first version of model runner and model feedback (#70)

* Implemented model.py

- Need to run within the RDAgent folder (relevant path)
- Each time copy a template & insert code & run qlib & store result back to experiment

* Create model.py

* Create conf.yaml

This is the sample conf.yaml to be copied each time.

This has gone several times of iteration and is now working for both tabular and Time-Series data.

* Create read_exp.py

This is to read the results within Qlib

* Create ReadMe.md

* Update model.py

* Create test_model.py

A testing file that separates model code generation and running&feedback section.

* move the template folder

* help xisen finish the model runner

* help xisen fix improve model feedback generation

* delete debug file

* rename readme.md

---------

Co-authored-by: Xisen Wang <118058822+Xisen-Wang@users.noreply.github.com>

rdagent/components/coder/model_coder/CoSTEER/evaluators.py

@@ -241,12 +241,12 @@ class ModelCoderEvaluator(Evaluator):
             )
         assert isinstance(target_task, ModelTask)
 
-        batch_size, num_features, num_timesteps = (
-            random.randint(6, 10),
-            random.randint(6, 10),
-            random.randint(6, 10),
-        )
-        input_value, param_init_value = random.random(), random.random()
+        # NOTE: Use fixed input to test the model to avoid randomness
+        batch_size = 8
+        num_features = 30
+        num_timesteps = 40
+        input_value = 0.4
+        param_init_value = 0.6
 
         assert isinstance(implementation, ModelImplementation)
         model_execution_feedback, gen_tensor = implementation.execute(

rdagent/components/coder/model_coder/CoSTEER/evolving_strategy.py

@@ -86,13 +86,12 @@ class ModelCoderEvolvingStrategy(EvolvingStrategy):
                     queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge_to_render[1:]
 
             code = json.loads(
-                APIBackend(use_chat_cache=True).build_messages_and_create_chat_completion(
+                APIBackend(use_chat_cache=False).build_messages_and_create_chat_completion(
                     user_prompt=user_prompt,
                     system_prompt=system_prompt,
                     json_mode=True,
                 ),
             )["code"]
-            # ast.parse(code)
             model_implementation = ModelImplementation(
                 target_task,
             )