def test_wta(self):
with pytest.warns(
UserWarning,
match="Could not simulate data, returning constant dataframe"):
gen_stationary_dyn_net_and_df(w_min_inter=1,
w_max_inter=2,
max_data_gen_trials=2)
def test_error_if_wmin_less_wmax(self):
with pytest.raises(
ValueError,
match=
"Absolute minimum weight must be less than or equal to maximum weight: 2 > 1",
):
gen_stationary_dyn_net_and_df(w_min_inter=2,
w_max_inter=1,
max_data_gen_trials=2)
def test_fail_to_find_stationary_network(self):
"""if fails to find suitable network, returns dataset of ones"""
np.random.seed(5)
_, df, _, _ = gen_stationary_dyn_net_and_df(
n_samples=1000,
p=1,
w_min_inter=0.6,
w_max_inter=0.6,
max_data_gen_trials=20,
degree_intra=4,
degree_inter=7,
)
assert np.any(np.ones(df.shape) == df)
def test_dense_networks(self):
"""dense network are more likely to be non stationary. we check that the simulator is still able to provide a
stationary time-deries in that case.
If df contain only ones it means that the generator failed to obtain a stationary structure"""
np.random.seed(4)
_, df, _, _ = gen_stationary_dyn_net_and_df(
n_samples=1000,
p=1,
w_min_inter=0.2,
w_max_inter=0.5,
max_data_gen_trials=10,
degree_intra=4,
degree_inter=7,
)
assert np.any(np.ones(df.shape) != df)
def test_seems_stationary(self, seed):
np.random.seed(seed)
_, df, _, _ = gen_stationary_dyn_net_and_df(w_min_inter=0.1,
w_max_inter=0.2,
max_data_gen_trials=2)
assert np.all(df.max() - df.min() < 10)
