tidymut.cleaners.protein_gym_cleaner module

class tidymut.cleaners.protein_gym_cleaner.ProteinGymCleanerConfig(pipeline_name: str = 'protein_gym_cleaner', num_workers: int = 16, validate_config: bool = True, column_mapping: Dict[str, str] = <factory>, filters: Dict[str, Any] = <factory>, type_conversions: Dict[str, str] = <factory>, validation_workers: int = 16, infer_wt_workers: int = 16, handle_multiple_wt: Literal['error', 'first', 'separate'] = 'error', label_columns: List[str] = <factory>, primary_label_column: str = 'DMS_score')[source]

Bases: BaseCleanerConfig

Configuration class for ProteinGym dataset cleaner

Inherits from BaseCleanerConfig and adds ProteinGym-specific configuration options.

column_mapping

Mapping from source to target column names

Type:

Dict[str, str]

filters

Filter conditions for data cleaning

Type:

Dict[str, Any]

type_conversions

Data type conversion specifications

Type:

Dict[str, str]

is_zero_based

Whether mutation positions are zero-based

Type:

bool

validation_workers

Number of workers for mutation validation

Type:

int

infer_wt_workers

Number of workers for wildtype sequence inference

Type:

int

handle_multiple_wt

Strategy for handling multiple wildtype sequences

Type:

Literal[“error”, “first”, “separate”]

label_columns

List of score columns to process

Type:

List[str]

primary_label_column

Primary score column for the dataset

Type:

str

column_mapping: Dict[str, str]
filters: Dict[str, Any]
handle_multiple_wt: Literal['error', 'first', 'separate'] = 'error'
infer_wt_workers: int = 16
label_columns: List[str]
pipeline_name: str = 'protein_gym_cleaner'
primary_label_column: str = 'DMS_score'
type_conversions: Dict[str, str]
validate() None[source]

Validate ProteinGym-specific configuration parameters

Raises:

ValueError – If configuration is invalid

validation_workers: int = 16
tidymut.cleaners.protein_gym_cleaner.clean_protein_gym_dataset(pipeline: Pipeline) Tuple[Pipeline, MutationDataset][source]

Clean ProteinGym dataset using configurable pipeline

Parameters:

pipeline (Pipeline) – ProteinGym dataset cleaning pipeline

Returns:

  • Pipeline: The cleaned pipeline

  • MutationDataset: The cleaned ProteinGym dataset

Return type:

Tuple[Pipeline, MutationDataset]

tidymut.cleaners.protein_gym_cleaner.create_protein_gym_cleaner(data_path: str | Path, config: ProteinGymCleanerConfig | Dict[str, Any] | str | Path | None = None) Pipeline[source]

Create ProteinGym dataset cleaning pipeline

Parameters:
  • data_path (Union[str, Path]) – Path to directory containing ProteinGym CSV files or path to zip file - Download from: https://proteingym.org/download - File: DMS_ProteinGym_substitutions.zip

  • config (Optional[Union[ProteinGymCleanerConfig, Dict[str, Any], str, Path]]) – Configuration for the cleaning pipeline. Can be: - ProteinGymCleanerConfig object - Dictionary with configuration parameters (merged with defaults) - Path to JSON configuration file (str or Path) - None (uses default configuration)

Returns:

The cleaning pipeline

Return type:

Pipeline

Raises:
  • TypeError – If config has invalid type

  • ValueError – If configuration validation fails

Examples

Process directory of ProteinGym CSV files: >>> pipeline = create_protein_gym_cleaner(“DMS_ProteinGym_substitutions/”) >>> pipeline, dataset = clean_protein_gym_dataset(pipeline)

Process zip file: >>> pipeline = create_protein_gym_cleaner(“DMS_ProteinGym_substitutions.zip”) >>> pipeline, dataset = clean_protein_gym_dataset(pipeline)

Custom configuration: >>> config = { … “validation_workers”: 8, … “handle_multiple_wt”: “first” … } >>> pipeline = create_protein_gym_cleaner(“data/”, config=config)

Load configuration from file: >>> pipeline = create_protein_gym_cleaner(“data/”, config=”config.json”)