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Update/main score selection #114

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Feb 18, 2023
Merged

Update/main score selection #114

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972a191
[ADD] Make dynamic main score selection optional if ss_main_score set…
singjc Feb 18, 2023
1a49566
[UPDATE] help message for ss_main_score
singjc Feb 18, 2023
266bab8
Remove debug echo statement
singjc Feb 18, 2023
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4 changes: 2 additions & 2 deletions pyprophet/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ def type_cast_value(self, ctx, value):
@click.option('--ss_initial_fdr', default=0.15, show_default=True, type=float, help='Initial FDR cutoff for best scoring targets.')
@click.option('--ss_iteration_fdr', default=0.05, show_default=True, type=float, help='Iteration FDR cutoff for best scoring targets.')
@click.option('--ss_num_iter', default=10, show_default=True, type=int, help='Number of iterations for semi-supervised learning step.')
@click.option('--ss_main_score', default="var_xcorr_shape", show_default=True, type=str, help='Main score to start semi-supervised-learning.')
@click.option('--ss_main_score', default="auto", show_default=True, type=str, help='Main score to start semi-supervised-learning. Default is set to auto, meaning each iteration of learning a dynamic main score selection process will occur. If you want to have a set starting main score for each learning iteration, you can set a specifc score, i.e. "var_xcorr_shape"')
@click.option('--ss_score_filter', default='', help='Specify scores which should used for scoring. In addition specific predefined profiles can be used. For example for metabolomis data use "metabolomics". Please specify any additional input as follows: "var_ms1_xcorr_coelution,var_library_corr,var_xcorr_coelution,etc."')
# Statistics
@click.option('--group_id', default="group_id", show_default=True, type=str, help='Group identifier for calculation of statistics.')
Expand Down Expand Up @@ -112,7 +112,7 @@ def score(infile, outfile, classifier, xgb_autotune, apply_weights, xeval_fracti
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, ss_score_filter, color_palette, main_score_selection_report).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, ss_score_filter, color_palette).run()
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, ss_score_filter, color_palette, main_score_selection_report).run()


# IPF
Expand Down
6 changes: 3 additions & 3 deletions pyprophet/pyprophet.py
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Expand Up @@ -352,10 +352,10 @@ def _build_result(self, table, final_classifier, score_columns, experiment):


@profile
def PyProphet(classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, 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, tric_chromprob, threads, test, ss_score_filter, color_palette, main_score_selection_report, outfile, level):
def PyProphet(classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, 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, tric_chromprob, threads, test, ss_score_filter, color_palette, main_score_selection_report, outfile, level, ss_use_dynamic_main_score):
if classifier == "LDA":
return HolyGostQuery(StandardSemiSupervisedLearner(LDALearner(), xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, test, main_score_selection_report, outfile, level), classifier, ss_num_iter, 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, tric_chromprob, threads, test, ss_score_filter, color_palette)
return HolyGostQuery(StandardSemiSupervisedLearner(LDALearner(), xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, test, main_score_selection_report, outfile, level, ss_use_dynamic_main_score), classifier, ss_num_iter, 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, tric_chromprob, threads, test, ss_score_filter, color_palette)
elif classifier == "XGBoost":
return HolyGostQuery(StandardSemiSupervisedLearner(XGBLearner(xgb_hyperparams, xgb_params, xgb_params_space, threads), xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, test, main_score_selection_report, outfile, level), classifier, ss_num_iter, 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, tric_chromprob, threads, test, ss_score_filter, color_palette)
return HolyGostQuery(StandardSemiSupervisedLearner(XGBLearner(xgb_hyperparams, xgb_params, xgb_params_space, threads), xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, test, main_score_selection_report, outfile, level, ss_use_dynamic_main_score), classifier, ss_num_iter, 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, tric_chromprob, threads, test, ss_score_filter, color_palette)
else:
raise click.ClickException("Classifier not supported.")
19 changes: 14 additions & 5 deletions pyprophet/runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -194,13 +194,21 @@ def read_osw(infile, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_m
con.close()
return(table)

# Check for auto main score selection
if ss_main_score=="auto":
# Set starting default main score
ss_main_score = "var_xcorr_shape"
use_dynamic_main_score = True
else:
use_dynamic_main_score = False

# Main function
if is_sqlite_file(infile):
self.mode = 'osw'
self.table = read_osw(infile, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn)
else:
self.mode = 'tsv'
self.table = read_tsv(infile)
self.table = read_tsv(infile)

self.infile = infile
self.outfile = outfile
Expand Down Expand Up @@ -233,6 +241,7 @@ def read_osw(infile, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_m
self.ss_score_filter = ss_score_filter
self.color_palette = color_palette
self.main_score_selection_report = main_score_selection_report
self.ss_use_dynamic_main_score = use_dynamic_main_score

self.prefix = os.path.splitext(outfile)[0]

