def test_init_integer(self):
"""Tests initializations which only specify the order."""
# Checks for a zero order Lfd object
lfd_0 = LinearDifferentialOperator(order=0)
weightfd = [FDataBasis(Constant(domain_range=(0, 1)), 1)]
self._assert_equal_weights(
lfd_0.weights, weightfd,
"Wrong list of weight functions of the linear operator")
# Checks for a non zero order Lfd object
lfd_3 = LinearDifferentialOperator(3)
consfd = FDataBasis(
Constant(domain_range=(0, 1)),
[[0], [0], [0], [1]],
)
bwtlist3 = list(consfd)
self._assert_equal_weights(
lfd_3.weights, bwtlist3,
"Wrong list of weight functions of the linear operator")
# Negative order must fail
with np.testing.assert_raises(ValueError):
LinearDifferentialOperator(-1)
def test_basis_fpca_transform_result(self):
n_basis = 9
n_components = 3
fd_data = fetch_weather()['data'].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix),
np.arange(0.5, 365, 1))
# initialize basis data
basis = Fourier(n_basis=n_basis, domain_range=(0, 365))
fd_basis = fd_data.to_basis(basis)
fpca = FPCA(n_components=n_components,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2),
regularization_parameter=1e5))
fpca.fit(fd_basis)
scores = fpca.transform(fd_basis)
# results obtained using Ramsay's R package
results = [[-7.68307641e+01, 5.69034443e+01, -1.22440149e+01],
[-9.02873996e+01, 1.46262257e+01, -1.78574536e+01],
[-8.21155683e+01, 3.19159491e+01, -2.56212328e+01],
[-1.14163637e+02, 3.66425562e+01, -1.00810836e+01],
[-6.97263223e+01, 1.22817168e+01, -2.39417618e+01],
[-6.41886364e+01, -1.07261045e+01, -1.10587407e+01],
[1.35824412e+02, 2.03484658e+01, -9.04815324e+00],
[-1.46816399e+01, -2.66867491e+01, -1.20233465e+01],
[1.02507511e+00, -2.29840736e+01, -9.06081296e+00],
[-3.62936903e+01, -2.09520442e+01, -1.14799951e+01],
[-4.20649313e+01, -1.13618094e+01, -6.24909009e+00],
[-7.38115985e+01, -3.18423866e+01, -1.50298626e+01],
[-6.69822456e+01, -3.35518632e+01, -1.25167352e+01],
[-1.03534763e+02, -1.29513941e+01, -1.49103879e+01],
[-1.04542036e+02, -1.36794907e+01, -1.41555965e+01],
[-7.35863347e+00, -1.41171956e+01, -2.97562788e+00],
[7.28804530e+00, -5.34421830e+01, -3.39823418e+00],
[5.59974094e+01, -4.02154080e+01, 3.78800103e-01],
[1.80778702e+02, 1.87798201e+01, -1.99043247e+01],
[-3.69700617e+00, -4.19441020e+01, 6.45820740e+00],
[3.76527216e+01, -4.23056953e+01, 1.04221757e+01],
[1.23850646e+02, -4.24648130e+01, -2.22336786e-01],
[-7.23588457e+00, -1.20579536e+01, 2.07502089e+01],
[-4.96871011e+01, 8.88483448e+00, 2.02882768e+01],
[-1.36726355e+02, -1.86472599e+01, 1.89076217e+01],
[-1.83878661e+02, 4.12118550e+01, 1.78960356e+01],
[-1.81568820e+02, 5.20817910e+01, 2.01078870e+01],
[-5.08775852e+01, 1.34600555e+01, 3.18602712e+01],
[-1.37633866e+02, 7.50809631e+01, 2.42320782e+01],
[4.98276375e+01, 1.33401270e+00, 3.50611066e+01],
[1.51149934e+02, -5.47417776e+01, 3.97592325e+01],
[1.58366096e+02, -3.80762686e+01, -5.62415023e+00],
[2.17139548e+02, 6.34055987e+01, -1.98853635e+01],
[2.33615480e+02, -7.90787574e-02, 2.69069525e+00],
[3.45371437e+02, 9.58703622e+01, 8.47570770e+00]]
results = np.array(results)
# compare results
np.testing.assert_allclose(scores, results, atol=1e-7)
def test_init_default(self):
"""Tests default initialization (do not penalize)."""
