Transformers

Specialized transformers to address Gordo specific problems. This function just like Scikit-Learn’s transformers and thus can be inserted into 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)

Bases: sklearn.base.TransformerMixin

Fill 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 (numeric) – Value to fill ‘inf’ values

• neg_inf_fill_value (numeric) – Value to fill ‘-inf’ values

• strategy (str) – 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 (float) – 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 and delta=2 any inf value will be filled with 12. Likewise, if the min feature was -10 any -inf will be filled with -12.

fit(X: Union[pandas.core.frame.DataFrame, numpy.ndarray], y=None)

get_params(deep=True)

transform(X: Union[pandas.core.frame.DataFrame, numpy.ndarray], y=None)