mirror of
https://github.com/NicolasBohn/NexQuant.git
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68d47e1f1f
* Init todo * Evaluation & dataset * Generate new data * dataset generation * add the result * Analysis * Factor update * Updates * Reformat analysis.py * CI fix * Revised Preprocessing & Supported Random Forest * Revised to support three models with feature * Further revised prompts * Slight Revision * docs: update contributors (#230) * Revised to support three models with feature * Further revised prompts * Slight Revision * feat: kaggle model and feature (#238) * update first version code * make hypothesis_gen and experiment_builder fit for both feature and model * feat: continue kaggle feature and model coder (#239) * use qlib docker to run qlib models * feature coder ready * model coder ready * fix CI * finish the first round of runner (#240) * Optimized the factor scenario and added the front-end. * fix a small bug * fix a typo * update the kaggle scenario * delete model_template folder * use experiment to run data preprocess script * add source data to scenarios * minor fix * minor bug fix * train.py debug * fixed a bug in train.py and added some TODOs * For Debugging * fix two small bugs in based_exp * fix some bugs * update preprocess * fix a bug in preprocess * fix a bug in train.py * reformat * Follow-up * fix a bug in train.py * fix a bug in workspace * fix a bug in feature duplication * fix a bug in feedback * fix a bug in preprocessed data * fix a bug om feature engineering * fix a ci error * Debugged & Connected * Fixed error on feedback & added other fixes * fix CI errors * fix a CI bug * fix: fix_dotenv_error (#257) * fix_dotenv_error * format with isort * Update rdagent/app/cli.py --------- Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * chore(main): release 0.2.1 (#249) Release-As: 0.2.1 * init a scenario for kaggle feature engineering * delete error codes * Delete rdagent/app/kaggle_feature/conf.py --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: Taozhi Wang <taozhi.mark.wang@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: cyncyw <47289405+taozhiwang@users.noreply.github.com> Co-authored-by: Xisen-Wang <xisen_application@163.com> Co-authored-by: Haotian Chen <113661982+Hytn@users.noreply.github.com> Co-authored-by: WinstonLiye <1957922024@qq.com> Co-authored-by: WinstonLiyt <104308117+WinstonLiyt@users.noreply.github.com> Co-authored-by: Linlang <30293408+SunsetWolf@users.noreply.github.com>
197 lines
6.3 KiB
Python
197 lines
6.3 KiB
Python
from __future__ import annotations
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import shutil
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import uuid
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from abc import ABC, abstractmethod
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from copy import deepcopy
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from pathlib import Path
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from typing import Any, Generic, Sequence, TypeVar
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from rdagent.core.conf import RD_AGENT_SETTINGS
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"""
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This file contains the all the class about organizing the task in RD-Agent.
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"""
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class Task(ABC):
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def __init__(self, version: int = 1) -> None:
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"""
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The version of the task, default is 1
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Because qlib tasks execution and kaggle tasks execution are different, we need to distinguish them.
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TODO: We may align them in the future.
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"""
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self.version = version
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@abstractmethod
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def get_task_information(self) -> str:
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"""
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Get the task information string to build the unique key
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"""
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ASpecificTask = TypeVar("ASpecificTask", bound=Task)
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class Workspace(ABC, Generic[ASpecificTask]):
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"""
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A workspace is a place to store the task implementation. It evolves as the developer implements the task.
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To get a snapshot of the workspace, make sure call `copy` to get a copy of the workspace.
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"""
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def __init__(self, target_task: ASpecificTask | None = None) -> None:
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self.target_task: ASpecificTask | None = target_task
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@abstractmethod
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def execute(self, *args: Any, **kwargs: Any) -> object | None:
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error_message = "execute method is not implemented."
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raise NotImplementedError(error_message)
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@abstractmethod
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def copy(self) -> Workspace:
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error_message = "copy method is not implemented."
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raise NotImplementedError(error_message)
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ASpecificWS = TypeVar("ASpecificWS", bound=Workspace)
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class WsLoader(ABC, Generic[ASpecificTask, ASpecificWS]):
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@abstractmethod
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def load(self, task: ASpecificTask) -> ASpecificWS:
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error_message = "load method is not implemented."
