calibrated_explanations.core.validation

Validation helpers shared across the core package.

These utilities centralize defensive argument checks while preserving existing behavior, ensuring future refactors can rely on a consistent error vocabulary.

ADR-002 compliance: Validation functions use the exception taxonomy (ValidationError, DataShapeError, NotFittedError, ConfigurationError, etc.) and accept optional details payloads.

calibrated_explanations.core.validation.validate_inputs(x: Any, y: Any | None = None, task: Literal['auto', 'classification', 'regression'] = 'auto', allow_nan: bool = False, require_y: bool = False, n_features: int | None = None, class_labels: Any | None = None, check_finite: bool = True) → None

Validate input features and target for downstream operations.

This function provides the primary validation entry point per ADR-002, accepting feature matrix x and optional target y with comprehensive shape, dtype, and value checks.

Parameters:

• x (array-like) – Feature matrix of shape (n_samples, n_features). Can be a NumPy array, pandas DataFrame, or similar.

• y (array-like, optional) – Target vector of shape (n_samples,). If provided, length must match x. Default is None.

• task ({"auto", "classification", "regression"}, default="auto") – Task type. When “auto”, inferred from model capabilities or y dtype.

• allow_nan (bool, default=False) – If False, raises ValidationError when x or y contain NaN values.

• require_y (bool, default=False) – If True, raises ValidationError when y is None.

• n_features (int, optional) – Expected number of features in x. If provided and mismatch occurs, raises DataShapeError.

• class_labels (array-like, optional) – Class labels for classification tasks. Stored for later use.

• check_finite (bool, default=True) – If True, checks that x and y contain only finite values (except NaN when allow_nan=True).

Raises:

• ValidationError – When y is required but None, or when values contain NaN/inf unexpectedly.

• DataShapeError – When x is not 2D, feature count mismatches, or y length mismatches.

Examples

>>> from calibrated_explanations.core.validation import validate_inputs
>>> import numpy as np
>>> x = np.array([[1.0, 2.0], [3.0, 4.0]])
>>> y = np.array([0, 1])
>>> validate_inputs(x, y, task="classification", require_y=True, n_features=2)
# Passes silently if valid

calibrated_explanations.core.validation.validate_not_none(value: Any, name: str) → None

Raise ValidationError when value is None.

calibrated_explanations.core.validation.validate_type(value: Any, expected_type: type, name: str) → None

Ensure that value is an instance of expected_type.

calibrated_explanations.core.validation.validate_non_empty(value: Any, name: str) → None

Ensure that length-aware inputs are not empty.

calibrated_explanations.core.validation.validate_inputs_matrix(x: Any, y: Any | None = None, *, task: Literal['auto', 'classification', 'regression'] = 'auto', allow_nan: bool = False, require_y: bool = False, n_features: int | None = None, check_finite: bool = True) → None

Validate a feature/target matrix pair for downstream operations.

• Ensure x is 2D and matches the expected feature count when provided.

• Confirm that y has the same number of samples when supplied.

• Guard against NaN or infinite values unless explicitly allowed.

calibrated_explanations.core.validation.validate_model(model: Any) → None

Validate minimal model protocol requirements.

calibrated_explanations.core.validation.validate_fit_state(obj: Any, *, require: bool = True) → None

Validate fit state flags before executing stateful operations.

calibrated_explanations.core.validation.infer_task(x: Any = None, y: Any = None, model: Any = None) → Literal['classification', 'regression']

Infer the task type using model capabilities or target dtype.

Priority is given to model capabilities (predict_proba implies classification). When a model is unavailable, heuristics based on the target dtype are used. Regression is the safe fallback.

calibrated_explanations.core.validation.validate(condition: bool, exc_cls: Type[CalibratedError], message: str, *, details: dict[str, Any] | None = None) → None

Conditional validation helper for common patterns.

Raises an exception when a condition is False, enabling concise guard clauses.

Parameters:

• condition (bool) – Condition to check. If False, raises exc_cls.

• exc_cls (Type[CalibratedError]) – Exception class to raise when condition is False.

• message (str) – Error message.

• details (dict, optional) – Structured error details to attach to the exception.

Raises:

exc_cls – If condition is False.

Examples

>>> from calibrated_explanations.core.validation import validate
>>> from calibrated_explanations.utils.exceptions import ValidationError
>>> validate(len(x) > 0, ValidationError, "x must not be empty", details={"param": "x"})