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/components/coder/model_coder/benchmark/eval.py

@@ -1,7 +1,7 @@
 # TODO: inherent from the benchmark base class
 import torch
 
-from rdagent.components.coder.model_coder.model import ModelImplementation
+from rdagent.components.coder.model_coder.model import ModelFBWorkspace
 
 
 def get_data_conf(init_val):
@@ -32,7 +32,7 @@ class ModelImpValEval:
     For each hidden output, we can calculate a correlation. The average correlation will be the metrics.
     """
 
-    def evaluate(self, gt: ModelImplementation, gen: ModelImplementation):
+    def evaluate(self, gt: ModelFBWorkspace, gen: ModelFBWorkspace):
         round_n = 10
 
         eval_pairs: list[tuple] = []