def test_convert_svmc_linear_raw_multi(self): iris = load_iris() X = iris.data[:, :2] y = iris.target y[-5:] = 3 prob = svmutil.svm_problem(y, X.tolist()) param = svmutil.svm_parameter() param.svm_type = SVC param.kernel_type = svmutil.LINEAR param.eps = 1 param.probability = 0 if noprint: param.print_func = noprint libsvm_model = svmutil.svm_train(prob, param) node = convert(libsvm_model, "LibSvmNuSvmcMultiRaw", [('input', FloatTensorType(shape=['None', 2]))]) self.assertTrue(node is not None) X2 = numpy.vstack([X[:2], X[60:62], X[110:112], X[147:149]]) # 5x0, 5x1 dump_data_and_model( X2.astype(numpy.float32), SkAPICl(libsvm_model), node, basename="LibSvmSvmcRaw-Dec3", verbose=False, allow_failure= "StrictVersion(onnxruntime.__version__) <= StrictVersion('0.1.3')")
def test_convert_svmc_raw(self): iris = load_iris() X = iris.data[:, :2] y = iris.target y[y == 2] = 1 prob = svmutil.svm_problem(y, X.tolist()) param = svmutil.svm_parameter() param.svm_type = SVC param.kernel_type = svmutil.RBF param.eps = 1 param.probability = 0 if noprint: param.print_func = noprint libsvm_model = svmutil.svm_train(prob, param) # known svm runtime dimension error in ONNX Runtime node = convert(libsvm_model, "LibSvmSvmcRaw", [('input', FloatTensorType(shape=['None', 'None']))]) self.assertTrue(node is not None) dump_data_and_model( X[:5].astype(numpy.float32), SkAPICl(libsvm_model), node, basename="LibSvmSvmcRaw", allow_failure= "StrictVersion(onnxruntime.__version__) < StrictVersion('0.5.0')")
def test_convert_nusvmc(self): iris = load_iris() X = iris.data[:, :2] y = iris.target y[y == 2] = 1 prob = svmutil.svm_problem(y, X.tolist()) param = svmutil.svm_parameter() param.svm_type = NuSVC param.kernel_type = svmutil.RBF param.eps = 1 param.probability = 1 if noprint: param.print_func = noprint libsvm_model = svmutil.svm_train(prob, param) node = convert(libsvm_model, "LibSvmNuSvmc", [('input', FloatTensorType(shape=['None', 'None']))]) self.assertTrue(node is not None) dump_data_and_model( X[:5].astype(numpy.float32), SkAPIClProba2(libsvm_model), node, basename="LibSvmNuSvmc-Dec2", allow_failure= "StrictVersion(onnxruntime.__version__) <= StrictVersion('0.1.3')")
def test_convert_svmc(self): iris = load_iris() X = iris.data[:, :2] y = iris.target y[y == 2] = 1 prob = svmutil.svm_problem(y, X.tolist()) param = svmutil.svm_parameter() param.svm_type = SVC param.kernel_type = svmutil.RBF param.eps = 1 param.probability = 1 if noprint: param.print_func = noprint libsvm_model = svmutil.svm_train(prob, param) node = convert(libsvm_model, "LibSvmSvmc", [('input', FloatTensorType())]) self.assertTrue(node is not None) dump_data_and_model(X[:5].astype(numpy.float32), SkAPIClProba2(libsvm_model), node, basename="LibSvmSvmc-Dec2")
def test_convert_svmc_linear_raw(self): iris = load_iris() X = iris.data[:, :2] y = iris.target y[y == 2] = 1 prob = svmutil.svm_problem(y, X.tolist()) param = svmutil.svm_parameter() param.svm_type = SVC param.kernel_type = svmutil.LINEAR param.eps = 1 param.probability = 0 if noprint: param.print_func = noprint libsvm_model = svmutil.svm_train(prob, param) node = convert(libsvm_model, "LibSvmSvmcLinearRaw", [('input', FloatTensorType(shape=[1, 'None']))]) self.assertTrue(node is not None) dump_data_and_model( X[:5].astype(numpy.float32), SkAPICl(libsvm_model), node, basename="LibSvmSvmcLinearRaw-Dec3", verbose=False, allow_failure= "StrictVersion(onnxruntime.__version__) <= StrictVersion('0.1.4')")
def test_convert_svmr_linear(self): iris = load_iris() X = iris.data[:, :2] y = iris.target prob = svmutil.svm_problem(y, X.tolist()) param = svmutil.svm_parameter() param.svm_type = SVR param.kernel_type = svmutil.LINEAR param.eps = 1 if noprint: param.print_func = noprint libsvm_model = svmutil.svm_train(prob, param) node = convert(libsvm_model, "LibSvmSvmrLinear", [('input', FloatTensorType(shape=[1, 'None']))]) self.assertTrue(node is not None) dump_data_and_model(X[:5].astype(numpy.float32), SkAPIReg(libsvm_model), node, basename="LibSvmSvmrLinear-Dec3")