requests from @mfeurer

Author: janvanrijn

openml/runs/__init__.py

@@ -1,4 +1,4 @@
 from .run import OpenMLRun
-from .functions import (run_task, get_run, list_runs, get_runs)
+from .functions import (run_task, get_run, list_runs, get_runs, initialize_model_from_run)
 
 __all__ = ['OpenMLRun', 'run_task', 'get_run', 'list_runs', 'get_runs']

openml/runs/functions.py

@@ -160,6 +160,8 @@ def _get_seeded_model(model, seed=None):
         if 'random_state' in param_name:
             currentValue = model_params[param_name]
             # important to draw the value at this point (and not in the if statement)
+            # this way we guarantee that if a different set of subflows is seeded,
+            # the same number of the random generator is used
             newValue = rs.randint(0, 2**16)
             if currentValue is None:
                 random_states[param_name] = newValue

tests/test_runs/test_run_functions.py

@@ -37,6 +37,7 @@ def _perform_run(self, task_id, num_instances, clf, check_setup=True):
         self.assertEqual(len(run.data_content), num_instances)
 
         if check_setup:
+            # test the initialize setup function
             run_id = run_.run_id
             run_server = openml.runs.get_run(run_id)
             clf_server = openml.setups.initialize_model(run_server.setup_id)
@@ -46,6 +47,11 @@ def _perform_run(self, task_id, num_instances, clf, check_setup=True):
 
             openml.flows.assert_flows_equal(flow_local, flow_server)
 
+            # and test the initialize setup from run function
+            clf_server2 = openml.runs.initialize_model_from_run(run_server.run_id)
+            flow_server2 = openml.flows.sklearn_to_flow(clf_server2)
+            openml.flows.assert_flows_equal(flow_local, flow_server2)
+
             #self.assertEquals(clf.get_params(), clf_prime.get_params())
             # self.assertEquals(clf, clf_prime)