Allow networkx >3 dependency

Signed-off-by: Ehud-Karavani <ehud.karavani@ibm.com>

causallib/simulation/CausalSimulator3.py

@@ -186,7 +186,7 @@ def __init__(self, topology, var_types, prob_categories, link_types, snr, treatm
 
         # Create a graph out of matrix topology:
         self.topology = topology
-        self.graph_topology = nx.from_numpy_matrix(topology.transpose(), create_using=nx.DiGraph())  # type:nx.DiGraph
+        self.graph_topology = nx.from_numpy_array(topology.transpose(), create_using=nx.DiGraph())  # type:nx.DiGraph
         self.graph_topology = nx.relabel_nodes(self.graph_topology,
                                                dict(list(zip(list(range(self.m)), self.var_names))))
 

causallib/tests/test_causal_simulator3.py

@@ -308,7 +308,7 @@ def test_random_topology_generation(self):
         np.testing.assert_array_equal(T.loc[X.columns, :].sum(axis="columns"), np.zeros(5))
 
         # Test for DAGness:
-        from networkx import DiGraph, from_numpy_matrix, is_directed_acyclic_graph
+        from networkx import DiGraph, from_numpy_array, is_directed_acyclic_graph
         NUM_TESTS = 50
         for test in range(NUM_TESTS):
             n_cov = np.random.randint(low=10, high=100)
@@ -317,7 +317,7 @@ def test_random_topology_generation(self):
             n_cen = np.random.randint(low=0, high=n_tre_out)
             T, _ = CS3m.generate_random_topology(n_covariates=n_cov, p=p, n_treatments=n_tre_out, n_outcomes=n_tre_out,
                                                  n_censoring=n_cen, given_vars=[], p_hidden=0)
-            G = from_numpy_matrix(T.values.transpose(), create_using=DiGraph())
+            G = from_numpy_array(T.values.transpose(), create_using=DiGraph())
             res = is_directed_acyclic_graph(G)
             self.assertTrue(res)
 

requirements.txt

@@ -1,4 +1,4 @@
-pandas>=0.25.2,<3
+pandas>=0.25.2,<4
 scipy>=0.19,<2
 statsmodels>=0.9,<1
 networkx>=1.1,<3