feat: implement isolated model feature selection loop (#370)

* rename meta_tpl

* use a isolated coder to deal with model feature selection and refine the structure

* fix CI

* fix: fix some errors in scenario.py, proposal.py and runner.py and several complex competition scenarios(#365)

* fix several bugs in proposal and runner

* fix a bug in feedback-prize-english-language-learning

* fix some bugs and templates

* fix the bug in optiver and nlp problem

* delete unnecessary codes

* remove unnecessary codes

* complete forest and s4e8

* push

* feedback & s4e8 &  forest

* optiver finished

* s3e11 & s3e26

* s4e9 finished

* sf-crime finished

* the last one finished

---------

Co-authored-by: WinstonLiyt <104308117+WinstonLiyt@users.noreply.github.com>
Co-authored-by: WinstonLiyte <1957922024@qq.com>

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

@@ -21,6 +21,7 @@ from rdagent.core.evolving_framework import EvolvingStrategy
 from rdagent.core.prompts import Prompts
 from rdagent.core.utils import multiprocessing_wrapper
 from rdagent.oai.llm_utils import APIBackend
+from rdagent.scenarios.kaggle.experiment.kaggle_experiment import KG_MODEL_MAPPING
 
 coder_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
 
@@ -41,14 +42,8 @@ class ModelCoderEvolvingStrategy(EvolvingStrategy):
             current_code = ""
             sota_exp_code_dict = current_exp.based_experiments[-1].experiment_workspace.code_dict
             if target_task.version == 2:
-                model_file_mapping = {
-                    "XGBoost": "model/model_xgboost.py",
-                    "RandomForest": "model/model_randomforest.py",
-                    "LightGBM": "model/model_lightgbm.py",
-                    "NN": "model/model_nn.py",
-                }
-                if model_type in model_file_mapping:
-                    current_code = sota_exp_code_dict.get(model_file_mapping[model_type], None)
+                if model_type in KG_MODEL_MAPPING:
+                    current_code = sota_exp_code_dict.get(KG_MODEL_MAPPING[model_type], None)
                 elif "model.py" in sota_exp_code_dict:
                     current_code = sota_exp_code_dict["model.py"]
                 else:

rdagent/components/coder/model_coder/model_execute_template_v2.txt

@@ -4,7 +4,7 @@ import pickle
 import numpy as np
 import pandas as pd
 import torch
-from model import fit, predict, select
+from model import fit, predict
 
 train_X = pd.DataFrame(np.random.randn(8, 30), columns=[f"{i}" for i in range(30)])
 train_y = pd.Series(np.random.randint(0, 2, 8))

rdagent/components/proposal/prompts.yaml

@@ -12,7 +12,7 @@ hypothesis_gen:
     {{ hypothesis_output_format }}
 
   user_prompt: |-
-    {% if hypothesis_and_feedback|length == 0 %}    It is the first round of hypothesis generation. The user has no hypothesis on this scenario yet.
+    {% if hypothesis_and_feedback|length == 0 %}It is the first round of hypothesis generation. The user has no hypothesis on this scenario yet.
     {% else %}It is not the first round, the user has made several hypothesis on this scenario and did several evaluation on them.
     The former hypothesis and the corresponding feedbacks are as follows (focus on the last one & the new hypothesis that it provides and reasoning to see if you agree):
     {{ hypothesis_and_feedback }}