def process(options, trainCollection, modelAnnotationName, trainAnnotationName, feature):
rootpath = options.rootpath
modelName = options.model
if 'fastlinear' == modelName:
from fastlinear.fastlinear import fastlinear_load_model as load_model
from fastlinear.fastlinear import fastlinear_save_model as save_model
else:
from fiksvm.fiksvm import fiksvm_load_model as load_model
from fiksvm.fiksvm import fiksvm_save_model as save_model
concepts = readConcepts(trainCollection, trainAnnotationName, rootpath)
concepts = [concepts[i] for i in range(len(concepts)) if (i%options.numjobs + 1) == options.job]
feat_file = BigFile(os.path.join(rootpath, trainCollection, "FeatureData", feature))
for concept in concepts:
modelfile = os.path.join(rootpath, trainCollection, 'Models', modelAnnotationName, feature, modelName, '%s.model' % concept)
model = load_model(modelfile)
(A0, B0) = model.get_probAB()
if abs(A0) > 1e-8 and not options.overwrite:
printStatus(INFO, "old parameters exist as A=%g, B=%g, skip" % (A0, B0))
continue
names,labels = readAnnotationsFrom(trainCollection, trainAnnotationName, concept, skip_0=True, rootpath=rootpath)
name2label = dict(zip(names, labels))
results = classify_large_data(model, names, feat_file, prob_output=False)
labels = [name2label[x[0]] for x in results]
dec_values = [x[1] for x in results]
printStatus(INFO, "%s +%d -%d" % (concept, len([x for x in labels if x==1]), len([x for x in labels if x==-1])))
[A,B] = sigmoid_train(dec_values, labels)
model.set_probAB(A, B)
save_model(modelfile, model)
(A1, B1) = model.get_probAB()
printStatus(INFO, "A: %g -> %g, B: %g -> %g" % (A0, A1, B0, B1))
def process(options, trainCollection, trainAnnotationName, valCollection,
valAnnotationName, feature, modelName):
assert (modelName in ['fik', 'fastlinear'])
rootpath = options.rootpath
autoweight = 1 #options.autoweight
beta = 0.5
metric = options.metric
scorer = getScorer(metric)
overwrite = options.overwrite
numjobs = options.numjobs
job = options.job
params = {}
if 'fik' == modelName:
from fiksvm.svmutil import svm_train as train_model
from fiksvm.fiksvm import svm_to_fiksvm as compress_model
from fiksvm.svm import KERNEL_TYPE
nr_bins = options.nr_bins
modelName += str(nr_bins)
params['nr_bins'] = nr_bins
minmax_file = os.path.join(rootpath, trainCollection, 'FeatureData',
feature, 'minmax.txt')
with open(minmax_file, 'r') as f:
params['min_vals'] = map(float, str.split(f.readline()))
params['max_vals'] = map(float, str.split(f.readline()))
else:
from fastlinear.liblinear193.python.liblinearutil import train as train_model
from fastlinear.fastlinear import liblinear_to_fastlinear as compress_model
modelName = 'fastlinear'
concepts = readConcepts(trainCollection,
trainAnnotationName,
rootpath=rootpath)
valConcepts = readConcepts(valCollection,
valAnnotationName,
rootpath=rootpath)
concept_num = len(concepts)
for i in range(concept_num):
assert (concepts[i] == valConcepts[i])
resultdir = os.path.join(
rootpath, trainCollection, 'Models', trainAnnotationName,
'%s,best_params' % modelName,
'%s,%s,%s' % (valCollection, valAnnotationName, feature))
todo = []
for concept in concepts:
resultfile = os.path.join(resultdir, concept + '.txt')
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
train_feat_file = BigFile(
os.path.join(rootpath, trainCollection, 'FeatureData', feature))
val_feat_file = BigFile(
os.path.join(rootpath, valCollection, 'FeatureData', feature))
feat_dim = train_feat_file.ndims
assert (feat_dim == val_feat_file.ndims)
for concept in todo:
names, labels = readAnnotationsFrom(trainCollection,
trainAnnotationName,
concept,
skip_0=True,
rootpath=rootpath)
name2label = dict(zip(names, labels))
renamed, vectors = train_feat_file.read(names)
Ys = [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
names, labels = readAnnotationsFrom(valCollection,
valAnnotationName,
concept,
skip_0=True,
rootpath=rootpath)
val_name2label = dict(zip(names, labels))
val_renamed, val_vectors = val_feat_file.read(names)
min_perf = 2.0
worst_C = 1.0
max_perf = 0.0
best_C = 1.0
best_scores = None
best_labels = None
for C in [1e-2, 1e-1, 1, 1e1, 1e2, 1e3, 1e4]:
if autoweight:
svm_params = '-w1 %g -w-1 %g' % (wp * C, wn * C)
else:
svm_params = '-c %g' % C
if modelName.startswith('fik'):
svm_params += ' -s 0 -t %d' % KERNEL_TYPE.index("HI")
else:
svm_params += ' -s 2 -B -1 '
#print modelName, '>'*20, svm_params
model = train_model(Ys, vectors, svm_params + ' -q')
