slight update

experiments/expt_twovars.py

@@ -40,16 +40,20 @@ def run_expt(num_graph, num_data_per_graph, n, d, s0, graph_type, sem_type, w_ra
     for ii in tqdm(range(num_graph)):
         B_true = utils.simulate_dag(d, s0, graph_type)
         W_true = utils.simulate_parameter(B_true, w_ranges=w_ranges)
-        np.savetxt(f'{expt_name}/graph{ii:05}_W_true.csv', W_true, delimiter=',')
+        W_true_fn = os.path.join(expt_name, f'graph{ii:05}_W_true.csv')
+        np.savetxt(W_true_fn, W_true, delimiter=',')
         for jj in range(num_data_per_graph):
             X = utils.simulate_linear_sem(W_true, n, sem_type, noise_scale=noise_scale)
-            np.savetxt(f'{expt_name}/graph{ii:05}_data{jj:05}_X.csv', X, delimiter=',')
+            X_fn = os.path.join(expt_name, f'graph{ii:05}_data{jj:05}_X.csv')
+            np.savetxt(X_fn, X, delimiter=',')
             # notears
-            W_est = notears.notears_linear_l1(X, lambda1=0, loss_type='l2')
-            assert utils.is_dag(W_est)
-            np.savetxt(f'{expt_name}/graph{ii:05}_data{jj:05}_W_notears.csv', W_est, delimiter=',')
+            W_notears = notears.notears_linear_l1(X, lambda1=0, loss_type='l2')
+            assert utils.is_dag(W_notears)
+            W_notears_fn = os.path.join(expt_name, f'graph{ii:05}_data{jj:05}_W_notears.csv')
+            np.savetxt(W_notears_fn, W_notears, delimiter=',')
             # eval
-            acc = utils.count_accuracy(B_true, W_est != 0)
+            B_notears = (W_notears != 0)
+            acc = utils.count_accuracy(B_true, B_notears)
             for metric in acc:
                 perf[metric].append(acc[metric])
     # print stats

src/utils.py

@@ -128,6 +128,10 @@ def _simulate_single_equation(X, w, scale):
 def count_accuracy(B_true, B_est):
     """Compute various accuracy metrics for B_est.
 
+    true positive = predicted association exists in condition in correct direction
+    reverse = predicted association exists in condition in opposite direction
+    false positive = predicted association does not exist in condition
+
     Args:
         B_true (np.ndarray): [d, d] ground truth graph, {0, 1}
         B_est (np.ndarray): [d, d] estimate, {0, 1, -1}, -1 is undirected edge in CPDAG
@@ -139,8 +143,16 @@ def count_accuracy(B_true, B_est):
         shd: undirected extra + undirected missing + reverse
         nnz: prediction positive
     """
-    if ((B_est == -1) & (B_est.T == -1)).any():
-        raise ValueError('undirected edge should only appear once')
+    if (B_est == -1).any():  # cpdag
+        if not ((B_est == 0) | (B_est == 1) | (B_est == -1)).all():
+            raise ValueError('B_est should take value in {0,1,-1}')
+        if ((B_est == -1) & (B_est.T == -1)).any():
+            raise ValueError('undirected edge should only appear once')
+    else:  # dag
+        if not ((B_est == 0) | (B_est == 1)).all():
+            raise ValueError('B_est should take value in {0,1}')
+        if not is_dag(B_est):
+            raise ValueError('B_est should be a DAG')
     d = B_true.shape[0]
     # linear index of nonzeros
     pred_und = np.flatnonzero(B_est == -1)