from cdt.causality.graph import CCDr
from cdt.causality.graph import CGNN
from cdt.causality.graph import GES
from cdt.causality.graph import LiNGAM
from cdt.causality.graph import PC
from cdt.causality.graph import CAM
from cdt.utils.graph import clr
import networkx as nx
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Read the data
data = pd.read_csv('combined.csv', header=None)
true_CM = pd.read_csv('true_CM.csv', header=None)
true_CM = np.array(true_CM)
start_time = time.time()
aupr_matrix, dir_adj_matrix = CDVAE()
end_time = time.time() - start_time
print("--- Execution time : %4.4s seconds ---" % end_time)
#Retrieve SHD and AUPR
shd = SHD(true_CM, dir_adj_matrix, double_for_anticausal=False)
print('SHD:', shd)
aupr, curve = precision_recall(true_CM, aupr_matrix)
print('AUPR for aupr_matrix:', aupr)
def test_precision_recall():
assert precision_recall(*init())
def baselines(data):
# Tests
start_time = time.time()
obj = PC()
output = obj.predict(data)
adj_mat = nx.adjacency_matrix(output).todense()
output = clr(adj_mat)
output[np.isnan(output)] = 0
output[np.isposinf(output)] = 1
predicted = retrieve_adjacency_matrix(output)
true_matrix = pd.read_csv('true_CM.csv', header=None)
true_matrix = np.array(true_matrix)
shd = SHD(np.array(true_matrix), predicted, double_for_anticausal=False)
aupr, curve = precision_recall(np.array(true_matrix), output)
end_time = (time.time() - start_time)
print("--- Execution time : %4.4s seconds ---" % end_time)
results_pc = ['PC', aupr, shd, end_time]
print(results_pc)
# Tests
start_time = time.time()
obj = GES()
output = obj.predict(data)
adj_mat = nx.adjacency_matrix(output).todense()
output = clr(adj_mat)
output[np.isnan(output)] = 0
output[np.isposinf(output)] = 1
predicted = retrieve_adjacency_matrix(output)
true_matrix = pd.read_csv('true_CM.csv', header=None)
true_matrix = np.array(true_matrix)
shd = SHD(np.array(true_matrix), predicted, double_for_anticausal=False)
aupr, curve = precision_recall(np.array(true_matrix), output)
end_time = (time.time() - start_time)
print("--- Execution time : %4.4s seconds ---" % end_time)
results_ges = ['GES', aupr, shd, end_time]
print(results_ges)
# Tests
start_time = time.time()
obj = LiNGAM()
output = obj.predict(data)
adj_mat = nx.adjacency_matrix(output).todense()
output = clr(adj_mat)
output[np.isnan(output)] = 0
output[np.isposinf(output)] = 1
predicted = retrieve_adjacency_matrix(output)
true_matrix = pd.read_csv('true_CM.csv', header=None)
true_matrix = np.array(true_matrix)
shd = SHD(np.array(true_matrix), predicted, double_for_anticausal=False)
aupr, curve = precision_recall(np.array(true_matrix), output)
end_time = (time.time() - start_time)
print("--- Execution time : %4.4s seconds ---" % end_time)
results_lingam = ['LiNGAM', aupr, shd, end_time]
print(results_lingam)
# Tests
start_time = time.time()
obj = CCDr()
output = obj.predict(data)
adj_mat = nx.adjacency_matrix(output).todense()
output = clr(adj_mat)
output[np.isnan(output)] = 0
output[np.isposinf(output)] = 1
predicted = retrieve_adjacency_matrix(output)
true_matrix = pd.read_csv('true_CM.csv', header=None)
true_matrix = np.array(true_matrix)
shd = SHD(np.array(true_matrix), predicted, double_for_anticausal=False)
aupr, curve = precision_recall(np.array(true_matrix), output)
end_time = (time.time() - start_time)
print("--- Execution time : %4.4s seconds ---" % end_time)
results_ccdr = ['CCDR', aupr, shd, end_time]
print(results_ccdr)
return results_pc, results_ges, results_lingam, results_ccdr
def graph_score(self, test_graph: nx.DiGraph) -> float:
pr_score, _ = precision_recall(self._ground_truth_graph, test_graph)
shd_score = SHD(self._ground_truth_graph, test_graph)
return float(pr_score - shd_score) # Higher better
results_ccdr = ['CCDR', aupr, shd, end_time]
print(results_ccdr)
return results_pc, results_ges, results_lingam, results_ccdr
# Tests
start_time = time.time()
obj = CGNN(nruns=1, train_epochs=500, test_epochs=500)
output = obj.predict(data)
adj_mat = nx.adjacency_matrix(output).todense()
output = clr(adj_mat)
output[np.isnan(output)] = 0
output[np.isposinf(output)] = 1
predicted = retrieve_adjacency_matrix(output)
true_matrix = pd.read_csv('true_CM.csv', header=None)
true_matrix = np.array(true_matrix)
shd = SHD(np.array(true_matrix), predicted, double_for_anticausal=False)
aupr, curve = precision_recall(np.array(true_matrix), output)
end_time = (time.time() - start_time)
print("--- Execution time : %4.4s seconds ---" % end_time)
results_cgnn = ['CGNN', aupr, shd, end_time]
print(results_cgnn)