lfd = LinearDifferentialOperator()
weightfd = [FDataBasis(Constant((0, 1)), 0)]
np.testing.assert_equal(
lfd.weights, weightfd,
"Wrong list of weight functions of the linear operator")
def test_init_wrong_params(self):
# Check specifying both arguments fail
with np.testing.assert_raises(ValueError):
LinearDifferentialOperator(1, weights=[1, 1])
# Check invalid domain range
monomial = Monomial((0, 1), n_basis=3)
fdlist = [FDataBasis(monomial, [1, 2, 3])]
with np.testing.assert_raises(ValueError):
LinearDifferentialOperator(weights=fdlist, domain_range=(0, 2))
# Check wrong types fail
with np.testing.assert_raises(ValueError):
LinearDifferentialOperator(weights=['a'])
with np.testing.assert_raises(ValueError):
LinearDifferentialOperator(weights='a')
def _test_penalty(self, basis, linear_diff_op, atol=0, result=None):
operator = LinearDifferentialOperator(linear_diff_op)
penalty = gramian_matrix(operator, basis)
numerical_penalty = gramian_matrix_numerical(operator, basis)
np.testing.assert_allclose(penalty, numerical_penalty, atol=atol)
if result is not None:
np.testing.assert_allclose(penalty, result, atol=atol)
def test_vector_valued_smoothing(self):
X, _ = skfda.datasets.fetch_weather(return_X_y=True)
basis_dim = skfda.representation.basis.Fourier(
n_basis=7, domain_range=X.domain_range)
basis = skfda.representation.basis.VectorValued([basis_dim] * 2)
for method in smoothing.BasisSmoother.SolverMethod:
with self.subTest(method=method):
basis_smoother = smoothing.BasisSmoother(
basis,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2)),
return_basis=True,
smoothing_parameter=1,
method=method)
basis_smoother_dim = smoothing.BasisSmoother(
basis_dim,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2)),
return_basis=True,
smoothing_parameter=1,
method=method)
X_basis = basis_smoother.fit_transform(X)
self.assertEqual(X_basis.dim_codomain, 2)
self.assertEqual(X_basis.coordinates[0].basis, basis_dim)
np.testing.assert_allclose(
X_basis.coordinates[0].coefficients,
basis_smoother_dim.fit_transform(
X.coordinates[0]).coefficients)
self.assertEqual(X_basis.coordinates[1].basis, basis_dim)
np.testing.assert_allclose(
X_basis.coordinates[1].coefficients,
basis_smoother_dim.fit_transform(
X.coordinates[1]).coefficients)
def test_init_list_int(self):
"""Tests initializations with integer weights."""
coefficients = [1, 3, 4, 5, 6, 7]
constant = Constant((0, 1))
fd = FDataBasis(constant, np.array(coefficients).reshape(-1, 1))
lfd = LinearDifferentialOperator(weights=coefficients)
np.testing.assert_equal(
lfd.weights, list(fd),
"Wrong list of weight functions of the linear operator")
def test_init_list_fdatabasis(self):
"""Test initialization with functional weights."""
n_basis = 4
n_weights = 6
monomial = Monomial((0, 1), n_basis=n_basis)
weights = np.arange(n_basis * n_weights).reshape((n_weights, n_basis))
fd = FDataBasis(monomial, weights)
fdlist = [FDataBasis(monomial, w) for w in weights]
lfd = LinearDifferentialOperator(weights=fdlist)
np.testing.assert_equal(
lfd.weights, list(fd),
"Wrong list of weight functions of the linear operator")
# Check failure if intervals do not match
constant = Constant((0, 2))
fdlist.append(FDataBasis(constant, 1))
with np.testing.assert_raises(ValueError):
LinearDifferentialOperator(weights=fdlist)
def test_bspline_penalty_special_case(self):
basis = BSpline(n_basis=5)
res = np.array([[1152., -2016., 1152., -288., 0.],
[-2016., 3600., -2304., 1008., -288.],
[1152., -2304., 2304., -2304., 1152.],
[-288., 1008., -2304., 3600., -2016.],
[0., -288., 1152., -2016., 1152.]])
