mirror of
https://github.com/NicolasBohn/NexQuant.git
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you-n-g
5d5084b87e
* copy init version * feat: new-york-city-taxi-fare-prediction_template * add move to linear model * Add more details about docker * auto lint * auto lint with new black
131 lines
4.4 KiB
Python
131 lines
4.4 KiB
Python
from abc import abstractmethod
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from pathlib import Path
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from typing import Tuple
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from jinja2 import Environment, StrictUndefined
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from rdagent.core.experiment import Experiment
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from rdagent.core.prompts import Prompts
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from rdagent.core.proposal import (
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Hypothesis,
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Hypothesis2Experiment,
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HypothesisGen,
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Scenario,
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Trace,
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)
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from rdagent.oai.llm_utils import APIBackend
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prompt_dict = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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class LLMHypothesisGen(HypothesisGen):
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def __init__(self, scen: Scenario):
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super().__init__(scen)
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# The following methods are scenario related so they should be implemented in the subclass
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@abstractmethod
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def prepare_context(self, trace: Trace) -> Tuple[dict, bool]: ...
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@abstractmethod
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def convert_response(self, response: str) -> Hypothesis: ...
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def gen(self, trace: Trace) -> Hypothesis:
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context_dict, json_flag = self.prepare_context(trace)
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system_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["hypothesis_gen"]["system_prompt"])
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.render(
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targets=self.targets,
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scenario=self.scen.get_scenario_all_desc(filtered_tag="hypothesis_and_experiment"),
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hypothesis_output_format=context_dict["hypothesis_output_format"],
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hypothesis_specification=context_dict["hypothesis_specification"],
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)
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)
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user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["hypothesis_gen"]["user_prompt"])
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.render(
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targets=self.targets,
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hypothesis_and_feedback=context_dict["hypothesis_and_feedback"],
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RAG=context_dict["RAG"],
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)
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)
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resp = APIBackend().build_messages_and_create_chat_completion(user_prompt, system_prompt, json_mode=json_flag)
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hypothesis = self.convert_response(resp)
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return hypothesis
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class FactorHypothesisGen(LLMHypothesisGen):
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def __init__(self, scen: Scenario):
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super().__init__(scen)
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self.targets = "factors"
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class ModelHypothesisGen(LLMHypothesisGen):
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def __init__(self, scen: Scenario):
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super().__init__(scen)
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self.targets = "model tuning"
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class FactorAndModelHypothesisGen(LLMHypothesisGen):
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def __init__(self, scen: Scenario):
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super().__init__(scen)
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self.targets = "feature engineering and model building"
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class LLMHypothesis2Experiment(Hypothesis2Experiment[Experiment]):
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@abstractmethod
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def prepare_context(self, hypothesis: Hypothesis, trace: Trace) -> Tuple[dict, bool]: ...
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@abstractmethod
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def convert_response(self, response: str, trace: Trace) -> Experiment: ...
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def convert(self, hypothesis: Hypothesis, trace: Trace) -> Experiment:
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context, json_flag = self.prepare_context(hypothesis, trace)
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system_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["hypothesis2experiment"]["system_prompt"])
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.render(
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targets=self.targets,
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scenario=trace.scen.get_scenario_all_desc(filtered_tag="hypothesis_and_experiment"),
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experiment_output_format=context["experiment_output_format"],
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)
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)
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user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["hypothesis2experiment"]["user_prompt"])
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.render(
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targets=self.targets,
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target_hypothesis=context["target_hypothesis"],
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hypothesis_and_feedback=context["hypothesis_and_feedback"],
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target_list=context["target_list"],
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RAG=context["RAG"],
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)
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)
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resp = APIBackend().build_messages_and_create_chat_completion(user_prompt, system_prompt, json_mode=json_flag)
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return self.convert_response(resp, trace)
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class FactorHypothesis2Experiment(LLMHypothesis2Experiment):
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def __init__(self):
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super().__init__()
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self.targets = "factors"
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class ModelHypothesis2Experiment(LLMHypothesis2Experiment):
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def __init__(self):
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super().__init__()
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self.targets = "model tuning"
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class FactorAndModelHypothesis2Experiment(LLMHypothesis2Experiment):
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def __init__(self):
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super().__init__()
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self.targets = "feature engineering and model building"
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