frequency param introduced + plots_utils file sketch

2 files changed, 39 insertions, 10 deletions

diff --git a/simulation/active_blocks.py b/simulation/active_blocks.py
index 1495eb8..be1fc3d 100644
--- a/simulation/active_blocks.py
+++ b/simulation/active_blocks.py
@@ -43,7 +43,9 @@ class ActiveLearning(blocks.extensions.SimpleExtension):
     def do(self, which_callback, *args):
         out_degree = np.sum(self.dataset.graph, axis=1)
         self.dataset.node_p = out_degree / np.sum(out_degree)
-        print(self.dataset.node_p)
+
+#    def do(self, which_callback, *args):
+
 
 
 class JSONDump(blocks.extensions.SimpleExtension):
@@ -149,7 +151,8 @@ def create_learned_data_stream(graph, batch_size):
 if __name__ == "__main__":
     batch_size = 100
     n_obs = 1000
-    graph = utils.create_wheel(10)
+    frequency = 1
+    graph = utils.create_wheel(1000)
     print('GRAPH:\n', graph, '\n-------------\n')
 
     g_shared = theano.shared(value=graph, name='graph')
@@ -160,17 +163,17 @@ if __name__ == "__main__":
     alg = algorithms.GradientDescent(
         cost=-cost, parameters=[params], step_rule=blocks.algorithms.AdaDelta()
     )
-    # data_stream = create_learned_data_stream(graph, batch_size)
-    data_stream = create_fixed_data_stream(n_obs, graph, batch_size)
+    data_stream = create_learned_data_stream(graph, batch_size)
+    #data_stream = create_fixed_data_stream(n_obs, graph, batch_size)
     loop = main_loop.MainLoop(
         alg, data_stream,
         extensions=[
-            be.FinishAfter(after_n_batches=10**3),
-            bm.TrainingDataMonitoring([cost, params,
-                                       rmse, error], every_n_batches=10),
-            be.Printing(every_n_batches=10),
-            JSONDump("log.json", every_n_batches=10)
-            # ActiveLearning(data_stream.dataset),
+            be.FinishAfter(after_n_batches=10**4),
+            bm.TrainingDataMonitoring([cost, rmse, error],
+                                       every_n_batches=frequency),
+            be.Printing(every_n_batches=frequency),
+            JSONDump("tmpactive_log.json", every_n_batches=frequency),
+            ActiveLearning(data_stream.dataset, every_n_batches=frequency)
         ],
     )
     loop.run()
diff --git a/simulation/plot_utils.py b/simulation/plot_utils.py
new file mode 100644
index 0000000..af5269c
--- /dev/null
+++ b/simulation/plot_utils.py
@@ -0,0 +1,26 @@
+import matplotlib.pyplot as plt
+import argparse
+import json
+import seaborn
+seaborn.set_style('whitegrid')
+
+parser = argparse.ArgumentParser(description='Process logs')
+parser.add_argument('-x', help='name of parameters on x axis', default='time')
+parser.add_argument('-y', help='name of parameters on y axis', default='rmse')
+parser.add_argument('f', help='list of logs to parse', nargs='+')
+parser.add_argument('-dest', help='name of figure to save', default='fig.png')
+args = parser.parse_args()
+
+for file_name in args.f:
+    x, y = [], []
+    with open(file_name) as f:
+        for line in f:
+            jason = json.loads(line)
+            x.append(jason[args.x])
+            y.append(jason[args.y])
+    plt.plot(x, y, label=file_name)
+
+plt.legend()
+plt.xlabel(args.x)
+plt.ylabel(args.y)
+plt.savefig(args.dest)