new_model = compress_model([model], [1.0], feat_dim, params)
ranklist = [(val_renamed[i], new_model.predict(val_vectors[i]))
for i in range(len(val_renamed))]
ranklist.sort(key=lambda v: v[1], reverse=True)
sorted_labels = [val_name2label[x[0]] for x in ranklist]
perf = scorer.score(sorted_labels)
if max_perf < perf:
max_perf = perf
best_C = C
best_scores = [x[1] for x in ranklist]
best_labels = list(sorted_labels)
if min_perf > perf:
min_perf = perf
worst_C = C
[A, B] = sigmoid_train(best_scores, best_labels)
resultfile = os.path.join(resultdir, '%s.txt' % concept)
printStatus(
INFO,
'%s -> worseAP=%g, worst_C=%g, bestAP=%g, best_C=%g, a=%g, b=%g' %
(concept, min_perf, worst_C, max_perf, best_C, A, B))
makedirsforfile(resultfile)
fw = open(resultfile, 'w')
fw.write('bestAP=%g, best_C=%g, a=%g, b=%g' % (max_perf, best_C, A, B))
fw.close()
def process(options, trainCollection, trainAnnotationName, valCollection, valAnnotationName, feature, modelName):
assert(modelName in ['fik', 'fastlinear'])
rootpath = options.rootpath
autoweight = 1 #options.autoweight
beta = 0.5
metric = options.metric
scorer = getScorer(metric)
overwrite = options.overwrite
numjobs = options.numjobs
job = options.job
params = {}
if 'fik' == modelName:
from fiksvm.svmutil import svm_train as train_model
from fiksvm.fiksvm import svm_to_fiksvm as compress_model
from fiksvm.svm import KERNEL_TYPE
nr_bins = options.nr_bins
modelName += str(nr_bins)
params['nr_bins'] = nr_bins
minmax_file = os.path.join(rootpath, trainCollection, 'FeatureData', feature, 'minmax.txt')
with open(minmax_file, 'r') as f:
params['min_vals'] = map(float, str.split(f.readline()))
params['max_vals'] = map(float, str.split(f.readline()))
else:
from fastlinear.liblinear193.python.liblinearutil import train as train_model
from fastlinear.fastlinear import liblinear_to_fastlinear as compress_model
modelName = 'fastlinear'
concepts = readConcepts(trainCollection,trainAnnotationName, rootpath=rootpath)
valConcepts = readConcepts(valCollection,valAnnotationName, rootpath=rootpath)
concept_num = len(concepts)
for i in range(concept_num):
assert(concepts[i] == valConcepts[i])
resultdir = os.path.join(rootpath, trainCollection, 'Models', trainAnnotationName, '%s,best_params'%modelName, '%s,%s,%s' % (valCollection,valAnnotationName,feature))
todo = []
for concept in concepts:
resultfile = os.path.join(resultdir, concept + '.txt')
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
train_feat_file = BigFile(os.path.join(rootpath,trainCollection,'FeatureData',feature))
val_feat_file = BigFile(os.path.join(rootpath,valCollection,'FeatureData',feature))
feat_dim = train_feat_file.ndims
assert(feat_dim == val_feat_file.ndims)
for concept in todo:
names,labels = readAnnotationsFrom(trainCollection, trainAnnotationName, concept, skip_0=True, rootpath=rootpath)
name2label = dict(zip(names,labels))
renamed,vectors = train_feat_file.read(names)
Ys = [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
names,labels = readAnnotationsFrom(valCollection, valAnnotationName, concept, skip_0=True, rootpath=rootpath)
val_name2label = dict(zip(names,labels))
val_renamed, val_vectors = val_feat_file.read(names)
min_perf = 2.0
worst_C = 1.0
max_perf = 0.0
best_C = 1.0
best_scores = None
best_labels = None
for C in [1e-2, 1e-1, 1, 1e1, 1e2, 1e3, 1e4]:
if autoweight:
svm_params = '-w1 %g -w-1 %g' % (wp*C, wn*C)
else:
svm_params = '-c %g' % C
if modelName.startswith('fik'):
svm_params += ' -s 0 -t %d' % KERNEL_TYPE.index("HI")
else:
svm_params += ' -s 2 -B -1 '
#print modelName, '>'*20, svm_params
model = train_model(Ys, vectors, svm_params + ' -q')
new_model = compress_model([model], [1.0], feat_dim, params)
ranklist = [(val_renamed[i], new_model.predict(val_vectors[i])) for i in range(len(val_renamed))]
ranklist.sort(key=lambda v:v[1], reverse=True)
sorted_labels = [val_name2label[x[0]] for x in ranklist]
perf = scorer.score(sorted_labels)
if max_perf < perf:
max_perf = perf
best_C = C
best_scores = [x[1] for x in ranklist]
best_labels = list(sorted_labels)
if min_perf > perf:
min_perf = perf
worst_C = C
[A,B] = sigmoid_train(best_scores, best_labels)
resultfile = os.path.join(resultdir, '%s.txt' % concept)
printStatus(INFO, '%s -> worseAP=%g, worst_C=%g, bestAP=%g, best_C=%g, a=%g, b=%g' % (concept, min_perf, worst_C, max_perf, best_C, A, B))
makedirsforfile(resultfile)
fw = open(resultfile, 'w')
fw.write('bestAP=%g, best_C=%g, a=%g, b=%g' % (max_perf, best_C, A, B))
fw.close()
