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.SklearnSVMModelAssembler(estimator)
actual = assembler.assemble()
def kernel_ast(sup_vec_value):
return 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.dual_coef_, estimator.intercept_,
[kernel_ast(1.0), kernel_ast(2.0)])
assert utils.cmp_exprs(actual, expected)
def test_unknown_kernel():
estimator = svm.SVC(kernel=lambda x, y: np.transpose(x) * y)
estimator.fit([[1], [2]], [1, 2])
assembler = assemblers.SklearnSVMModelAssembler(estimator)
assembler.assemble()
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.SklearnSVMModelAssembler(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_rbf_kernel():
estimator = svm.SVC(kernel="rbf", random_state=1, gamma=2.0)
estimator.fit([[1], [2]], [1, 2])
assembler = assemblers.SklearnSVMModelAssembler(estimator)
actual = assembler.assemble()
kernels = [_rbf_kernel_ast(estimator, 1.), _rbf_kernel_ast(estimator, 2.)]
expected = _create_expected_single_output_ast(
estimator.dual_coef_, estimator.intercept_, kernels)
assert utils.cmp_exprs(actual, expected)