AbstractSemiSupervisedLearner

class pyprophet.scoring.semi_supervised.AbstractSemiSupervisedLearner(xeval_fraction, xeval_num_iter, test)

Bases: object

Abstract base class for semi-supervised learning workflows.

xeval_fraction

Fraction of data used for cross-validation.

Type:

float

xeval_num_iter

Number of iterations for cross-validation.

Type:

int

test

Whether to enable testing mode.

Type:

bool

__init__(xeval_fraction, xeval_num_iter, test)

__weakref__

list of weak references to the object (if defined)

averaged_learner(params, **kwargs)

Abstract method to create an averaged learner from multiple parameter sets.

Parameters:

• params (list) – List of parameter sets.

• kwargs – Additional arguments.

iter_semi_supervised_learning(train)

Abstract method for iterative semi-supervised learning.

Parameters:

train (Experiment) – Training data.

learn_final(experiment)

Performs final learning on cross-validated scores.

Parameters:

experiment (Experiment) – The experiment data.

Returns:

Final model parameters.

Return type:

dict

learn_randomized(experiment, score_columns, working_thread_number)

Performs randomized semi-supervised learning with cross-validation.

Parameters:

• experiment (Experiment) – The experiment data.

• score_columns (list) – List of score column names.

• working_thread_number (int) – Number of threads to use.

Returns:

Target scores, decoy scores, and model parameters.

Return type:

tuple

score(df, params)

Abstract method to score the given data using the trained model.

Parameters:

• df (pd.DataFrame) – Input data.

• params (dict) – Model parameters.

start_semi_supervised_learning(train, score_columns, working_thread_number)

Abstract method to start the semi-supervised learning process.

Parameters:

• train (Experiment) – Training data.

• score_columns (list) – List of score column names.

• working_thread_number (int) – Number of threads to use.