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TemporalHoldoutSplitter

Splitter
DashAI.back.splitters.TemporalHoldoutSplitter

Split a series in time order: train first, then validation, then test.

Rows are cut where they lie rather than sampled, so every row a model is scored on comes after every row it was fitted on. That is the only honest way to estimate how a model will do on data it has not seen yet, because the alternative lets it learn from the future.

Shuffling is not merely discouraged here, it is impossible: a request to shuffle is overruled. Two ways of getting it wrong are worth naming, since neither reports an error, they just return a score that is too good.

  • A random split of a series lets the model interpolate between rows it has already seen instead of extrapolating past them.
  • On the output of TimeSeriesWindowConverter it is worse still, because consecutive rows share window_size - 1 of their values, so a random split puts near duplicates of the training rows into the test set. That route goes through RegressionTask, which this splitter is not offered for; use HoldoutSplitter with shuffling turned off there.

Row order is taken as time order. This splitter receives the selected input columns, which for a windowed dataset no longer include a date, so it cannot re-sort and does not try.

Parameters​

train : number, default=0.6
Proportion of the earliest rows used for training.
validation : number, default=0.2
Proportion of the rows after training used for validation.
test : number, default=0.2
Proportion of the most recent rows used for testing.

Methods​

split_indexes(self, x: "'DashAIDataset'", y: "'DashAIDataset'") -> 'Tuple[List, List, List]'

Defined on TemporalHoldoutSplitter

Cut the rows into three consecutive blocks in time order.

Parameters

x : DashAIDataset
Input dataset to partition.
y : DashAIDataset
Target values associated with x. Unused: nothing here depends on the target, unlike a stratified split.

Returns

tuple[List, List, List]
Train, test and validation indexes. Note the return order matches the holdout contract, while the partitions themselves run train, validation, test along the timeline.

explainable_partitions(cls, split_indexes)

Defined on PartitionSplitter

Return the train, test and validation partitions of a holdout run.

Parameters

split_indexes : dict
The Run.split_indexes payload, already parsed.

Returns

dict
Row indexes for the train, test and val partitions.

explainable_splits(cls, split_indexes: 'Dict[str, Any]') -> 'List[Dict[str, Any]]'

Defined on BaseSplitter

Describe the partitions of a run that an explainer may target.

Parameters

split_indexes : dict
The Run.split_indexes payload, already parsed.

Returns

list[dict]
One {"name", "rows"} entry per non-empty partition, followed by an all entry. Empty when the run has no data to explain.

get_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str
Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential
An instance of the requested credential component.

get_metadata(cls) -> 'Dict[str, Any]'

Defined on BaseSplitter

Return metadata describing how this splitter carves the dataset.

Returns

Dict[str, Any]
Mapping with partitioning, which the frontend uses to decide whether the splitter belongs to the holdout or the cross-validation strategy.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

prepare_y(self, y)

Defined on BaseSplitter

Encode the target variable for stratified splitting.

Parameters

y : object
Target values to encode. This may be a list, a pandas-like object, or a DashAI dataset that exposes a single target column.

Returns

object
Encoded labels suitable for stratified splitting.

split(self, x: 'DashAIDataset', y: 'DashAIDataset') -> 'Tuple[DatasetDict, DatasetDict, Dict[str, Any]]'

Defined on PartitionSplitter

Split the input data into holdout partitions and return the resulting datasets.

Parameters

x : DashAIDataset
Input dataset to partition.
y : DashAIDataset
Target values associated with x.

Returns

tuple
A tuple containing the partitioned input and output datasets, along with the indices used for each split.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict
A dictionary with the data provided by the user to initialize the model.

Returns

dict
A validated dictionary with the necessary objects.

Compatible with​