def fit(self,X,y):
     '''
     Fits ElasticNet Regression with kernelised features
     
     Parameters
     ----------
     X: array-like of size [n_samples, n_features]
        Matrix of explanatory variables
        
     y: array-like of size (n_samples,)
        Vector of dependent variable
     
     Returns
     -------
     obj: self
       self
     '''
     X,y = check_X_y(X,y, dtype = np.float64)
     K   = get_kernel(X, X, self.gamma, self.degree, self.coef0, self.kernel, 
                      self.kernel_params )
     model = ElasticNet(self.alpha, self.l1_ratio, self.fit_intercept,
                        self.normalize, self.precompute, self.max_iter,
                        self.copy_X, self.tol, self.warm_start, self.positive,
                        self.random_state, self.selection)
     self._model = model.fit(K,y)
     self.relevant_indices_ = np.where(self._model.coef_ != 0)[0]
     self.relevant_vectors_ = X[self.relevant_indices_,:]
     return self

def test_sparse_coef():
    # Check that the sparse_coef property works
    clf = ElasticNet()
    clf.coef_ = [1, 2, 3]

    assert_true(sp.isspmatrix(clf.sparse_coef_))
    assert_equal(clf.sparse_coef_.toarray().tolist()[0], clf.coef_)

def test_sparse_coef():
    """ Check that the sparse_coef propery works """
    clf = ElasticNet()
    clf.coef_ = [1, 2, 3]

    assert_true(sp.isspmatrix(clf.sparse_coef_))
    assert_equal(clf.sparse_coef_.todense().tolist()[0], clf.coef_)

def test_enet_positive_constraint():
    X = [[-1], [0], [1]]
    y = [1, 0, -1]       # just a straight line with negative slope

    enet = ElasticNet(alpha=0.1, max_iter=1000, positive=True)
    enet.fit(X, y)
    assert min(enet.coef_) >= 0

def test_enet_toy_list_input():
    # Test ElasticNet for various values of alpha and l1_ratio with list X

    X = np.array([[-1], [0], [1]])
    X = sp.csc_matrix(X)
    Y = [-1, 0, 1]       # just a straight line
    T = np.array([[2], [3], [4]])  # test sample

    # this should be the same as unregularized least squares
    clf = ElasticNet(alpha=0, l1_ratio=1.0)
    # catch warning about alpha=0.
    # this is discouraged but should work.
    ignore_warnings(clf.fit)(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [1])
    assert_array_almost_equal(pred, [2, 3, 4])
    assert_almost_equal(clf.dual_gap_, 0)

    clf = ElasticNet(alpha=0.5, l1_ratio=0.3, max_iter=1000)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
    assert_array_almost_equal(pred, [1.0163,  1.5245,  2.0327], decimal=3)
    assert_almost_equal(clf.dual_gap_, 0)

    clf = ElasticNet(alpha=0.5, l1_ratio=0.5)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.45454], 3)
    assert_array_almost_equal(pred, [0.9090,  1.3636,  1.8181], 3)
    assert_almost_equal(clf.dual_gap_, 0)

def test_enet_copy_X_True(check_input):
    X, y, _, _ = build_dataset()
    X = X.copy(order='F')

    original_X = X.copy()
    enet = ElasticNet(copy_X=True)
    enet.fit(X, y, check_input=check_input)

    assert_array_equal(original_X, X)

def test_enet_copy_X_False_check_input_False():
    X, y, _, _ = build_dataset()
    X = X.copy(order='F')

    original_X = X.copy()
    enet = ElasticNet(copy_X=False)
    enet.fit(X, y, check_input=False)

    # No copying, X is overwritten
    assert np.any(np.not_equal(original_X, X))

def test_warm_start():
    X, y, _, _ = build_dataset()
    clf = ElasticNet(alpha=1.0, max_iter=50, warm_start=True)
    clf.fit(X, y)
    clf.set_params(alpha=0.1)
    clf.fit(X, y)

    clf2 = ElasticNet(alpha=0.1, max_iter=500)
    clf2.fit(X, y)
    assert_array_almost_equal(clf2.coef_, clf.coef_)

def test_enet_multitarget():
    n_targets = 3
    X, y, _, _ = build_dataset(n_samples=10, n_features=8, n_informative_features=10, n_targets=n_targets)
    estimator = ElasticNet(alpha=0.01, fit_intercept=True)
    estimator.fit(X, y)
    coef, intercept, dual_gap = (estimator.coef_, estimator.intercept_, estimator.dual_gap_)

    for k in range(n_targets):
        estimator.fit(X, y[:, k])
        assert_array_almost_equal(coef[k, :], estimator.coef_)
        assert_array_almost_equal(intercept[k], estimator.intercept_)
        assert_array_almost_equal(dual_gap[k], estimator.dual_gap_)

def test_enet_multitarget():
    n_targets = 3
    X, y = make_sparse_data(n_targets=n_targets)

    estimator = ElasticNet(alpha=0.01, fit_intercept=True, precompute=None)
    # XXX: There is a bug when precompute is not None!
    estimator.fit(X, y)
    coef, intercept, dual_gap = (estimator.coef_, estimator.intercept_, estimator.dual_gap_)

