def test_bootstrap_invalid_data():
# causal direction: x0 --> x1 --> x3
x0 = np.random.uniform(size=1000)
x1 = 2.0 * x0 + np.random.uniform(size=1000)
x2 = np.random.uniform(size=1000)
x3 = 4.0 * x1 + np.random.uniform(size=1000)
X1 = pd.DataFrame(np.array([x0, x1, x2, x3]).T,
columns=['x0', 'x1', 'x2', 'x3'])
x0 = np.random.uniform(size=1000)
x1 = 2.1 * x0 + np.random.uniform(size=1000)
x2 = np.random.uniform(size=1000)
x3 = 4.2 * x1 + np.random.uniform(size=1000)
X2 = pd.DataFrame(np.array([x0, x1, x2, x3]).T,
columns=['x0', 'x1', 'x2', 'x3'])
X_list = [X1, X2]
# Invalid argument: bootstrap(n_sampling=-1)
model = MultiGroupDirectLiNGAM()
try:
result = model.bootstrap(X_list, n_sampling=-1)
except ValueError:
pass
else:
raise AssertionError
# Invalid argument: bootstrap(n_sampling='3')
model = MultiGroupDirectLiNGAM()
try:
result = model.bootstrap(X_list, n_sampling='3')
except ValueError:
pass
else:
raise AssertionError
def test_bootstrap_success():
# causal direction: x0 --> x1 --> x3
x0 = np.random.uniform(size=1000)
x1 = 2.0 * x0 + np.random.uniform(size=1000)
x2 = np.random.uniform(size=1000)
x3 = 4.0 * x1 + np.random.uniform(size=1000)
X1 = pd.DataFrame(np.array([x0, x1, x2, x3]).T,
columns=['x0', 'x1', 'x2', 'x3'])
x0 = np.random.uniform(size=1000)
x1 = 2.1 * x0 + np.random.uniform(size=1000)
x2 = np.random.uniform(size=1000)
x3 = 4.2 * x1 + np.random.uniform(size=1000)
X2 = pd.DataFrame(np.array([x0, x1, x2, x3]).T,
columns=['x0', 'x1', 'x2', 'x3'])
X_list = [X1, X2]
model = MultiGroupDirectLiNGAM()
result_list = model.bootstrap(X_list, n_sampling=5)
result = result_list[0]
result.adjacency_matrices_
result.total_effects_
# No argument
cdc = result.get_causal_direction_counts()
# n_directions=2
cdc = result.get_causal_direction_counts(n_directions=2)
# min_causal_effect=0.2
cdc = result.get_causal_direction_counts(min_causal_effect=0.2)
# split_by_causal_effect_sign=True
cdc = result.get_causal_direction_counts(split_by_causal_effect_sign=True)
# No argument
dagc = result.get_directed_acyclic_graph_counts()
# n_dags=2
dagc = result.get_directed_acyclic_graph_counts(n_dags=2)
# min_causal_effect=0.6
dagc = result.get_directed_acyclic_graph_counts(min_causal_effect=0.6)
# split_by_causal_effect_sign=True
dagc = result.get_directed_acyclic_graph_counts(
split_by_causal_effect_sign=True)
# get_probabilities
probs = result.get_probabilities()
# get_probabilities
probs = result.get_probabilities(min_causal_effect=0.6)
# get_total_causal_effects
ce = result.get_total_causal_effects()
# get_total_causal_effects
ce = result.get_total_causal_effects(min_causal_effect=0.6)