def test_f1score_generated_poisson(self):
""" Poisson strucutre learned should have good f1 score """
np.random.seed(10)
sm = generate_structure(5, 3.0)
df = generate_count_dataframe(
sm, 1000, intercept=False, zero_inflation_factor=0.0, seed=10
)
df = np.asarray(df)
dist_type_schema = {i: "poiss" for i in range(df.shape[1])}
sm_fitted = from_numpy(
df,
dist_type_schema=dist_type_schema,
lasso_beta=0.1,
ridge_beta=0.0,
w_threshold=0.1,
use_bias=False,
)
right_edges = sm.edges
n_predictions_made = len(sm_fitted.edges)
n_correct_predictions = len(set(sm_fitted.edges).intersection(set(right_edges)))
n_relevant_predictions = len(right_edges)
precision = n_correct_predictions / n_predictions_made
recall = n_correct_predictions / n_relevant_predictions
f1_score = 2 * (precision * recall) / (precision + recall)
assert f1_score > 0.7
def test_zero_lambda(self):
"""
A wrong initialisation could lead to counts always being zero if they dont
have parents.
"""
graph = StructureModel()
graph.add_nodes_from(list(range(20)))
df = generate_count_dataframe(graph, 10000)
assert not np.any(df.mean() == 0)
def test_dataframe(self, graph, intercept, seed, kernel,
zero_inflation_factor):
"""
Tests equivalence of dataframe wrapper
"""
data = generate_count_dataframe(
graph,
100,
zero_inflation_factor=zero_inflation_factor,
seed=seed,
intercept=intercept,
kernel=kernel,
)
df = generate_count_dataframe(
graph,
100,
zero_inflation_factor=zero_inflation_factor,
seed=seed,
intercept=intercept,
kernel=kernel,
)
assert np.array_equal(data, df[list(graph.nodes())].values)