ddm_test_validate and plot notebooks

.gitignore

@@ -81,4 +81,7 @@ mlruns/
 pytest-coverage.txt
 
 # saved models
-saved_models/
+saved_models/
+
+# sims
+sim/

conf/data/moab_5Kepi.yaml

@@ -1,13 +1,23 @@
 data:
-  path: csv_data/moab_sim_5KepiPosVelMinStates.csv
+  path: csv_data/moab_sim_5Kepi.csv #PosVelMinStates.csv
   full_or_relative: relative
-  inputs: state
-  outputs: state
+  inputs: [
+      "state_ball_x",
+      "state_ball_y",
+      "state_ball_vel_x",
+      "state_ball_vel_y",
+    ]
+  outputs: [
+      "state_ball_x",
+      "state_ball_y",
+      "state_ball_vel_x",
+      "state_ball_vel_y",
+    ]
   augmented_cols: ["action_input_roll","action_input_pitch"]
   iteration_order: -1
   episode_col: episode
   iteration_col: iteration
   max_rows: 230000 # 251000 used in LNN to generate 232432 samples of which 200K is trained
   scale_data: True
-  diff_state: True
+  diff_state: False
   test_perc: 0.25

ddm_test_validate.py

@@ -14,11 +14,14 @@
 logger = logging.getLogger("datamodeler")
 
 import hydra
-from omegaconf import DictConfig
+from omegaconf import DictConfig, ListConfig
 
 from all_models import available_models
 
 ## Add a local simulator in a `sim` folder to validate data-driven model
+# Commented example import of sim model from the sim folder can be similar to mba example
+# from sim.moab.moab_main import SimulatorSession,  env_setup
+# from sim.moab.policies import coast, random_policy, small_perturbations
 ## Example: Quanser from a Microsoft Bonsai
 """
 ├───ddm_test_validate.py
@@ -54,7 +57,7 @@ def __init__(
         self.action_keys = actions
         self.sim_orig = (
             sim_orig()
-        )  # include simulator function if comparing to simulator
+        )  # include simulator instance if comparing to simulator
         self.diff_state = diff_state
         if log_file == "enable":
             current_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
@@ -109,6 +112,7 @@ def episode_step(self, action: Dict[str, int]):
             list(self.config.values()),
             list(action.values()),
         ]
+        print(input_list)
 
         input_array = [item for subl in input_list for item in subl]
         X = np.array(input_array).reshape(1, -1)
@@ -267,6 +271,15 @@ def main(cfg: DictConfig):
     output_cols = cfg["data"]["outputs"]
     augmented_cols = cfg["data"]["augmented_cols"]
 
+    if type(input_cols) == ListConfig:
+        input_cols = list(input_cols)
+    if type(output_cols) == ListConfig:
+        output_cols = list(output_cols)
+    if type(augmented_cols) == ListConfig:
+        augmented_cols = list(augmented_cols)
+    print(type(input_cols))
+    print(type(augmented_cols))
+
     input_cols = input_cols + augmented_cols
 
     ddModel = available_models[model_name]
@@ -284,9 +297,11 @@ def main(cfg: DictConfig):
         model.load_model(filename=save_path, scale_data=scale_data)
 
     # Grab standardized way to interact with sim API
-    sim = Simulator(model, states, actions, configs, logflag, diff_state)
+    sim = Simulator(
+        model, states, actions, configs, logflag, diff_state
+    )  # , SimulatorSession)
 
-    test_sim_model(1, 250, logflag, sim)
+    test_sim_model(10, 250, logflag, sim)
 
     return sim