streamline.models.learning_based module

class streamline.models.learning_based.ExSTraCSClassifier(cv_folds=3, scoring_metric='balanced_accuracy', metric_direction='maximize', random_state=None, cv=None, n_jobs=None, iterations=None, N=None, nu=None, expert_knowledge=None)

Bases: BaseModel, ABC

Base Model Class for all ML Models

Parameters:

• model –

• model_name –

• cv_folds –

• scoring_metric –

• metric_direction –

• random_state –

• cv –

• sampler –

• n_jobs –

color = 'lawngreen'

model_name = 'ExSTraCS'

objective(trial, params=None)

Unimplemented objective function stub, needs to be overridden :param trial: optuna trial object :param params: dict of optional params or None

small_name = 'ExSTraCS'

class streamline.models.learning_based.XCSClassifier(cv_folds=3, scoring_metric='balanced_accuracy', metric_direction='maximize', random_state=None, cv=None, n_jobs=None, iterations=None, N=None, nu=None)

Bases: BaseModel, ABC

Base Model Class for all ML Models

Parameters:

• model –

• model_name –

• cv_folds –

• scoring_metric –

• metric_direction –

• random_state –

• cv –

• sampler –

• n_jobs –

color = 'olive'

model_name = 'XCS'

objective(trial, params=None)

Unimplemented objective function stub, needs to be overridden :param trial: optuna trial object :param params: dict of optional params or None

small_name = 'XCS'

class streamline.models.learning_based.eLCSClassifier(cv_folds=3, scoring_metric='balanced_accuracy', metric_direction='maximize', random_state=None, cv=None, n_jobs=None, iterations=None, N=None, nu=None)

Bases: BaseModel, ABC

Base Model Class for all ML Models

Parameters:

• model –

• model_name –

• cv_folds –

• scoring_metric –

• metric_direction –

• random_state –

• cv –

• sampler –

• n_jobs –

color = 'green'

model_name = 'eLCS'

objective(trial, params=None)

Unimplemented objective function stub, needs to be overridden :param trial: optuna trial object :param params: dict of optional params or None

small_name = 'eLCS'