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iLearn-descriptor-estimater.py
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iLearn-descriptor-estimater.py
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#!/usr/bin/env python
# _*_ coding: utf-8 _*_
import argparse
import sys, os, re
import numpy as np
from pubscripts import *
from machinelearning import *
if __name__ == '__main__':
parser = argparse.ArgumentParser(usage="it's usage tip.",
description="running the iLearn pipeline")
parser.add_argument("--config", default='config.txt', help="the config file")
args = parser.parse_args()
# read config
parameters = read_config.read_config(args.config)
if parameters['Sequence_Type'] == 'Protein':
from descproteins import *
else:
from descnucleotide import *
# commands for encoding
dna_cmd_coding = {
'Kmer': ['Kmer.Kmer(training_data, k=%s, **kw)' % parameters['Kmer_Size'],
'Kmer.Kmer(testing_data, k=%s, **kw)' % parameters['Kmer_Size']],
'RCKmer': ['RCKmer.RCKmer(training_data, k=%s, **kw)' % parameters['Kmer_Size'],
'RCKmer.RCKmer(testing_data, k=%s, **kw)' % parameters['Kmer_Size']],
'NAC': ['NAC.NAC(training_data, **kw)', 'NAC.NAC(testing_data, **kw)'],
'DNC': ['DNC.DNC(training_data, **kw)', 'DNC.DNC(testing_data, **kw)'],
'TNC': ['TNC.TNC(training_data, **kw)', 'TNC.TNC(testing_data, **kw)'],
'ANF': ['ANF.ANF(training_data, **kw)', 'ANF.ANF(testing_data, **kw)'],
'ENAC': ['ENAC.ENAC(training_data, window=%s, **kw)' % parameters['Sliding_Window'],
'ENAC.ENAC(testing_data, window=%s, **kw)' % parameters['Sliding_Window']],
'binary': ['binary.binary(training_data, **kw)', 'binary.binary(testing_data, **kw)'],
'CKSNAP': ['CKSNAP.CKSNAP(training_data, gap=%s, **kw)' % parameters['K_Space'],
'CKSNAP.CKSNAP(testing_data, gap=%s, **kw)' % parameters['K_Space']],
'NCP': ['NCP.NCP(training_data, **kw)', 'NCP.NCP(testing_data, **kw)'],
'PSTNPss': ['PSTNPss.PSTNPss(PSTNP_training_data, **kw)', 'PSTNPss.PSTNPss(PSTNP_testing_data, **kw)'],
'PSTNPds': ['PSTNPds.PSTNPds(PSTNP_training_data, **kw)', 'PSTNPds.PSTNPds(PSTNP_testing_data, **kw)'],
'EIIP': ['EIIP.EIIP(training_data, **kw)', 'EIIP.EIIP(testing_data, **kw)'],
'PseEIIP': ['PseEIIP.PseEIIP(training_data, **kw)', 'PseEIIP.PseEIIP(testing_data, **kw)'],
'DAC': ['ACC.make_ac_vector(training_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value'],
'ACC.make_ac_vector(testing_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value']],
'DCC': ['ACC.make_cc_vector(training_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value'],
'ACC.make_cc_vector(testing_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value']],
'DACC': [
'ACC.make_acc_vector(training_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value'],
'ACC.make_acc_vector(testing_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value']],
'TAC': ['ACC.make_ac_vector(training_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value'],
'ACC.make_ac_vector(testing_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value']],
'TCC': ['ACC.make_cc_vector(training_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value'],
'ACC.make_cc_vector(testing_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value']],
'TACC': [
'ACC.make_acc_vector(training_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value'],
'ACC.make_acc_vector(testing_data, my_property_name, my_property_value, %s, my_kmer)' % parameters[
'Lag_Value']],
'PseDNC': [
'Pse.make_PseDNC_vector(training_data, my_property_name, my_property_value, my_lamada, my_weight)',
'Pse.make_PseDNC_vector(testing_data, my_property_name, my_property_value, my_lamada, my_weight)'],
'PseKNC': [
'Pse.make_PseKNC_vector(training_data, my_property_name, my_property_value, my_lamada, my_weight, %d)' % int(
parameters['Kmer_Size']),
'Pse.make_PseKNC_vector(testing_data, my_property_name, my_property_value, my_lamada, my_weight, %d)' % int(
parameters['Kmer_Size'])],
'PCPseDNC': [
'Pse.make_PseDNC_vector(training_data, my_property_name, my_property_value, my_lamada, my_weight)',
