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Code Issues Packages Projects Releases Wiki Activity

Align factor coder into new framework (#47)

Browse Source 
* 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>
This commit is contained in:
Xu Yang
2024-07-05 17:42:00 +08:00
committed by GitHub
parent f61453fbb1
commit 1d9b4cd2ec
57 changed files with 974 additions and 1093 deletions
Download Patch File Download Diff File Expand all files Collapse all files
rdagent/app/CI/run.py
+18 -16
View File
@@ -13,6 +13,16 @@ from pathlib import Path
from typing import Any, Literal
import tree_sitter_python
from rich import print
from rich.panel import Panel
from rich.progress import Progress, SpinnerColumn, TimeElapsedColumn
from rich.prompt import Prompt
from rich.rule import Rule
from rich.syntax import Syntax
from rich.table import Table
from rich.text import Text
from tree_sitter import Language, Node, Parser
from rdagent.core.evaluation import Evaluator
from rdagent.core.evolving_agent import EvoAgent
from rdagent.core.evolving_framework import (
@@ -24,15 +34,6 @@ from rdagent.core.evolving_framework import (
)
from rdagent.core.prompts import Prompts
from rdagent.oai.llm_utils import APIBackend
from rich import print
from rich.panel import Panel
from rich.progress import Progress, SpinnerColumn, TimeElapsedColumn
from rich.prompt import Prompt
from rich.rule import Rule
from rich.syntax import Syntax
from rich.table import Table
from rich.text import Text
from tree_sitter import Language, Node, Parser
py_parser = Parser(Language(tree_sitter_python.language()))
CI_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
@@ -355,7 +356,6 @@ class RuffEvaluator(Evaluator):
class MypyEvaluator(Evaluator):
def __init__(self, command: str | None = None) -> None:
if command is None:
self.command = "mypy . --pretty --no-error-summary --show-column-numbers"
@@ -411,12 +411,10 @@ class MypyEvaluator(Evaluator):
class MultiEvaluator(Evaluator):
def __init__(self, *evaluators: Evaluator) -> None:
self.evaluators = evaluators
def evaluate(self, evo: Repo, **kwargs: dict) -> CIFeedback:
all_errors = defaultdict(list)
for evaluator in self.evaluators:
feedback: CIFeedback = evaluator.evaluate(evo, **kwargs)
@@ -438,7 +436,6 @@ class CIEvoStr(EvolvingStrategy):
knowledge_l: list[Knowledge] | None = None,
**kwargs: dict,
) -> Repo:
@dataclass
class CodeFixGroup:
start_line: int
@@ -633,12 +630,16 @@ class CIEvoStr(EvolvingStrategy):
for i in diff:
if i.startswith("+"):
table.add_row(
"", Text(str(diff_new_lineno), style="green bold"), Text(i, style="green"),
"",
Text(str(diff_new_lineno), style="green bold"),
Text(i, style="green"),
)
diff_new_lineno += 1
elif i.startswith("-"):
table.add_row(
Text(str(diff_original_lineno), style="red bold"), "", Text(i, style="red"),
Text(str(diff_original_lineno), style="red bold"),
"",
Text(i, style="red"),
)
diff_original_lineno += 1
elif i.startswith("?"):
@@ -691,13 +692,13 @@ class CIEvoStr(EvolvingStrategy):
return evo
class CIEvoAgent(EvoAgent):
def __init__(self, evolving_strategy: CIEvoStr) -> None:
super().__init__(max_loop=1, evolving_strategy=evolving_strategy)
self.evolving_trace = []
def multistep_evolve(self, evo: Repo, eva: Evaluator, **kwargs: Any) -> Repo:
evo = self.evolving_strategy.evolve(
evo=evo,
evolving_trace=self.evolving_trace,
@@ -707,6 +708,7 @@ class CIEvoAgent(EvoAgent):
return evo
DIR = None
while DIR is None or not DIR.exists():
DIR = Prompt.ask("Please input the [cyan]project directory[/cyan]")
rdagent/app/factor_extraction_and_implementation/factor_extract_and_implement.py
+2 -2
View File
@@ -2,7 +2,7 @@
from dotenv import load_dotenv
from rdagent.scenarios.qlib.factor_experiment_loader.pdf_loader import (
FactorImplementationExperimentLoaderFromPDFfiles,
FactorExperimentLoaderFromPDFfiles,
)
from rdagent.scenarios.qlib.factor_task_implementation import (
COSTEERFG_QUANT_FACTOR_IMPLEMENTATION,
@@ -12,7 +12,7 @@ assert load_dotenv()
def extract_factors_and_implement(report_file_path: str) -> None:
factor_tasks = FactorImplementationExperimentLoaderFromPDFfiles().load(report_file_path)
factor_tasks = FactorExperimentLoaderFromPDFfiles().load(report_file_path)
implementation_result = COSTEERFG_QUANT_FACTOR_IMPLEMENTATION().generate(factor_tasks)
# Qlib to run the implementation
return implementation_result
rdagent/app/model_extraction_and_implementation/model_extraction_and_implementation.py
+4 -6
View File
@@ -1,16 +1,14 @@
# %%
from dotenv import load_dotenv
from rdagent.components.task_implementation.model_implementation.one_shot import (
ModelCodeWriter,
)
from rdagent.components.task_implementation.model_implementation.task_loader import (
ModelImplementationExperimentLoaderFromPDFfiles,
from rdagent.components.coder.model_coder.one_shot import ModelCodeWriter
from rdagent.components.coder.model_coder.task_loader import (
ModelExperimentLoaderFromPDFfiles,
)
def extract_models_and_implement(report_file_path: str = "../test_doc") -> None:
factor_tasks = ModelImplementationExperimentLoaderFromPDFfiles().load(report_file_path)
factor_tasks = ModelExperimentLoaderFromPDFfiles().load(report_file_path)
implementation_result = ModelCodeWriter().generate(factor_tasks)
return implementation_result
rdagent/app/model_implementation/eval.py
+3 -7
View File
@@ -2,16 +2,12 @@ from pathlib import Path
DIRNAME = Path(__file__).absolute().resolve().parent
from rdagent.components.task_implementation.model_implementation.benchmark.eval import (
ModelImpValEval,
)
from rdagent.components.task_implementation.model_implementation.model import (
from rdagent.components.coder.model_coder.benchmark.eval import ModelImpValEval
from rdagent.components.coder.model_coder.model import (
ModelImpLoader,
ModelTaskLoaderJson,
)
from rdagent.components.task_implementation.model_implementation.one_shot import (
ModelCodeWriter,
)
from rdagent.components.coder.model_coder.one_shot import ModelCodeWriter
bench_folder = DIRNAME.parent.parent / "components" / "task_implementation" / "model_implementation" / "benchmark"
mtl = ModelTaskLoaderJson(str(bench_folder / "model_dict.json"))
rdagent/app/qlib_rd_loop/conf.py
+1 -1
View File
@@ -7,7 +7,7 @@ class PropSetting(BaseSettings):
scen: str = "rdagent.scenarios.qlib.experiment.factor_experiment.QlibFactorScenario"
hypothesis_gen: str = "rdagent.scenarios.qlib.factor_proposal.QlibFactorHypothesisGen"
hypothesis2experiment: str = "rdagent.scenarios.qlib.factor_proposal.QlibFactorHypothesis2Experiment"