    for k in range(n_targets):
        estimator.fit(X, y[:, k])
        assert_array_almost_equal(coef[k, :], estimator.coef_)
        assert_array_almost_equal(intercept[k], estimator.intercept_)
        assert_array_almost_equal(dual_gap[k], estimator.dual_gap_)

def test_fit_simple_backupsklearn():
    df = pd.read_csv("./open_data/simple.txt", delim_whitespace=True)
    X = np.array(df.iloc[:, :df.shape[1] - 1], dtype='float32', order='C')
    y = np.array(df.iloc[:, df.shape[1] - 1], dtype='float32', order='C')
    Solver = h2o4gpu.ElasticNet

    enet = Solver(glm_stop_early=False)
    print("h2o4gpu fit()")
    enet.fit(X, y)
    print("h2o4gpu predict()")
    print(enet.predict(X))
    print("h2o4gpu score()")
    print(enet.score(X,y))

    enet_wrapper = Solver(positive=True, random_state=1234)
    print("h2o4gpu scikit wrapper fit()")
    enet_wrapper.fit(X, y)
    print("h2o4gpu scikit wrapper predict()")
    print(enet_wrapper.predict(X))
    print("h2o4gpu scikit wrapper score()")
    print(enet_wrapper.score(X, y))

    from sklearn.linear_model.coordinate_descent import ElasticNet
    enet_sk = ElasticNet(positive=True, random_state=1234)
    print("Scikit fit()")
    enet_sk.fit(X, y)
    print("Scikit predict()")
    print(enet_sk.predict(X))
    print("Scikit score()")
    print(enet_sk.score(X, y))

    enet_sk_coef = csr_matrix(enet_sk.coef_, dtype=np.float32).toarray()

    print(enet_sk.coef_)

    print(enet_sk_coef)

    print(enet_wrapper.coef_)

    print(enet_sk.intercept_)
    print(enet_wrapper.intercept_)

    print(enet_sk.n_iter_)
    print(enet_wrapper.n_iter_)

    print("Coeffs, intercept, and n_iters should match")
    assert np.allclose(enet_wrapper.coef_, enet_sk_coef)
    assert np.allclose(enet_wrapper.intercept_, enet_sk.intercept_)

def test_same_multiple_output_sparse_dense():
    for normalize in [True, False]:
        l = ElasticNet(normalize=normalize)
        X = [[0, 1, 2, 3, 4], [0, 2, 5, 8, 11], [9, 10, 11, 12, 13], [10, 11, 12, 13, 14]]
        y = [[1, 2, 3, 4, 5], [1, 3, 6, 9, 12], [10, 11, 12, 13, 14], [11, 12, 13, 14, 15]]
        ignore_warnings(l.fit)(X, y)
        sample = np.array([1, 2, 3, 4, 5]).reshape(1, -1)
        predict_dense = l.predict(sample)

        l_sp = ElasticNet(normalize=normalize)
        X_sp = sp.coo_matrix(X)
        ignore_warnings(l_sp.fit)(X_sp, y)
        sample_sparse = sp.coo_matrix(sample)
        predict_sparse = l_sp.predict(sample_sparse)

        assert_array_almost_equal(predict_sparse, predict_dense)

def test_check_input_false():
    X, y, _, _ = build_dataset(n_samples=20, n_features=10)
    X = check_array(X, order='F', dtype='float64')
    y = check_array(X, order='F', dtype='float64')
    clf = ElasticNet(selection='cyclic', tol=1e-8)
    # Check that no error is raised if data is provided in the right format
    clf.fit(X, y, check_input=False)
    # With check_input=False, an exhaustive check is not made on y but its
    # dtype is still cast in _preprocess_data to X's dtype. So the test should
    # pass anyway
    X = check_array(X, order='F', dtype='float32')
    clf.fit(X, y, check_input=False)
    # With no input checking, providing X in C order should result in false
    # computation
    X = check_array(X, order='C', dtype='float64')
    assert_raises(ValueError, clf.fit, X, y, check_input=False)

def test_check_input_false():
    X, y, _, _ = build_dataset(n_samples=20, n_features=10)
    X = check_array(X, order='F', dtype='float64')
    y = check_array(X, order='F', dtype='float64')
    clf = ElasticNet(selection='cyclic', tol=1e-8)
    # Check that no error is raised if data is provided in the right format
    clf.fit(X, y, check_input=False)
    X = check_array(X, order='F', dtype='float32')
    clf.fit(X, y, check_input=True)
    # Check that an error is raised if data is provided in the wrong dtype,
    # because of check bypassing
    assert_raises(ValueError, clf.fit, X, y, check_input=False)

    # With no input checking, providing X in C order should result in false
    # computation
    X = check_array(X, order='C', dtype='float64')
    assert_raises(ValueError, clf.fit, X, y, check_input=False)

def test_enet_toy_explicit_sparse_input():
    """Test ElasticNet for various values of alpha and l1_ratio with sparse
    X"""
    f = ignore_warnings
    # training samples
    X = sp.lil_matrix((3, 1))
    X[0, 0] = -1
    # X[1, 0] = 0
    X[2, 0] = 1
    Y = [-1, 0, 1]       # just a straight line (the identity function)