operator = LinearDifferentialOperator(basis.order - 1)
penalty = gramian_matrix(operator, basis)
numerical_penalty = gramian_matrix_numerical(operator, basis)
np.testing.assert_allclose(penalty, res)
np.testing.assert_allclose(numerical_penalty, res)
def test_qr(self):
t = np.linspace(0, 1, 5)
x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t)
basis = BSpline((0, 1), n_basis=5)
fd = FDataGrid(data_matrix=x, sample_points=t)
smoother = smoothing.BasisSmoother(
basis=basis,
smoothing_parameter=10,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2)),
method='qr',
return_basis=True)
fd_basis = smoother.fit_transform(fd)
np.testing.assert_array_almost_equal(
fd_basis.coefficients.round(2),
np.array([[0.60, 0.47, 0.20, -0.07, -0.20]]))
def test_basis_fpca_fit_result(self):
n_basis = 9
n_components = 3
fd_data = fetch_weather()['data'].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix),
np.arange(0.5, 365, 1))
# initialize basis data
basis = Fourier(n_basis=n_basis, domain_range=(0, 365))
fd_basis = fd_data.to_basis(basis)
fpca = FPCA(n_components=n_components,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2),
regularization_parameter=1e5))
fpca.fit(fd_basis)
# results obtained using Ramsay's R package
results = [[
0.92407552, 0.13544888, 0.35399023, 0.00805966, -0.02148108,
-0.01709549, -0.00208469, -0.00297439, -0.00308224
],
[
-0.33314436, -0.05116842, 0.89443418, 0.14673902,
0.21559073, 0.02046924, 0.02203431, -0.00787185,
0.00247492
],
[
-0.14241092, 0.92131899, 0.00514715, 0.23391411,
-0.19497613, 0.09800817, 0.01754439, -0.00205874,
0.01438185
]]
results = np.array(results)
# compare results obtained using this library. There are slight
# variations due to the fact that we are in two different packages
for i in range(n_components):
if np.sign(fpca.components_.coefficients[i][0]) != np.sign(
results[i][0]):
results[i, :] *= -1
np.testing.assert_allclose(fpca.components_.coefficients,
results,
atol=1e-7)
def test_monomial_smoothing(self):
# It does not have much sense to apply smoothing in this basic case
# where the fit is very good but its just for testing purposes
t = np.linspace(0, 1, 5)
x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t)
basis = Monomial(n_basis=4)
fd = FDataGrid(data_matrix=x, sample_points=t)
smoother = smoothing.BasisSmoother(
basis=basis,
smoothing_parameter=1,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2)),
return_basis=True)
fd_basis = smoother.fit_transform(fd)
# These results where extracted from the R package fda
np.testing.assert_array_almost_equal(
fd_basis.coefficients.round(2),
np.array([[0.61, -0.88, 0.06, 0.02]]))
def test_regression_mixed_regularization(self):
multivariate = np.array([[0, 0], [2, 7], [1, 7], [3, 9], [4, 16],
[2, 14], [3, 5]])
X = [
multivariate,
FDataBasis(Monomial(n_basis=3),
[[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0],
[0, 1, 0], [0, 0, 1]])
]
# y = 2 + sum([3, 1] * array) + int(3 * function)
intercept = 2
coefs_multivariate = np.array([3, 1])
y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1])
y_sum = multivariate @ coefs_multivariate
y = 2 + y_sum + y_integral
scalar = LinearRegression(regularization=[
TikhonovRegularization(lambda x: x),
TikhonovRegularization(LinearDifferentialOperator(2))
