend (Literal['stan', 'numba']) – Prediction backend. stan uses generated quantities. numba runs deterministic hidden-state recursion for each posterior parameter draw.

  • output (Literal['stan', 'draws', 'summary']) – Desired output type.

  • quantiles (Optional[list[float]]) – Quantiles used for summary output.

  • stan_output (Optional[dict[str, CmdStanGQ]]) – Precomputed Stan generated quantities output.

  • n_cores (int) – Number of cores used for summary processing.

Return type:

dict[str, csp.CmdStanGQ] | dict[str, pd.DataFrame] | pd.DataFrame

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