'Pse.make_PseDNC_vector(testing_data, my_property_name, my_property_value, my_lamada, my_weight)'],
'PCPseTNC': [
'Pse.make_PCPseTNC_vector(training_data, my_property_name, my_property_value, my_lamada, my_weight)',
'Pse.make_PCPseTNC_vector(testing_data, my_property_name, my_property_value, my_lamada, my_weight)'],
'SCPseDNC': [
'Pse.make_SCPseDNC_vector(training_data, my_property_name, my_property_value, my_lamada, my_weight)',
'Pse.make_SCPseDNC_vector(testing_data, my_property_name, my_property_value, my_lamada, my_weight)'],
'SCPseTNC': [
'Pse.make_SCPseTNC_vector(training_data, my_property_name, my_property_value, my_lamada, my_weight)',
'Pse.make_SCPseTNC_vector(testing_data, my_property_name, my_property_value, my_lamada, my_weight)'],
}
protein_cmd_coding = {
'AAC': ['AAC.AAC(training_data, **kw)', 'AAC.AAC(testing_data, **kw)'],
'EAAC': ['EAAC.EAAC(training_data, window=%d, **kw)' % int(parameters['Sliding_Window']),
'EAAC.EAAC(testing_data, window=%d, **kw)' % int(parameters['Sliding_Window'])],
'CKSAAP': ['CKSAAP.CKSAAP(training_data, gap=%d, **kw)' % int(parameters['K_Space']),
'CKSAAP.CKSAAP(testing_data, gap=%d, **kw)' % int(parameters['K_Space'])],
'DPC': ['DPC.DPC(training_data, **kw)', 'DPC.DPC(testing_data, **kw)'],
'DDE': ['DDE.DDE(training_data, **kw)', 'DDE.DDE(testing_data, **kw)'],
'TPC': ['TPC.TPC(training_data, **kw)', 'TPC.TPC(testing_data, **kw)'],
'binary': ['binary.binary(training_data, **kw)', 'binary.binary(testing_data, **kw)'],
'GAAC': ['GAAC.GAAC(training_data, **kw)', 'GAAC.GAAC(testing_data, **kw)'],
'EGAAC': ['EGAAC.EGAAC(training_data, window=%d, **kw)' % int(parameters['Sliding_Window']),
'EGAAC.EGAAC(testing_data, window=%d, **kw)' % int(parameters['Sliding_Window'])],
'CKSAAGP': ['CKSAAGP.CKSAAGP(training_data, gap=%d, **kw)' % int(parameters['K_Space']),
'CKSAAGP.CKSAAGP(testing_data, gap=%d, **kw)' % int(parameters['K_Space'])],
'GDPC': ['GDPC.GDPC(training_data, **kw)', 'GDPC.GDPC(testing_data, **kw)'],
'GTPC': ['GTPC.GTPC(training_data, **kw)', 'GTPC.GTPC(testing_data, **kw)'],
'AAINDEX': ['AAINDEX.AAINDEX(training_data, props=props, **kw)',
'AAINDEX.AAINDEX(testing_data, props=props, **kw)'],
'ZSCALE': ['ZSCALE.ZSCALE(training_data, **kw)', 'ZSCALE.ZSCALE(testing_data, **kw)'],
'BLOSUM62': ['BLOSUM62.BLOSUM62(training_data, **kw)', 'BLOSUM62.BLOSUM62(testing_data, **kw)'],
'Moran': ['Moran.Moran(training_data, props=props, nlag=%d, **kw)' % int(parameters['Lag_Value']),
'Moran.Moran(testing_data, props=props, nlag=%d, **kw)' % int(parameters['Lag_Value'])],
'Geary': ['Geary.Geary(training_data, props=props, nlag=%d, **kw)' % int(parameters['Lag_Value']),
'Geary.Geary(testing_data, props=props, nlag=%d, **kw)' % int(parameters['Lag_Value'])],
'NMBroto': ['NMBroto.NMBroto(training_data, props=props, nlag=%d, **kw)' % int(parameters['Lag_Value']),
'NMBroto.NMBroto(testing_data, props=props, nlag=%d, **kw)' % int(parameters['Lag_Value'])],
'CTDC': ['CTDC.CTDC(training_data, **kw)', 'CTDC.CTDC(testing_data, **kw)'],
'CTDT': ['CTDT.CTDT(training_data, **kw)', 'CTDT.CTDT(testing_data, **kw)'],
'CTDD': ['CTDD.CTDD(training_data, **kw)', 'CTDD.CTDD(testing_data, **kw)'],
'CTriad': ['CTriad.CTriad(training_data, gap=0, **kw)', 'CTriad.CTriad(testing_data, gap=0, **kw)'],
'KSCTriad': ['KSCTriad.KSCTriad(training_data, gap=%d, **kw)' % int(parameters['K_Space']),
'KSCTriad.KSCTriad(testing_data, gap=%d, **kw)' % int(parameters['K_Space'])],
'SOCNumber': ['SOCNumber.SOCNumber(training_data, nlag=%d, **kw)' % int(parameters['Lag_Value']),
'SOCNumber.SOCNumber(testing_data, nlag=%d, **kw)' % int(parameters['Lag_Value'])],
'QSOrder': ['QSOrder.QSOrder(training_data, nlag=%d, w=%f, **kw)' % (
int(parameters['Lag_Value']), float(parameters['Weight_Value'])),
'QSOrder.QSOrder(testing_data, nlag=%d, w=%f, **kw)' % (
int(parameters['Lag_Value']), float(parameters['Weight_Value']))],
'PAAC': ['PAAC.PAAC(training_data, lambdaValue=%d, w=%f, **kw)' % (
