from subprocess import call
from classes.FeatureSet import FeatureSet
# Parameters:
#
# -p: File name for positive feature set (any file type)
# -n: File name for negative feature set (any file type)
# -o: Name of output LibSVM model
opts, extraparams = getopt.getopt(sys.argv[1:], 'o:p:n:')
for o,p in opts:
if o == '-p':
posPath = p
if o == '-n':
negPath = p
if o == '-o':
outPath = p
# Aggregate inputs, export to libsvm file
fs = FeatureSet()
fs.load('data/'+posPath, patternClass = 'real')
fs.add_instances('data/'+negPath, patternClass = 'pseudo')
fs.weka_smote()
fs.libsvm_scale(paramOut = 'models/'+outPath+'.scale')
fs.export('tmp.libsvm')
# Build model
# Micropred: -c 100 -d 1 -h 1 -e 0.001 -g 0.0019531
# HeteroMir: -c 1 -d 1 -h 1 -e 0.001 -g 0.06
call('progs/libsvm-3.14/svm-train -c 1 -d 1 -h 1 -e 0.001 -g 0.06 -b 1 tmp.libsvm models/'+outPath+'.model', shell=True)
# Clean up
call('rm tmp.libsvm', shell=True)
call('python extract_hairpins.py -i '+negPath, shell=True)
print "### Extracting micropred features from coding regions"
sl = SequenceList()
sl.load_fasta('data/'+negPath+'.nr.hairpins')
sl.select_random(10000)
sl.export_fasta('data/'+negPath+'.nr.hairpins')
call('python build_micropred_features.py -i '+negPath+'.nr.hairpins -n '+numThreads, shell=True)
# call('python build_huntmi_features.py -i '+negPath+'.nr.hairpins')
################################################
# Build LibSVM model
################################################
print "### Building LibSVM model"
call('python build_model.py -p '+speciesFilename+'.features -n '+negPath+'.nr.hairpins.micropred -o '+speciesFilename, shell=True)
################################################
# Build feature set from hairpin candidates in genome of interest
################################################
print "### Building hairpins from genome under exploration"
call('python extract_hairpins.py -i '+inPath, shell=True)
print "### Extracting micropred features from genome under exploration"
call('python build_micropred_features.py -i '+inPath+'.nr.hairpins -n '+numThreads, shell=True)
################################################
# Run svm-predict on all hairpin candidates in genome of interest
################################################
fs = FeatureSet()
fs.load('data/'+inPath+'.nr.hairpins.micropred', patternClass = 'real')
fs.libsvm_scale(params='models/'+speciesFilename+'.scale')
fs.export('data/'+inPath+'.nr.hairpins.libsvm')
call('progs/libsvm-3.14/svm-predict -b 1 data/'+inPath+'.nr.hairpins.libsvm models/'+speciesFilename+'.model data/'+inPath+'.nr.hairpins.results', shell=True)
import sys
import getopt
from subprocess import call
from classes.FeatureSet import FeatureSet
from classes.ResultSet import ResultSet
# Parameters:
#
# -m: model name (<-m>.scale and <-m>.model should exist in models directory)
# -i: File containing input feature data
opts, extraparams = getopt.getopt(sys.argv[1:], 'm:i:')
for o,p in opts:
if o == '-m':
modelName = p
if o == '-i':
inPath = p
print '1'
fs = FeatureSet()
print '2'
fs.load('data/'+inPath, patternClass = 'real')
print '3'
fs.libsvm_scale(params='models/'+modelName+'.scale')
print '4'
fs.export('tmp.libsvm')
call('progs/libsvm-3.14/svm-predict -b 1 tmp.libsvm models/'+modelName+'.model data/'+inPath+'.results', shell=True)
# call('rm tmp.libsvm', shell=True)
import getopt, sys
from classes.FeatureSet import FeatureSet
opts, extraparams = getopt.getopt(sys.argv[1:], 'i:f:')
for o, p in opts:
if o == '-i':
inPath = p
if o == '-f':
outFormat = p
inFormat = inPath.split('.')[-1]
noFormatName = ""
for text in inPath.split('.')[:-1]:
noFormatName += text
noFormatName += '.'
outPath = noFormatName + outFormat
if inFormat in [
'micropred', 'features', 'huntmi', 'csv', 'svm', 'libsvm', 'arff'
]:
fs = FeatureSet()
fs.load(inPath)
fs.export(outPath)