qlib_factor_coder: str = "rdagent.components.task_implementation.factor_implementation.CoSTEER.CoSTEERFG"
qlib_factor_coder: str = "rdagent.scenarios.qlib.factor_task_implementation.QlibFactorCoSTEER"
qlib_factor_runner: str = "rdagent.scenarios.qlib.task_generator.data.QlibFactorRunner"
qlib_factor_summarizer: str = "rdagent.scenarios.qlib.task_generator.feedback.QlibFactorExperiment2Feedback"
rdagent/app/qlib_rd_loop/factor.py
+6 -2
View File
@@ -2,6 +2,10 @@
TODO: Factor Structure RD-Loop
"""
from dotenv import load_dotenv
load_dotenv(override=True)
# import_from
from rdagent.app.qlib_rd_loop.conf import PROP_SETTING
from rdagent.core.proposal import (
@@ -20,8 +24,8 @@ hypothesis_gen: HypothesisGen = import_class(PROP_SETTING.hypothesis_gen)(scen)
hypothesis2experiment: Hypothesis2Experiment = import_class(PROP_SETTING.hypothesis2experiment)()
qlib_factor_coder: TaskGenerator = import_class(PROP_SETTING.qlib_factor_coder)()
qlib_factor_runner: TaskGenerator = import_class(PROP_SETTING.qlib_factor_runner)()
qlib_factor_coder: TaskGenerator = import_class(PROP_SETTING.qlib_factor_coder)(scen)
qlib_factor_runner: TaskGenerator = import_class(PROP_SETTING.qlib_factor_runner)(scen)
qlib_factor_summarizer: Experiment2Feedback = import_class(PROP_SETTING.qlib_factor_summarizer)()
rdagent/app/qlib_rd_loop/model.py
+1
View File
@@ -5,6 +5,7 @@ TODO: move the following code to a new class: Model_RD_Agent
# import_from
from rdagent.app.model_proposal.conf import MODEL_PROP_SETTING
from rdagent.core.proposal import (
Experiment2Feedback,
Hypothesis2Experiment,
rdagent/components/benchmark/eval_method.py
+24 -28
View File
@@ -4,22 +4,18 @@ from typing import List, Tuple, Union
from tqdm import tqdm
from rdagent.components.task_implementation.factor_implementation.config import (
FACTOR_IMPLEMENT_SETTINGS,
)
from rdagent.components.task_implementation.factor_implementation.evolving.evaluators import (
FactorImplementationCorrelationEvaluator,
FactorImplementationEvaluator,
FactorImplementationIndexEvaluator,
FactorImplementationIndexFormatEvaluator,
FactorImplementationMissingValuesEvaluator,
FactorImplementationRowCountEvaluator,
FactorImplementationSingleColumnEvaluator,
FactorImplementationValuesEvaluator,
)
from rdagent.components.task_implementation.factor_implementation.factor import (
FileBasedFactorImplementation,
from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
from rdagent.components.coder.factor_coder.CoSTEER.evaluators import (
FactorCorrelationEvaluator,
FactorEqualValueCountEvaluator,
FactorEvaluator,
FactorIndexEvaluator,
FactorMissingValuesEvaluator,
FactorOutputFormatEvaluator,
FactorRowCountEvaluator,
FactorSingleColumnEvaluator,
)
from rdagent.components.coder.factor_coder.factor import FileBasedFactorImplementation
from rdagent.core.exception import ImplementRunException
from rdagent.core.experiment import Implementation, Task
from rdagent.core.task_generator import TaskGenerator
@@ -43,7 +39,7 @@ class BaseEval:
def __init__(
self,
evaluator_l: List[FactorImplementationEvaluator],
evaluator_l: List[FactorEvaluator],
test_cases: List[TestCase],
generate_method: TaskGenerator,
catch_eval_except: bool = True,
@@ -52,7 +48,7 @@ class BaseEval:
----------
test_cases : List[TestCase]
cases to be evaluated, ground truth are included in the test cases.
evaluator_l : List[FactorImplementationEvaluator]
evaluator_l : List[FactorEvaluator]
A list of evaluators to evaluate the generated code.
catch_eval_except : bool
If we want to debug the evaluators, we recommend to set the this parameter to True.
@@ -81,7 +77,7 @@ class BaseEval:
self,
case_gt: Implementation,
case_gen: Implementation,
) -> List[Union[Tuple[FactorImplementationEvaluator, object], Exception]]:
) -> List[Union[Tuple[FactorEvaluator, object], Exception]]:
"""Parameters
----------
case_gt : FactorImplementation
@@ -91,14 +87,14 @@ class BaseEval:
Returns
-------
List[Union[Tuple[FactorImplementationEvaluator, object],Exception]]
List[Union[Tuple[FactorEvaluator, object],Exception]]
for each item
If the evaluation run successfully, return the evaluate results. Otherwise, return the exception.
"""
eval_res = []
for ev in self.evaluator_l:
try:
eval_res.append((ev, ev.evaluate(case_gt, case_gen)))
eval_res.append((ev, ev.evaluate(implementation=case_gen, gt_implementation=case_gt)))
# if the corr ev is successfully evaluated and achieve the best performance, then break
except ImplementRunException as e:
return e
@@ -116,18 +112,18 @@ class FactorImplementEval(BaseEval):
self,
test_cases: TestCase,
method: TaskGenerator,
test_round: int = 10,
*args,
test_round: int = 10,
**kwargs,
):
online_evaluator_l = [
FactorImplementationSingleColumnEvaluator(),
FactorImplementationIndexFormatEvaluator(),
FactorImplementationRowCountEvaluator(),
FactorImplementationIndexEvaluator(),
FactorImplementationMissingValuesEvaluator(),
FactorImplementationValuesEvaluator(),
FactorImplementationCorrelationEvaluator(hard_check=False),
FactorSingleColumnEvaluator(),
FactorOutputFormatEvaluator(),
FactorRowCountEvaluator(),
FactorIndexEvaluator(),
FactorMissingValuesEvaluator(),
FactorEqualValueCountEvaluator(),
FactorCorrelationEvaluator(hard_check=False),
]
super().__init__(online_evaluator_l, test_cases, method, *args, **kwargs)
self.test_round = test_round
rdagent/components/task_implementation/factor_implementation/CoSTEER.py → rdagent/components/coder/factor_coder/CoSTEER/__init__.py
+27 -31
View File
@@ -1,40 +1,38 @@
import pickle
from pathlib import Path
from typing import List
from rdagent.components.task_implementation.factor_implementation.config import (
FACTOR_IMPLEMENT_SETTINGS,
from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
from rdagent.components.coder.factor_coder.CoSTEER.evaluators import (
FactorEvaluatorForCoder,
FactorMultiEvaluator,
)
from rdagent.components.task_implementation.factor_implementation.evolving.evaluators import (
FactorImplementationEvaluatorV1,
FactorImplementationsMultiEvaluator,
)
from rdagent.components.task_implementation.factor_implementation.evolving.evolvable_subjects import (
from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
FactorEvolvingItem,
)
from rdagent.components.task_implementation.factor_implementation.evolving.evolving_strategy import (
from rdagent.components.coder.factor_coder.CoSTEER.evolving_strategy import (
FactorEvolvingStrategyWithGraph,
)
from rdagent.components.task_implementation.factor_implementation.evolving.knowledge_management import (
