Merge pull request #512 from marrlab/fbopt_phase_portrait

phase portrait type plots for fbopt

domainlab/utils/generate_fbopt_phase_portrait.py

@@ -0,0 +1,63 @@
+import glob
+import os
+
+import matplotlib.pyplot as plt
+from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
+
+
+# FIXME: maybe adjust the output path where the png is saved
+output_dir = "../.."
+
+def get_xy_from_event_file(event_file, tf_size_guidance=None):
+    if tf_size_guidance is None:
+        # settings for which/how much data is loaded from the tensorboard event files
+        tf_size_guidance = {
+            'compressedHistograms': 0,
+            'images': 0,
+            'scalars': 1e10,  # keep unlimited number
+            'histograms': 0
+        }
+    # load event file
+    event = EventAccumulator(event_file, tf_size_guidance)
+    event.Reload()
+    # extract the reg/dyn0 values
+    y_event = event.Scalars('x-axis=task vs y-axis=reg/dyn0')
+    y = [s.value for s in y_event]
+    x_int = [s.step for s in y_event]  # the .step data are saved as ints in tensorboard, so we will re-extact from 'task'
+    # extract the corresponding 'task' values
+    x_event = event.Scalars('task')
+    x = [s.value for s in x_event]
+    # sanity check:
+    for i in range(len(x)):
+        assert int(x[i]) == x_int[i]
+    return x, y
+
+def phase_portrain_combined(event_files, colors):
+    plt.figure()
+
+    for event_i in range(len(event_files)):
+        x, y = get_xy_from_event_file(event_files[event_i])
+
+        assert len(x) == len(y)
+        for i in range(len(x)-1):
+            plt.arrow(x[i], y[i], (x[i+1]-x[i]), (y[i+1]-y[i]),
+                      head_width=0.2, head_length=0.2, length_includes_head=True,
+                      fc=colors[event_i], ec=colors[event_i], alpha=0.4)
+
+        plt.plot(x[0], y[0], 'ko')
+        plt.scatter(x, y, s=1, c='black')
+
+        plt.xlabel("task")
+        plt.ylabel("reg/dyn0")
+        plt.title("x-axis=task vs y-axis=reg/dyn0")
+
+    plt.savefig(os.path.join(output_dir, 'phase_portrain_combined.png'), dpi=300)
+
+
+if __name__ == "__main__":
+    event_files = glob.glob("../../runs/*/events*")
+    print("Using the following tensorboard event files:\n{}".format("\n".join(event_files)))
+    cmap = plt.get_cmap('tab10')  # Choose a colormap
+    colors = [cmap(i) for i in range(len(event_files))]  # Different colors for the different runs
+    phase_portrain_combined(event_files, colors)
+