def test_independence(self, graph_gen, seed, num_nodes):
"""
test whether the relation is accurate, implicitely tests sequence of
nodes.
"""
sm = graph_gen(num_nodes=num_nodes, seed=seed, weight=None)
nodes = sm.nodes()
df = generate_binary_dataframe(
sm,
n_samples=100000,
distribution="normal",
seed=seed,
noise_scale=0.5,
intercept=False,
)
tol = 0.05
for node in nodes:
if node == "aa":
continue
joint_proba, factored_proba = calculate_proba(df, "aa", node)
if node == "ab":
# this is the only link
assert not np.isclose(
joint_proba, factored_proba, atol=tol, rtol=0), df.mean()
else:
assert np.isclose(joint_proba,
factored_proba,
atol=tol,
rtol=0)
def test_independence(self, graph_gen, seed, num_nodes, n_categories,
distribution):
"""
test whether the relation is accurate, implicitely tests sequence of
nodes.
"""
sm = graph_gen(num_nodes=num_nodes, seed=seed, weight=None)
nodes = sm.nodes()
df = generate_categorical_dataframe(
sm,
n_samples=100000,
distribution=distribution,
n_categories=n_categories,
seed=seed,
noise_scale=1,
intercept=False,
)
tol = 0.05
# independent links
for node in nodes:
if node == "aa":
continue
joint_proba, factored_proba = calculate_proba(
df, "aa_0", node + "_0")
if node == "ab":
assert not np.isclose(
joint_proba, factored_proba, rtol=tol, atol=0)
else:
assert np.isclose(joint_proba,
factored_proba,
rtol=tol,
atol=0)
def test_order_is_correct(self, graph_gen, num_nodes, seed):
"""
Check if the order of the nodes is the same order as `sm.nodes`, which in turn is the same order as the
adjacency matrix.
To do so, we create graphs with degree in {0,1} by doing permutations on identity.
The edge values are always 100 and the noise is 1, so we expect `edge_from` < `edge_to` in absolute value
almost every time.
"""
sm = graph_gen(num_nodes=num_nodes, seed=seed, weight=None)
nodes = sm.nodes()
node_seq = {node: ix for ix, node in enumerate(sm.nodes())}
data = generate_binary_data(
sm,
n_samples=10000,
distribution="normal",
seed=seed,
noise_scale=0.1,
intercept=False,
)
tol = 0.15
# since we dont have an intercept, the mean proba for the parent is 0.5,
# which has the highest possible std for a binary feature (std= p(1-p)),
# hence, any child necessarily has a lower probability.
assert data[:, node_seq["aa"]].std() > data[:, node_seq["ab"]].std()
for node in nodes:
if node == "aa":
continue
joint_proba, factored_proba = calculate_proba(
data, node_seq["aa"], node_seq[node])
if node == "ab":
# this is the only link
assert not np.isclose(
joint_proba, factored_proba, rtol=tol, atol=0)
else:
assert np.isclose(joint_proba,
factored_proba,
rtol=tol,
atol=0)