FactorImplementationGraphKnowledgeBase,
FactorImplementationGraphRAGStrategy,
FactorImplementationKnowledgeBaseV1,
)
from rdagent.components.task_implementation.factor_implementation.factor import (
FactorExperiment,
from rdagent.components.coder.factor_coder.CoSTEER.knowledge_management import (
FactorGraphKnowledgeBase,
FactorGraphRAGStrategy,
FactorKnowledgeBaseV1,
)
from rdagent.components.coder.factor_coder.factor import FactorExperiment
from rdagent.core.evolving_agent import RAGEvoAgent
from rdagent.core.experiment import Experiment
from rdagent.core.scenario import Scenario
from rdagent.core.task_generator import TaskGenerator
class CoSTEERFG(TaskGenerator[FactorExperiment]):
class FactorCoSTEER(TaskGenerator[FactorExperiment]):
def __init__(
self,
*args,
with_knowledge: bool = True,
with_feedback: bool = True,
knowledge_self_gen: bool = True,
**kwargs,
) -> None:
super().__init__(*args, **kwargs)
self.max_loop = FACTOR_IMPLEMENT_SETTINGS.max_loop
self.knowledge_base_path = (
Path(FACTOR_IMPLEMENT_SETTINGS.knowledge_base_path)
@@ -49,30 +47,28 @@ class CoSTEERFG(TaskGenerator[FactorExperiment]):
self.with_knowledge = with_knowledge
self.with_feedback = with_feedback
self.knowledge_self_gen = knowledge_self_gen
self.evolving_strategy = FactorEvolvingStrategyWithGraph()
self.evolving_strategy = FactorEvolvingStrategyWithGraph(scen=self.scen)
# declare the factor evaluator
self.factor_evaluator = FactorImplementationsMultiEvaluator(FactorImplementationEvaluatorV1())
self.factor_evaluator = FactorMultiEvaluator(FactorEvaluatorForCoder(scen=self.scen), scen=self.scen)
self.evolving_version = 2
def load_or_init_knowledge_base(self, former_knowledge_base_path: Path = None, component_init_list: list = []):
if former_knowledge_base_path is not None and former_knowledge_base_path.exists():
factor_knowledge_base = pickle.load(open(former_knowledge_base_path, "rb"))
if self.evolving_version == 1 and not isinstance(
factor_knowledge_base, FactorImplementationKnowledgeBaseV1
):
if self.evolving_version == 1 and not isinstance(factor_knowledge_base, FactorKnowledgeBaseV1):
raise ValueError("The former knowledge base is not compatible with the current version")
elif self.evolving_version == 2 and not isinstance(
factor_knowledge_base,
FactorImplementationGraphKnowledgeBase,
FactorGraphKnowledgeBase,
):
raise ValueError("The former knowledge base is not compatible with the current version")
else:
factor_knowledge_base = (
FactorImplementationGraphKnowledgeBase(
FactorGraphKnowledgeBase(
init_component_list=component_init_list,
)
if self.evolving_version == 2
else FactorImplementationKnowledgeBaseV1()
else FactorKnowledgeBaseV1()
)
return factor_knowledge_base
@@ -83,15 +79,15 @@ class CoSTEERFG(TaskGenerator[FactorExperiment]):
component_init_list=[],
)
# init rag method
self.rag = FactorImplementationGraphRAGStrategy(factor_knowledge_base)
self.rag = FactorGraphRAGStrategy(factor_knowledge_base)
# init intermediate items
factor_implementations = FactorEvolvingItem(sub_tasks=exp.sub_tasks)
factor_experiment = FactorEvolvingItem(sub_tasks=exp.sub_tasks)
self.evolve_agent = RAGEvoAgent(max_loop=self.max_loop, evolving_strategy=self.evolving_strategy, rag=self.rag)
factor_implementations = self.evolve_agent.multistep_evolve(
factor_implementations,
factor_experiment = self.evolve_agent.multistep_evolve(
factor_experiment,
self.factor_evaluator,
with_knowledge=self.with_knowledge,
with_feedback=self.with_feedback,
@@ -103,4 +99,4 @@ class CoSTEERFG(TaskGenerator[FactorExperiment]):
pickle.dump(factor_knowledge_base, open(self.new_knowledge_base_path, "wb"))
self.knowledge_base = factor_knowledge_base
self.latest_factor_implementations = exp.sub_tasks
return factor_implementations
return factor_experiment
rdagent/components/coder/factor_coder/CoSTEER/evaluators.py
+610
View File
@@ -0,0 +1,610 @@
import io
import json
import re
from abc import abstractmethod
from pathlib import Path
from typing import List, Tuple
import pandas as pd
from jinja2 import Environment, StrictUndefined
from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
FactorEvolvingItem,
)
from rdagent.components.coder.factor_coder.factor import FactorTask
from rdagent.core.conf import RD_AGENT_SETTINGS
from rdagent.core.evaluation import Evaluator
from rdagent.core.evolving_framework import Feedback, QueriedKnowledge
from rdagent.core.experiment import Implementation, Task
from rdagent.core.log import RDAgentLog
from rdagent.core.prompts import Prompts
from rdagent.core.utils import multiprocessing_wrapper
from rdagent.oai.llm_utils import APIBackend
evaluate_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
class FactorEvaluator(Evaluator):
# TODO:
# I think we should have unified interface for all evaluates, for examples.
# So we should adjust the interface of other factors
@abstractmethod
def evaluate(
self,
target_task: Task,
implementation: Implementation,
gt_implementation: Implementation,
**kwargs,
) -> Tuple[str, object]:
"""You can get the dataframe by
.. code-block:: python
_, gen_df = implementation.execute()
_, gt_df = gt_implementation.execute()
Returns
-------
Tuple[str, object]
- str: the text-based description of the evaluation result
- object: a comparable metric (bool, integer, float ...) None for evaluator with only text-based result
"""
raise NotImplementedError("Please implement the `evaluator` method")
def _get_df(self, gt_implementation: Implementation, implementation: Implementation):
if gt_implementation is not None:
_, gt_df = gt_implementation.execute()
if isinstance(gt_df, pd.Series):
gt_df = gt_df.to_frame("gt_factor")
if isinstance(gt_df, pd.DataFrame):
gt_df = gt_df.sort_index()
else:
gt_df = None
_, gen_df = implementation.execute()
if isinstance(gen_df, pd.Series):
gen_df = gen_df.to_frame("source_factor")
if isinstance(gen_df, pd.DataFrame):
gen_df = gen_df.sort_index()
return gt_df, gen_df
def __str__(self) -> str:
return self.__class__.__name__
class FactorCodeEvaluator(FactorEvaluator):
def evaluate(
self,
target_task: FactorTask,
implementation: Implementation,
execution_feedback: str,
factor_value_feedback: str = "",
gt_implementation: Implementation = None,
**kwargs,
):
factor_information = target_task.get_factor_information()
code = implementation.code
system_prompt = (
Environment(undefined=StrictUndefined)
.from_string(evaluate_prompts["evaluator_code_feedback_v1_system"])
.render(scenario=self.scen.get_scenario_all_desc() if self.scen is not None else "No scenario description.")
