def computePOIM(x,y,poim_degree,kernel_degree,savepath):
feats_train = StringCharFeatures(x,DNA)
labels = BinaryLabels(y);
kernel = WeightedDegreePositionStringKernel(feats_train, feats_train, kernel_degree)
C=1
svm = LibSVM(C, kernel, labels)
svm.train()
tally=len(x[0])
ma = poim.compute_poims(svm,kernel,poim_degree,tally)
fobj = open(savepath,'wb')
pickle.dump(ma,fobj)
fobj.close()
return ma
def computePOIM(x, y, poim_degree, kernel_degree, savepath):
feats_train = StringCharFeatures(x, DNA)
labels = BinaryLabels(np.array(y))
print "compute kernel matrix"
kernel = WeightedDegreePositionStringKernel(feats_train, feats_train, kernel_degree)
C = 1
svm = LibSVM(C, kernel, labels)
print "train support vector machine"
svm.train()
pdb.set_trace()
tally = len(x[0])
print "compute poim"
ma = poim.compute_poims(svm, kernel, poim_degree, tally)
fobj = open(savepath, "wb")
pickle.dump(ma, fobj)
fobj.close()
return ma
def svm_poim(argv):
"""A top level script to parse input parameters and plot poims"""
assert(argv[1]=='poim')
if len(argv)<7:sys.stderr.write("usage: %s poim C poimdegree wd [kernelparameters] [arff|fasta] inputfiles poim.png [dna|protein] non(nucleotide|amino)converter\n" % argv[0]);sys.exit(-1)
# parse input parameters
C = float(argv[2])
poimdegree = int(argv[3])
(kernelname,kparam,argv_rest) = parse.parse_kernel_param(argv[4:], False)
(examples,labels,argv_rest) = parse.parse_input_file_train(kernelname, argv_rest)
if len(argv_rest)<1:sys.stderr.write("poim.png [dna|protein] non(nucleotide|amino)converter are missing\n");sys.exit(-1)
if len(argv_rest)<2:sys.stderr.write("[dna|protein] non(nucleotide|amino)converter are missing\n");sys.exit(-1)
if len(argv_rest)<3:
if argv_rest[-1] == 'dna':
sys.stderr.write("non-nucleotide converter like [A|T|C|G|R|Y|N] is missing. Cannot continue.\n")
sys.exit(-1)
elif argv_rest[-1] == 'protein':
sys.stderr.write("non-amino acid converter like [G|P|A|V|L|I|M|C|F|Y|W|H|K|R|Q|N|E|D|S|T|random] is missing. Cannot continue.\n")
sys.exit(-1)
else:
sys.stderr.write("Here expect FASTA sequence type as [dna|protein] instead of -"+ argv_rest[-1] +"- Cannot continue.\n")
sys.exit(-1)
if len(argv_rest)>3:sys.stderr.write("Too many arguments\n");sys.exit(-1)
poimfilename = argv_rest[0]
seq_source = argv_rest[1]
nuc_con = argv_rest[2]
utils.check_params(kparam, C, len(examples[0]))
# train svm and compute POIMs
(svm, kernel, feats_train, preproc) = train(examples,labels,C,kernelname,kparam,seq_source,nuc_con)
print "done with training "
(poim, max_poim, diff_poim, poim_totalmass) = compute_poims(svm, kernel, poimdegree, len(examples[0]))
# plot poims
plots.plot_poims(poimfilename, poim, max_poim, diff_poim, poim_totalmass, poimdegree, len(examples[0]))
def svm_poim(argv):
"""A top level script to parse input parameters and plot poims"""
assert(argv[1]=='poim')
if len(argv)<7:sys.stderr.write("usage: %s poim C poimdegree wd [kernelparameters] [arff|fasta] inputfiles poim.png [dna|protein] non(nucleotide|amino)converter\n" % argv[0]);sys.exit(-1)
# parse input parameters
C = float(argv[2])
poimdegree = int(argv[3])
(kernelname,kparam,argv_rest) = parse.parse_kernel_param(argv[4:], False)
(examples,labels,argv_rest) = parse.parse_input_file_train(kernelname, argv_rest)
if len(argv_rest)<1:sys.stderr.write("poim.png [dna|protein] non(nucleotide|amino)converter are missing\n");sys.exit(-1)
if len(argv_rest)<2:sys.stderr.write("[dna|protein] non(nucleotide|amino)converter are missing\n");sys.exit(-1)
if len(argv_rest)<3:
if argv_rest[-1] == 'dna':
sys.stderr.write("non-nucleotide converter like [A|T|C|G|R|Y|N] is missing. Cannot continue.\n")
sys.exit(-1)
elif argv_rest[-1] == 'protein':
sys.stderr.write("non-amino acid converter like [G|P|A|V|L|I|M|C|F|Y|W|H|K|R|Q|N|E|D|S|T|random] is missing. Cannot continue.\n")
sys.exit(-1)
else:
sys.stderr.write("Here expect FASTA sequence type as [dna|protein] instead of -"+ argv_rest[-1] +"- Cannot continue.\n")
sys.exit(-1)
if len(argv_rest)>3:sys.stderr.write("Too many arguments\n");sys.exit(-1)
poimfilename = argv_rest[0]
seq_source = argv_rest[1]
nuc_con = argv_rest[2]
utils.check_params(kparam, C, len(examples[0]))
# train svm and compute POIMs
(svm, kernel, feats_train, preproc) = train(examples,labels,C,kernelname,kparam,seq_source,nuc_con)
(poim, max_poim, diff_poim, poim_totalmass) = compute_poims(svm, kernel, poimdegree, len(examples[0]))
# plot poims
plots.plot_poims(poimfilename, poim, max_poim, diff_poim, poim_totalmass, poimdegree, len(examples[0]))