int(parameters['Lamada_Value']), float(parameters['Weight_Value'])),
'PAAC.PAAC(testing_data, lambdaValue=%d, w=%f, **kw)' % (
int(parameters['Lamada_Value']), float(parameters['Weight_Value']))],
'APAAC': ['APAAC.APAAC(training_data, lambdaValue=%d, w=%f, **kw)' % (
int(parameters['Lamada_Value']), float(parameters['Weight_Value'])),
'APAAC.APAAC(testing_data, lambdaValue=%d, w=%f, **kw)' % (
int(parameters['Lamada_Value']), float(parameters['Weight_Value']))],
'KNNprotein': ['KNNprotein.KNNprotein(PSTNP_training_data, **kw)', 'KNNprotein.KNNprotein(PSTNP_testing_data, **kw)'],
'KNNpeptide': ['KNNpeptide.KNNpeptide(PSTNP_training_data, **kw)', 'KNNpeptide.KNNpeptide(PSTNP_testing_data, **kw)'],
'Kmer': ['Kmer.Kmer(training_data, k=%d, type="Protein", **kw)' % int(parameters['Kmer_Size']),
'Kmer.Kmer(testing_data, k=%d, type="Protein", **kw)' % int(parameters['Kmer_Size'])],
'type1': ['type1.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster1']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type1.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster1']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type2': ['type2.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster2']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type2.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster2']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type3A': ['type3A.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster3A']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type3A.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster3A']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type3B': ['type3B.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster3B']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type3B.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster3B']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type4': ['type4.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster4']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type4.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster4']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type5': ['type5.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster5']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type5.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster5']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type6A': ['type6A.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster6A']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type6A.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster6A']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type6B': ['type6B.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster6B']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type6B.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster6B']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type6C': ['type6C.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster6C']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type6C.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster6C']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type7': ['type7.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster7']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type7.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster7']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type8': ['type8.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster8']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type8.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster8']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type9': ['type9.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster9']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type9.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster9']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type10': ['type10.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster10']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type10.