)
execution_feedback_to_render = execution_feedback
for _ in range(10): # 10 times to split the content is enough
user_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
evaluate_prompts["evaluator_code_feedback_v1_user"],
)
.render(
factor_information=factor_information,
code=code,
execution_feedback=execution_feedback_to_render,
factor_value_feedback=factor_value_feedback,
gt_code=gt_implementation.code if gt_implementation else None,
)
)
if (
APIBackend().build_messages_and_calculate_token(
user_prompt=user_prompt,
system_prompt=system_prompt,
)
> RD_AGENT_SETTINGS.chat_token_limit
):
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
else:
break
critic_response = APIBackend().build_messages_and_create_chat_completion(
user_prompt=user_prompt,
system_prompt=system_prompt,
json_mode=False,
)
return critic_response, None
class FactorSingleColumnEvaluator(FactorEvaluator):
def evaluate(
self,
implementation: Implementation,
gt_implementation: Implementation,
) -> Tuple[str, object]:
_, gen_df = self._get_df(gt_implementation, implementation)
if len(gen_df.columns) == 1:
return "The source dataframe has only one column which is correct.", True
else:
return (
"The source dataframe has more than one column. Please check the implementation. We only evaluate the first column.",
False,
)
class FactorOutputFormatEvaluator(FactorEvaluator):
def evaluate(
self,
implementation: Implementation,
gt_implementation: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt_implementation, implementation)
if gen_df is None:
return (
"The source dataframe is None. Skip the evaluation of the output format.",
False,
)
buffer = io.StringIO()
gen_df.info(buf=buffer)
gen_df_info_str = buffer.getvalue()
system_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
evaluate_prompts["evaluator_output_format_system"],
)
.render(scenario=self.scen.get_scenario_all_desc() if self.scen is not None else "No scenario description.")
)
resp = APIBackend().build_messages_and_create_chat_completion(
user_prompt=gen_df_info_str, system_prompt=system_prompt, json_mode=True
)
resp_dict = json.loads(resp)
if isinstance(resp_dict["output_format_decision"], str) and resp_dict["output_format_decision"].lower() in (
"true",
"false",
):
resp_dict["output_format_decision"] = bool(resp_dict["output_format_decision"])
return (
resp_dict["output_format_feedback"],
resp_dict["output_format_decision"],
)
class FactorRowCountEvaluator(FactorEvaluator):
def evaluate(
self,
implementation: Implementation,
gt_implementation: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt_implementation, implementation)
if gen_df.shape[0] == gt_df.shape[0]:
return "Both dataframes have the same rows count.", True
else:
return (
f"The source dataframe and the ground truth dataframe have different rows count. The source dataframe has {gen_df.shape[0]} rows, while the ground truth dataframe has {gt_df.shape[0]} rows. Please check the implementation.",
False,
)
class FactorIndexEvaluator(FactorEvaluator):
def evaluate(
self,
implementation: Implementation,
gt_implementation: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt_implementation, implementation)
if gen_df.index.equals(gt_df.index):
return "Both dataframes have the same index.", True
else:
return (
"The source dataframe and the ground truth dataframe have different index. Please check the implementation.",
False,
)
class FactorMissingValuesEvaluator(FactorEvaluator):
def evaluate(
self,
implementation: Implementation,
gt_implementation: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt_implementation, implementation)
if gen_df.isna().sum().sum() == gt_df.isna().sum().sum():
return "Both dataframes have the same missing values.", True
else:
return (
f"The dataframes do not have the same missing values. The source dataframe has {gen_df.isna().sum().sum()} missing values, while the ground truth dataframe has {gt_df.isna().sum().sum()} missing values. Please check the implementation.",
False,
)
class FactorEqualValueCountEvaluator(FactorEvaluator):
def evaluate(
self,
implementation: Implementation,
gt_implementation: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt_implementation, implementation)
try:
close_values = gen_df.sub(gt_df).abs().lt(1e-6)
result_int = close_values.astype(int)
pos_num = result_int.sum().sum()
acc_rate = pos_num / close_values.size
except:
close_values = gen_df
if close_values.all().iloc[0]:
return (
"All values in the dataframes are equal within the tolerance of 1e-6.",
acc_rate,
)
else:
return (
"Some values differ by more than the tolerance of 1e-6. Check for rounding errors or differences in the calculation methods.",
acc_rate,
)
class FactorCorrelationEvaluator(FactorEvaluator):
def __init__(self, hard_check: bool, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.hard_check = hard_check
def evaluate(
self,
implementation: Implementation,
gt_implementation: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt_implementation, implementation)
concat_df = pd.concat([gen_df, gt_df], axis=1)
concat_df.columns = ["source", "gt"]
ic = concat_df.groupby("datetime").apply(lambda df: df["source"].corr(df["gt"])).dropna().mean()
ric = (
concat_df.groupby("datetime")
.apply(lambda df: df["source"].corr(df["gt"], method="spearman"))
.dropna()
.mean()
)
if self.hard_check:
if ic > 0.99 and ric > 0.99:
return (
f"The dataframes are highly correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}.",
True,
)
else:
return (
f"The dataframes are not sufficiently high correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}. Investigate the factors that might be causing the discrepancies and ensure that the logic of the factor calculation is consistent.",
False,
)
else:
return f"The ic is ({ic:.6f}) and the rankic is ({ric:.6f}).", ic
class FactorValueEvaluator(FactorEvaluator):
def evaluate(
self,
implementation: Implementation,
gt_implementation: Implementation,
**kwargs,
) -> Tuple:
conclusions = []
# Check if both dataframe has only one columns
feedback_str, _ = FactorSingleColumnEvaluator(self.scen).evaluate(implementation, gt_implementation)
conclusions.append(feedback_str)
# Check if the index of the dataframe is ("datetime", "instrument")
feedback_str, _ = FactorOutputFormatEvaluator(self.scen).evaluate(implementation, gt_implementation)
conclusions.append(feedback_str)
# Check if both dataframe have the same rows count
if gt_implementation is not None:
feedback_str, _ = FactorRowCountEvaluator(self.scen).evaluate(implementation, gt_implementation)
conclusions.append(feedback_str)
feedback_str, same_index_result = FactorIndexEvaluator(self.scen).evaluate(
implementation, gt_implementation
)
conclusions.append(feedback_str)
feedback_str, _ = FactorMissingValuesEvaluator(self.scen).evaluate(implementation, gt_implementation)
conclusions.append(feedback_str)
feedback_str, equal_value_ratio_result = FactorEqualValueCountEvaluator(self.scen).evaluate(
implementation, gt_implementation
)
conclusions.append(feedback_str)
if same_index_result:
feedback_str, high_correlation_result = FactorCorrelationEvaluator(
hard_check=True, scen=self.scen
).evaluate(implementation, gt_implementation)
else:
high_correlation_result = False
feedback_str = "The source dataframe and the ground truth dataframe have different index. Give up comparing the values and correlation because it's useless"
conclusions.append(feedback_str)
# Combine all conclusions into a single string
conclusion_str = "\n".join(conclusions)
same_value_or_high_correlation = (
((equal_value_ratio_result > 0.99) or high_correlation_result) if gt_implementation is not None else False
)
return conclusion_str, same_value_or_high_correlation
class FactorFinalDecisionEvaluator(Evaluator):
def evaluate(
self,
target_task: FactorTask,
execution_feedback: str,
value_feedback: str,
code_feedback: str,
**kwargs,
) -> Tuple:
system_prompt = (
Environment(undefined=StrictUndefined)
.from_string(evaluate_prompts["evaluator_final_decision_v1_system"])
.render(scenario=self.scen.get_scenario_all_desc() if self.scen is not None else "No scenario description.")
