def test_XX_meanconst(self):
est = Covariance(lagtime=self.lag,
compute_c0t=False,
bessels_correction=False)
cc = est.fit(self.data - self.mean_const).fetch_model()
np.testing.assert_allclose(cc.mean_0, self.mx_c_lag0)
np.testing.assert_allclose(cc.cov_00, self.Mxx_c_lag0)
cc = est.fit(self.data - self.mean_const,
column_selection=self.cols_2).fetch_model()
np.testing.assert_allclose(cc.cov_00, self.Mxx_c_lag0[:, self.cols_2])
def test_non_matching_length(self):
n = 100
data = [np.random.random(size=(n, 2)) for n in range(3)]
data = (data[:-1], data[1:])
weights = [np.random.random(n) for _ in range(3)]
weights[0] = weights[0][:-3]
with self.assertRaises(ValueError):
Covariance(1, compute_c0t=True).fit(data, weights=weights)
with self.assertRaises(ValueError):
Covariance(1, compute_c0t=True).fit(data, weights=weights[:10])
def test_XX_meanfree(self):
# many passes
est = Covariance(lagtime=self.lag,
compute_c0t=False,
remove_data_mean=True,
bessels_correction=False)
cc = est.fit(self.data).fetch_model()
np.testing.assert_allclose(cc.mean_0, self.mx_lag0)
np.testing.assert_allclose(cc.cov_00, self.Mxx0_lag0)
cc = est.fit(self.data, column_selection=self.cols_2).fetch_model()
np.testing.assert_allclose(cc.cov_00, self.Mxx0_lag0[:, self.cols_2])
def test_XY_sym_meanconst(self):
est = Covariance(lagtime=self.lag,
compute_c0t=True,
reversible=True,
bessels_correction=False)
cc = est.fit(self.Xc_lag0).fetch_model()
np.testing.assert_allclose(cc.mean_0, self.m_c_sym)
np.testing.assert_allclose(cc.cov_00, self.Mxx_c_sym)
np.testing.assert_allclose(cc.cov_0t, self.Mxy_c_sym)
cc = est.fit(self.Xc_lag0, column_selection=self.cols_2).fetch_model()
np.testing.assert_allclose(cc.cov_00, self.Mxx_c_sym[:, self.cols_2])
np.testing.assert_allclose(cc.cov_0t, self.Mxy_c_sym[:, self.cols_2])
def test_XY_weighted_meanconst(self):
est = Covariance(lagtime=self.lag,
compute_c0t=True,
bessels_correction=False)
cc = est.fit(self.Xc_lag0, weights=self.data_weights).fetch_model()
np.testing.assert_allclose(cc.mean_0, self.mx_c_wobj)
np.testing.assert_allclose(cc.mean_t, self.my_c_wobj)
np.testing.assert_allclose(cc.cov_00, self.Mxx_c_wobj)
np.testing.assert_allclose(cc.cov_0t, self.Mxy_c_wobj)
cc = est.fit(self.Xc_lag0,
weights=self.data_weights,
column_selection=self.cols_2).fetch_model()
np.testing.assert_allclose(cc.cov_00, self.Mxx_c_wobj[:, self.cols_2])
np.testing.assert_allclose(cc.cov_0t, self.Mxy_c_wobj[:, self.cols_2])
def test_XXXY_sym_meanfree(self):
# many passes
est = Covariance(lagtime=self.lag,
remove_data_mean=True,
compute_c0t=True,
reversible=True,
bessels_correction=False)
cc = est.fit(self.data, lagtime=self.lag).fetch_model()
np.testing.assert_allclose(cc.mean_0, self.m_sym)
np.testing.assert_allclose(cc.cov_00, self.Mxx0_sym)
np.testing.assert_allclose(cc.cov_0t, self.Mxy0_sym)
cc = est.fit(self.data, column_selection=self.cols_2).fetch_model()
np.testing.assert_allclose(cc.cov_00, self.Mxx0_sym[:, self.cols_2])
np.testing.assert_allclose(cc.cov_0t, self.Mxy0_sym[:, self.cols_2])
def test_re_estimate_weight_types(self):
# check different types are allowed and re-estimation works
x = np.random.random((100, 2))
c = Covariance(lagtime=1, compute_c0t=True)
c.fit(x, weights=np.ones((len(x), ))).fetch_model()
c.fit(x, weights=np.ones((len(x), ))).fetch_model()
