rearrange files and fix typo

ppo_bihyb_dag.yaml → config/ppo_bihyb_dag.yaml

@@ -1,29 +1,29 @@
-# environment configs
-scheduler_type: cp
-num_init_dags: 50
-gamma: 0.95
-resource_limit: 6000
-train_sample: 50
-test_sample: 10
-
-# learning configs
-batch_size: 1
-lr_steps: []
-max_timesteps: 20
-k_epochs: 10
-update_timestep: 20
-learning_rate: 0.001
-eps_clip: 0.1
-
-# model parameters
-node_output_size: 64 #16
-gnn_layers: 5
-
-# test parameters
-search_size: 3
-
-# misc configs
-log_interval: 1
-test_interval: 50
-max_episodes: 5000
-test_model_weight: pretrained/PPO_cp_dag_num50_beam3_ratio0.0892.pt
+# environment configs
+scheduler_type: cp
+num_init_dags: 50
+gamma: 0.95
+resource_limit: 6000
+train_sample: 50
+test_sample: 10
+
+# learning configs
+batch_size: 1
+lr_steps: []
+max_timesteps: 20
+k_epochs: 10
+update_timestep: 20
+learning_rate: 0.001
+eps_clip: 0.1
+
+# model parameters
+node_output_size: 64 #16
+gnn_layers: 5
+
+# test parameters
+search_size: 3
+
+# misc configs
+log_interval: 1
+test_interval: 50
+max_episodes: 5000
+test_model_weight: pretrained/PPO_cp_dag_num50_beam3_ratio0.0892.pt

ppo_bihyb_ged.yaml → config/ppo_bihyb_ged.yaml

@@ -1,28 +1,28 @@
-# environment configs
-solver_type: ipfp
-dataset: AIDS-20-30
-gamma: 0.95
-train_sample: 50
-test_sample: 10
-
-# learning configs
-batch_size: 1
-lr_steps: []
-max_timesteps: 10
-k_epochs: 10
-update_timestep: 10
-learning_rate: 0.001
-eps_clip: 0.1
-
-# model parameters
-node_output_size: 64
-gnn_layers: 3
-
-# test parameters
-search_size: 3
-
-# misc configs
-log_interval: 1
-test_interval: 200
-max_episodes: 10000
-test_model_weight: pretrained/PPO_ipfp_datasetAIDS-20-30_beam3_ratio0.2117.pt
+# environment configs
+solver_type: ipfp
+dataset: AIDS-20-30
+gamma: 0.95
+train_sample: 50
+test_sample: 10
+
+# learning configs
+batch_size: 1
+lr_steps: []
+max_timesteps: 10
+k_epochs: 10
+update_timestep: 10
+learning_rate: 0.001
+eps_clip: 0.1
+
+# model parameters
+node_output_size: 64
+gnn_layers: 3
+
+# test parameters
+search_size: 3
+
+# misc configs
+log_interval: 1
+test_interval: 200
+max_episodes: 10000
+test_model_weight: pretrained/PPO_ipfp_datasetAIDS-20-30_beam3_ratio0.2117.pt

ppo_bihyb_hcp.yaml → config/ppo_bihyb_hcp.yaml

@@ -1,29 +1,29 @@
-# environment configs
-solver_type: lkh-fast
-min_size: 34
-max_size: 104
-gamma: 0.95
-train_sample: 50
-test_sample: 20
-
-# learning configs
-batch_size: 1
-lr_steps: []
-max_timesteps: 10
-k_epochs: 10
-update_timestep: 20
-learning_rate: 0.001
-eps_clip: 0.1
-
-# model parameters
-node_output_size: 16
-gnn_layers: 3
-
-# test parameters
-search_size: 12
-
-# misc configs
-log_interval: 1
-test_interval: 100
-max_episodes: 200
-test_model_weight: pretrained/PPO_lkh-fast_min34_max103_beam12_opt0.2500.pt
+# environment configs
+solver_type: lkh-fast
+min_size: 34
+max_size: 104
+gamma: 0.95
+train_sample: 50
+test_sample: 20
+
+# learning configs
+batch_size: 1
+lr_steps: []
+max_timesteps: 10
+k_epochs: 10
+update_timestep: 20
+learning_rate: 0.001
+eps_clip: 0.1
+
+# model parameters
+node_output_size: 16
+gnn_layers: 3
+
+# test parameters
+search_size: 12
+
+# misc configs
+log_interval: 1
+test_interval: 100
+max_episodes: 200
+test_model_weight: pretrained/PPO_lkh-fast_min34_max103_beam12_opt0.2500.pt