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster10']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type11': ['type11.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster11']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type11.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster11']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type12': ['type12.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster12']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type12.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster12']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type13': ['type13.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster13']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type13.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster13']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type14': ['type14.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster14']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type14.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster14']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type15': ['type15.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster15']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type15.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster15']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
'type16': ['type16.type1(training_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster16']), int(parameters['Ktuple']),
int(parameters['GapLamada'])), 'type16.type1(testing_data, "%s", %d, %d, %d)' % (
parameters['PseKRAAC_Model'], int(parameters['RAACCluster16']), int(parameters['Ktuple']),
int(parameters['GapLamada']))],
}
# read fasta sequence and specify cmd
fastas = []
cmd_coding = {}
if parameters['Sequence_Type'] in ('DNA', 'RNA'):
fastas = read_fasta_sequences.read_nucleotide_sequences(parameters['Sequence_File'])
cmd_coding = dna_cmd_coding
if parameters['Sequence_Type'] == 'Protein':
fastas = read_fasta_sequences.read_protein_sequences(parameters['Sequence_File'])
cmd_coding = protein_cmd_coding
kw = {'nclusters': 3, 'sof': 'sample', 'order': ''}
kw['order'] = 'ACGT' if parameters['Sequence_Type'] == 'DNA' or parameters['Sequence_Type'] == 'RNA' else 'ACDEFGHIKLMNPQRSTVWY'
# divide training and testing data
training_data = []
testing_data = []
PSTNP_training_data = []
PSTNP_testing_data = []
classes = set()
for sequence in fastas:
if sequence[3] == 'training':
training_data.append(sequence)
PSTNP_training_data.append(sequence)
PSTNP_training_data.append([sequence[0], sequence[1], sequence[2], 'testing'])
PSTNP_testing_data.append(sequence)
classes.add(sequence[2])
else:
testing_data.append(sequence)
PSTNP_testing_data.append(sequence)
# get property for AAindex, NMBroto, Geary, Moran
props = parameters['AAindex'].split(';') if parameters['AAindex'] != '' else ['CIDH920105', 'BHAR880101',
'CHOC760101', 'BIGC670101',
'CHAM810101', 'DAYM780201']
# get property for ACC descriptors and Pse descriptors
my_property_name, my_property_value, my_kmer, my_lamada, my_weight = 0, 0, 0, 0, 0
#
method_array = parameters['Method'].split(';')
ML_array = parameters['ML'].split(';')
for ML in ML_array:
# parameters for binary classification task
CV_records = {}
IND_records = {}
CV_record_metrics = {}
IND_record_metrics = {}
# parameters for muti-classification task
muti_record_metrics = {}
for method in method_array:
print('Building model for %s - %s' %(ML, method))
if method in ('DAC', 'DCC', 'DACC', 'TAC', 'TCC', 'TACC'):
my_property_name, my_property_value, my_kmer = check_parameters.check_acc_arguments_pipeline(parameters,
method)
if method in ('PseDNC', 'PseKNC', 'PCPseDNC', 'PCPseTNC', 'SCPseDNC', 'SCPseTNC'):
my_property_name, my_property_value, my_lamada, my_weight = check_parameters.check_Pse_arguments_pipeline(
parameters, method, fastas)
training_code = eval(cmd_coding[method][0])
testing_dode = []
