def test_pc_stable_max_conds_dim(self):
# Setting up strict test level
pc_alpha = 0.05 #[0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
tau_max = 2
alpha_level = 0.01
# true_parents_here = {0: [],
# 1: [(1, -1), (0, -1)],
# 2: []
# }
dataframe = pp.DataFrame(self.data)
cond_ind_test = ParCorr(verbosity=verbosity)
pcmci = PCMCI(selected_variables=None,
dataframe=dataframe,
cond_ind_test=cond_ind_test,
verbosity=verbosity)
pcmci.run_pc_stable(
selected_links=None,
tau_min=1,
tau_max=tau_max,
save_iterations=False,
pc_alpha=pc_alpha,
max_conds_dim=2,
max_combinations=1,
)
parents = pcmci.all_parents
# print parents
# print _get_parent_graph(true_parents)
assert_graphs_equal(parents, self.true_parents)
def run_pc_stable_parallel(j):
"""Wrapper around PCMCI.run_pc_stable estimating the parents for a single
variable j.
Parameters
----------
j : int
Variable index.
Returns
-------
j, pcmci_of_j, parents_of_j : tuple
Variable index, PCMCI object, and parents of j
"""
# CondIndTest is initialized globally below
# Further parameters of PCMCI as described in the documentation can be
# supplied here:
pcmci_of_j = PCMCI(dataframe=dataframe,
cond_ind_test=cond_ind_test,
selected_variables=[j],
verbosity=verbosity)
# Run PC condition-selection algorithm. Also here further parameters can be
# specified:
parents_of_j = pcmci_of_j.run_pc_stable(
selected_links=selected_links,
tau_max=tau_max,
pc_alpha=pc_alpha,
)
# We return also the PCMCI object because it may contain pre-computed
# results can be re-used in the MCI step (such as residuals or null
# distributions)
return j, pcmci_of_j, parents_of_j
def run_pc_stable_parallel(j, dataframe, cond_ind_test, params):
"""Wrapper around PCMCI.run_pc_stable estimating the parents for a single
variable j.
Parameters
----------
j : int
Variable index.
Returns
-------
j, pcmci_of_j, parents_of_j : tuple
Variable index, PCMCI object, and parents of j
"""
N = dataframe.values.shape[1]
# CondIndTest is initialized globally below
# Further parameters of PCMCI as described in the documentation can be
# supplied here:
pcmci_of_j = PCMCI(
dataframe=dataframe,
cond_ind_test=cond_ind_test,
selected_variables=[j],
# var_names=var_names,
verbosity=verbosity)
# Run PC condition-selection algorithm. Also here further parameters can be
# specified:
if method_arg == 'pcmci':
parents_of_j = pcmci_of_j.run_pc_stable(
selected_links=params['selected_links'],
tau_max=params['tau_max'],
pc_alpha=params['pc_alpha'],
)
elif method_arg == 'gc':
parents_of_j = {}
for i in range(N):
if i == j:
parents_of_j[i] = [
(var, -lag) for var in range(N)
for lag in range(params['tau_min'], params['tau_max'] + 1)
]
else:
parents_of_j[i] = []
elif method_arg == 'corr':
parents_of_j = {}
for i in range(N):
parents_of_j[i] = []
# We return also the PCMCI object because it may contain pre-computed
# results can be re-used in the MCI step (such as residuals or null
# distributions)
return j, pcmci_of_j, parents_of_j
def run_pc_stable_parallel(j):
"""Wrapper around PCMCI.run_pc_stable estimating the parents for a single
variable j.
Parameters
----------
j : int
Variable index.
Returns
-------
j, pcmci_of_j, parents_of_j : tuple
Variable index, PCMCI object, and parents of j
"""
# CondIndTest is initialized globally below
# Further parameters of PCMCI as described in the documentation can be
# supplied here:
pcmci_of_j = PCMCI(
dataframe=dataframe,
cond_ind_test=cond_ind_test,
selected_variables=[j],
verbosity=verbosity)
# Run PC condition-selection algorithm. Also here further parameters can be
# specified:
parents_of_j = pcmci_of_j.run_pc_stable(
selected_links=selected_links,
tau_max=tau_max,
pc_alpha=pc_alpha,
)
# We return also the PCMCI object because it may contain pre-computed
# results can be re-used in the MCI step (such as residuals or null
# distributions)
return j, pcmci_of_j, parents_of_j