)
execution_feedback_to_render = execution_feedback
for _ in range(10): # 10 times to split the content is enough
user_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
evaluate_prompts["evaluator_final_decision_v1_user"],
)
.render(
factor_information=target_task.get_factor_information(),
execution_feedback=execution_feedback_to_render,
code_feedback=code_feedback,
factor_value_feedback=(
value_feedback
if value_feedback is not None
else "No Ground Truth Value provided, so no evaluation on value is performed."
),
)
)
if (
APIBackend().build_messages_and_calculate_token(
user_prompt=user_prompt,
system_prompt=system_prompt,
)
> RD_AGENT_SETTINGS.chat_token_limit
):
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
else:
break
final_evaluation_dict = json.loads(
APIBackend().build_messages_and_create_chat_completion(
user_prompt=user_prompt,
system_prompt=system_prompt,
json_mode=True,
),
)
if isinstance(final_evaluation_dict["final_decision"], str) and final_evaluation_dict[
"final_decision"
].lower() in ("true", "false"):
final_evaluation_dict["final_decision"] = bool(final_evaluation_dict["final_decision"])
return (
final_evaluation_dict["final_decision"],
final_evaluation_dict["final_feedback"],
)
class FactorSingleFeedback:
"""This class is a feedback to single implementation which is generated from an evaluator."""
def __init__(
self,
execution_feedback: str = None,
value_generated_flag: bool = False,
code_feedback: str = None,
factor_value_feedback: str = None,
final_decision: bool = None,
final_feedback: str = None,
final_decision_based_on_gt: bool = None,
) -> None:
self.execution_feedback = execution_feedback
self.value_generated_flag = value_generated_flag
self.code_feedback = code_feedback
self.factor_value_feedback = factor_value_feedback
self.final_decision = final_decision
self.final_feedback = final_feedback
self.final_decision_based_on_gt = final_decision_based_on_gt
def __str__(self) -> str:
return f"""------------------Factor Execution Feedback------------------
{self.execution_feedback}
------------------Factor Code Feedback------------------
{self.code_feedback}
------------------Factor Value Feedback------------------
{self.factor_value_feedback}
------------------Factor Final Feedback------------------
{self.final_feedback}
------------------Factor Final Decision------------------
This implementation is {'SUCCESS' if self.final_decision else 'FAIL'}.
"""
class FactorMultiFeedback(
Feedback,
List[FactorSingleFeedback],
):
"""Feedback contains a list, each element is the corresponding feedback for each factor implementation."""
class FactorEvaluatorForCoder(FactorEvaluator):
"""This class is the v1 version of evaluator for a single factor implementation.
It calls several evaluators in share modules to evaluate the factor implementation.
"""
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.value_evaluator = FactorValueEvaluator(self.scen)
self.code_evaluator = FactorCodeEvaluator(self.scen)
self.final_decision_evaluator = FactorFinalDecisionEvaluator(self.scen)
def evaluate(
self,
target_task: FactorTask,
implementation: Implementation,
gt_implementation: Implementation = None,
queried_knowledge: QueriedKnowledge = None,
**kwargs,
) -> FactorSingleFeedback:
if implementation is None:
return None
target_task_information = target_task.get_factor_information()
if (
queried_knowledge is not None
and target_task_information in queried_knowledge.success_task_to_knowledge_dict
):
return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
return FactorSingleFeedback(
execution_feedback="This task has failed too many times, skip implementation.",
value_generated_flag=False,
code_feedback="This task has failed too many times, skip code evaluation.",
factor_value_feedback="This task has failed too many times, skip value evaluation.",
final_decision=False,
final_feedback="This task has failed too many times, skip final decision evaluation.",
final_decision_based_on_gt=False,
)
else:
factor_feedback = FactorSingleFeedback()
# 1. Get factor execution feedback to generated implementation and remove the long list of numbers in execution feedback
(
execution_feedback,
gen_df,
) = implementation.execute()
execution_feedback = re.sub(r"(?<=\D)(,\s+-?\d+\.\d+){50,}(?=\D)", ", ", execution_feedback)
factor_feedback.execution_feedback = "\n".join(
[line for line in execution_feedback.split("\n") if "warning" not in line.lower()]
)
# 2. Get factor value feedback
if gen_df is None:
factor_feedback.factor_value_feedback = "No factor value generated, skip value evaluation."
factor_feedback.value_generated_flag = False
same_value_or_high_correlation = None
else:
factor_feedback.value_generated_flag = True
(
factor_feedback.factor_value_feedback,
same_value_or_high_correlation,
) = self.value_evaluator.evaluate(implementation=implementation, gt_implementation=gt_implementation)
factor_feedback.final_decision_based_on_gt = gt_implementation is not None
if same_value_or_high_correlation is not None and same_value_or_high_correlation is True:
# To avoid confusion, when same_value_or_high_correlation is True, we do not need code feedback
factor_feedback.code_feedback = "Final decision is True and there are no code critics."
factor_feedback.final_decision = same_value_or_high_correlation
factor_feedback.final_feedback = "Value evaluation passed, skip final decision evaluation."
else:
factor_feedback.code_feedback, _ = self.code_evaluator.evaluate(
target_task=target_task,
implementation=implementation,
execution_feedback=factor_feedback.execution_feedback,
value_feedback=factor_feedback.factor_value_feedback,
gt_implementation=gt_implementation,
)
(
factor_feedback.final_decision,
factor_feedback.final_feedback,
) = self.final_decision_evaluator.evaluate(
target_task=target_task,
execution_feedback=factor_feedback.execution_feedback,
value_feedback=factor_feedback.factor_value_feedback,
code_feedback=factor_feedback.code_feedback,
)
return factor_feedback
class FactorMultiEvaluator(Evaluator):
def __init__(self, single_evaluator, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.single_factor_implementation_evaluator = single_evaluator
def evaluate(
self,
evo: FactorEvolvingItem,
queried_knowledge: QueriedKnowledge = None,
**kwargs,
) -> FactorMultiFeedback:
multi_implementation_feedback = FactorMultiFeedback()
# for index in range(len(evo.sub_tasks)):
# corresponding_implementation = evo.sub_implementations[index]
# corresponding_gt_implementation = (
# evo.sub_gt_implementations[index] if evo.sub_gt_implementations is not None else None
# )
# multi_implementation_feedback.append(
# self.single_factor_implementation_evaluator.evaluate(
# target_task=evo.sub_tasks[index],
# implementation=corresponding_implementation,
# gt_implementation=corresponding_gt_implementation,
# queried_knowledge=queried_knowledge,
# )
# )
calls = []
for index in range(len(evo.sub_tasks)):
corresponding_implementation = evo.sub_implementations[index]
corresponding_gt_implementation = (
evo.sub_gt_implementations[index] if evo.sub_gt_implementations is not None else None
)
calls.append(
(
self.single_factor_implementation_evaluator.evaluate,
(
evo.sub_tasks[index],
corresponding_implementation,
corresponding_gt_implementation,
queried_knowledge,
),
),
)
multi_implementation_feedback = multiprocessing_wrapper(calls, n=FACTOR_IMPLEMENT_SETTINGS.evo_multi_proc_n)
final_decision = [
None if single_feedback is None else single_feedback.final_decision
for single_feedback in multi_implementation_feedback
]
RDAgentLog().info(f"Final decisions: {final_decision} True count: {final_decision.count(True)}")
return multi_implementation_feedback
# TODO:
def shorten_prompt(tpl: str, render_kwargs: dict, shorten_key: str, max_trail: int = 10) -> str:
"""When the prompt is too long. We have to shorten it.
