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/factor_coder/CoSTEER/evaluators.py

@@ -161,7 +161,7 @@ class FactorOutputFormatEvaluator(FactorEvaluator):
             )
         buffer = io.StringIO()
         gen_df.info(buf=buffer)
-        gen_df_info_str = buffer.getvalue()
+        gen_df_info_str = f"The use is currently working on a feature related task.\nThe output dataframe info is:\n{buffer.getvalue()}"
         system_prompt = (
             Environment(undefined=StrictUndefined)
             .from_string(
@@ -378,6 +378,7 @@ class FactorValueEvaluator(FactorEvaluator):
         self,
         implementation: Workspace,
         gt_implementation: Workspace,
+        version: int = 1,  # 1 for qlib factors and 2 for kaggle factors
         **kwargs,
     ) -> Tuple:
         conclusions = []
@@ -389,18 +390,21 @@ class FactorValueEvaluator(FactorEvaluator):
         equal_value_ratio_result = 0
         high_correlation_result = False
 
-        # Check if both dataframe has only one columns
-        feedback_str, _ = FactorSingleColumnEvaluator(self.scen).evaluate(implementation, gt_implementation)
-        conclusions.append(feedback_str)
+        # Check if both dataframe has only one columns Mute this since factor task might generate more than one columns now
+        if version == 1:
+            feedback_str, _ = FactorSingleColumnEvaluator(self.scen).evaluate(implementation, gt_implementation)
+            conclusions.append(feedback_str)
 
         # Check if the index of the dataframe is ("datetime", "instrument")
         feedback_str, _ = FactorOutputFormatEvaluator(self.scen).evaluate(implementation, gt_implementation)
         conclusions.append(feedback_str)
-
-        feedback_str, daily_check_result = FactorDatetimeDailyEvaluator(self.scen).evaluate(
-            implementation, gt_implementation
-        )
-        conclusions.append(feedback_str)
+        if version == 1:
+            feedback_str, daily_check_result = FactorDatetimeDailyEvaluator(self.scen).evaluate(
+                implementation, gt_implementation
+            )
+            conclusions.append(feedback_str)
+        else:
+            daily_check_result = None
 
         # Check if both dataframe have the same rows count
         if gt_implementation is not None:
@@ -627,7 +631,9 @@ class FactorEvaluatorForCoder(FactorEvaluator):
                 (
                     factor_feedback.factor_value_feedback,
                     decision_from_value_check,
-                ) = self.value_evaluator.evaluate(implementation=implementation, gt_implementation=gt_implementation)
+                ) = self.value_evaluator.evaluate(
+                    implementation=implementation, gt_implementation=gt_implementation, version=target_task.version
+                )
 
             factor_feedback.final_decision_based_on_gt = gt_implementation is not None
 
@@ -647,7 +653,7 @@ class FactorEvaluatorForCoder(FactorEvaluator):
                     target_task=target_task,
                     implementation=implementation,
                     execution_feedback=factor_feedback.execution_feedback,
-                    value_feedback=factor_feedback.factor_value_feedback,
+                    factor_value_feedback=factor_feedback.factor_value_feedback,
                     gt_implementation=gt_implementation,
                 )
                 (