Rename and save pre.data for each partition

dtree_synth.py

@@ -3,6 +3,7 @@
 import itertools
 import json
 import matplotlib.pyplot as plt
+import pathlib
 import pickle
 import traceback
 from typing import Dict, Hashable, List, Tuple
@@ -115,6 +116,7 @@ def validate_cexs(state_dim: int, perc_dim: int,
         print("Spurious CEXs:", *spurious_cexs, sep='\n')
         return False
 
+
 def search_part(partition, state):
     assert len(partition) == len(state)
     bounds = []
@@ -122,7 +124,7 @@ def search_part(partition, state):
         i = np.searchsorted(sorted_list, v)
         if i == 0 or i == len(sorted_list):
             return None
-        bounds.append( (sorted_list[i-1], sorted_list[i]) )
+        bounds.append((sorted_list[i-1], sorted_list[i]))
     return tuple(bounds)
 
 
@@ -180,7 +182,7 @@ def main():
     )
 
     result = []
-    for part, (pos_exs, neg_exs, num_nan) in part_to_examples.items():
+    for i, (part, (pos_exs, neg_exs, num_nan)) in enumerate(part_to_examples.items()):
         print("#"*80)
         print(f"# positive: {len(pos_exs)}; "
               f"# negative: {len(neg_exs)}; "
@@ -196,16 +198,6 @@ def main():
                           perc_dim=teacher.perc_dim, timeout=20000)
         learner.set_grammar([(Teacher.PERC_GT, np.zeros(2))])
 
-        if pos_exs:
-            pos_fv_arr = np.asfarray([learner._s2f_func(exs) for exs in pos_exs])
-            plt.scatter(pos_fv_arr[:, 0], pos_fv_arr[:, 1], c="g", marker="o")
-        if neg_exs:
-            neg_fv_arr = np.asfarray([learner._s2f_func(exs) for exs in neg_exs])
-            plt.scatter(neg_fv_arr[:, 0], neg_fv_arr[:, 1], c="r", marker="x")
-        plt.show()
-
-        continue  # XXX Temporary skip learning and only plot feature vectors
-
         learner.add_positive_examples(*pos_exs)
         learner.add_negative_examples(*neg_exs)
         try:
@@ -227,12 +219,15 @@ def main():
                            "result": traceback.format_exc()})
             print(e)
         finally:
+            data_file = pathlib.Path("out/pre.data")
+            data_file.rename(f"out/part-{i:03}-pre.data")
             del teacher
             del learner
 
     with open(f"out/dtree_synth.{NUM_Y_PARTS}x{NUM_YAW_PARTS}.out.json", "w") as f:
         json.dump(result, f)
 
+
 if __name__ == "__main__":
     # test_synth_dtree()
     main()