feat: Kaggle loop update (Feature & Model) (#241)

* Init todo

* Evaluation & dataset

* Generate new data

* dataset generation

* add the result

* Analysis

* Factor update

* Updates

* Reformat analysis.py

* CI fix

* Revised Preprocessing & Supported Random Forest

* Revised to support three models with feature

* Further revised prompts

* Slight Revision

* docs: update contributors (#230)

* Revised to support three models with feature

* Further revised prompts

* Slight Revision

* feat: kaggle model and feature (#238)

* update first version code

* make hypothesis_gen and experiment_builder fit for both feature and model

* feat: continue kaggle feature and model coder (#239)

* use qlib docker to run qlib models

* feature coder ready

* model coder ready

* fix CI

* finish the first round of runner (#240)

* Optimized the factor scenario and added the front-end.

* fix a small bug

* fix a typo

* update the kaggle scenario

* delete model_template folder

* use experiment to run data preprocess script

* add source data to scenarios

* minor fix

* minor bug fix

* train.py debug

* fixed a bug in train.py and added some TODOs

* For Debugging

* fix two small bugs in based_exp

* fix some bugs

* update preprocess

* fix a bug in preprocess

* fix a bug in train.py

* reformat

* Follow-up

* fix a bug in train.py

* fix a bug in workspace

* fix a bug in feature duplication

* fix a bug in feedback

* fix a bug in preprocessed data

* fix a bug om feature engineering

* fix a ci error

* Debugged & Connected

* Fixed error on feedback & added other fixes

* fix CI errors

* fix a CI bug

* fix: fix_dotenv_error (#257)

* fix_dotenv_error

* format with isort

* Update rdagent/app/cli.py

---------

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* chore(main): release 0.2.1 (#249)

Release-As: 0.2.1

* init a scenario for kaggle feature engineering

* delete error codes

* Delete rdagent/app/kaggle_feature/conf.py

---------

Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: Taozhi Wang <taozhi.mark.wang@gmail.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
Co-authored-by: cyncyw <47289405+taozhiwang@users.noreply.github.com>
Co-authored-by: Xisen-Wang <xisen_application@163.com>
Co-authored-by: Haotian Chen <113661982+Hytn@users.noreply.github.com>
Co-authored-by: WinstonLiye <1957922024@qq.com>
Co-authored-by: WinstonLiyt <104308117+WinstonLiyt@users.noreply.github.com>
Co-authored-by: Linlang <30293408+SunsetWolf@users.noreply.github.com>

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

@@ -24,7 +24,7 @@ from rdagent.oai.llm_utils import APIBackend
 evaluate_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
 
 
-def shape_evaluator(prediction: torch.Tensor, target_shape: Tuple = None) -> Tuple[str, bool]:
+def shape_evaluator(prediction: torch.Tensor | np.ndarray, target_shape: Tuple = None) -> Tuple[str, bool]:
     if target_shape is None or prediction is None:
         return (
             "No output generated from the model. No shape evaluation conducted.",
@@ -279,12 +279,8 @@ class ModelCoderEvaluator(Evaluator):
         else:
             gt_tensor = None
 
-        if target_task.model_type == "XGBoost":
-            shape_feedback = "Not applicable for XGBoost models"
-            shape_decision = True
-        else:
-            shape_feedback, shape_decision = shape_evaluator(gen_tensor, (batch_size, 1))
-        value_feedback, value_decision = value_evaluator(gt_tensor, gen_tensor)
+        shape_feedback, shape_decision = shape_evaluator(gen_tensor, (batch_size, 1))
+        value_feedback, value_decision = value_evaluator(gen_tensor, gt_tensor)
         code_feedback, _ = ModelCodeEvaluator(scen=self.scen).evaluate(
             target_task=target_task,
             implementation=implementation,