c.fit(x, weights=None).fetch_model()
c.fit(x, weights=x[:, 0]).fetch_model()
def test_XX_weightobj_withmean(self):
# many passes
est = Covariance(lagtime=self.lag,
compute_c0t=False,
remove_data_mean=False,
bessels_correction=False)
cc = est.fit(self.data, n_splits=10,
weights=self.data_weights).fetch_model()
np.testing.assert_allclose(cc.mean_0, self.mx_wobj_lag0)
np.testing.assert_allclose(cc.cov_00, self.Mxx_wobj_lag0)
cc = est.fit(self.data,
column_selection=self.cols_2,
weights=self.data_weights).fetch_model()
np.testing.assert_allclose(cc.cov_00, self.Mxx_wobj_lag0[:,
self.cols_2])
def test_XXXY_weightobj_withmean(self):
# many passes
est = Covariance(lagtime=self.lag,
remove_data_mean=False,
compute_c0t=True,
bessels_correction=False)
cc = est.fit(self.data, weights=self.data_weights).fetch_model()
np.testing.assert_allclose(cc.mean_0, self.mx_wobj)
np.testing.assert_allclose(cc.mean_t, self.my_wobj)
np.testing.assert_allclose(cc.cov_00, self.Mxx_wobj)
np.testing.assert_allclose(cc.cov_0t, self.Mxy_wobj)
cc = est.fit(self.data,
weights=self.data_weights,
column_selection=self.cols_2).fetch_model()
np.testing.assert_allclose(cc.cov_00, self.Mxx_wobj[:, self.cols_2])
np.testing.assert_allclose(cc.cov_0t, self.Mxy_wobj[:, self.cols_2])
def test_XXXY_withmean(self):
# many passes
est = Covariance(lagtime=self.lag,
remove_data_mean=False,
compute_c0t=True,
bessels_correction=False)
cc = est.fit(self.data, n_splits=1).fetch_model()
assert not cc.bessels_correction
np.testing.assert_allclose(cc.mean_0, self.mx)
np.testing.assert_allclose(cc.mean_t, self.my)
np.testing.assert_allclose(cc.cov_00, self.Mxx)
np.testing.assert_allclose(cc.cov_0t, self.Mxy)
cc = est.fit(self.data, n_splits=1,
column_selection=self.cols_2).fetch_model()
np.testing.assert_allclose(cc.cov_00, self.Mxx[:, self.cols_2])
np.testing.assert_allclose(cc.cov_0t, self.Mxy[:, self.cols_2])
def test_XXXY_weightobj_meanfree(self):
#TODO: tests do not pass for n_splits > 1!
# many passes
est = Covariance(lagtime=self.lag,
remove_data_mean=True,
compute_c0t=True,
bessels_correction=False)
cc = est.fit(self.data, weights=self.data_weights,
n_splits=1).fetch_model()
np.testing.assert_allclose(cc.mean_0, self.mx_wobj)
np.testing.assert_allclose(cc.mean_t, self.my_wobj)
np.testing.assert_allclose(cc.cov_00, self.Mxx0_wobj)
np.testing.assert_allclose(cc.cov_0t, self.Mxy0_wobj)
cc = est.fit(self.data,
weights=self.data_weights,
column_selection=self.cols_2,
n_splits=1).fetch_model()
np.testing.assert_allclose(cc.cov_00, self.Mxx0_wobj[:, self.cols_2])
np.testing.assert_allclose(cc.cov_0t, self.Mxy0_wobj[:, self.cols_2])
def test_weights_close_to_zero(self):
n = 1000
data = [np.random.random(size=(n, 2)) for _ in range(5)]
# create some artificial correlations
data[0][:, 0] *= np.random.randint(n)
data = np.asarray(data)
weights = [np.ones(n, dtype=np.float32) for _ in range(5)]
# omit the first trajectory by setting a weight close to zero.
weights[0][:] = 1E-44
weights = np.asarray(weights)
est = Covariance(lagtime=1, compute_c0t=True)
for data_traj, weights_traj in zip(data, weights):
est.partial_fit((data_traj[:-3], data_traj[3:]),
weights=weights_traj[:-3])
cov = est.fetch_model()
# cov = covariance_lagged(data, lag=3, weights=weights, chunksize=10)
assert np.all(cov.cov_00 < 1)