Change require_converged behavior

cmdstanpy/model.py

@@ -1142,8 +1142,19 @@ def variational(
             errors = re.findall(pat, contents)
             if len(errors) > 0:
                 valid = False
-        if require_converged and not valid:
-            raise RuntimeError('The algorithm may not have converged.')
+        if not valid:
+            if require_converged:
+                raise RuntimeError(
+                    'The algorithm may not have converged.\n'
+                    'If you would like to inspect the output, '
+                    're-call with require_converged=False'
+                )
+            # else:
+            get_logger().warning(
+                '%s\n%s',
+                'The algorithm may not have converged.',
+                'Proceeding because require_converged is set to False',
+            )
         if not runset._check_retcodes():
             msg = 'Error during variational inference:\n{}'.format(
                 runset.get_err_msgs()

test/test_variational.py

@@ -235,12 +235,22 @@ def test_variational_eta_fail(self):
         )
         model = CmdStanModel(stan_file=stan)
         with self.assertRaisesRegex(
-            RuntimeError, 'algorithm may not have converged'
+            RuntimeError,
+            r'algorithm may not have converged\.\n.*require_converged',
         ):
             model.variational(algorithm='meanfield', seed=12345)
 
-        model.variational(
-            algorithm='meanfield', seed=12345, require_converged=False
+        with LogCapture() as log:
+            model.variational(
+                algorithm='meanfield', seed=12345, require_converged=False
+            )
+        log.check_present(
+            (
+                'cmdstanpy',
+                'WARNING',
+                'The algorithm may not have converged.\n'
+                'Proceeding because require_converged is set to False',
+            )
         )