def test_7_calculate_fdr_step(self):
print("test_7_calculate_fdr_step")
predicates = calculate_fdr.default_predicates()
self.fdr_results = calculate_fdr.main(self.classification_results_file, self.ms2_decon_file,
self.test_model_file_path, suffix_len=1,
num_decoys_per_real_mass=self.num_decoys,
predicate_fns=predicates, n_processes=self.num_procs)
self.assertTrue(os.path.exists(self.classification_results_file[:-5] + "_fdr.json"))
def calculate_false_discovery_rate(scored_predictions_file, deconvoluted_spectra=None, model_file_path=None,
decoy_matches_path=None,
method="full_random_forest", ms1_match_tolerance=1e-5, ms2_match_tolerance=2e-5,
out=None, n_decoys=1, predicates=None, random_only=False,
n_processes=6, prefix_len=0, suffix_len=1):
predicates = predicates if predicates is not None else calculate_fdr.default_predicates()
outfile_path = calculate_fdr.main(scored_matches_path=scored_predictions_file, decon_data=deconvoluted_spectra,
model_file_path=model_file_path, decoy_matches_path=decoy_matches_path,
outfile_path=out, num_decoys_per_real_mass=n_decoys,
predicate_fns=predicates, prefix_len=prefix_len, suffix_len=suffix_len,
by_mod_sig=False, random_only=random_only,
ms1_tolerance=ms1_match_tolerance, ms2_tolerance=ms2_match_tolerance,
method=method, method_init_args=None,
method_fit_args=None, n_processes=n_processes)
return outfile_path
