def a_pcmciplus_order_independence(request):
# Set the parameters
links_coeffs, time, seed_val = request.param
# Retrieve lags
tau_min, tau_max = pp._get_minmax_lag(links_coeffs)
# Generate the data
data, _ = pp.structural_causal_process(links=links_coeffs,
T=time,
noises=None,
seed=seed_val)
# Get the true parents
# true_parents = pp._get_parents(links_coeffs, exclude_contemp=False)
true_graph = pp.links_to_graph(links_coeffs, tau_max=tau_max)
return pp.DataFrame(data), true_graph, links_coeffs, tau_min, tau_max
if __name__ == '__main__':
import tigramite.data_processing as pp
np.random.seed(6)
def lin_f(x):
return x
links = {
0: [((0, -1), 0.8, lin_f)],
1: [((1, -1), 0.8, lin_f), ((0, -1), 0.5, lin_f)],
2: [((2, -1), 0.8, lin_f), ((1, 0), -0.6, lin_f)]
}
# noises = [np.random.randn for j in links.keys()]
data, nonstat = pp.structural_causal_process(links, T=10000)
true_parents = pp._get_true_parent_neighbor_dict(links)
dataframe = pp.DataFrame(data)
med = Models(dataframe=dataframe,
model=sklearn.linear_model.LinearRegression(),
data_transform=None)
# Fit the model
med.get_fit(all_parents=true_parents, tau_max=3)
print(med.get_val_matrix())
# for j, i, tau, coeff in pp._iter_coeffs(links):
# print(i, j, tau, coeff, med.get_coeff(i=i, tau=tau, j=j))
# for causal_coeff in [med.get_ce(i=0, tau=-2, j=2),