simpleml.pipelines.validation_split_mixins module

Module for different split methods for cross validation

1. No Split – Just use all the data
2. Explicit Split – dataset class defines the split
3. Percentage – random split support for train, validation, test
4. Chronological – time based split support for train, validation, test
5. KFold

class simpleml.pipelines.validation_split_mixins.ChronologicalSplitMixin(**kwargs)

Bases: simpleml.pipelines.validation_split_mixins.SplitMixin

class simpleml.pipelines.validation_split_mixins.ExplicitSplitMixin

Bases: simpleml.pipelines.validation_split_mixins.SplitMixin

split_dataset()

Method to split the dataframe into different sets. Assumes dataset explicitly delineates between train, validation, and test

class simpleml.pipelines.validation_split_mixins.KFoldSplitMixin

Bases: simpleml.pipelines.validation_split_mixins.SplitMixin

TBD on how to implement this. KFold requires K models and unique datasets so may be easier to wrap a parallelized implementation that internally creates K new Pipeline and Model objects

class simpleml.pipelines.validation_split_mixins.NoSplitMixin

Bases: simpleml.pipelines.validation_split_mixins.SplitMixin

split_dataset()

Method to split the dataframe into different sets. By default sets everything to TRAIN, but can be overwritten to add validation, test…

TODO: Work in support for generators (k-fold)

class simpleml.pipelines.validation_split_mixins.RandomSplitMixin(train_size, test_size=None, validation_size=0.0, random_state=123, shuffle=True, **kwargs)

Bases: simpleml.pipelines.validation_split_mixins.SplitMixin

Class to randomly split dataset into different sets

split_dataset()

Overwrite method to split by percentage

class simpleml.pipelines.validation_split_mixins.SplitMixin

Bases: object

split_dataset()

Set the split criteria

Must set self._dataset_splits