But we should not truncate the prompt directly, so we should find the key we want to shorten and then shorten it.
"""
# TODO: this should replace most of code in
# - FactorFinalDecisionEvaluator.evaluate
# - FactorCodeEvaluator.evaluate
rdagent/components/task_implementation/factor_implementation/evolving/evolvable_subjects.py → rdagent/components/coder/factor_coder/CoSTEER/evolvable_subjects.py
+1 -1
View File
@@ -1,4 +1,4 @@
from rdagent.components.task_implementation.factor_implementation.factor import (
from rdagent.components.coder.factor_coder.factor import (
FactorExperiment,
FactorTask,
FileBasedFactorImplementation,
rdagent/components/task_implementation/factor_implementation/evolving/evolving_strategy.py → rdagent/components/coder/factor_coder/CoSTEER/evolving_strategy.py
+14 -16
View File
@@ -8,23 +8,19 @@ from typing import TYPE_CHECKING
from jinja2 import Environment, StrictUndefined
from rdagent.components.task_implementation.factor_implementation.config import (
FACTOR_IMPLEMENT_SETTINGS,
)
from rdagent.components.task_implementation.factor_implementation.evolving.evolvable_subjects import (
from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
FactorEvolvingItem,
)
from rdagent.components.task_implementation.factor_implementation.evolving.scheduler import (
from rdagent.components.coder.factor_coder.CoSTEER.scheduler import (
LLMSelect,
RandomSelect,
)
from rdagent.components.task_implementation.factor_implementation.factor import (
from rdagent.components.coder.factor_coder.factor import (
FactorTask,
FileBasedFactorImplementation,
)
from rdagent.components.task_implementation.factor_implementation.utils import (
get_data_folder_intro,
)
from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
from rdagent.core.conf import RD_AGENT_SETTINGS
from rdagent.core.evolving_framework import EvolvingStrategy, QueriedKnowledge
from rdagent.core.experiment import Implementation
@@ -33,9 +29,9 @@ from rdagent.core.utils import multiprocessing_wrapper
from rdagent.oai.llm_utils import APIBackend
if TYPE_CHECKING:
from rdagent.components.task_implementation.factor_implementation.evolving.knowledge_management import (
FactorImplementationQueriedKnowledge,
FactorImplementationQueriedKnowledgeV1,
from rdagent.components.coder.factor_coder.CoSTEER.knowledge_management import (
FactorQueriedKnowledge,
FactorQueriedKnowledgeV1,
)
implement_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
@@ -54,7 +50,7 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
self,
*,
evo: FactorEvolvingItem,
queried_knowledge: FactorImplementationQueriedKnowledge | None = None,
queried_knowledge: FactorQueriedKnowledge | None = None,
**kwargs,
) -> FactorEvolvingItem:
self.num_loop += 1
@@ -93,6 +89,7 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
implementation_factors_per_round,
new_evo,
queried_knowledge.former_traces,
self.scen,
)
result = multiprocessing_wrapper(
@@ -120,7 +117,7 @@ class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
def implement_one_factor(
self,
target_task: FactorTask,
queried_knowledge: FactorImplementationQueriedKnowledgeV1 = None,
queried_knowledge: FactorQueriedKnowledgeV1 = None,
) -> Implementation:
factor_information_str = target_task.get_factor_information()
@@ -197,7 +194,8 @@ class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
def __init__(self) -> None:
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.num_loop = 0
self.haveSelected = False
@@ -244,7 +242,7 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
implement_prompts["evolving_strategy_factor_implementation_v1_system"],
)
.render(
data_info=get_data_folder_intro(),
scenario=self.scen.get_scenario_all_desc(),
queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
)
)
rdagent/components/task_implementation/factor_implementation/evolving/knowledge_management.py → rdagent/components/coder/factor_coder/CoSTEER/knowledge_management.py
+27 -31
View File
@@ -10,19 +10,15 @@ from typing import Union
from jinja2 import Environment, StrictUndefined
from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
from rdagent.components.coder.factor_coder.CoSTEER.evaluators import (
FactorSingleFeedback,
)
from rdagent.components.coder.factor_coder.factor import FactorTask
from rdagent.components.knowledge_management.graph import (
UndirectedGraph,
UndirectedNode,
)
from rdagent.components.task_implementation.factor_implementation.config import (
FACTOR_IMPLEMENT_SETTINGS,
)
from rdagent.components.task_implementation.factor_implementation.evolving.evaluators import (
FactorImplementationSingleFeedback,
)
from rdagent.components.task_implementation.factor_implementation.evolving.evolving_strategy import (
FactorTask,
)
from rdagent.core.evolving_framework import (
EvolvableSubjects,
EvoStep,
@@ -40,12 +36,12 @@ from rdagent.oai.llm_utils import (
)
class FactorImplementationKnowledge(Knowledge):
class FactorKnowledge(Knowledge):
def __init__(
self,
target_task: FactorTask,
implementation: Implementation,
feedback: FactorImplementationSingleFeedback,
feedback: FactorSingleFeedback,
) -> None:
"""
Initialize a FactorKnowledge object. The FactorKnowledge object is used to store a factor implementation without the ground truth code and value.
