mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Xu Yang
22e1aa3330
* store code into FBImplementation * fix path related bugs * fix a bug * fix factor related small bugs * re-submit all model related code * new code to model coder * finish the model evolving code --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
from pathlib import Path
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from rdagent.components.coder.model_coder.CoSTEER import ModelCoSTEER
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from rdagent.components.loader.task_loader import ModelImpLoader, ModelTaskLoaderJson
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from rdagent.scenarios.qlib.experiment.model_experiment import (
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QlibModelExperiment,
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QlibModelScenario,
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)
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DIRNAME = Path(__file__).absolute().resolve().parent
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from rdagent.components.coder.model_coder.benchmark.eval import ModelImpValEval
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from rdagent.components.coder.model_coder.one_shot import ModelCodeWriter
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bench_folder = DIRNAME.parent.parent / "components" / "coder" / "model_coder" / "benchmark"
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mtl = ModelTaskLoaderJson(str(bench_folder / "model_dict.json"))
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task_l = mtl.load()
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task_l = [t for t in task_l if t.name == "A-DGN"] # FIXME: other models does not work well
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model_experiment = QlibModelExperiment(sub_tasks=task_l)
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# mtg = ModelCodeWriter(scen=QlibModelScenario())
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mtg = ModelCoSTEER(scen=QlibModelScenario())
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model_experiment = mtg.generate(model_experiment)
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# TODO: Align it with the benchmark framework after @wenjun's refine the evaluation part.
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# Currently, we just handcraft a workflow for fast evaluation.
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mil = ModelImpLoader(bench_folder / "gt_code")
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mie = ModelImpValEval()
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# Evaluation:
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eval_l = []
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for impl in model_experiment.sub_implementations:
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print(impl.target_task)
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gt_impl = mil.load(impl.target_task)
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eval_l.append(mie.evaluate(gt_impl, impl))
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print(eval_l)
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