])
scalar.fit(X, y)
np.testing.assert_allclose(scalar.intercept_, intercept, atol=0.01)
np.testing.assert_allclose(scalar.coef_[0], [2.536739, 1.072186],
atol=0.01)
np.testing.assert_allclose(scalar.coef_[1].coefficients,
[[2.125676, 2.450782, 5.808745e-4]],
atol=0.01)
y_pred = scalar.predict(X)
np.testing.assert_allclose(y_pred, [
5.349035, 16.456464, 13.361185, 23.930295, 32.650965, 23.961766,
16.29029
],
atol=0.01)
def smooth_grids(self,
param_values: list = None,
smoother=None,
scorer=LinearSmootherGeneralizedCVScorer(),
return_history=False):
'''
Search hyperparameter of user's estimator, then transform datagrid with it
If no param_values specified, algorithm will try 100 values between 10**-8 et 10**8
smoother must be either a skfda.BasisSmoother or a list of skfda.BasisSmoother one by variable
'''
if param_values is None:
param_values = np.logspace(-8, 8, num=100)
if smoother is None:
print("Default Smoother used")
smoother = BasisSmoother(basis,
regularization=TikhonovRegularization(
LinearDifferentialOperator(order=2)))
if isinstance(smoother, list) or isinstance(smoother, np.ndarray):
if len(smoother) != self._nVar:
raise ValueError(
"number of smoothers must be equal to the number of variable or equal to 1"
)
else:
smoother.domain_range = self.init_grid.domain_range
smoother = [smoother] * self._nVar
smoothed_grids = []
history = []
print("Smoothing data...")
for i in range(self._nVar):
data_grid = self.coordinates_grids[i].copy()
grid_search = SmoothingParameterSearch(estimator=smoother[i],
param_values=param_values,
scoring=scorer)
_ = grid_search.fit(data_grid)
history.append(grid_search.cv_results_['mean_test_score'])
best_est = grid_search.best_estimator_
smoothed_grids.append(best_est.fit_transform(data_grid))
print("Smoothing Done")
self.coordinates_grids = smoothed_grids
self._smoothed = True
self.coordinates_grids_dx1 = []
self.coordinates_grids_dx2 = []
self.coefficients = []
for i in range(self._nVar):
basis_representation = self.coordinates_grids[i].copy().to_basis(
basis=smoother[i].basis)
self.coefficients.append(basis_representation.coefficients)
self.coordinates_grids_dx1.append(
basis_representation.derivative(order=1).to_grid(
self.sample_points))
self.coordinates_grids_dx2.append(
basis_representation.derivative(order=2).to_grid(
self.sample_points))
self.coefficients = np.array(self.coefficients)
if return_history:
return np.array(history)
else:
return None
def test_grid_fpca_regularization_fit_result(self):
n_components = 1
fd_data = fetch_weather()['data'].coordinates[0]
fd_data = FDataGrid(np.squeeze(fd_data.data_matrix),
np.arange(0.5, 365, 1))
fpca = FPCA(n_components=n_components,
weights=[1] * 365,
regularization=TikhonovRegularization(
LinearDifferentialOperator(2)))
fpca.fit(fd_data)
# results obtained using fda.usc for the first component
results = [[
-0.06961236, -0.07027042, -0.07090496, -0.07138247, -0.07162215,
-0.07202264, -0.07264893, -0.07279174, -0.07274672, -0.07300075,
-0.07365471, -0.07489002, -0.07617455, -0.07658708, -0.07551923,
-0.07375128, -0.0723776, -0.07138373, -0.07080555, -0.07111745,
-0.0721514, -0.07395427, -0.07558341, -0.07650959, -0.0766541,
-0.07641352, -0.07660864, -0.07669081, -0.0765396, -0.07640671,
