Feature Validation Helper

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Feature validation helper for parameter and state validation.

This module provides utilities for validating feature-related parameters and states in the FeatureManager, separating validation logic from core business logic.

class mdxplain.feature.helper.feature_validation_helper.FeatureValidationHelper

Helper class for feature parameter and state validation.

Provides static methods for validating various types of parameters used in feature management, including threshold parameters, feature existence checks, and dependency validation.

static validate_threshold_parameters(threshold_min: int | float | None, threshold_max: int | float | None, metric: str) → None

Validate threshold parameters for data reduction.

Parameters

threshold_minfloat or None: Minimum threshold value to validate
threshold_maxfloat or None: Maximum threshold value to validate
metricstr: Metric name for context in error messages

Returns

None: Validates parameters or raises ValueError

Raises

ValueError: If threshold parameters are invalid (wrong type or invalid range)

Examples

>>> FeatureValidationHelper.validate_threshold_parameters(0.1, 0.9, "cv")
>>> # No error - valid parameters

>>> FeatureValidationHelper.validate_threshold_parameters(0.9, 0.1, "cv")
ValueError: Invalid threshold range...

static validate_feature_exists(pipeline_data: PipelineData, feature_key: str) → bool

Check if feature exists and has computed data.

Parameters

pipeline_dataPipelineData: Pipeline data object to check
feature_keystr: Feature key to validate

Returns

bool: True if feature exists and has data, False otherwise

Examples

>>> exists = FeatureValidationHelper.validate_feature_exists(
...     pipeline_data, "distances"
... )

static validate_dependencies(pipeline_data: PipelineData, feature_type: FeatureTypeBase, feature_key: str, traj_indices: List[int]) → None

Validate that all feature dependencies are available.

Parameters

pipeline_dataPipelineData: Pipeline data object to check
feature_typeFeatureTypeBase: Feature type object to check dependencies for
feature_keystr: Feature key for error messages

Returns

None: Validates dependencies or raises ValueError

Raises

ValueError: If required dependency is missing

Examples

>>> FeatureValidationHelper.validate_dependencies(
...     pipeline_data, contacts_feature, "contacts"
... )

static validate_computation_requirements(pipeline_data: PipelineData, feature_type: FeatureTypeBase) → None

Validate that all requirements for feature computation are met.

Parameters

pipeline_dataPipelineData: Pipeline data object to validate
feature_typeFeatureTypeBase: Feature type object to validate

Returns

None: Validates requirements or raises ValueError

Raises

ValueError: If trajectory labels are not set or trajectories are not loaded

Examples

>>> FeatureValidationHelper.validate_computation_requirements(
...     pipeline_data, distances_feature
... )

static validate_reduction_state(pipeline_data: PipelineData, feature_key: str, traj_indices: List[int]) → None

Validate that feature is ready for reduction for specified trajectories.

Parameters

pipeline_dataPipelineData: Pipeline data object to validate
feature_keystr: Feature key to validate
traj_indicesList[int]: List of trajectory indices to validate

Returns

None: Validates state or raises ValueError

Raises

ValueError: If feature has no data or reduction already performed for specified trajectories

Examples

>>> FeatureValidationHelper.validate_reduction_state(
...     pipeline_data, "distances", [0, 1, 2]
... )