Transformers#
Specialized transformers to address Gordo specific problems.
This function just like Scikit-Learn’s transformers and thus can be
inserted into sklearn.pipeline.Pipeline objects.
Imputers#
- class gordo.machine.model.transformers.imputer.InfImputer(inf_fill_value=None, neg_inf_fill_value=None, strategy='minmax', delta: float = 2.0)[source]#
Bases:
TransformerMixinFill inf/-inf values of a 2d array/dataframe with imputed or provided values By default it will find the min and max of each feature/column and fill -infs/infs with those values +/-
delta- Parameters:
inf_fill_value – Value to fill ‘inf’ values
neg_inf_fill_value – Value to fill ‘-inf’ values
strategy – How to fill values, irrelevant if fill value is provided. choices: ‘extremes’, ‘minmax’ -‘extremes’ will use the min and max values for the current datatype. such that ‘inf’ in a float32 dataset will have float32’s largest value inserted. - ‘minmax’ will look at the min and max values in the feature where the -inf / inf appears and fill with the max/min found in that feature.
delta – Only applicable if
strategy='minmax'Will add/subtract the max/min value, by feature, by this delta. If the max value in a feature was 10 anddelta=2any inf value will be filled with 12. Likewise, if the min feature was -10 any -inf will be filled with -12.