-0.07634668, -0.07626304, -0.07603638, -0.07549114, -0.07410347,
-0.07181791, -0.06955356, -0.06824034, -0.06834077, -0.06944125,
-0.07133598, -0.07341109, -0.07471501, -0.07568844, -0.07631904,
-0.07647264, -0.07629453, -0.07598431, -0.07628157, -0.07654062,
-0.07616026, -0.07527189, -0.07426683, -0.07267961, -0.07079998,
-0.06927394, -0.068412, -0.06838534, -0.06888439, -0.0695309,
-0.07005508, -0.07066637, -0.07167196, -0.07266978, -0.07275299,
-0.07235183, -0.07207819, -0.07159814, -0.07077697, -0.06977026,
-0.0691952, -0.06965756, -0.07058327, -0.07075751, -0.07025415,
-0.06954233, -0.06899785, -0.06891026, -0.06887079, -0.06862183,
-0.06830082, -0.06777765, -0.06700202, -0.06639394, -0.06582435,
-0.06514987, -0.06467236, -0.06425272, -0.06359187, -0.062922,
-0.06300068, -0.06325494, -0.06316979, -0.06296254, -0.06246343,
-0.06136836, -0.0600936, -0.05910688, -0.05840872, -0.0576547,
-0.05655684, -0.05546518, -0.05484433, -0.05465746, -0.05449286,
-0.05397004, -0.05300742, -0.05196686, -0.05133129, -0.05064617,
-0.04973418, -0.04855687, -0.04714356, -0.04588103, -0.04547284,
-0.04571493, -0.04580704, -0.04523509, -0.04457293, -0.04405309,
-0.04338468, -0.04243512, -0.04137278, -0.04047946, -0.03984531,
-0.03931376, -0.0388847, -0.03888507, -0.03908662, -0.03877577,
-0.03830952, -0.03802713, -0.03773521, -0.03752388, -0.03743759,
-0.03714113, -0.03668387, -0.0363703, -0.03642288, -0.03633051,
-0.03574618, -0.03486536, -0.03357797, -0.03209969, -0.0306837,
-0.02963987, -0.029102, -0.0291513, -0.02932013, -0.02912619,
-0.02869407, -0.02801974, -0.02732363, -0.02690451, -0.02676622,
-0.0267323, -0.02664896, -0.02661708, -0.02637166, -0.02577496,
-0.02490428, -0.02410813, -0.02340367, -0.02283356, -0.02246305,
-0.0224229, -0.0225435, -0.02295603, -0.02324663, -0.02310005,
-0.02266893, -0.02221522, -0.02168056, -0.02129419, -0.02064909,
-0.02007801, -0.01979083, -0.01979541, -0.01978879, -0.01954269,
-0.0191623, -0.01879572, -0.01849678, -0.01810297, -0.01769666,
-0.01753802, -0.01794351, -0.01871307, -0.01930005, -0.01933,
-0.01901017, -0.01873486, -0.01861838, -0.01870777, -0.01879,
-0.01904219, -0.01945078, -0.0200607, -0.02076936, -0.02100213,
-0.02071439, -0.02052113, -0.02076313, -0.02128468, -0.02175631,
-0.02206387, -0.02201054, -0.02172142, -0.02143092, -0.02133647,
-0.02144956, -0.02176286, -0.02212579, -0.02243861, -0.02278316,
-0.02304113, -0.02313356, -0.02349275, -0.02417028, -0.0245954,
-0.0244062, -0.02388557, -0.02374682, -0.02401071, -0.02431126,
-0.02433125, -0.02427656, -0.02430442, -0.02424977, -0.02401619,
-0.02402294, -0.02415424, -0.02413262, -0.02404076, -0.02397651,
-0.0243893, -0.0253322, -0.02664395, -0.0278802, -0.02877936,
-0.02927182, -0.02937318, -0.02926277, -0.02931632, -0.02957945,
-0.02982133, -0.03023224, -0.03060406, -0.03066011, -0.03070932,
-0.03116429, -0.03179009, -0.03198094, -0.03149462, -0.03082037,
-0.03041594, -0.0303307, -0.03028465, -0.03052841, -0.0311837,
-0.03199307, -0.03262025, -0.03345083, -0.03442665, -0.03521313,
-0.0356433, -0.03606037, -0.03677406, -0.03735165, -0.03746578,
-0.03744154, -0.03752143, -0.03780898, -0.03837639, -0.03903232,
-0.03911629, -0.03857567, -0.03816592, -0.03819285, -0.03818405,
-0.03801684, -0.03788493, -0.03823232, -0.03906142, -0.04023251,
