mutcleaner.cleaners.ctxm_cleaner#

Functions

`clean_ctxm_dataset`(pipeline)	Clean CTXM dataset using configurable pipeline
`create_ctxm_cleaner`([dataset_or_path, config])	Create CTXM dataset cleaning pipeline

Classes

CTXMCleanerConfig([pipeline_name, ...])

Configuration class for CTXM dataset cleaner.

class mutcleaner.cleaners.ctxm_cleaner.CTXMCleanerConfig(pipeline_name='CTXM Cleaning Pipeline', num_workers=16, validate_config=True, column_mapping=<factory>, filters=<factory>, type_conversions=<factory>, process_workers=16, validate_mut_workers=16, label_columns=<factory>, ambler_to_seq_mapping=<factory>, wt_name='CTXM_ampicillin', primary_label_column='fitness')[source]#

Bases: BaseCleanerConfig

Configuration class for CTXM dataset cleaner. Inherits from BaseCleanerConfig and adds CTXM-specific configuration options.

Simply run mutcleaner.download_ctxm_source_file() to download the dataset.

Alternatively, the raw CTXM file can be obtained from:

Attributes:

column_mappingDict[str, str]: Mapping from source to target column names
filtersDict[str, Callable]: Filter conditions for data cleaning
type_conversionsDict[str, str]: Data type conversion specifications
validate_mut_workersint: Number of workers for mutation validation, set to -1 to use all available CPUs
validate_wt_workersint: Number of workers for wildtype sequence validation, set to -1 to use all available CPUs
label_columnsList[str]: List of score columns to process
primary_label_columnstr: Primary score column for the dataset

Methods

`from_dict`(config_dict)	Create configuration object from dictionary
`from_json`(json_path)	Load configuration from JSON file
`get_summary`()	Get a human-readable summary of the configuration
`merge`(partial_config)	Merge partial configuration with current configuration
`to_dict`([exclude_callables])	Convert configuration to dictionary
`to_json`(json_path, **json_kwargs)	Save configuration to JSON file
`validate`()	Validate CTXM-specific configuration parameters

ambler_to_seq_mapping: Dict[str, str]#

column_mapping: Dict[str, str]#

filters: Dict[str, Callable]#

label_columns: List[str]#

pipeline_name: str = 'CTXM Cleaning Pipeline'#

primary_label_column: str = 'fitness'#

process_workers: int = 16#

type_conversions: Dict[str, str]#

validate()[source]#

Validate CTXM-specific configuration parameters

Raises:: ValueError – If configuration is invalid
Return type:: None

validate_mut_workers: int = 16#

wt_name: str = 'CTXM_ampicillin'#

wt_sequence = 'RMMFAAAACIPLLLGSAPLYAQTSAVQQKLAALEKSSGGRLGVALIDTADNTQVLYRGDERFPMCSTSKVMAAAAVLKQSETQKQLLNQPVEIKPADLVNYNPIAEKHVNGTMTLAELSAAALQYSDNTAMNKLIAQLGGPGGVTAFARAIGDETFRLDRTEPTLNTAIPGDPRDTTTPRAMAQTLRQLTLGHALGETQRAQLVTWLKGNTTGAASIRAGLPTSWTVGDKTGSGDYGTTNDIAVIWPQGRAPLVLVTYFTQPQQNAESRRDVLASAARIIAEGL'#

mutcleaner.cleaners.ctxm_cleaner.clean_ctxm_dataset(pipeline)[source]#

Clean CTXM dataset using configurable pipeline

Parameters:

pipeline (Pipeline) – CTXM dataset cleaning pipeline

Return type:

Tuple[Pipeline, MutationDataset]

Returns:

Pipeline: The cleaned pipeline - MutationDataset: The cleaned CTXM dataset

Examples

>>> pipeline = create_CTXM_cleaner(df)  # df is raw CTXM dataset file
Use default configuration:

>>> pipeline, dataset = clean_CTXM_dataset(pipeline)

Use partial configuration:

>>> pipeline, dataset = clean_CTXM_dataset(df, config={
...     "process_workers": 8,
... })

Load configuration from file:

>>> pipeline, dataset = clean_CTXM_dataset(df, config="config.json")

mutcleaner.cleaners.ctxm_cleaner.create_ctxm_cleaner(dataset_or_path=None, config=None)[source]#

Create CTXM dataset cleaning pipeline

Parameters:

dataset_or_path (Union[DataFrame, str, Path, None]) – Raw dataset DataFrame or file path to CTXM dataset.
config (Union[CTXMCleanerConfig, Dict[str, Any], str, Path, None]) – Configuration for the cleaning pipeline. Can be: - CTXMCleanerConfig object - Dictionary with configuration parameters (merged with defaults) - Path to JSON configuration file (str or Path) - None (uses default configuration)

Return type:

Pipeline

Returns:

Pipeline: The cleaning pipeline used

Raises:

TypeError – If config has invalid type
ValueError – If configuration validation fails