ConstantTemplate (the underlying sklearn estimator for ConstantTimeseriesRegressor) only uses the input data X to determine the output shape of the predictions. It shouldn't actually be necessary for X to even contain data, or be array-like at all, as long as it specifies a length (implements the __len__() dunder).
In #75 and #76 I already loosened the validation on X by allowing NaN/Inf values, but the requirements are still needlessly restrictive because of the assumptions in BaseTimeseriesRegressor. I would like the ability at least to pass an empty dataframe X = pd.DataFrame(index=range(100)).
Moreover, it should be noted that scikit-learn actually has DummyRegressor, implementing the same logic as ConstantTemplate. Although I haven't tested it, ConstantTemplate is probably equivalent to DummyRegressor, and if so can be removed completely in favor of the latter.
ConstantTemplate(the underlying sklearn estimator forConstantTimeseriesRegressor) only uses the input data X to determine the output shape of the predictions. It shouldn't actually be necessary for X to even contain data, or be array-like at all, as long as it specifies a length (implements the__len__()dunder).In #75 and #76 I already loosened the validation on X by allowing NaN/Inf values, but the requirements are still needlessly restrictive because of the assumptions in
BaseTimeseriesRegressor. I would like the ability at least to pass an empty dataframeX = pd.DataFrame(index=range(100)).Moreover, it should be noted that scikit-learn actually has
DummyRegressor, implementing the same logic asConstantTemplate. Although I haven't tested it,ConstantTemplateis probably equivalent toDummyRegressor, and if so can be removed completely in favor of the latter.