def valid(self,datasets,opt,opp,method = fold,part_ids = None,seed = None,test_data = None):
if seed is None:
# If seed is not set. UNIX time is used as seed.
seed = time.time()
saving_seed = "%s/log/%s.log.seed" % (self._dir,self._name)
with open(saving_seed,"w") as fp:
# Save used seed value.
fp.write("seed:%f\n" % seed)
if part_ids is None:
part_ids = datasets.pids
groups = [(test,train) for test,train in method(part_ids,seed = seed)]
for cnt,pdtsts in enumerate(groups):
# cnt is number of cluster.
if test_data is None:
test = False
ltest,dtest,itest = test2svm_prob(datasets.mkTest(pdtsts[0]))
else:
test = True
ltest,dtest,itest = test2svm_prob(test_data.mkTest(test_data.pids))
print "start %s validation" % (cnt)
ptrn,itrain = train2svm_prob(datasets.mkTrain(pdtsts[1]))
#opt = svm.svm_parameter(opt)
model = svmutil.svm_train(ptrn,opt)
plbl,pacc,pval = svmutil.svm_predict(ltest,dtest,model,opp)
# create saving direcotry
#self._mkdir(cnt)
# create log files
self._save_log(itest,plbl,pval,cnt,test)
model_name = "%s/model/%s.model.%s" % (self._dir,self._name,cnt)
def __init__(self, train_feature_file=TRAIN_FEATURE_FILE):
if os.path.exists(SAVED_MODEL):
self.model = svmutil.svm_load_model(SAVED_MODEL)
else:
y, x = svmutil.svm_read_problem(train_feature_file)
self.model = svmutil.svm_train(y, x, '-c 4')
svmutil.svm_save_model(SAVED_MODEL, self.model)
def svm():
# Training Parameters
# Defines how high the cost is of a misclassification
# versus making the decision plane more complex.
# Low COST makes decisions very simple but creates classification errors
COST = 0.9
# Used for generalisation
# - Low GAMMA means high generalisation
# - High GAMMA is closer to original dataset
GAMMA = 6
KERNEL = RBF
svm_model.predict = lambda self, x: svm_predict([0], [x], self)[0][0]
# Get the data
SPARSE_LENGTH = 16
sparseCodings = sparse_coding.generateFull(SPARSE_LENGTH)
dataset, data, outputs, classes = sparse_coding.toSVMProblem(sparseCodings)
# Set the parameters for the SVM
parameters = svm_parameter()
parameters.kernel_type = KERNEL
parameters.C = COST
parameters.gamma = GAMMA
# Train the SVM
solver = svm_train(dataset, parameters)
# Create the output path if it doesn't exist
generated_dir = path.abspath(
path.join(
"generated",
"Q2Task1-TrainedSVM-{}".format(strftime("%Y-%m-%d_%H-%M-%S"))))
if not path.exists(generated_dir):
makedirs(generated_dir)
uniqueFileName = path.normpath(path.join(generated_dir, "data.pkl"))
svm_save_model(uniqueFileName, solver)
# Compare the results to the extected values
figure = plot.figure()
axis = figure.add_subplot(111)
colors = ['r', 'y', 'g', 'c', 'b', 'k']
for sample in sparseCodings:
classifier = sparse_coding.getClassifier(sample)
activationResult = svm_predict([0.], [sample], solver, '-q')[0][0]
axis.bar(classifier,
activationResult,
color=colors[classifier % len(colors)])
plot.savefig(path.normpath(path.join(generated_dir, "activations.png")))
plot.show()
def create_model(self,datasets,opt,opp,part_ids = None): # Should groups and ngroups be idch ? if part_ids is None: part_ids = datasets.pids ptrn,itrain = train2svm_prob(datasets.mkTrain(part_ids)) print "create model ..." #opt = svm.svm_parameter(opt) model = svmutil.svm_train(ptrn,opt) # create saving direcotry #self._mkdir(cnt) # create log files #self._save_log(itest,plbl,pval,cnt) model_name = "%s/model/%s.model" % (self._dir,self._name) svmutil.svm_save_model(model_name, model)
def valid(self,datasets,opt,opp,method = fold,seed = None): # Should groups and ngroups be idch ? groups = [(test,train) for test,train in method(datasets.pids,seed = seed)] ngroups = [(test,train) for test,train in method(datasets.nids,seed = seed)] for cnt,(pdtsts,ndtsts) in enumerate(zip(groups,ngroups)): # cnt is number of cluster. ltest,dtest,itest = test2svm_prob(datasets.mkTest(pdtsts[0],ndtsts[0])) ptrn,itrain = train2svm_prob(datasets.mkTrain(pdtsts[1],ndtsts[1])) print "start %s validation" % (cnt) #opt = svm.svm_parameter(opt) model = svmutil.svm_train(ptrn,opt) plbl,pacc,pval = svmutil.svm_predict(ltest,dtest,model,opp) # create saving direcotry #self._mkdir(cnt) # create log files self._save_log(itest,plbl,pval,cnt) model_name = "%s/model/%s.model.%s" % (self._dir,self._name,cnt)
def taska():
# Training Parameters
# Defines how high the cost is of a misclassification
# versus making the decision plane more complex.
# Low COST makes decisions very simple but creates classification errors
COST = 0.9
# Used for generalisation
# - Low GAMMA means high generalisation
# - High GAMMA is closer to original dataset
GAMMA = 6
# Get the data
dataset, data, outputs, classes = csv.loadSVMProblem(
"spirals\\SpiralOut.txt")
# Set the parameters for the SVM
parameters = svm_parameter()
parameters.kernel_type = KERNEL
parameters.C = COST
parameters.gamma = GAMMA
# Train the SVM
start = time()
solver = svm_train(dataset, parameters)
end = time()
trainingTime = end - start
print trainingTime
uniqueFileName = "generated\\Q1DTaskA-TrainedSVM-" + strftime(
"%Y-%m-%d_%H-%M-%S") + '.pkl'
svm_save_model(uniqueFileName, solver)
# Compare the results to the extected values
plot = plotSVM(solver, -6.0, 6.0, 0.2)
plot.show()
def taska():
# Training Parameters
# Defines how high the cost is of a misclassification
# versus making the decision plane more complex.
# Low COST makes decisions very simple but creates classification errors
COST = 0.9
# Used for generalisation
# - Low GAMMA means high generalisation
# - High GAMMA is closer to original dataset
GAMMA = 6
# Get the data
dataset, data, outputs, classes = csv.loadSVMProblem("spirals\\SpiralOut.txt")
# Set the parameters for the SVM
parameters = svm_parameter()
parameters.kernel_type = KERNEL
parameters.C = COST
parameters.gamma = GAMMA
# Train the SVM
start = time()
solver = svm_train(dataset, parameters)
end = time()
trainingTime = end - start
print trainingTime
uniqueFileName = "generated\\Q1DTaskA-TrainedSVM-" + strftime("%Y-%m-%d_%H-%M-%S") + '.pkl'
svm_save_model(uniqueFileName,solver)
# Compare the results to the extected values
plot = plotSVM(solver, -6.0, 6.0, 0.2)
plot.show()