@@ -68,15 +64,15 @@ class FactorImplementationKnowledge(Knowledge):
"""
class FactorImplementationQueriedKnowledge(QueriedKnowledge):
class FactorQueriedKnowledge(QueriedKnowledge):
def __init__(self, success_task_to_knowledge_dict: dict = {}, failed_task_info_set: set = set()) -> None:
self.success_task_to_knowledge_dict = success_task_to_knowledge_dict
self.failed_task_info_set = failed_task_info_set
class FactorImplementationKnowledgeBaseV1(KnowledgeBase):
class FactorKnowledgeBaseV1(KnowledgeBase):
def __init__(self) -> None:
self.implementation_trace: dict[str, FactorImplementationKnowledge] = dict()
self.implementation_trace: dict[str, FactorKnowledge] = dict()
self.success_task_info_set: set[str] = set()
self.task_to_embedding = dict()
@@ -88,15 +84,15 @@ class FactorImplementationKnowledgeBaseV1(KnowledgeBase):
raise NotImplementedError
class FactorImplementationQueriedKnowledgeV1(FactorImplementationQueriedKnowledge):
class FactorQueriedKnowledgeV1(FactorQueriedKnowledge):
def __init__(self) -> None:
self.working_task_to_former_failed_knowledge_dict = dict()
self.working_task_to_similar_successful_knowledge_dict = dict()
super().__init__()
class FactorImplementationRAGStrategyV1(RAGStrategy):
def __init__(self, knowledgebase: FactorImplementationKnowledgeBaseV1) -> None:
class FactorRAGStrategyV1(RAGStrategy):
def __init__(self, knowledgebase: FactorKnowledgeBaseV1) -> None:
super().__init__(knowledgebase)
self.current_generated_trace_count = 0
@@ -123,7 +119,7 @@ class FactorImplementationRAGStrategyV1(RAGStrategy):
single_feedback = feedback[task_index]
if single_feedback is None:
continue
single_knowledge = FactorImplementationKnowledge(
single_knowledge = FactorKnowledge(
target_task=target_task,
implementation=implementation,
feedback=single_feedback,
@@ -149,7 +145,7 @@ class FactorImplementationRAGStrategyV1(RAGStrategy):
v1_query_similar_success_limit = FACTOR_IMPLEMENT_SETTINGS.v1_query_similar_success_limit
fail_task_trial_limit = FACTOR_IMPLEMENT_SETTINGS.fail_task_trial_limit
queried_knowledge = FactorImplementationQueriedKnowledgeV1()
queried_knowledge = FactorQueriedKnowledgeV1()
for target_factor_task in evo.sub_tasks:
target_factor_task_information = target_factor_task.get_factor_information()
if target_factor_task_information in self.knowledgebase.success_task_info_set:
@@ -199,7 +195,7 @@ class FactorImplementationRAGStrategyV1(RAGStrategy):
return queried_knowledge
class FactorImplementationQueriedGraphKnowledge(FactorImplementationQueriedKnowledge):
class FactorQueriedGraphKnowledge(FactorQueriedKnowledge):
# Aggregation of knowledge
def __init__(
self,
@@ -214,8 +210,8 @@ class FactorImplementationQueriedGraphKnowledge(FactorImplementationQueriedKnowl
super().__init__(**kwargs)
class FactorImplementationGraphRAGStrategy(RAGStrategy):
def __init__(self, knowledgebase: FactorImplementationGraphKnowledgeBase) -> None:
class FactorGraphRAGStrategy(RAGStrategy):
def __init__(self, knowledgebase: FactorGraphKnowledgeBase) -> None:
super().__init__(knowledgebase)
self.current_generated_trace_count = 0
self.prompt = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
@@ -242,7 +238,7 @@ class FactorImplementationGraphRAGStrategy(RAGStrategy):
single_feedback = feedback[task_index]
if single_feedback is None:
continue
single_knowledge = FactorImplementationKnowledge(
single_knowledge = FactorKnowledge(
target_task=target_task,
implementation=implementation,
feedback=single_feedback,
@@ -288,7 +284,7 @@ class FactorImplementationGraphRAGStrategy(RAGStrategy):
def query(self, evo: EvolvableSubjects, evolving_trace: list[EvoStep]) -> QueriedKnowledge | None:
conf_knowledge_sampler = FACTOR_IMPLEMENT_SETTINGS.v2_knowledge_sampler
factor_implementation_queried_graph_knowledge = FactorImplementationQueriedGraphKnowledge(
factor_implementation_queried_graph_knowledge = FactorQueriedGraphKnowledge(
success_task_to_knowledge_dict=self.knowledgebase.success_task_to_knowledge_dict,
)
@@ -390,7 +386,7 @@ class FactorImplementationGraphRAGStrategy(RAGStrategy):
def former_trace_query(
self,
evo: EvolvableSubjects,
factor_implementation_queried_graph_knowledge: FactorImplementationQueriedGraphKnowledge,
factor_implementation_queried_graph_knowledge: FactorQueriedGraphKnowledge,
v2_query_former_trace_limit: int = 5,
) -> Union[QueriedKnowledge, set]:
"""
@@ -440,11 +436,11 @@ class FactorImplementationGraphRAGStrategy(RAGStrategy):
def component_query(
self,
evo: EvolvableSubjects,
factor_implementation_queried_graph_knowledge: FactorImplementationQueriedGraphKnowledge,
factor_implementation_queried_graph_knowledge: FactorQueriedGraphKnowledge,
v2_query_component_limit: int = 5,
knowledge_sampler: float = 1.0,
) -> QueriedKnowledge | None:
# queried_component_knowledge = FactorImplementationQueriedGraphComponentKnowledge()
# queried_component_knowledge = FactorQueriedGraphComponentKnowledge()
for target_factor_task in evo.sub_tasks:
target_factor_task_information = target_factor_task.get_factor_information()
if (
@@ -580,11 +576,11 @@ class FactorImplementationGraphRAGStrategy(RAGStrategy):
def error_query(
self,
evo: EvolvableSubjects,
factor_implementation_queried_graph_knowledge: FactorImplementationQueriedGraphKnowledge,
factor_implementation_queried_graph_knowledge: FactorQueriedGraphKnowledge,
v2_query_error_limit: int = 5,
knowledge_sampler: float = 1.0,
) -> QueriedKnowledge | None:
# queried_error_knowledge = FactorImplementationQueriedGraphErrorKnowledge()
# queried_error_knowledge = FactorQueriedGraphErrorKnowledge()
for task_index, target_factor_task in enumerate(evo.sub_tasks):
target_factor_task_information = target_factor_task.get_factor_information()
factor_implementation_queried_graph_knowledge.error_with_success_task[target_factor_task_information] = {}
@@ -712,7 +708,7 @@ class FactorImplementationGraphRAGStrategy(RAGStrategy):
return factor_implementation_queried_graph_knowledge
class FactorImplementationGraphKnowledgeBase(KnowledgeBase):
class FactorGraphKnowledgeBase(KnowledgeBase):
def __init__(self, init_component_list=None) -> None:
"""
Load knowledge, offer brief information of knowledge and common handle interfaces
@@ -735,7 +731,7 @@ class FactorImplementationGraphKnowledgeBase(KnowledgeBase):
# Add already success task
self.success_task_to_knowledge_dict = {}
# key:node_id(for task trace and success implement), value:knowledge instance(aka 'FactorImplementationKnowledge')
# key:node_id(for task trace and success implement), value:knowledge instance(aka 'FactorKnowledge')
self.node_to_implementation_knowledge_dict = {}
# store the task description to component nodes
rdagent/components/task_implementation/factor_implementation/evolving/scheduler.py → rdagent/components/coder/factor_coder/CoSTEER/scheduler.py
+17 -13
View File
@@ -1,17 +1,17 @@
import json
from pathlib import Path
from typing import Dict
from jinja2 import Environment, StrictUndefined
from rdagent.components.task_implementation.factor_implementation.evolving.evolvable_subjects import (
from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
FactorEvolvingItem,
)
from rdagent.components.task_implementation.factor_implementation.utils import (
get_data_folder_intro,
)
from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
from rdagent.core.conf import RD_AGENT_SETTINGS
from rdagent.core.log import RDAgentLog
from rdagent.core.prompts import Prompts
from rdagent.core.scenario import Scenario
from rdagent.oai.llm_utils import APIBackend
scheduler_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
@@ -29,7 +29,13 @@ def RandomSelect(to_be_finished_task_index, implementation_factors_per_round):
return to_be_finished_task_index
def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo: FactorEvolvingItem, former_trace):
def LLMSelect(
to_be_finished_task_index,
implementation_factors_per_round,
evo: FactorEvolvingItem,
former_trace: Dict,
scen: Scenario,
):
tasks = []
for i in to_be_finished_task_index:
# find corresponding former trace for each task
@@ -43,14 +49,10 @@ def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo:
scheduler_prompts["select_implementable_factor_system"],
)
.render(
data_info=get_data_folder_intro(),
