seqgra.constants module

class ComparatorID

Bases: object

ALL_COMPARATOR_IDS: FrozenSet[str] = frozenset({'curve-table', 'fi-eval-table', 'pr', 'roc', 'table'})

CURVE_TABLE: str = 'curve-table'

FEATURE_IMPORTANCE_EVALUATOR_TABLE: str = 'fi-eval-table'

PR: str = 'pr'

ROC: str = 'roc'

TABLE: str = 'table'

class DataSet

Bases: object

ALL_SETS: List[str] = ['training', 'validation', 'test']

TEST: str = 'test'

TRAINING: str = 'training'

VALIDATION: str = 'validation'

class EvaluatorID

Bases: object

ALL_EVALUATOR_IDS: FrozenSet[str] = frozenset({'contrastive-excitation-backprop', 'deconv', 'deep-lift', 'excitation-backprop', 'feedback', 'grad-cam', 'gradient', 'gradient-x-input', 'guided-backprop', 'integrated-gradients', 'metrics', 'nonlinear-integrated-gradients', 'pr', 'predict', 'roc', 'saliency', 'sis', 'smooth-grad'})

CONTRASTIVE_EXCITATION_BACKPROP: str = 'contrastive-excitation-backprop'

CONVENTIONAL_EVALUATORS: FrozenSet[str] = frozenset({'metrics', 'pr', 'predict', 'roc'})

CORE_FEATURE_IMPORTANCE_EVALUATORS: FrozenSet[str] = frozenset({'deconv', 'deep-lift', 'gradient', 'gradient-x-input', 'guided-backprop', 'integrated-gradients', 'saliency'})

DECONV: str = 'deconv'

DEEP_LIFT: str = 'deep-lift'

EXCITATION_BACKPROP: str = 'excitation-backprop'

FEATURE_IMPORTANCE_EVALUATORS: FrozenSet[str] = frozenset({'contrastive-excitation-backprop', 'deconv', 'deep-lift', 'excitation-backprop', 'feedback', 'grad-cam', 'gradient', 'gradient-x-input', 'guided-backprop', 'integrated-gradients', 'nonlinear-integrated-gradients', 'saliency', 'sis', 'smooth-grad'})

FEEDBACK: str = 'feedback'

GRADIENT: str = 'gradient'

GRADIENT_X_INPUT: str = 'gradient-x-input'

GRAD_CAM: str = 'grad-cam'

GUIDED_BACKPROP: str = 'guided-backprop'

INTEGRATED_GRADIENTS: str = 'integrated-gradients'

METRICS: str = 'metrics'

MODEL_AGNOSTIC_EVALUATORS: FrozenSet[str] = frozenset({'metrics', 'pr', 'predict', 'roc', 'sis'})

NONLINEAR_INTEGRATED_GRADIENTS: str = 'nonlinear-integrated-gradients'

PR: str = 'pr'

PREDICT: str = 'predict'

ROC: str = 'roc'

SALIENCY: str = 'saliency'

SIS: str = 'sis'

SMOOTH_GRAD: str = 'smooth-grad'

class LibraryType

Bases: object

ALL_LIBRARIES: FrozenSet[str] = frozenset({'BayesOptimalClassifier', 'PyTorch', 'TensorFlow'})

BAYES_OPTIMAL_CLASSIFIER: str = 'BayesOptimalClassifier'

TENSORFLOW: str = 'TensorFlow'

TORCH: str = 'PyTorch'

class PositionType

Bases: object

AA_MASKED: str = 'X'

BACKGROUND: str = '_'

CONFOUNDER: str = 'C'

DNA_MASKED: str = 'N'

GRAMMAR: str = 'G'

class SequenceSpaceType

Bases: object

ALL_SEQUENCE_SPACES: FrozenSet[str] = frozenset({'DNA', 'protein'})

DNA: str = 'DNA'

PROTEIN: str = 'protein'

class TaskType

Bases: object

ALL_TASKS: FrozenSet[str] = frozenset({'multi-class classification', 'multi-label classification', 'multiple regression', 'multivariate regression'})

MULTIPLE_REGRESSION: str = 'multiple regression'

MULTIVARIATE_REGRESSION: str = 'multivariate regression'

MULTI_CLASS_CLASSIFICATION: str = 'multi-class classification'

MULTI_LABEL_CLASSIFICATION: str = 'multi-label classification'