<commit_before>"""OVK learning, unit tests.

The :mod:`sklearn.tests.test_KenrelRidge` tests OVK ridge regression estimator.
"""

from sklearn.utils.estimator_checks import check_estimator
from scipy.optimize import check_grad
from numpy.random import RandomState, rand, randn
from numpy import sort, pi, sin, cos, array, dot, eye, arange, newaxis, cov
from numpy.linalg import norm, cholesky
from distutils.version import LooseVersion

import operalib as ovk

seed = 0
rng = RandomState(seed)
X = sort(200 * rng.rand(1000, 1) - 100, axis=0)
y = array([pi * sin(X).ravel(), pi * cos(X).ravel()]).T
Tr = 2 * rand(2, 2) - 1
Tr = dot(Tr, Tr.T)
Tr = Tr / norm(Tr, 2)
U = cholesky(Tr)
y = dot(y, U)

Sigma = 2 * rand(2, 2) - 1
Sigma = dot(Sigma, Sigma.T)
Sigma = 1. * Sigma / norm(Sigma, 2)
Cov = cholesky(Sigma)
y += dot(randn(y.shape[0], y.shape[1]), Cov.T)

X_test = arange(-100.0, 100.0, .5)[:, newaxis]
y_t = dot(array([pi * sin(X_test).ravel(),
                 pi * cos(X_test).ravel()]).T, U)


def test_valid_estimator():
    """Test whether ovk.Ridge is a valid sklearn estimator."""
    from sklearn import __version__
    if LooseVersion(__version__) >= LooseVersion('0.18.0'):
        check_estimator(ovk.Ridge)
    else:
        check_estimator(ovk.Ridge())


def test_ridge_grad_id():
    """Test ovk.KernelRidgeRisk gradient with finite differences."""
    K = ovk.DecomposableKernel(A=eye(2))
    risk = ovk.KernelRidgeRisk(0.01)
    assert check_grad(lambda *args: risk.functional_grad_val(*args)[0],
                      lambda *args: risk.functional_grad_val(*args)[1],
                      rand(X.shape[0] * y.shape[1]),
                      y.ravel(), K(X, X)) < 1e-3


def test_ridge_grad_cov():
    """Test ovk.KernelRidgeRisk gradient with finite differences."""
    K = ovk.DecomposableKernel(A=eye(2))
    risk = ovk.KernelRidgeRisk(0.01)
    assert check_grad(lambda *args: risk.functional_grad_val(*args)[0],
                      lambda *args: risk.functional_grad_val(*args)[1],
                      rand(X.shape[0] * y.shape[1]),
                      y.ravel(), K(X, X)) < 1e-3<commit_msg>skipping check_estimator for sklean with version <= 0.17x<commit_after>"""OVK learning, unit tests.

The :mod:`sklearn.tests.test_KenrelRidge` tests OVK ridge regression estimator.
"""

from sklearn.utils.estimator_checks import check_estimator
from scipy.optimize import check_grad
from numpy.random import RandomState, rand, randn
from numpy import sort, pi, sin, cos, array, dot, eye, arange, newaxis, cov
from numpy.linalg import norm, cholesky
from distutils.version import LooseVersion
from warnings import warn

import operalib as ovk

seed = 0
rng = RandomState(seed)
X = sort(200 * rng.rand(1000, 1) - 100, axis=0)
y = array([pi * sin(X).ravel(), pi * cos(X).ravel()]).T
Tr = 2 * rand(2, 2) - 1
Tr = dot(Tr, Tr.T)
Tr = Tr / norm(Tr, 2)
U = cholesky(Tr)
y = dot(y, U)

Sigma = 2 * rand(2, 2) - 1
Sigma = dot(Sigma, Sigma.T)
Sigma = 1. * Sigma / norm(Sigma, 2)
Cov = cholesky(Sigma)
y += dot(randn(y.shape[0], y.shape[1]), Cov.T)

X_test = arange(-100.0, 100.0, .5)[:, newaxis]
y_t = dot(array([pi * sin(X_test).ravel(),
                 pi * cos(X_test).ravel()]).T, U)


def test_valid_estimator():
    """Test whether ovk.Ridge is a valid sklearn estimator."""
    from sklearn import __version__
    if LooseVersion(__version__) >= LooseVersion('0.18.0'):
        warn('sklearn\'s check_estimator seems to be broken in __version__ <= \
              0.17.x... skipping')
    else:
        check_estimator(ovk.Ridge())


def test_ridge_grad_id():
    """Test ovk.KernelRidgeRisk gradient with finite differences."""
    K = ovk.DecomposableKernel(A=eye(2))
    risk = ovk.KernelRidgeRisk(0.01)
    assert check_grad(lambda *args: risk.functional_grad_val(*args)[0],
                      lambda *args: risk.functional_grad_val(*args)[1],
                      rand(X.shape[0] * y.shape[1]),
                      y.ravel(), K(X, X)) < 1e-3


def test_ridge_grad_cov():
    """Test ovk.KernelRidgeRisk gradient with finite differences."""
    K = ovk.DecomposableKernel(A=eye(2))
    risk = ovk.KernelRidgeRisk(0.01)
    assert check_grad(lambda *args: risk.functional_grad_val(*args)[0],
                      lambda *args: risk.functional_grad_val(*args)[1],
                      rand(X.shape[0] * y.shape[1]),
                      y.ravel(), K(X, X)) < 1e-3
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