    # test samples
    T = sp.lil_matrix((3, 1))
    T[0, 0] = 2
    T[1, 0] = 3
    T[2, 0] = 4

    # this should be the same as lasso
    clf = ElasticNet(alpha=0, l1_ratio=1.0)
    f(clf.fit)(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [1])
    assert_array_almost_equal(pred, [2, 3, 4])
    assert_almost_equal(clf.dual_gap_, 0)

    clf = ElasticNet(alpha=0.5, l1_ratio=0.3, max_iter=1000)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
    assert_array_almost_equal(pred, [1.0163,  1.5245,  2.0327], decimal=3)
    assert_almost_equal(clf.dual_gap_, 0)

    clf = ElasticNet(alpha=0.5, l1_ratio=0.5)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.45454], 3)
    assert_array_almost_equal(pred, [0.9090,  1.3636,  1.8181], 3)
    assert_almost_equal(clf.dual_gap_, 0)

def test_warm_start():
    X, y, _, _ = build_dataset()
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", UserWarning)
        clf = ElasticNet(alpha=0.1, max_iter=5, warm_start=True)
        clf.fit(X, y)
        clf.fit(X, y)  # do a second round with 5 iterations

        clf2 = ElasticNet(alpha=0.1, max_iter=10)
        clf2.fit(X, y)
        assert_array_almost_equal(clf2.coef_, clf.coef_)

def test_normalize_option():
    """ Check that the normalize option in enet works """
    X = sp.csc_matrix([[-1], [0], [1]])
    y = [-1, 0, 1]
    clf_dense = ElasticNet(fit_intercept=True, normalize=True)
    clf_sparse = ElasticNet(fit_intercept=True, normalize=True)
    clf_dense.fit(X, y)
    X = sp.csc_matrix(X)
    clf_sparse.fit(X, y)
    assert_almost_equal(clf_dense.dual_gap_, 0)
    assert_array_almost_equal(clf_dense.coef_, clf_sparse.coef_)

def test_warm_start_convergence():
    X, y, _, _ = build_dataset()
    model = ElasticNet(alpha=1e-3, tol=1e-3).fit(X, y)
    n_iter_reference = model.n_iter_

    # This dataset is not trivial enough for the model to converge in one pass.
    assert_greater(n_iter_reference, 2)

    # Check that n_iter_ is invariant to multiple calls to fit
    # when warm_start=False, all else being equal.
    model.fit(X, y)
    n_iter_cold_start = model.n_iter_
    assert_equal(n_iter_cold_start, n_iter_reference)

    # Fit the same model again, using a warm start: the optimizer just performs
    # a single pass before checking that it has already converged
    model.set_params(warm_start=True)
    model.fit(X, y)
    n_iter_warm_start = model.n_iter_
    assert_equal(n_iter_warm_start, 1)

def test_random_descent():
    # Test that both random and cyclic selection give the same results.
    # Ensure that the test models fully converge and check a wide
    # range of conditions.

    # This uses the coordinate descent algo using the gram trick.
    X, y, _, _ = build_dataset(n_samples=50, n_features=20)
    clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
    clf_cyclic.fit(X, y)
    clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
    clf_random.fit(X, y)
    assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
    assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)

    # This uses the descent algo without the gram trick
    clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
    clf_cyclic.fit(X.T, y[:20])
    clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
    clf_random.fit(X.T, y[:20])
    assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
    assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)

    # Sparse Case
    clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
    clf_cyclic.fit(sparse.csr_matrix(X), y)
    clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
    clf_random.fit(sparse.csr_matrix(X), y)
    assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
    assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)

    # Multioutput case.
    new_y = np.hstack((y[:, np.newaxis], y[:, np.newaxis]))
    clf_cyclic = MultiTaskElasticNet(selection='cyclic', tol=1e-8)
    clf_cyclic.fit(X, new_y)
    clf_random = MultiTaskElasticNet(selection='random', tol=1e-8,
                                     random_state=42)
    clf_random.fit(X, new_y)
    assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
    assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)

    # Raise error when selection is not in cyclic or random.
    clf_random = ElasticNet(selection='invalid')
    assert_raises(ValueError, clf_random.fit, X, y)

def test_warm_start():
    X, y, _, _ = build_dataset()
    # Test that explicit warm restart...
    clf = ElasticNet(alpha=1.0, max_iter=50)
    clf.fit(X, y)

    clf2 = ElasticNet(alpha=0.1, max_iter=50)
    clf2.fit(X, y, coef_init=clf.coef_.copy())

    # ... and implicit warm restart are equivalent.
    clf3 = ElasticNet(alpha=1.0, max_iter=50, warm_start=True)
    clf3.fit(X, y)

    assert_array_almost_equal(clf3.coef_, clf.coef_)

    clf3.set_params(alpha=0.1)
    clf3.fit(X, y)

    assert_array_almost_equal(clf3.coef_, clf2.coef_)