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raise NotImplementedError(error_message)
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class FBWorkspace(Workspace):
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"""
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File-based task workspace
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The implemented task will be a folder which contains related elements.
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- Data
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- Code Workspace
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- Output
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- After execution, it will generate the final output as file.
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A typical way to run the pipeline of FBWorkspace will be:
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(We didn't add it as a method due to that we may pass arguments into
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`prepare` or `execute` based on our requirements.)
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.. code-block:: python
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def run_pipeline(self, **files: str):
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self.prepare()
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self.inject_code(**files)
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self.execute()
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"""
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, **kwargs)
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self.code_dict: dict[str, Any] = {}
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self.code_dict = (
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{}
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) # The code injected into the folder, store them in the variable to reproduce the former result
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self.workspace_path: Path = RD_AGENT_SETTINGS.workspace_path / uuid.uuid4().hex
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@property
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def code(self) -> str:
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code_string = ""
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for file_name, code in self.code_dict.items():
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code_string += f"File: {file_name}\n{code}\n"
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return code_string
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def prepare(self) -> None:
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"""
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Prepare the workspace except the injected code
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- Data
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- Documentation
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typical usage of `*args, **kwargs`:
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Different methods shares the same data. The data are passed by the arguments.
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"""
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self.workspace_path.mkdir(parents=True, exist_ok=True)
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def inject_code(self, **files: str) -> None:
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"""
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Inject the code into the folder.
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{
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<file name>: <code>
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}
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"""
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self.prepare()
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for k, v in files.items():
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self.code_dict[k] = v
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target_file_path = self.workspace_path / k
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if not target_file_path.parent.exists():
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target_file_path.parent.mkdir(parents=True, exist_ok=True)
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with Path.open(self.workspace_path / k, "w") as f:
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f.write(v)
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def get_files(self) -> list[Path]:
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"""
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Get the environment description.
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To be general, we only return a list of filenames.
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How to summarize the environment is the responsibility of the Developer.
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"""
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return list(self.workspace_path.iterdir())
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def inject_code_from_folder(self, folder_path: Path) -> None:
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"""
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Load the workspace from the folder
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"""
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for file_path in folder_path.rglob("*"):
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if file_path.suffix in (".py", ".yaml", ".md"):
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relative_path = file_path.relative_to(folder_path)
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self.inject_code(**{str(relative_path): file_path.read_text()})
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def copy(self) -> FBWorkspace:
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"""
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copy the workspace from the original one
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"""
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return deepcopy(self)
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def clear(self) -> None:
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"""
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Clear the workspace
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"""
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shutil.rmtree(self.workspace_path)
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self.code_dict = {}
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def execute(self) -> object | None:
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"""
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Before each execution, make sure to prepare and inject code
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"""
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self.prepare()
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self.inject_code(**self.code_dict)
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return None
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ASpecificWSForExperiment = TypeVar("ASpecificWSForExperiment", bound=Workspace)
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ASpecificWSForSubTasks = TypeVar("ASpecificWSForSubTasks", bound=Workspace)
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class Experiment(ABC, Generic[ASpecificTask, ASpecificWSForExperiment, ASpecificWSForSubTasks]):
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"""
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The experiment is a sequence of tasks and the implementations of the tasks after generated by the Developer.
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"""
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def __init__(self, sub_tasks: Sequence[ASpecificTask]) -> None:
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self.sub_tasks = sub_tasks
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self.sub_workspace_list: list[ASpecificWSForSubTasks | None] = [None] * len(self.sub_tasks)
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self.based_experiments: Sequence[ASpecificWSForExperiment] = []
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self.result: object = None # The result of the experiment, can be different types in different scenarios.
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self.experiment_workspace: ASpecificWSForExperiment | None = None
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ASpecificExp = TypeVar("ASpecificExp", bound=Experiment)
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TaskOrExperiment = TypeVar("TaskOrExperiment", Task, Experiment)
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class Loader(ABC, Generic[TaskOrExperiment]):
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@abstractmethod
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def load(self, *args: Any, **kwargs: Any) -> TaskOrExperiment:
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err_msg = "load method is not implemented."
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raise NotImplementedError(err_msg)
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