Expand Down Expand Up @@ -415,7 +424,7 @@ def save_bin_weights(self, weights, extra_writes):
class PyProphetLearner(PyProphetRunner):

def run_algo(self):
(result, scorer, weights) = PyProphet(self.classifier, self.xgb_hyperparams, self.xgb_params, self.xgb_params_space, self.xeval_fraction, self.xeval_num_iter, self.ss_initial_fdr, self.ss_iteration_fdr, self.ss_num_iter, self.group_id, self.parametric, self.pfdr, self.pi0_lambda, self.pi0_method, self.pi0_smooth_df, self.pi0_smooth_log_pi0, self.lfdr_truncate, self.lfdr_monotone, self.lfdr_transformation, self.lfdr_adj, self.lfdr_eps, self.tric_chromprob, self.threads, self.test, self.ss_score_filter, self.color_palette, self.main_score_selection_report, self.outfile, self.level).learn_and_apply(self.table)
(result, scorer, weights) = PyProphet(self.classifier, self.xgb_hyperparams, self.xgb_params, self.xgb_params_space, self.xeval_fraction, self.xeval_num_iter, self.ss_initial_fdr, self.ss_iteration_fdr, self.ss_num_iter, self.group_id, self.parametric, self.pfdr, self.pi0_lambda, self.pi0_method, self.pi0_smooth_df, self.pi0_smooth_log_pi0, self.lfdr_truncate, self.lfdr_monotone, self.lfdr_transformation, self.lfdr_adj, self.lfdr_eps, self.tric_chromprob, self.threads, self.test, self.ss_score_filter, self.color_palette, self.main_score_selection_report, self.outfile, self.level, self.ss_use_dynamic_main_score).learn_and_apply(self.table)
return (result, scorer, weights)

def extra_writes(self):
Expand All @@ -432,8 +441,8 @@ def extra_writes(self):

class PyProphetWeightApplier(PyProphetRunner):

def __init__(self, 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, ss_score_filter, color_palette):
super(PyProphetWeightApplier, self).__init__(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, ss_score_filter, color_palette)
def __init__(self, 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, ss_score_filter, color_palette, main_score_selection_report):
super(PyProphetWeightApplier, self).__init__(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, ss_score_filter, color_palette, main_score_selection_report)
if not os.path.exists(apply_weights):
raise click.ClickException("Weights file %s does not exist." % apply_weights)
if self.mode == "tsv":
Expand Down Expand Up @@ -481,7 +490,7 @@ def __init__(self, infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb
raise

def run_algo(self):
(result, scorer, weights) = PyProphet(self.classifier, self.xgb_hyperparams, self.xgb_params, self.xgb_params_space, self.xeval_fraction, self.xeval_num_iter, self.ss_initial_fdr, self.ss_iteration_fdr, self.ss_num_iter, self.group_id, self.parametric, self.pfdr, self.pi0_lambda, self.pi0_method, self.pi0_smooth_df, self.pi0_smooth_log_pi0, self.lfdr_truncate, self.lfdr_monotone, self.lfdr_transformation, self.lfdr_adj, self.lfdr_eps, self.tric_chromprob, self.threads, self.test, self.ss_score_filter, self.color_palette).apply_weights(self.table, self.persisted_weights)
(result, scorer, weights) = PyProphet(self.classifier, self.xgb_hyperparams, self.xgb_params, self.xgb_params_space, self.xeval_fraction, self.xeval_num_iter, self.ss_initial_fdr, self.ss_iteration_fdr, self.ss_num_iter, self.group_id, self.parametric, self.pfdr, self.pi0_lambda, self.pi0_method, self.pi0_smooth_df, self.pi0_smooth_log_pi0, self.lfdr_truncate, self.lfdr_monotone, self.lfdr_transformation, self.lfdr_adj, self.lfdr_eps, self.tric_chromprob, self.threads, self.test, self.ss_score_filter, self.color_palette, self.main_score_selection_report, self.outfile, self.level, self.ss_use_dynamic_main_score).apply_weights(self.table, self.persisted_weights)
return (result, scorer, weights)

def extra_writes(self):
Expand Down
5 changes: 3 additions & 2 deletions pyprophet/semi_supervised.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@ def learn_randomized(self, experiment, score_columns, working_thread_number):
# Get current main score column name
old_main_score_column = [col for col in score_columns if 'main' in col][0]
# Only Update if chosen main score column has changed
if use_as_main_score != old_main_score_column:
if use_as_main_score != old_main_score_column and self.ss_use_dynamic_main_score:
train, _ = update_chosen_main_score_in_table(train, score_columns, use_as_main_score)
train.rank_by("main_score")
experiment, score_columns = update_chosen_main_score_in_table(experiment, score_columns, use_as_main_score)
Expand Down Expand Up @@ -106,7 +106,7 @@ def learn_final(self, experiment):

class StandardSemiSupervisedLearner(AbstractSemiSupervisedLearner):

def __init__(self, inner_learner, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, test, main_score_selection_report, outfile, level):
def __init__(self, inner_learner, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, test, main_score_selection_report, outfile, level, ss_use_dynamic_main_score):
assert isinstance(inner_learner, AbstractLearner)
AbstractSemiSupervisedLearner.__init__(self, xeval_fraction, xeval_num_iter, test)
self.inner_learner = inner_learner
Expand All @@ -123,6 +123,7 @@ def __init__(self, inner_learner, xeval_fraction, xeval_num_iter, ss_initial_fdr
self.main_score_selection_report = main_score_selection_report
self.outfile = outfile
self.level = level
self.ss_use_dynamic_main_score = ss_use_dynamic_main_score

def select_train_peaks(self, train, sel_column, cutoff_fdr, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, mapper=None, main_score_selection_report=False, outfile=None, level=None, working_thread_number=None):
assert isinstance(train, Experiment)
Expand Down