Source code for contextualized.easy.ContextualizedRegressor

"""
sklearn-like interface to Contextualized Regressors.
"""
from contextualized.regression import (
    NaiveContextualizedRegression,
    ContextualizedRegression,
)
from contextualized.easy.wrappers import SKLearnWrapper
from contextualized.regression import RegressionTrainer

# TODO: Multitask metamodels
# TODO: Task-specific link functions.


[docs]class ContextualizedRegressor(SKLearnWrapper): """ Contextualized Linear Regression quantifies context-varying linear relationships. Args: n_bootstraps (int, optional): Number of bootstraps to use. Defaults to 1. num_archetypes (int, optional): Number of archetypes to use. Defaults to 0, which used the NaiveMetaModel. If > 0, uses archetypes in the ContextualizedMetaModel. encoder_type (str, optional): Type of encoder to use ("mlp", "ngam", "linear"). Defaults to "mlp". loss_fn (torch.nn.Module, optional): Loss function. Defaults to LOSSES["mse"]. link_fn (torch.nn.Module, optional): Link function. Defaults to LINK_FUNCTIONS["identity"]. alpha (float, optional): Regularization strength. Defaults to 0.0. mu_ratio (float, optional): Float in range (0.0, 1.0), governs how much the regularization applies to context-specific parameters or context-specific offsets. Defaults to 0.0. l1_ratio (float, optional): Float in range (0.0, 1.0), governs how much the regularization penalizes l1 vs l2 parameter norms. Defaults to 0.0. """ def __init__(self, **kwargs): self.num_archetypes = kwargs.get("num_archetypes", 0) if self.num_archetypes == 0: constructor = NaiveContextualizedRegression elif self.num_archetypes > 0: constructor = ContextualizedRegression else: print( f""" Was told to construct a ContextualizedRegressor with {self.num_archetypes} archetypes, but this should be a non-negative integer.""" ) extra_model_kwargs = ["base_param_predictor", "base_y_predictor", "y_dim"] extra_data_kwargs = ["Y_val"] trainer_constructor = RegressionTrainer super().__init__( constructor, extra_model_kwargs, extra_data_kwargs, trainer_constructor, **kwargs, ) def _split_train_data(self, C, X, Y=None, Y_required=False, **kwargs): return super()._split_train_data(C, X, Y, Y_required=True, **kwargs)