ppo_single_dag.yaml → config/ppo_single_dag.yaml

@@ -1,27 +1,27 @@
-# environment configs
-num_init_dags: 50
-gamma: 0.99
-resource_limit: 6000
-train_sample: 50
-test_sample: 10
-
-# learning configs
-batch_size: 1
-lr_steps: []
-max_timesteps: 5000
-k_epochs: 10
-update_timestep: 50
-learning_rate: 0.001
-eps_clip: 0.1
-
-# model parameters
-node_output_size: 64
-gnn_layers: 5
-
-# test parameters
-search_size: 10
-
-# misc configs
-log_interval: 1
-test_interval: 10 #50
-max_episodes: 5000
+# environment configs
+num_init_dags: 50
+gamma: 0.99
+resource_limit: 6000
+train_sample: 50
+test_sample: 10
+
+# learning configs
+batch_size: 1
+lr_steps: []
+max_timesteps: 5000
+k_epochs: 10
+update_timestep: 50
+learning_rate: 0.001
+eps_clip: 0.1
+
+# model parameters
+node_output_size: 64
+gnn_layers: 5
+
+# test parameters
+search_size: 10
+
+# misc configs
+log_interval: 1
+test_interval: 10 #50
+max_episodes: 5000

ppo_single_ged.yaml → config/ppo_single_ged.yaml

@@ -1,26 +1,26 @@
-# environment configs
-dataset: AIDS-20-30
-gamma: 0.99
-train_sample: 50
-test_sample: 10
-
-# learning configs
-batch_size: 1
-lr_steps: []
-max_timesteps: 200
-k_epochs: 10
-update_timestep: 50
-learning_rate: 0.001
-eps_clip: 0.1
-
-# model parameters
-node_output_size: 64
-gnn_layers: 3
-
-# test parameters
-search_size: 10
-
-# misc configs
-log_interval: 1
-test_interval: 200
-max_episodes: 10000
+# environment configs
+dataset: AIDS-20-30
+gamma: 0.99
+train_sample: 50
+test_sample: 10
+
+# learning configs
+batch_size: 1
+lr_steps: []
+max_timesteps: 200
+k_epochs: 10
+update_timestep: 50
+learning_rate: 0.001
+eps_clip: 0.1
+
+# model parameters
+node_output_size: 64
+gnn_layers: 3
+
+# test parameters
+search_size: 10
+
+# misc configs
+log_interval: 1
+test_interval: 200
+max_episodes: 10000

ppo_single_hcp.yaml → config/ppo_single_hcp.yaml

@@ -1,27 +1,27 @@
-# environment configs
-min_size: 34
-max_size: 103
-gamma: 0.95
-train_sample: 50
-test_sample: 20
-
-# learning configs
-batch_size: 1
-lr_steps: []
-max_timesteps: 500
-k_epochs: 10
-update_timestep: 8
-learning_rate: 0.001
-eps_clip: 0.1
-
-# model parameters
-node_output_size: 16
-gnn_layers: 3
-
-# test parameters
-search_size: 12
-
-# misc configs
-log_interval: 1
-test_interval: 100
-max_episodes: 200
+# environment configs
+min_size: 34
+max_size: 103
+gamma: 0.95
+train_sample: 50
+test_sample: 20
+
+# learning configs
+batch_size: 1
+lr_steps: []
+max_timesteps: 500
+k_epochs: 10
+update_timestep: 8
+learning_rate: 0.001
+eps_clip: 0.1
+
+# model parameters
+node_output_size: 16
+gnn_layers: 3
+
+# test parameters
+search_size: 12
+
+# misc configs
+log_interval: 1
+test_interval: 100
+max_episodes: 200

dag_ppo_bihyb_eval.py

@@ -176,7 +176,7 @@ def evaluate(policy_net, dag_graph, eval_graphs, max_steps=10, search_size=10, m
     import random
     from torch.multiprocessing import Pool, cpu_count
 
-    from dag_graph import DAGraph
+    from utils.dag_graph import DAGraph
     from dag_data.dag_generator import load_tpch_tuples
     from dag_ppo_bihyb_train import ActorCritic, parse_arguments
 

dag_ppo_bihyb_train.py

@@ -11,10 +11,10 @@
 from torch.multiprocessing import Pool, cpu_count
 from copy import deepcopy
 
-from dag_ppo_bihyb_model import ActorNet, CriticNet, GraphEncoder
-from utils import print_args
-from tfboard_helper import TensorboardUtil
-from dag_graph import DAGraph
+from src.dag_ppo_bihyb_model import ActorNet, CriticNet, GraphEncoder
+from utils.utils import print_args
+from utils.tfboard_helper import TensorboardUtil
+from utils.dag_graph import DAGraph
 from dag_data.dag_generator import load_tpch_tuples
 from dag_ppo_bihyb_eval import evaluate
 
@@ -442,7 +442,7 @@ def parse_arguments():
     args = parser.parse_args()
 
     if args.config:
-        with open(args.config) as f:
+        with open('config/' + args.config) as f:
             cfg_dict = yaml.load(f)
             for key, val in cfg_dict.items():
                 assert hasattr(args, key), f'Unknown config key: {key}'

dag_ppo_single_eval.py

@@ -158,7 +158,7 @@ def evaluate(policy_net, dag_graph, eval_graphs, max_steps=10, search_size=10, m
     import random
     from torch.multiprocessing import Pool, cpu_count
 
-    from dag_graph import DAGraph
+    from utils.dag_graph import DAGraph
     from dag_data.dag_generator import load_tpch_tuples
     from dag_ppo_single_train import ActorCritic, parse_arguments