changing active learning definition plus main loops of mle/vi_blocks

1 files changed, 8 insertions, 9 deletions

diff --git a/simulation/vi_blocks.py b/simulation/vi_blocks.py
index b78375b..5deb6f6 100644
--- a/simulation/vi_blocks.py
+++ b/simulation/vi_blocks.py
@@ -51,10 +51,10 @@ def create_vi_model(n_nodes, n_samp=100):
 
 
 if __name__ == "__main__":
-    n_cascades = 10000
-    batch_size = 10
+    batch_size = 100
+    frequency = 10
     n_samples = 50
-    graph = utils.create_random_graph(n_nodes=4)
+    graph = utils.create_random_graph(n_nodes=10)
     print('GRAPH:\n', graph, '\n-------------\n')
 
     x, s, mu, sig, cost = create_vi_model(len(graph), n_samples)
@@ -65,17 +65,16 @@ if __name__ == "__main__":
 
     alg = algorithms.GradientDescent(cost=cost, parameters=[mu, sig],
                                      step_rule=step_rules)
-    data_stream = ub.fixed_data_stream(n_cascades, graph, batch_size,
-                                       shuffle=False)
-    # data_stream = ub.dynamic_data_stream(graph, batch_size)
+    data_stream = ub.dynamic_data_stream(graph, batch_size)
     loop = main_loop.MainLoop(
         alg, data_stream,
         log_backend="sqlite",
         extensions=[
             be.FinishAfter(after_n_batches=10**4),
-            bm.TrainingDataMonitoring([cost, mu, sig, rmse],
-                                      every_n_batches=10),
-            be.Printing(every_n_batches=100, after_epoch=False),
+            bm.TrainingDataMonitoring([cost, rmse, mu], every_n_batches=frequency),
+            be.Printing(every_n_batches=frequency, after_epoch=False),
+            ub.JSONDump("logs/tmp.json", every_n_batches=10),
+            #ub.ActiveLearning(dataset=data_stream.dataset, params=graph)
         ]
     )
     loop.run()