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)[source]#

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()[source]#

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)[source]#

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