InputConsistencyChecker

class openstef_models.transforms.validation.input_consistency_checker.InputConsistencyChecker(**data: Any) → None

Bases: BaseConfig, TimeSeriesTransform

Validates input data consistency during transform operations.

Ensures that input features match those seen during fitting and maintains consistent column ordering. Logs warnings and removes extra columns.

Invariants

• Must be fitted before transform() can be called

• Validates presence of all features seen during fitting

• Logs warnings for extra columns not seen during fitting

• Removes extra columns from output

• Maintains consistent column ordering in output

Parameters:

data (Any)

property is_fitted: bool

Check if the transform has been fitted.

fit(data: TimeSeriesDataset) → None

Fit the transform to the input data.

This method should be called before applying the transform to the data. It allows the transform to learn any necessary parameters from the data.

Parameters:

• data (TimeSeriesDataset) – The input data to fit the transform on.

• data

Return type:

None

transform(data: TimeSeriesDataset) → TimeSeriesDataset

Transform the input data.

This method should apply a transformation to the input data and return a new instance.

Parameters:

• data (TimeSeriesDataset) – The input data to be transformed.

• data

Returns:

A new instance of the transformed data.

Raises:

NotFittedError – If the transform has not been fitted yet.

Return type:

TimeSeriesDataset

features_added() → list[str]

List of feature names added by this transform.

Return type:

list[str]

Returns:

A list of strings representing the names of features added to the dataset by this transform. Default is an empty list.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': False, 'extra': 'ignore', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• context (Any) – The context.

• self

• context

Return type:

None