def run_CDT(alg, x, y, vis=True, verbose=False):
if alg == 'PC':
obj = PC()
elif alg == 'SAM':
obj = SAM()
elif alg == 'CAM':
obj = CAM()
elif alg == 'GES':
obj = GES()
else:
assert (False)
obj.verbose = verbose
output = obj.predict(x)
# and well, I want to return: prec, recall, shd
# or, I can just have the adjacency matrix, and compute the matrix myself
#
# FIXME the default order seems to be correct
mat = nx.to_numpy_matrix(output)
print('adjacency matrix:')
print(mat)
if vis:
nx.draw_networkx(output)
plt.show()
return mat
def test_SAM():
data, graph = load_dataset("sachs")
obj = SAM()
#The predict() method works without a graph, or with a
#directed or undirected graph provided as an input
#
# (HEBI: this seems to be very slow, 6+ hours)
output = obj.predict(data) #No graph provided as an argument
output = obj.predict(data, nx.Graph(graph)) #With an undirected graph
output = obj.predict(data, graph) #With a directed graph
#To view the graph created, run the below commands:
nx.draw_networkx(output, font_size=8)
plt.show()
def test_SAM():
m = SAM(train_epochs=10, test_epochs=10, nh=10, dnh=10, nruns=1, njobs=1)
assert isinstance(m.predict(data_graph), nx.DiGraph)
return 0