Helpers
¶
Various helper functions
- class helpers.LightwoodAutocast(enabled=True)[source]¶
Equivalent to torch.cuda.amp.autocast, but checks device compute capability to activate the feature only when the GPU has tensor cores to leverage AMP.
- helpers.add_tn_num_conf_bounds(data, tss_args)[source]¶
Deprecated. Instead we now opt for the much better solution of having scores for each timestep (see all TS classes in analysis/nc)
Add confidence (and bounds if applicable) to t+n predictions, for n>1 TODO: active research question: how to guarantee 1-e coverage for t+n, n>1 For now, (conservatively) increases width by the confidence times the log of the time step (and a scaling factor).
- helpers.get_group_matches(data, combination, group_columns, copy=False)[source]¶
Given a particular group combination, return the data subset that belongs to it.
- Return type
Tuple
[list
,DataFrame
]
- helpers.is_none(value)[source]¶
Pandas has no way to guarantee “stability” for the type of a column, it choses to arbitrarily change it based on the values. Pandas also change the values in the columns based on the types. Lightwood relies on having
None
values for a cells that represent “missing” or “corrupt”.When we assign
None
to a cell in a dataframe this might get turned to nan or other values, this function checks if a cell isNone
or any other values a pd.DataFrame might convertNone
to.It also checks some extra values (like
''
) that pandas never convertsNone
to (hopefully). But lightwood would still consider those values “None values”, and this will allow for more generic use later.