Scorer

class pyprophet.scoring.pyprophet.Scorer(classifier, score_columns, experiment, group_id, error_estimation_config: ErrorEstimationConfig, tric_chromprob, ss_score_filter, ss_scale_features, color_palette, level)[source]

Bases: object

Handles scoring, error estimation, and hypothesis testing for experiments.

classifier

The trained classifier used for scoring.

score_columns

List of score column names.

mu, nu

Parameters for d-score normalization.

error_stat

Error statistics for target and decoy scores.

pi0

Estimated proportion of null hypotheses.

level

Analysis level (e.g., peptide, protein).

__getstate__()[source]

when pickling

__init__(classifier, score_columns, experiment, group_id, error_estimation_config: ErrorEstimationConfig, tric_chromprob, ss_score_filter, ss_scale_features, color_palette, level)[source]

Initializes the Scorer with the given classifier, experiment, and configuration.

Parameters:

  • classifier – The trained classifier.

  • score_columns – List of score column names.

  • experiment – The experiment data.

  • group_id – Group ID for scoring.

  • error_estimation_config – Configuration for error estimation.

  • tric_chromprob – Flag for chromatogram probabilities.

  • ss_score_filter – Filter for semi-supervised scoring.

  • ss_scale_features – Flag for feature scaling.

  • color_palette – Color palette for visualization.

  • level – Analysis level (e.g., peptide, protein).

__setstate__(data)[source]

when unpickling

__weakref__

list of weak references to the object (if defined)

add_chromatogram_probabilities(scored_table, texp)[source]

Adds chromatogram probabilities to the scored table.

Parameters:

  • scored_table – The scored table.

  • texp – The experiment data.

Returns:

The updated scored table with chromatogram probabilities.

Return type:

pd.DataFrame

get_error_stats()[source]

Retrieves the final and summary error statistics.

Returns:

Final error table and summary error table.

Return type:

tuple

minimal_error_stat()[source]

Creates a minimal error statistics object for serialization.

Returns:

The minimal error statistics object.

Return type:

ErrorStatistics

score(table)[source]

Scores the given table using the trained classifier.

Parameters:

table – The input data table.

Returns:

The scored table with additional columns for scores and error metrics.

Return type:

pd.DataFrame