def test_unknown_kernel():
estimator = svm.SVC(kernel=lambda x, y: np.transpose(x) * y)
estimator.fit([[1], [2]], [1, 2])
assembler = assemblers.SVMModelAssembler(estimator)
assembler.assemble()
def test_rbf_kernel():
estimator = svm.SVC(kernel="rbf", random_state=1, gamma=2.0)
estimator.fit([[1], [2]], [1, 2])
assembler = assemblers.SVMModelAssembler(estimator)
actual = assembler.assemble()
negative_gamma_ast = ast.BinNumExpr(ast.NumVal(0),
ast.NumVal(estimator.gamma),
ast.BinNumOpType.SUB)
def kernel_ast(sup_vec_value):
return ast.SubroutineExpr(
ast.ExpExpr(
ast.BinNumExpr(
negative_gamma_ast,
ast.PowExpr(
ast.BinNumExpr(ast.NumVal(sup_vec_value),
ast.FeatureRef(0),
ast.BinNumOpType.SUB), ast.NumVal(2)),
ast.BinNumOpType.MUL)))
expected = _create_expected_ast(
estimator, [kernel_ast(1.0), kernel_ast(2.0)])
assert utils.cmp_exprs(actual, expected)
def test_rbf_kernel():
estimator = svm.SVC(kernel="rbf", random_state=1, gamma=2.0)
estimator.fit([[1], [2]], [1, 2])
assembler = assemblers.SVMModelAssembler(estimator)
actual = assembler.assemble()
kernels = [_rbf_kernel_ast(estimator, 1.), _rbf_kernel_ast(estimator, 2.)]
expected = _create_expected_single_output_ast(estimator, kernels)
assert utils.cmp_exprs(actual, expected)
def test_linear_kernel():
estimator = svm.SVC(kernel="linear", random_state=1)
estimator.fit([[1], [2]], [1, 2])
assembler = assemblers.SVMModelAssembler(estimator)
actual = assembler.assemble()
def kernel_ast(sup_vec_value):
return ast.SubroutineExpr(
ast.BinNumExpr(ast.NumVal(sup_vec_value), ast.FeatureRef(0),
ast.BinNumOpType.MUL))
expected = _create_expected_single_output_ast(
estimator, [kernel_ast(1.0), kernel_ast(2.0)])
assert utils.cmp_exprs(actual, expected)
def test_multi_class_rbf_kernel():
estimator = svm.SVC(kernel="rbf", random_state=1, gamma=2.0)
estimator.fit([[1], [2], [3]], [1, 2, 3])
assembler = assemblers.SVMModelAssembler(estimator)
actual = assembler.assemble()
kernels = [
_rbf_kernel_ast(estimator, float(i), to_reuse=True)
for i in range(1, 4)
]
expected = ast.VectorVal([
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(kernels[1], ast.NumVal(-1.0),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(kernels[0], ast.NumVal(1.0), ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(kernels[2], ast.NumVal(-1.0),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(kernels[0], ast.NumVal(1.0), ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(kernels[2], ast.NumVal(-1.0),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(kernels[1], ast.NumVal(1.0), ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD)
])
assert utils.cmp_exprs(actual, expected)
def test_poly_kernel():
estimator = svm.SVC(kernel="poly", random_state=1, gamma=2.0, degree=2)
estimator.fit([[1], [2]], [1, 2])
assembler = assemblers.SVMModelAssembler(estimator)
actual = assembler.assemble()
def kernel_ast(sup_vec_value):
return ast.SubroutineExpr(
ast.PowExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(estimator.gamma),
ast.BinNumExpr(ast.NumVal(sup_vec_value),
ast.FeatureRef(0),
ast.BinNumOpType.MUL),
ast.BinNumOpType.MUL), ast.NumVal(0.0),
ast.BinNumOpType.ADD), ast.NumVal(estimator.degree)))
expected = _create_expected_single_output_ast(
estimator, [kernel_ast(1.0), kernel_ast(2.0)])
assert utils.cmp_exprs(actual, expected)