save_file.save_file(training_code, format=parameters['Output_Format'], file='training_code.txt')
if len(testing_data) > 0:
testing_dode = eval(cmd_coding[method][1])
save_file.save_file(testing_dode, format=parameters['Output_Format'], file='testing_code.txt')
X, y, independent = 0, 0, np.array([])
X, y = read_code_ml.read_code('training_code.txt', format=parameters['Output_Format'])
if len(testing_data) > 0:
ind_X, ind_y = read_code_ml.read_code('testing_code.txt', format=parameters['Output_Format'])
independent = np.zeros((ind_X.shape[0], ind_X.shape[1] + 1))
independent[:, 0], independent[:, 1:] = ind_y, ind_X
fold = parameters['Validation'] if parameters['Validation'] != '' else 5
# svm
kernel = parameters['Kernel'] if parameters['Kernel'] != '' else 'rbf'
auto = False
cost = 1.0
gamma = 1 / len(training_data)
if parameters['Auto_Opterimize'] == 'True':
auto = True
cost = 1.0
gamma = 1 / len(training_data)
else:
cost = float(parameters['Cost']) if parameters['Cost'] != '' else 1.0
gamma = float(parameters['Gamma']) if parameters['Gamma'] != '' else 1 / len(training_data)
# RF
n_trees = parameters['Tree_Number'] if parameters['Tree_Number'] != '' else 100
# KNN
top_K = int(parameters['K_Nearest_Neighbour']) if parameters['K_Nearest_Neighbour'] != '' else 3
# ANN
hidden_layer_size = eval('(' + re.sub(';', ', ', parameters['Hidden_Layer_Size']) + ')') if parameters['Hidden_Layer_Size'] != '' else (32, 32)
# training model
para_info, cv_res, ind_res = 0, 0, 0
if ML == 'SVM':
para_info, cv_res, ind_res = SVM.SVM_Classifier(X, y, indep=independent, fold=fold, batch=0.8, auto=auto, kernel=kernel, gamma=gamma, C=cost)
if ML == 'RF':
para_info, cv_res, ind_res = RF.RF_Classifier(X, y, indep=independent, fold=fold, n_trees=n_trees)
if ML == 'LR':
para_info, cv_res, ind_res = LR.LR_Classifier_binary(X, y, indep=independent, fold=fold)
if ML == 'KNN':
para_info, cv_res, ind_res = KNN.KNN_Classifier_binary(X, y, indep=independent, fold=fold, n_neighbors=top_K)
if ML == 'MLP':
para_info, cv_res, ind_res = MLP.MLP_Classifier(X, y, indep=independent, fold=fold, hidden_layer_size=hidden_layer_size)
# binary classification task
if len(classes) == 2:
CV_records['%s-%s' %(ML, method)] = cv_res
IND_records['%s-%s' %(ML, method)] = ind_res
cv_metrics = calculate_prediction_metrics.calculate_metrics_cv(cv_res, label_column=0, score_column=2)
my_metrics = {'Sensitivity': 0, 'Specificity': 0, 'Accuracy': 0, 'MCC': 0, 'Recall': 0, 'Precision': 0, 'F1-score': 0}
for i in range(len(cv_metrics)):
for key in my_metrics:
try:
my_metrics[key] += cv_metrics[i][key]
except TypeError as e:
pass
for key in my_metrics:
my_metrics[key] /= len(cv_metrics)
CV_record_metrics['%s-%s' %(ML, method)] = my_metrics
if len(testing_data) > 0:
ind_metrics = calculate_prediction_metrics.calculate_metrics(ind_res[:, 0], ind_res[:, 2])
IND_record_metrics['%s-%s' %(ML, method)] = ind_metrics
# muti-classification task
if len(classes) >= 3:
muti_record_metrics['%s-%s' %(ML, method)] = [calculate_prediction_metrics.calculate_metrics_cv_muti_V2(cv_res, classes, label_column=0)]
if len(testing_data) > 0:
muti_record_metrics['%s-%s' % (ML, method)].append(calculate_prediction_metrics.calculate_metrics_ind_muti_V2(ind_res, classes, label_column=0))
else:
muti_record_metrics['%s-%s' % (ML, method)].append('NA')
if len(classes) == 2:
draw_plot.plot_mean_roc_cv(CV_records, '%s_CV_ROC.png' % ML, label_column=0, score_column=2)
draw_plot.plot_mean_prc_CV(CV_records, '%s_CV_PRC.png' % ML, label_column=0, score_column=2)
save_file.save_prediction_metrics_2_classes(CV_record_metrics, 'Metrics_CV.txt')
if len(testing_data) > 0:
draw_plot.plot_roc_muti_ind(IND_records, '%s_IND_ROC.png' % ML, label_column=0, score_column=2)
draw_plot.plot_prc_muti_ind(IND_records, '%s_IND_PRC.png' % ML, label_column=0, score_column=2)
save_file.save_prediction_metrics_2_classes(IND_record_metrics, 'Metrics_IND.txt')
if len(classes) >= 3:
save_file.save_prediction_metrics_muti_V2(muti_record_metrics, '%s_Metrics_CV.txt' %ML)