def test_cosine_kernel():
estimator = KernelSVC(kernel="cosine", random_state=1, gamma=2.0)
estimator.fit(np.array([[1], [2]]), [1, 2])
assembler = assemblers.LightningSVMModelAssembler(estimator)
actual = assembler.assemble()
def kernel_ast(sup_vec_value):
feature_norm = ast.SqrtExpr(
ast.BinNumExpr(
ast.FeatureRef(0),
ast.FeatureRef(0),
ast.BinNumOpType.MUL),
to_reuse=True)
return ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(sup_vec_value),
ast.FeatureRef(0),
ast.BinNumOpType.MUL),
ast.IfExpr(
ast.CompExpr(
feature_norm,
ast.NumVal(0.0),
ast.CompOpType.EQ),
ast.NumVal(1.0),
feature_norm),
ast.BinNumOpType.DIV)
expected = _create_expected_single_output_ast(
estimator.coef_, estimator.intercept_,
[kernel_ast(1.0), kernel_ast(1.0)])
assert utils.cmp_exprs(actual, expected)
def test_lightning_multi_class_rbf_kernel():
estimator = KernelSVC(kernel="rbf", random_state=1, gamma=2.0)
estimator.fit(np.array([[1], [2], [3]]), np.array([1, 2, 3]))
assembler = LightningSVMModelAssembler(estimator)
actual = assembler.assemble()
kernels = [_rbf_kernel_ast(estimator, float(i)) for i in range(1, 4)]
expected = ast.VectorVal([
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(kernels[0], ast.NumVal(0.5342246289),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(kernels[1], ast.NumVal(-0.5046204480),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(kernels[2], ast.NumVal(-0.4659431306),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(kernels[0], ast.NumVal(-0.5386765707),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(kernels[1], ast.NumVal(0.5729019463),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(kernels[2], ast.NumVal(-0.5386765707),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(kernels[0], ast.NumVal(-0.4659431306),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(kernels[1], ast.NumVal(-0.5046204480),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(kernels[2], ast.NumVal(0.5342246289),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD)
])
assert cmp_exprs(actual, expected)
pl.scatter(sv[:, 0], sv[:, 1], s=100, c="g")
# Plot decision surface.
A, B = np.meshgrid(np.linspace(-6, 6, 50), np.linspace(-6, 6, 50))
C = np.array([[x1, x2] for x1, x2 in zip(np.ravel(A), np.ravel(B))])
Z = clf.decision_function(C).reshape(A.shape)
pl.contour(A, B, Z, [0.0], colors="k", linewidths=1, origin="lower")
pl.axis("tight")
# Generate synthetic data from 2 classes.
X1, y1, X2, y2 = gen_non_lin_separable_data()
# Combine them to form a training set.
X = np.vstack((X1, X2))
y = np.hstack((y1, y2))
# Train the classifiers.
clf = SparseNonlinearClassifier(gamma=0.1, alpha=1.0 / 0.05)
clf.fit(X, y)
clf2 = KernelSVC(gamma=0.1, kernel="rbf", alpha=1e-2)
clf2.fit(X, y)
# Plot contours.
plot_contour(X, X1, X2, clf, "Sparse")
plot_contour(X, X1, X2, clf2, "Kernel SVM")
pl.show()
pl.scatter(sv[:, 0], sv[:, 1], s=100, c="g")
# Plot decision surface.
A, B = np.meshgrid(np.linspace(-6, 6, 50), np.linspace(-6, 6, 50))
C = np.array([[x1, x2] for x1, x2 in zip(np.ravel(A), np.ravel(B))])
Z = clf.decision_function(C).reshape(A.shape)
pl.contour(A, B, Z, [0.0], colors='k', linewidths=1, origin='lower')
pl.axis("tight")
# Generate synthetic data from 2 classes.
X1, y1, X2, y2 = gen_non_lin_separable_data()
# Combine them to form a training set.
X = np.vstack((X1, X2))
y = np.hstack((y1, y2))
# Train the classifiers.
clf = SparseNonlinearClassifier(gamma=0.1, alpha=1. / 0.05)
clf.fit(X, y)
clf2 = KernelSVC(gamma=0.1, kernel="rbf", alpha=1e-2)
clf2.fit(X, y)
# Plot contours.
plot_contour(X, X1, X2, clf, "Sparse")
plot_contour(X, X1, X2, clf2, "Kernel SVM")
pl.show()