data['rec_crossed_model']),
output = "test_crossed_results_" + args.output,
base_folder = base_folder, folder="")
#Read scores
if algorithm == "CROSSED" or algorithm == "REC":
crossed_scores = functions.read_crossed(
filename = crossed_output, original = args.test,
length = length)
ric_scores = None
rric_scores = None
else:
crossed_scores = None
if algorithm != "CROSSED":
if algorithm != "rRIC":
ric_scores = functions.read_ric(ric_output)
else:
ric_scores = None
if algorithm != "RIC":
rric_scores = functions.read_ric(rric_output)
else:
rric_scores = None
#Classify sequences
result = functions.classify(algorithm = algorithm,
crossed_scores = crossed_scores, ric_scores = ric_scores,
rric_scores = rric_scores, data = data, length = length)
#Save result
functions.save(output = args.output, result = result,
algorithm = algorithm, original = args.test,
ric_negatives = functions.convert_ric(
filename=args.negative, length=length,
output="train_ric_negatives.fasta", folder=unique)
#Aply RIC to the test files
ric_positives_output = functions.run_ric(test_file=ric_positives,
length=length, base_folder=base_folder,
training_file=ric_positives, output="test_ric_positives",
folder=unique)
ric_negatives_output = functions.run_ric(test_file=ric_negatives,
length=length, base_folder=base_folder,
training_file=ric_positives, output="test_ric_negatives",
folder=unique)
os.remove(ric_negatives)
#Read scores
ric_positive_scores = functions.read_ric(
filename=ric_positives_output)
os.remove(ric_positives_output)
ric_negative_scores = functions.read_ric(
filename=ric_negatives_output)
os.remove(ric_negatives_output)
ric_value_fdr = functions.get_threshold_unique(
positive = ric_positive_scores,
negative = ric_negative_scores,
optim = "FDR")
ric_value_sen = functions.get_threshold_unique(
positive = ric_positive_scores,
negative = ric_negative_scores,
optim = "SEN")
#Do CRoSSeD
