Align factor coder into new framework (#47)

* use CoSTEER as component name

* rename factorimplementation to avoid confusion

* rename modelimplementation

* align benchmark and evolving evaluators

* add scenario to evaluator init function

* rename all factorimplementationknowledge in CoSTEER

* remove all scenario related information in component

* remove useless code

---------

Co-authored-by: xuyang1 <xuyang1@microsoft.com>

rdagent/components/coder/model_coder/one_shot/__init__.py

@@ -0,0 +1,45 @@
+import re
+from pathlib import Path
+
+from jinja2 import Environment, StrictUndefined
+
+from rdagent.components.coder.model_coder.model import (
+    ModelExperiment,
+    ModelImplementation,
+)
+from rdagent.core.prompts import Prompts
+from rdagent.core.task_generator import TaskGenerator
+from rdagent.oai.llm_utils import APIBackend
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+
+class ModelCodeWriter(TaskGenerator[ModelExperiment]):
+    def generate(self, exp: ModelExperiment) -> ModelExperiment:
+        mti_l = []
+        for t in exp.sub_tasks:
+            mti = ModelImplementation(t)
+            mti.prepare()
+            pr = Prompts(file_path=DIRNAME / "prompt.yaml")
+
+            user_prompt_tpl = Environment(undefined=StrictUndefined).from_string(pr["code_implement_user"])
+            sys_prompt_tpl = Environment(undefined=StrictUndefined).from_string(pr["code_implement_sys"])
+
+            user_prompt = user_prompt_tpl.render(
+                name=t.name,
+                description=t.description,
+                formulation=t.formulation,
+                variables=t.variables,
+                execute_desc=mti.execute_desc(),
+            )
+            system_prompt = sys_prompt_tpl.render()
+
+            resp = APIBackend().build_messages_and_create_chat_completion(user_prompt, system_prompt)
+
+            # Extract the code part from the response
+            match = re.search(r".*```[Pp]ython\n(.*)\n```.*", resp, re.DOTALL)
+            code = match.group(1)
+            mti.inject_code(**{"model.py": code})
+            mti_l.append(mti)
+        exp.sub_implementations = mti_l
+        return exp

rdagent/components/coder/model_coder/one_shot/prompt.yaml

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+
+
+code_implement_sys: -|
+  You are an assistant whose job is to answer user's question."
+code_implement_user: -|
+  With the following given information, write a python code using pytorch and torch_geometric to implement the model.
+  This model is in the graph learning field, only have one layer.
+  The input will be node_feature [num_nodes, dim_feature] and edge_index [2, num_edges]  (It would be the input of the forward model)
+  There is not edge attribute or edge weight as input. The model should detect the node_feature and edge_index shape, if there is Linear transformation layer in the model, the input and output shape should be consistent. The in_channels is the dimension of the node features.
+  Implement the model forward function based on the following information:model formula information.
+  1. model name:{{name}}
+  2. model description:{{description}}
+  3. model formulation:{{formulation}}
+  4. model variables:{{variables}}.
+  You must complete the forward function as far as you can do.
+  \# Execution
+  Your implemented code will be exectued in the follow way
+  {{execute_desc}}