no_split

Empty Data Splitter class.

Module Contents

Classes Summary

NoSplit

Does not split the training data into training and validation sets.

Contents

class evalml.preprocessing.data_splitters.no_split.NoSplit(random_seed=0)

Does not split the training data into training and validation sets.

All data is passed as the training set, test data is simply an array of None. To be used for future unsupervised learning, should not be used in any of the currently supported pipelines.

Parameters

random_seed (int) – The seed to use for random sampling. Defaults to 0. Not used.

Methods

get_metadata_routing	Get metadata routing of this object.
get_n_splits	Return the number of splits of this object.
is_cv	Returns whether or not the data splitter is a cross-validation data splitter.
split	Divide the data into training and testing sets, where the testing set is empty.

get_metadata_routing(self)

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routing – A MetadataRequest encapsulating routing information.

Return type

MetadataRequest

static get_n_splits()

Return the number of splits of this object.

Returns

Always returns 0.

Return type

int

property is_cv(self)

Returns whether or not the data splitter is a cross-validation data splitter.

Returns

If the splitter is a cross-validation data splitter

Return type

bool

split(self, X, y=None)

Divide the data into training and testing sets, where the testing set is empty.

Parameters

• X (pd.DataFrame) – Dataframe of points to split

• y (pd.Series) – Series of points to split

Returns

Indices to split data into training and test set

Return type

list