def rs_svm_large_file(train_file, test_file, kernel):
print("Ramdom Sampling SVM for large dataset")
start_time = time.time()
RS_SVM = RandomSamplingSVM(kernel)
model = RS_SVM.train_large_file(train_file, debug=True, temp_folder='./')
print("Remain SVs: " + str(model.n_support_))
print("Training time: %s" % (time.time() - start_time))
X_test, y_test = datasets.load_svmlight_file(test_file)
ratio = model.score(X_test,y_test)
print("Accuracy %f" % ratio)
print("Total time: %s" % (time.time() - start_time))
def signle_svm(train_file, test_file, kernel):
print("Single SVM", flush=True)
start_time = time.time()
RS_SVM = RandomSamplingSVM(kernel)
model = RS_SVM.train_single(train_file)
print("Remain SVs: " + str(model.n_support_), flush=True)
print("Training time: %s" % (time.time() - start_time), flush=True)
X_test, y_test = datasets.load_svmlight_file(test_file)
ratio = model.score(X_test,y_test)
print("Accuracy %f" % ratio, flush=True)
print("Total time: %s" % (time.time() - start_time), flush=True)
def rs_svm_ratio(train_file, test_file, kernel):
xTrain, yTrain = datasets.load_svmlight_file(train_file)
xTest, yTest = datasets.load_svmlight_file(test_file)
nTestingCore = [1, 2, 4, 8, 16]
RS_SVM = RandomSamplingSVM(kernel)
for iTestingCore in range(5):
start_time = time.time()
model = RS_SVM.trainWithRatio(0.5, xTrain, yTrain, beta=0.01, nCore=nTestingCore[iTestingCore])
print("Remain SVs: " + str(model.n_support_), flush=True)
print("Training time: %s" % (time.time() - start_time), flush=True)
testRatio = model.score(xTest, yTest)
print("Accuracy %f" % testRatio, flush=True)
print("Total time: %s" % (time.time() - start_time), flush=True)
print(flush=True)
