Clean up print messages

dtree_learner.py

@@ -124,8 +124,6 @@ class DTreeLearner(LearnerBase):
 
     def add_positive_examples(self, *args) -> None:
         feature_vec_list = [self._s2f_func(sample) for sample in args]
-
-        print("Positive feature vectors:", feature_vec_list)
         self._append_to_data_file(feature_vec_list, "true")
 
     def add_negative_examples(self, *args) -> None:
@@ -134,7 +132,6 @@ class DTreeLearner(LearnerBase):
                 self.count_neg_dup += 1
                 raise ValueError("repeated negative example: " + str(samp))
             perc_samp = tuple(self._s2f_func(samp))
-            print(tuple(perc_samp))
             if perc_samp in self.debug_neg_perc:
                 raise ValueError("repeated negative example: " + str(perc_samp))
             self.debug_neg_perc.add(perc_samp)

dtree_synth.py

@@ -45,7 +45,7 @@ def test_synth_dtree():
     #  0.0 <= x <= 30.0 and -1.0 <= y <= 0.9 and 0.2 <= theta <= 0.22
     teacher.set_old_state_bound(lb=[0.0, -1.0, 0.2], ub=[30.0, -0.9, 0.22])
 
-    synth_dtree(positive_examples, teacher, num_max_iterations=50)
+    synth_dtree(positive_examples, teacher, num_max_iterations=10)
 
 
 def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
@@ -60,6 +60,7 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
 
     past_candidate_list = []
     for k in range(num_max_iterations):
+        print("="*80)
         print(f"Iteration {k}:", sep='')
         print("learning ....")
 
@@ -71,7 +72,7 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
 
         # QUERYING TEACHER IF THERE ARE NEGATIVE EXAMPLES
         result = teacher.check(candidate)
-        print(result)
+        print(f"Satisfiability: {result}")
         if result == z3.sat:
             negative_examples = teacher.model()
             assert len(negative_examples) > 0

dtree_teacher_gem_stanley.py

@@ -34,7 +34,6 @@ class DTreeGEMStanleyGurobiTeacher(GEMStanleyGurobiTeacher):
             raise RuntimeError("Only support affine expressions.")
 
     def _set_candidate(self, conjunct: sympy.logic.boolalg.Boolean) -> None:
-        print(conjunct)
         # Variable Aliases
         m = self._gp_model
 
@@ -95,7 +94,6 @@ class DTreeGEMStanleyGurobiTeacher(GEMStanleyGurobiTeacher):
                 self._gp_model.optimize()
 
             if self._gp_model.status in [gp.GRB.OPTIMAL, gp.GRB.SUBOPTIMAL]:
-                print(f"ObjValue: {self._gp_model.getObjective().getValue()}")
                 cex = tuple(self._old_state.x) + tuple(self._percept.x)
                 self._cexs.append(cex)
             elif self._gp_model.status == gp.GRB.INFEASIBLE: