fixed minor issues str to enum conv (#14)

jobs/bayesian_pipeline.py

@@ -103,14 +103,14 @@ def generate_initial_bounds(self, system: System) -> torch.Tensor:
         t_bounds = torch.tensor([int(self.bayesian_setting["bounds"]["T_min"]),
                                  int(self.bayesian_setting["bounds"]["T_max"])])
         policy_bounds = torch.tensor([0, 1])
-        if self.model_type == "QHybrid":
+        if self.model_type == Policy.QFixed:
             q_bounds = torch.tensor([1, self.bayesian_setting["bounds"]["T_max"] - 1])
             bounds = torch.stack([h_bounds, t_bounds, q_bounds], dim=-1)
-        elif self.model_type == "YZHybrid":
+        elif self.model_type == Policy.YZHybrid:
             y_bounds = torch.tensor([1, self.bayesian_setting["bounds"]["T_max"] - 1])
             z_bounds = torch.tensor([1, self.bayesian_setting["bounds"]["T_max"] - 1])
             bounds = torch.stack([h_bounds, t_bounds, y_bounds, z_bounds], dim=-1)
-        # elif self.model_type == "KHybrid": # TODO add support for KHybrid model
+        # elif self.model_type == Policy.KHybrid: # TODO add support for KHybrid model
         else:
             bounds = torch.stack([h_bounds, t_bounds, policy_bounds], dim=-1)
         return bounds
@@ -184,7 +184,7 @@ def get_next_points(self, x: torch.Tensor, y: torch.Tensor, best_y: float, bound
             single_model = MixedSingleTaskGP(x, y, cat_dims=[1, 2], input_transform=Normalize(d=x.shape[1],
                                                                                               bounds=bounds),
                                              outcome_transform=Standardize(m=1))
-        elif self.model_type == "YZHybrid":
+        elif self.model_type == Policy.YZHybrid:
             single_model = MixedSingleTaskGP(x, y, cat_dims=[1, 2, 3], input_transform=Normalize(d=x.shape[1],
                                                                                                  bounds=bounds),
                                              outcome_transform=Standardize(m=1))
@@ -273,8 +273,6 @@ def _update_best_designs(self, best_designs: List[Tuple[LSMDesign, float]], new_
             # TODO: code for KHybrid to be added
             else:
                 h, size_ratio, policy = x[0], x[1], x[2]
-                if(policy.item == 0.0):
-                    print ("0 value for policy!")
                 best_designs.append((LSMDesign(h.item(), np.ceil(size_ratio.item()), policy.item()), y.item()))
         return best_designs
 

jobs/bo_job_runs.py

@@ -49,16 +49,16 @@ def compare_designs(n_runs=3, csv_filename='design_comparison.csv'):
         writer.writerow(['Entries per page(E)', 'Physical Entries per page(B)', 'Selectivity(s)',
                          'Max bits per element(H)', 'Total elements (N)', 'Empty Reads', 'Non-Empty Reads',
                          'Range Queries', 'Writes', 'BO Design', 'Analytical Design', 'BO Cost',
-                         'Analytical Cost', 'Diff(Analytical-Bayesian)', "Elapsed Time"])
+                         'Analytical Cost', 'Diff(Analytical-Bayesian)'])
 
         for i in range(n_runs):
             print(f"Iteration {i + 1}/{n_runs} running")
             system = generator._sample_system()
             z0, z1, q, w = generator._sample_workload(4)
-            bo_design, bo_cost, time = bayesian_optimizer.run(system, z0, z1, q, w)
+            bo_design, bo_cost = bayesian_optimizer.run(system, z0, z1, q, w)
             analytical_design, analytical_cost = bayesian_optimizer._find_analytical_results(system, z0, z1, q, w)
             writer.writerow([system.E, system.B, system.s, system.H, system.N, z0, z1, q, w,
-                             bo_design, analytical_design, bo_cost, analytical_cost, analytical_cost - bo_cost, time])
+                             bo_design, analytical_design, bo_cost, analytical_cost, analytical_cost - bo_cost])
 
 
 if __name__ == "__main__":