def test_error_X_beta_len_distinct(self):
""" Test that the number of beta bases and explanatory variables
are not different """
x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7))
y = [1 for _ in range(7)]
beta = Fourier(n_basis=5)
scalar = LinearScalarRegression([beta])
np.testing.assert_raises(ValueError, scalar.fit, [x_fd, x_fd], y)
scalar = LinearScalarRegression([beta, beta])
np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y)
def test_regression_fit(self):
x_basis = Monomial(n_basis=7)
x_fd = FDataBasis(x_basis, np.identity(7))
beta_basis = Fourier(n_basis=5)
beta_fd = FDataBasis(beta_basis, [1, 1, 1, 1, 1])
y = [
1.0000684777229512, 0.1623672257830915, 0.08521053851548224,
0.08514200869281137, 0.09529138749665378, 0.10549625973303875,
0.11384314859153018
]
scalar = LinearScalarRegression([beta_basis])
scalar.fit([x_fd], y)
np.testing.assert_array_almost_equal(scalar.beta_[0].coefficients,
beta_fd.coefficients)
def test_error_y_X_samples_different(self):
""" Test that the number of response samples and explanatory samples
are not different """
x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7))
y = [1 for _ in range(8)]
beta = Fourier(n_basis=5)
scalar = LinearScalarRegression([beta])
np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y)
x_fd = FDataBasis(Monomial(n_basis=8), np.identity(8))
y = [1 for _ in range(7)]
beta = Fourier(n_basis=5)
scalar = LinearScalarRegression([beta])
np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y)
def test_error_y_is_FData(self):
"""Tests that none of the explained variables is an FData object
"""
x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7))
y = list(FDataBasis(Monomial(n_basis=7), np.identity(7)))
scalar = LinearScalarRegression([Fourier(n_basis=5)])
np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y)
def test_error_beta_not_basis(self):
""" Test that all beta are Basis objects. """
x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7))
y = [1 for _ in range(7)]
beta = FDataBasis(Monomial(n_basis=7), np.identity(7))
scalar = LinearScalarRegression([beta])
np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y)
def test_error_X_not_FData(self):
"""Tests that at least one of the explanatory variables
is an FData object. """
x_fd = np.identity(7)
y = np.zeros(7)
scalar = LinearScalarRegression([Fourier(n_basis=5)])
np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y)
def test_error_weights_negative(self):
""" Test that none of the weights are negative. """
x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7))
y = [1 for _ in range(7)]
weights = [-1 for _ in range(7)]
beta = Monomial(n_basis=7)
scalar = LinearScalarRegression([beta])
np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y, weights)
def test_error_weights_lenght(self):
""" Test that the number of weights is equal to the
number of samples """
x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7))
y = [1 for _ in range(7)]
weights = [1 for _ in range(8)]
beta = Monomial(n_basis=7)
scalar = LinearScalarRegression([beta])
np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y, weights)
def test_regression_predict_multiple_explanatory(self):
y = [1, 2, 3, 4, 5, 6, 7]
x0 = FDataBasis(Constant(domain_range=(0, 1)), np.ones((7, 1)))
x1 = FDataBasis(Monomial(n_basis=7), np.identity(7))
beta0 = Constant(domain_range=(0, 1))
beta1 = BSpline(domain_range=(0, 1), n_basis=5)
scalar = LinearScalarRegression([beta0, beta1])
scalar.fit([x0, x1], y)
betas = scalar.beta_
np.testing.assert_array_almost_equal(betas[0].coefficients.round(4),
np.array([[32.6518]]))
np.testing.assert_array_almost_equal(
betas[1].coefficients.round(4),
np.array([[-28.6443, 80.3996, -188.587, 236.5832, -481.3449]]))