Several update on the repo (see desc) (#76)

* ignore result csv file

* fix app scripts

* rename taskgenerator to developer and generate to develop

* fix a config bug in coder

* fix a small bug in factor coder evaluators

* remove a single logger in factor coder evaluators

* fix a small bug in model coder main.py

* rename Implementation to Workspace

* move the prepare the inject_code into FBWorkspace to align all the behavior

* fix a small bug in model feedback

* remove debug lines for multi processing and simplify evaluators multi proc

* add a copy function to workspace to freeze the workspace && add config prefix to speed up debugging

* make hypothesisgen a abc class

* use Qlib***Experiment

* fix a small bug

* rename Imp to Ws

* rename sub_implementations to sub_workspace_list

* fix a bug in feedback not presented as content in prompts

* move proposal pys to proposal folder

* reformat the folder

* align factor and model qlib workspace and use template to handle the workspace

* add a filter to evoagent to filter out false evo

* align multi_proc_n into RDAGENT seeting

* handle when runner gets empty experiment

* fix logger merge remaining problems

* fix black and isort automatically

rdagent/app/model_implementation/eval.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 
 from rdagent.components.coder.model_coder.CoSTEER import ModelCoSTEER
-from rdagent.components.loader.task_loader import ModelImpLoader, ModelTaskLoaderJson
+from rdagent.components.loader.task_loader import ModelTaskLoaderJson, ModelWsLoader
 from rdagent.scenarios.qlib.experiment.model_experiment import (
     QlibModelExperiment,
     QlibModelScenario,
@@ -23,17 +23,17 @@ model_experiment = QlibModelExperiment(sub_tasks=task_l)
 # mtg = ModelCodeWriter(scen=QlibModelScenario())
 mtg = ModelCoSTEER(scen=QlibModelScenario())
 
-model_experiment = mtg.generate(model_experiment)
+model_experiment = mtg.develop(model_experiment)
 
 # TODO: Align it with the benchmark framework after @wenjun's refine the evaluation part.
 # Currently, we just handcraft a workflow for fast evaluation.
 
-mil = ModelImpLoader(bench_folder / "gt_code")
+mil = ModelWsLoader(bench_folder / "gt_code")
 
 mie = ModelImpValEval()
 # Evaluation:
 eval_l = []
-for impl in model_experiment.sub_implementations:
+for impl in model_experiment.sub_workspace_list:
     print(impl.target_task)
     gt_impl = mil.load(impl.target_task)
     eval_l.append(mie.evaluate(gt_impl, impl))