pyprophet/main.py
def score(infile, outfile, classifier, xgb_autotune, apply_weights, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test):
    """
    Conduct semi-supervised learning and error-rate estimation for MS1, MS2 and transition-level data. 
    """

    if outfile is None:
        outfile = infile
    else:
        outfile = outfile

    # Prepare XGBoost-specific parameters
    xgb_hyperparams = {'autotune': xgb_autotune, 'autotune_num_rounds': 10, 'num_boost_round': 100, 'early_stopping_rounds': 10, 'test_size': 0.33}

    xgb_params = {'eta': 0.3, 'gamma': 0, 'max_depth': 6, 'min_child_weight': 1, 'subsample': 1, 'colsample_bytree': 1, 'colsample_bylevel': 1, 'colsample_bynode': 1, 'lambda': 1, 'alpha': 0, 'scale_pos_weight': 1, 'silent': 1, 'objective': 'binary:logitraw', 'nthread': 1, 'eval_metric': 'auc'}

    xgb_params_space = {'eta': hp.uniform('eta', 0.0, 0.3), 'gamma': hp.uniform('gamma', 0.0, 0.5), 'max_depth': hp.quniform('max_depth', 2, 8, 1), 'min_child_weight': hp.quniform('min_child_weight', 1, 5, 1), 'subsample': 1, 'colsample_bytree': 1, 'colsample_bylevel': 1, 'colsample_bynode': 1, 'lambda': hp.uniform('lambda', 0.0, 1.0), 'alpha': hp.uniform('alpha', 0.0, 1.0), 'scale_pos_weight': 1.0, 'silent': 1, 'objective': 'binary:logitraw', 'nthread': 1, 'eval_metric': 'auc'}

    if not apply_weights:
        PyProphetLearner(infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test).run()
    else:
        PyProphetWeightApplier(infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, apply_weights).run()
def score(infile, outfile, classifier, xgb_autotune, apply_weights, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test):
    if outfile is None:
        outfile = infile
    else:
        outfile = outfile

    # Prepare XGBoost-specific parameters
    xgb_hyperparams = {'autotune': xgb_autotune, 'autotune_num_rounds': 10, 'num_boost_round': 100, 'early_stopping_rounds': 10, 'test_size': 0.33}

    xgb_params = {'eta': 0.3, 'gamma': 0, 'max_depth': 6, 'min_child_weight': 1, 'subsample': 1, 'colsample_bytree': 1, 'colsample_bylevel': 1, 'colsample_bynode': 1, 'lambda': 1, 'alpha': 0, 'scale_pos_weight': 1, 'silent': 1, 'objective': 'binary:logitraw', 'nthread': 1, 'eval_metric': 'auc'}

    xgb_params_space = {'eta': hp.uniform('eta', 0.0, 0.3), 'gamma': hp.uniform('gamma', 0.0, 0.5), 'max_depth': hp.quniform('max_depth', 2, 8, 1), 'min_child_weight': hp.quniform('min_child_weight', 1, 5, 1), 'subsample': 1, 'colsample_bytree': 1, 'colsample_bylevel': 1, 'colsample_bynode': 1, 'lambda': hp.uniform('lambda', 0.0, 1.0), 'alpha': hp.uniform('alpha', 0.0, 1.0), 'scale_pos_weight': 1.0, 'silent': 1, 'objective': 'binary:logitraw', 'nthread': 1, 'eval_metric': 'auc'}

    if not apply_weights:
        PyProphetLearner(infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test).run()
    else:
        PyProphetWeightApplier(infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, apply_weights).run()
def score ( infile , outfile , classifier , xgb_autotune , apply_weights , xeval_fraction , xeval_num_iter , ss_initial_fdr , ss_iteration_fdr , ss_num_iter , ss_main_score , group_id , parametric , pfdr , pi0_lambda , pi0_method , pi0_smooth_df , pi0_smooth_log_pi0 , lfdr_truncate , lfdr_monotone , lfdr_transformation , lfdr_adj , lfdr_eps , level , ipf_max_peakgroup_rank , ipf_max_peakgroup_pep , ipf_max_transition_isotope_overlap , ipf_min_transition_sn , tric_chromprob , threads , test ) : if outfile is None : outfile = infile else : outfile = outfile # Prepare XGBoost-specific parameters xgb_hyperparams = { 'autotune' : xgb_autotune , 'autotune_num_rounds' : 10 , 'num_boost_round' : 100 , 'early_stopping_rounds' : 10 , 'test_size' : 0.33 } xgb_params = { 'eta' : 0.3 , 'gamma' : 0 , 'max_depth' : 6 , 'min_child_weight' : 1 , 'subsample' : 1 , 'colsample_bytree' : 1 , 'colsample_bylevel' : 1 , 'colsample_bynode' : 1 , 'lambda' : 1 , 'alpha' : 0 , 'scale_pos_weight' : 1 , 'silent' : 1 , 'objective' : 'binary:logitraw' , 'nthread' : 1 , 'eval_metric' : 'auc' } xgb_params_space = { 'eta' : hp . uniform ( 'eta' , 0.0 , 0.3 ) , 'gamma' : hp . uniform ( 'gamma' , 0.0 , 0.5 ) , 'max_depth' : hp . quniform ( 'max_depth' , 2 , 8 , 1 ) , 'min_child_weight' : hp . quniform ( 'min_child_weight' , 1 , 5 , 1 ) , 'subsample' : 1 , 'colsample_bytree' : 1 , 'colsample_bylevel' : 1 , 'colsample_bynode' : 1 , 'lambda' : hp . uniform ( 'lambda' , 0.0 , 1.0 ) , 'alpha' : hp . uniform ( 'alpha' , 0.0 , 1.0 ) , 'scale_pos_weight' : 1.0 , 'silent' : 1 , 'objective' : 'binary:logitraw' , 'nthread' : 1 , 'eval_metric' : 'auc' } if not apply_weights : PyProphetLearner ( infile , outfile , classifier , xgb_hyperparams , xgb_params , xgb_params_space , xeval_fraction , xeval_num_iter , ss_initial_fdr , ss_iteration_fdr , ss_num_iter , ss_main_score , group_id , parametric , pfdr , pi0_lambda , pi0_method , pi0_smooth_df , pi0_smooth_log_pi0 , lfdr_truncate , lfdr_monotone , lfdr_transformation , lfdr_adj , lfdr_eps , level , ipf_max_peakgroup_rank , ipf_max_peakgroup_pep , ipf_max_transition_isotope_overlap , ipf_min_transition_sn , tric_chromprob , threads , test ) . run ( ) else : PyProphetWeightApplier ( infile , outfile , classifier , xgb_hyperparams , xgb_params , xgb_params_space , xeval_fraction , xeval_num_iter , ss_initial_fdr , ss_iteration_fdr , ss_num_iter , ss_main_score , group_id , parametric , pfdr , pi0_lambda , pi0_method , pi0_smooth_df , pi0_smooth_log_pi0 , lfdr_truncate , lfdr_monotone , lfdr_transformation , lfdr_adj , lfdr_eps , level , ipf_max_peakgroup_rank , ipf_max_peakgroup_pep , ipf_max_transition_isotope_overlap , ipf_min_transition_sn , tric_chromprob , threads , test , apply_weights ) . run ( )
Conduct semi-supervised learning and error-rate estimation for MS1, MS2 and transition-level data.
Conduct semi - supervised learning and error - rate estimation for MS1 MS2 and transition - level data .