Add random MRF tests

.gitignore

@@ -10,6 +10,7 @@ build/
 
 tests/uai/MAR_prob/*.dfg
 tests/plots
+plots/
 libdai/Makefile.conf
 libdai/lib/libdai.a
 libdai/utils/createfg

tbp/core.py

@@ -805,6 +805,42 @@ def ising_g(N, unary_min, unary_max, pairwise_min, pairwise_max):
     return Graph(g), DecomposedGraph(dg)
 
 
+def random_g(N, edge_prob, unary_min, unary_max, pairwise_min, pairwise_max):
+    """
+    Connected N-variable MRF with Ising potentials uniformly chosen within the given limits. Each possible edge is
+    present with independent probability edge_prob.
+    :return: Tuple (g, dg) containing the MRF as a Graph and DecomposedGraph instance respectively. The tensor
+    decomposition used is described in Wrigley, Lee and Ye (2017), supplementary material.
+    """
+    g = []
+    dg = []
+    while not g or len(Graph(g).get_connected_components()) > 1:
+        for i in range(N):
+
+            # Unary potential
+            theta = np.random.uniform(unary_min, unary_max)
+            g.append(Factor([i], np.exp([theta, -theta])))
+            dg.append(DecomposedFactor([i], np.array([1.0]), [np.exp([[theta], [-theta]])]))
+
+            for j in range(i + 1, N):
+
+                if np.random.rand() < edge_prob:
+
+                    # Pairwise potential
+                    theta = np.random.uniform(pairwise_min, pairwise_max)
+                    f = Factor([i, j], np.exp([[theta, -theta], [-theta, theta]]))
+                    G, H = factorise_ising(np.exp(theta))
+                    df = DecomposedFactor([i, j], np.array([1.0, 1.0]), [G, H])
+
+                    # Check decomposed potential is equal to original
+                    assert df.expand() == f
+
+                    g.append(f)
+                    dg.append(df)
+
+    return Graph(g), DecomposedGraph(dg)
+
+
 def l1_error(marg_1, marg_2, binary=False):
     """
     Mean L1 marginal error.

tests/icml17.py

@@ -211,11 +211,62 @@ def load_ising_tests(ks, sample_size=100) -> List[Tuple]:
     return tests
 
 
+def load_random_tests(ks, sample_size=100) -> List[Tuple]:
+    """
+    Load the random MRF tests used to produce Wrigley, Lee and Ye (2017), Figure 2.
+    """
+
+    unary_str = 1
+    pairwise_str = 2
+    edge_prob = 0.5
+    N = 15
+    tests = []
+
+    # First two tests - varying k
+    for i in range(sample_size):
+        test_name = 'random_k_{}_mixed_{}'.format(N, i)
+        print("Generating test case {}".format(test_name))
+        g, dg = tbp.random_g(N, edge_prob, -unary_str, unary_str, -pairwise_str, pairwise_str)
+        true_marg = g.exact_marg_elim()
+        tests.append({
+            'name': test_name,
+            'g': g,
+            'dg': dg,
+            'ks': ks,
+            'plot_series': 'mixed',
+            'true_marg': true_marg,
+        })
+
+    for i in range(sample_size):
+        test_name = 'random_k_{}_attractive_{}'.format(N, i)
+        print("Generating test case {}".format(test_name))
+        g, dg = tbp.random_g(N, edge_prob, -unary_str, unary_str, 0, pairwise_str)
+        true_marg = g.exact_marg_elim()
+        tests.append({
+            'name': test_name,
+            'g': g,
+            'dg': dg,
+            'ks': ks,
+            'plot_series': 'attractive',
+            'true_marg': true_marg,
+        })
+
+    # TODO Second two tests - varying N
+
+
+    return tests
+
 def run_ising():
     tests_ising = load_ising_tests(ks=[10, 100, 1000, 10000, 100000], sample_size=20)
     run_tests(tests_ising, binary_err=True)
     plot_tests(tests_ising, 'icml17-ising')
 
+def run_random():
+    tests_random = load_random_tests(ks=[10, 100, 1000, 10000, 100000], sample_size=100)
+    # tests_random = load_random_tests(ks=[10, 100, 1000], ns=[10, 15], sample_size=2)
+    run_tests(tests_random, binary_err=True)
+    plot_tests(tests_random, 'icml17-random')
+
 def run_uai():
     tests_uai_2 = load_uai_tests(['linkage_*.uai', 'Promedus_*.uai'], r=2, ks=[10, 100, 1000, 10000])
     tests_uai_4 = load_uai_tests(['linkage_*.uai', 'Promedus_*.uai'], r=4, ks=[10, 100, 1000, 10000])
@@ -227,6 +278,6 @@ def run_all():
     run_uai()
 
 if __name__ == '__main__':
-    run_ising()
+    run_random()