def test_error_summary(self, r_network):
tmle = NetworkTMLE(network=r_network, exposure='A', outcome='Y')
tmle.exposure_model('W')
tmle.exposure_map_model('W', distribution=None)
tmle.outcome_model('A + W')
with pytest.raises(ValueError):
tmle.summary()
df['NETID_split'] = df['Net_str'].str.split()
G = nx.DiGraph()
G.add_nodes_from(df['IDs'])
# Adding edges
for i, c in zip(df['IDs'], df['NETID_split']):
if type(c) is list:
for j in c:
G.add_edge(i, int(j[1:]))
# Adding attributes
for node in G.nodes():
G.nodes[node]['W'] = np.int(df.loc[df['IDs'] == node, 'W1'])
G.nodes[node]['A'] = np.int(df.loc[df['IDs'] == node, 'A'])
G.nodes[node]['Y'] = np.int(df.loc[df['IDs'] == node, 'Y'])
tmle = NetworkTMLE(network=G, exposure='A', outcome='Y', verbose=True)
tmle.exposure_model('W + W_sum')
tmle.exposure_map_model('A + W + W_sum', measure=None, distribution=None)
tmle.outcome_model('A + A_sum + W + W_sum')
tmle.fit(p=0.35, samples=1000, bound=0.005)
tmle.summary(decimal=6)
tmle = NetworkTMLE(network=G, exposure='A', outcome='Y', verbose=True)
tmle.exposure_model('W + W_sum')
tmle.exposure_map_model('A + W + W_sum', measure=None, distribution=None)
tmle.outcome_model('A + A_sum + W + W_sum')
tmle.fit(p=0.65, samples=1000, bound=0.005)
tmle.summary(decimal=6)