-0.04112434, -0.04188011, -0.04254759, -0.043, -0.04340181,
-0.04412687, -0.04484482, -0.04577669, -0.04700832, -0.04781373,
-0.04842662, -0.04923723, -0.05007637, -0.05037817, -0.05009794,
-0.04994083, -0.05012712, -0.05094001, -0.05216065, -0.05350458,
-0.05469781, -0.05566309, -0.05641011, -0.05688106, -0.05730818,
-0.05759156, -0.05763771, -0.05760073, -0.05766117, -0.05794587,
-0.05816696, -0.0584046, -0.05905105, -0.06014331, -0.06142231,
-0.06270788, -0.06388225, -0.06426245, -0.06386721, -0.0634656,
-0.06358049, -0.06442514, -0.06570047, -0.06694328, -0.0682621,
-0.06897846, -0.06896583, -0.06854621, -0.06797142, -0.06763755,
-0.06784024, -0.06844314, -0.06918567, -0.07021928, -0.07148473,
-0.07232504, -0.07272276, -0.07287021, -0.07289836, -0.07271531,
-0.07239956, -0.07214086, -0.07170078, -0.07081195, -0.06955202,
-0.06825156, -0.06690167, -0.06617102, -0.06683291, -0.06887539,
-0.07089424, -0.07174837, -0.07150888, -0.07070378, -0.06960066,
-0.06842496, -0.06777666, -0.06728403, -0.06681262, -0.06679066
]]
results = np.array(results)
# compare results obtained using this library. There are slight
# variations due to the fact that we are in two different packages
for i in range(n_components):
if np.sign(fpca.components_.data_matrix[i][0]) != np.sign(
results[i][0]):
results[i, :] *= -1
np.testing.assert_allclose(fpca.components_.data_matrix.reshape(
fpca.components_.data_matrix.shape[:-1]),
results,
rtol=1e-2)
def test_regression_regularization(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, [1.0403, 0, 0, 0, 0])
y = [
1.0000684777229512, 0.1623672257830915, 0.08521053851548224,
0.08514200869281137, 0.09529138749665378, 0.10549625973303875,
0.11384314859153018
]
y_pred_compare = [
0.890341, 0.370162, 0.196773, 0.110079, 0.058063, 0.023385,
-0.001384
]
scalar = LinearRegression(coef_basis=[beta_basis],
regularization=TikhonovRegularization(
LinearDifferentialOperator(2)))
scalar.fit(x_fd, y)
np.testing.assert_allclose(scalar.coef_[0].coefficients,
beta_fd.coefficients,
atol=1e-3)
np.testing.assert_allclose(scalar.intercept_, -0.15, atol=1e-4)
y_pred = scalar.predict(x_fd)
np.testing.assert_allclose(y_pred, y_pred_compare, atol=1e-4)
x_basis = Monomial(n_basis=3)
x_fd = FDataBasis(x_basis,
[[1, 0, 0], [0, 1, 0], [0, 0, 1], [2, 0, 1]])
beta_fd = FDataBasis(x_basis, [3, 2, 1])
y = [1 + 13 / 3, 1 + 29 / 12, 1 + 17 / 10, 1 + 311 / 30]
# Non regularized
scalar = LinearRegression()
scalar.fit(x_fd, y)
np.testing.assert_allclose(scalar.coef_[0].coefficients,
beta_fd.coefficients)
np.testing.assert_allclose(scalar.intercept_, 1)
y_pred = scalar.predict(x_fd)
np.testing.assert_allclose(y_pred, y)
# Regularized
beta_fd_reg = FDataBasis(x_basis, [2.812, 3.043, 0])
y_reg = [5.333, 3.419, 2.697, 11.366]
scalar_reg = LinearRegression(regularization=TikhonovRegularization(
LinearDifferentialOperator(2)))
scalar_reg.fit(x_fd, y)
np.testing.assert_allclose(scalar_reg.coef_[0].coefficients,
beta_fd_reg.coefficients,
atol=0.001)
np.testing.assert_allclose(scalar_reg.intercept_, 0.998, atol=0.001)
y_pred = scalar_reg.predict(x_fd)
np.testing.assert_allclose(y_pred, y_reg, atol=0.001)
def get_fpca(fdo):
"""
Performs functional PCA on funtional data object and returns results.
