def hiksvm_train(labels, features, beta):
# calculate class prior
np = len([1 for lab in labels if 1 == lab])
nn = len([1 for lab in labels if -1 == lab])
wp = float(beta)/np
wn = (1.0-beta)/nn
wp *= (np+nn)
wn *= (np+nn)
parameters = "-s 0 -c 1 -t %d -w-1 %g -w1 %g" % (KERNEL_TYPE.index("HI"), wn, wp)
model = svm_train(labels, features, parameters)
return model
def hiksvm_train(labels, features, beta):
# calculate class prior
np = len([1 for lab in labels if 1 == lab])
nn = len([1 for lab in labels if -1 == lab])
wp = float(beta) / np
wn = (1.0 - beta) / nn
wp *= (np + nn)
wn *= (np + nn)
parameters = "-s 0 -c 1 -t %d -w-1 %g -w1 %g" % (KERNEL_TYPE.index("HI"),
wn, wp)
model = svm_train(labels, features, parameters)
return model
def process(options, trainCollection, trainAnnotationName, feature):
import re
p = re.compile(
r'best_C=(?P<C>[\.\d]+),\sa=(?P<a>[\.\-\d]+),\sb=(?P<b>[\.\-\d]+)')
rootpath = options.rootpath
overwrite = options.overwrite
#autoweight = options.autoweight
numjobs = options.numjobs
job = options.job
nr_bins = options.nr_bins
best_param_dir = options.best_param_dir
beta = 0.5
modelName = 'fik%d' % nr_bins
if best_param_dir:
modelName += '-tuned'
concepts = readConcepts(trainCollection,
trainAnnotationName,
rootpath=rootpath)
resultdir = os.path.join(rootpath, trainCollection, 'Models',
trainAnnotationName, feature, modelName)
todo = []
for concept in concepts:
resultfile = os.path.join(resultdir, concept + '.model')
if not checkToSkip(resultfile, overwrite):
todo.append(concept)
todo = [todo[i] for i in range(len(todo)) if i % numjobs == (job - 1)]
printStatus(INFO,
'to process %d concepts: %s' % (len(todo), ' '.join(todo)))
if not todo:
return 0
feat_dir = os.path.join(rootpath, trainCollection, 'FeatureData', feature)
feat_file = BigFile(feat_dir)
params = {'nr_bins': nr_bins}
with open(os.path.join(feat_dir, 'minmax.txt'), 'r') as f:
params['min_vals'] = map(float, str.split(f.readline()))
params['max_vals'] = map(float, str.split(f.readline()))
for concept in todo:
if best_param_dir:
param_file = os.path.join(best_param_dir, '%s.txt' % concept)
m = p.search(open(param_file).readline().strip())
C = float(m.group('C'))
A = float(m.group('a'))
B = float(m.group('b'))
else:
C = 1
A = 0
B = 0
printStatus(INFO, '%s, C=%g, A=%g, B=%g' % (concept, C, A, B))
model_file_name = os.path.join(resultdir, concept + '.model')
names, labels = readAnnotationsFrom(trainCollection,
trainAnnotationName,
concept,
skip_0=True,
rootpath=rootpath)
name2label = dict(zip(names, labels))
renamed, vectors = feat_file.read(names)
y = [name2label[x] for x in renamed]
np = len([1 for lab in labels if 1 == lab])
nn = len([1 for lab in labels if -1 == lab])
wp = float(beta) * (np + nn) / np
wn = (1.0 - beta) * (np + nn) / nn
svm_params = '-w1 %g -w-1 %g' % (wp * C, wn * C)
model = svm_train(
y, vectors,
svm_params + ' -s 0 -t %d -q' % KERNEL_TYPE.index("HI"))
newmodel = svm_to_fiksvm([model], [1.0], feat_file.ndims, params)
newmodel.set_probAB(A, B)
makedirsforfile(model_file_name)
printStatus(INFO, '-> %s' % model_file_name)
fiksvm_save_model(model_file_name, newmodel)
# reload the model file to do a simple check
fiksvm_load_model(model_file_name)
assert (abs(newmodel.get_probAB()[0] - A) < 1e-6)
assert (abs(newmodel.get_probAB()[1] - B) < 1e-6)
return len(todo)
def process(options, trainCollection, trainAnnotationName, feature):
import re
p = re.compile(r'best_C=(?P<C>[\.\d]+),\sa=(?P<a>[\.\-\d]+),\sb=(?P<b>[\.\-\d]+)')
rootpath = options.rootpath
overwrite = options.overwrite
#autoweight = options.autoweight
numjobs = options.numjobs
job = options.job
nr_bins = options.nr_bins
best_param_dir = options.best_param_dir
beta = 0.5
modelName = 'fik%d' % nr_bins
if best_param_dir:
modelName += '-tuned'
concepts = readConcepts(trainCollection, trainAnnotationName, rootpath=rootpath)
resultdir = os.path.join(rootpath, trainCollection, 'Models', trainAnnotationName, feature, modelName)
todo = []
for concept in concepts:
resultfile = os.path.join(resultdir, concept + '.model')
if not checkToSkip(resultfile, overwrite):
todo.append(concept)
todo = [todo[i] for i in range(len(todo)) if i%numjobs==(job-1)]
printStatus(INFO, 'to process %d concepts: %s' % (len(todo), ' '.join(todo)))
if not todo:
return 0
feat_dir = os.path.join(rootpath,trainCollection,'FeatureData',feature)
feat_file = BigFile(feat_dir)
params = {'nr_bins': nr_bins}
with open(os.path.join(feat_dir, 'minmax.txt'), 'r') as f:
params['min_vals'] = map(float, str.split(f.readline()))
params['max_vals'] = map(float, str.split(f.readline()))
for concept in todo:
if best_param_dir:
param_file = os.path.join(best_param_dir, '%s.txt' % concept)
m = p.search(open(param_file).readline().strip())
C = float(m.group('C'))
A = float(m.group('a'))
B = float(m.group('b'))
else:
C = 1
A = 0
B = 0
printStatus(INFO, '%s, C=%g, A=%g, B=%g' % (concept, C, A, B))
model_file_name = os.path.join(resultdir, concept + '.model')
names,labels = readAnnotationsFrom(trainCollection, trainAnnotationName, concept, skip_0=True, rootpath=rootpath)
name2label = dict(zip(names,labels))
renamed,vectors = feat_file.read(names)
y = [name2label[x] for x in renamed]
np = len([1 for lab in labels if 1 == lab])
nn = len([1 for lab in labels if -1== lab])
wp = float(beta) * (np+nn) / np
wn = (1.0-beta) * (np+nn) /nn
svm_params = '-w1 %g -w-1 %g' % (wp*C, wn*C)
model = svm_train(y, vectors, svm_params + ' -s 0 -t %d -q' % KERNEL_TYPE.index("HI"))
newmodel = svm_to_fiksvm([model], [1.0], feat_file.ndims, params)
newmodel.set_probAB(A, B)
makedirsforfile(model_file_name)
printStatus(INFO, '-> %s'%model_file_name)
fiksvm_save_model(model_file_name, newmodel)
# reload the model file to do a simple check
fiksvm_load_model(model_file_name)
assert(abs(newmodel.get_probAB()[0]-A)<1e-6)
assert(abs(newmodel.get_probAB()[1]-B)<1e-6)
return len(todo)
