langtest.augmentation.base.AugmentRobustness#

class AugmentRobustness(task: TaskManager, h_report: DataFrame, config: Dict, custom_proportions: List | Dict | None = None, max_prop=0.5)#

Bases: BaseAugmentaion

A class for performing a specified task with historical results.

task#

A string indicating the task being performed.

Type:

str

config#

A dictionary containing configuration parameters for the task.

Type:

dict

h_report#

A DataFrame containing a report of historical results for the task.

Type:

pandas.DataFrame

max_prop#

The maximum proportion of improvement that can be suggested by the class methods. Defaults to 0.5.

Type:

float

__init__(self, task, h_report, config, max_prop=0.5) → None#

Initializes an instance of MyClass with the specified parameters.

fix(self) → List[Sample]#

.

suggestions(self, prop) → pandas.DataFrame#

Calculates suggestions for improving test performance based on a given report.

__init__(task: TaskManager, h_report: DataFrame, config: Dict, custom_proportions: List | Dict | None = None, max_prop=0.5) → None#

Initializes an instance of MyClass with the specified parameters.

Parameters:

  • task (str) – A string indicating the task being performed.

  • h_report (pandas.DataFrame) – A DataFrame containing a report of historical results for the task.

  • config (dict) – A dictionary containing configuration parameters for the task.

  • custom_proportions –

  • max_prop (float) – The maximum proportion of improvement that can be suggested by the class methods. Defaults to 0.5.

Returns:

None

Methods

__init__(task, h_report, config[, ...])

Initializes an instance of MyClass with the specified parameters.

fix(training_data, output_path[, export_mode])

Applies perturbations to the input data based on the recommendations from harness reports.

suggestions(report)

Calculates suggestions for improving test performance based on a given report.
fix(training_data: dict, output_path: str, export_mode: str = 'add')#

Applies perturbations to the input data based on the recommendations from harness reports.

Parameters:

  • training_data (dict) – A dictionary containing the input data for augmentation.

  • output_path (str) – The path to save the augmented data file.

  • export_mode (str, optional) – Determines how the samples are modified or exported. - ‘inplace’: Modifies the list of samples in place. - ‘add’: Adds new samples to the input data. - ‘transformed’: Exports only the transformed data, excluding untransformed samples. Defaults to ‘add’.

Returns:

A list of augmented data samples.

Return type:

List[Dict[str, Any]]

suggestions(report: DataFrame) → DataFrame#

Calculates suggestions for improving test performance based on a given report.

Parameters:

report (pandas.DataFrame) – A DataFrame containing test results by category and test type, including pass rates and minimum pass rates.

Returns:

A DataFrame containing the following columns for each suggestion:

  • category: the test category

  • test_type: the type of test

  • ratio: the pass rate divided by the minimum pass rate for the test

  • proportion_increase: a proportion indicating how much the pass rate

    should increase to reach the minimum pass rate

Return type:

pandas.DataFrame