Args:
fdo (object): Functional data object.
Returns:
dict: Functional PCA results, includes the following keys:
- 'C' : Centered coefficients matrix 'C'
- 'Cm' : Mean coefficients 'Cm'
- 'inertia' : Inertia
- 'W' : Function-to-discrete metric equivalence matrix 'W'
- 'M' : Weighting matrix 'M'
- 'values' : PCs values
- 'pval' : Percentage of variance of PCs.
- 'vecnotWM': PCs vectors
- 'vectors' : PCs weighted vectors
- 'axes' : ? #TODO Etienne?
- 'pc' : PCs projected on the modes
"""
# get number of samples, basis, and dimensions
nsam = fdo.n_samples
nbas = fdo.n_basis
ndim = len(fdo.dim_names)
# if 3d-array coefficients, convert to a 2d-array (n_samples, n_basis)
if ndim>1:
C = np.zeros((nsam, nbas*ndim))
for k in range(ndim):
j0 = nbas * k
j1 = nbas * (k+1)
C[:, j0:j1] = fdo.coefficients[:, :, k]
else:
C = fdo.coefficients
# compute centered coefficients matrix by subtractig the mean
Cm = np.mean(C, axis=0)
Cc = C - Cm[np.newaxis,:]
# get basis penalty matrix
regularization = TikhonovRegularization(LinearDifferentialOperator(0))
penalty = regularization.penalty_matrix(fdo.basis)
# compute crossed-covariance matrix of C and Inertia
inertia = np.zeros(ndim)
for k in range(ndim):
j0 = nbas * k
j1 = nbas * (k+1)
V = Cc[:, j0:j1].T @ Cc[:, j0:j1] @ penalty / nsam
inertia[k] = np.trace( V )
# compute weighting matrix 'M' to balance variables of different units
M = np.zeros((ndim*nbas,ndim*nbas))
Mdeminv = M.copy()
W = M.copy()
aux = np.diag(np.ones(nbas))
for k in range(ndim):
i0, j0 = nbas * k , nbas * k
i1, j1 = nbas * (k+1), nbas * (k+1)
M [i0:i1, j0:j1] = aux/inertia[k]
Mdeminv[i0:i1, j0:j1] = aux*np.sqrt(inertia[k])
W [i0:i1, j0:j1] = penalty
Mdem = np.sqrt(M);
# compute function-to-discrete metric equivalence matrix 'W
W = (W+W.T)/2.
Wdem = np.linalg.cholesky(W).T
Wdeminv = np.linalg.inv(Wdem)
# compute crossed-covariance matrix 'V'
V = Mdem @ Wdem @ Cc.T @ Cc @ Wdem.T @ Mdem / nsam
# compute eigenvalues and eigenvectors
pca_values, pca_vectors = np.linalg.eig(V)
idx = pca_values.argsort()[::-1]
pca_values = pca_values[idx]
pca_vectors = pca_vectors[:,idx]
pca_vectors_notWM = pca_vectors
pca_vectors = Mdeminv @ Wdeminv @ pca_vectors
# compute principal components projected on the modes
pc = Cc @ W.T @ M @ pca_vectors
# build PCA dictionary
fpca = {}
fpca['C' ] = C
fpca['Cm' ] = Cm
fpca['inertia' ] = inertia
fpca['W' ] = W
fpca['M' ] = M
fpca['values' ] = pca_values
fpca['pval' ] = 100 * pca_values.real / np.sum(pca_values.real)
fpca['vecnotWM' ] = pca_vectors_notWM
fpca['vectors' ] = pca_vectors
fpca['axes' ] = pca_vectors * np.sqrt(pca_values)[:,np.newaxis].T
fpca['pc' ] = pc.real
# Warn if eigen values are not orthogonal
v1 = fpca['vectors'][:,0].T @ W @ M @ fpca['vectors'][:,0] - 1.
v2 = fpca['vectors'][:,0].T @ W @ M @ fpca['vectors'][:,1]
if v1>1.e-10 or v2>1.e-10:
print('Warning : Eigen values not orthogonal (%s, %s)' % (v1, v2))
return fpca
