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fixed minor issues str to enum conv #14

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Feb 26, 2024
Merged

fixed minor issues str to enum conv #14

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10 changes: 4 additions & 6 deletions jobs/bayesian_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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))
Expand Down Expand Up @@ -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

Expand Down
6 changes: 3 additions & 3 deletions jobs/bo_job_runs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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__":
Expand Down
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