scenario=scen.get_scenario_all_desc(),
)
)
session = APIBackend(use_chat_cache=False).build_chat_session(
session_system_prompt=system_prompt,
)
while True:
user_prompt = (
Environment(undefined=StrictUndefined)
@@ -63,15 +65,17 @@ def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo:
)
)
if (
session.build_chat_completion_message_and_calculate_token(
user_prompt,
APIBackend().build_messages_and_calculate_token(
user_prompt=user_prompt,
system_prompt=system_prompt,
)
< RD_AGENT_SETTINGS.chat_token_limit
):
break
response = session.build_chat_completion(
response = APIBackend().build_messages_and_create_chat_completion(
user_prompt=user_prompt,
system_prompt=system_prompt,
json_mode=True,
)
try:
rdagent/components/task_implementation/factor_implementation/config.py → rdagent/components/coder/factor_coder/config.py
View File
rdagent/components/task_implementation/factor_implementation/factor.py → rdagent/components/coder/factor_coder/factor.py
+1 -3
View File
@@ -9,9 +9,7 @@ from typing import Tuple, Union
import pandas as pd
from filelock import FileLock
from rdagent.components.task_implementation.factor_implementation.config import (
FACTOR_IMPLEMENT_SETTINGS,
)
from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
from rdagent.core.exception import (
CodeFormatException,
NoOutputException,
rdagent/components/task_implementation/factor_implementation/prompts.yaml → rdagent/components/coder/factor_coder/prompts.yaml
+55 -59
View File
@@ -1,8 +1,10 @@
evaluator_code_feedback_v1_system: |-
Your job is to give critic to user's code. User's code is expected to implement some factors in quant investment. The code contains reading data from a HDF5(H5) file, calculate the factor to each instrument on each datetime, and save the result pandas dataframe to a HDF5(H5) file.
User will firstly provide you the information of the factor, which includes the name of the factor, description of the factor, the formulation of the factor and the description of the formulation. You can check whether user's code is align with the factor.
User is trying to implement some factors in the following scenario:
{{ scenario }}
User will provide you the information of the factor.
Your job is to check whether user's code is align with the factor and the scenario.
The user will provide the source python code and the execution error message if execution failed.
The user might provide you the ground truth code for you to provide the critic. You should not leak the ground truth code to the user in any form but you can use it to provide the critic.
@@ -14,6 +16,7 @@ evaluator_code_feedback_v1_system: |-
You should provide the suggestion to each of your critic to help the user improve the code. Please response the critic in the following format. Here is an example structure for the output:
critic 1: The critic message to critic 1
critic 2: The critic message to critic 2
evaluator_code_feedback_v1_user: |-
--------------Factor information:---------------
{{ factor_information }}
@@ -29,23 +32,19 @@ evaluator_code_feedback_v1_user: |-
--------------Ground truth Python code:---------------
{{ gt_code }}
{% endif %}
evolving_strategy_factor_implementation_v1_system: |-
The user is trying to implement some factors in quant investment, and you are the one to help write the python code.
{{ data_info }}
The user will provide you a formulation of the factor, which contains some function calls and some operators. You need to implement the function calls and operators in python. Your code is expected to align the formulation in any form which means The user needs to get the exact factor values with your code as expected.
Your code should contain the following part: the import part, the function part, and the main part. You should write a main function name: "calculate_{function_name}" and call this function in "if __name__ == __main__" part. Don't write any try-except block in your code. The user will catch the exception message and provide the feedback to you.
User will write your code into a python file and execute the file directly with "python {your_file_name}.py". You should calculate the factor values and save the result into a HDF5(H5) file named "result.h5" in the same directory as your python file. The result file is a HDF5(H5) file containing a pandas dataframe. The index of the dataframe is the "datetime" and "instrument", and the single column name is the factor name,and the value is the factor value. The result file should be saved in the same directory as your python file.
User is trying to implement some factors in the following scenario:
{{ scenario }}
Your code is expected to align the scenario in any form which means The user needs to get the exact factor values with your code as expected.
To help you write the correct code, the user might provide multiple information that helps you write the correct code:
1. The user might provide you the correct code to similar factors. Your should learn from these code to write the correct code.
2. The user might provide you the failed former code and the corresponding feedback to the code. The feedback contains to the execution, the code and the factor value. You should analyze the feedback and try to correct the latest code.
3. The user might provide you the suggestion to the latest fail code and some similar fail to correct pairs. Each pair contains the fail code with similar error and the corresponding corrected version code. You should learn from these suggestion to write the correct code.
Your must write your code based on your former latest attempt below which consists of your former code and code feedback, you should read the former attempt carefully and must not modify the right part of your former code.
{% if queried_former_failed_knowledge|length != 0 %}
--------------Your former latest attempt:---------------
{% for former_failed_knowledge in queried_former_failed_knowledge %}
@@ -56,17 +55,6 @@ evolving_strategy_factor_implementation_v1_system: |-
{% endfor %}
{% endif %}
A typical format of `result.h5` may be like following:
datetime instrument
2020-01-02 SZ000001 -0.001796
SZ000166 0.005780
SZ000686 0.004228
SZ000712 0.001298
SZ000728 0.005330
...
2021-12-31 SZ000750 0.000000
SZ000776 0.002459
Please response the code in the following json format. Here is an example structure for the JSON output:
{
"code": "The Python code as a string."
@@ -96,31 +84,6 @@ evolving_strategy_factor_implementation_v1_user: |-
{% endfor %}
{% endif %}
evaluator_final_decision_v1_system: |-
User is trying to implement some factor in quant investment and has finished a version of implementation. User has finished evaluation and got some feedback from the evaluator.
The evaluator run the code and get the factor value dataframe and provide several feedback regarding user's code and code output. You should analyze the feedback and considering the factor description to give a final decision about the evaluation result. The final decision concludes whether the factor is implemented correctly and if not, detail feedback containing reason and suggestion if the final decision is False.
The implementation final decision is considered in the following logic:
1. If the value and the ground truth value are exactly the same under a small tolerance, the implementation is considered correct.
2. If the value and the ground truth value have a high correlation on ic or rank ic, the implementation is considered correct.
3. If no ground truth value is not provided, the implementation is considered correct if the code execution is successful and the code feedback is reasonable.
Please response the critic in the json format. Here is an example structure for the JSON output, please strictly follow the format:
{
"final_decision": True,
"final_feedback": "The final feedback message",
}
evaluator_final_decision_v1_user: |-
--------------Factor information:---------------
{{ factor_information }}
--------------Execution feedback:---------------
{{ execution_feedback }}
--------------Code feedback:---------------
{{ code_feedback }}
--------------Factor value feedback:---------------
{{ factor_value_feedback }}
evolving_strategy_factor_implementation_v2_user: |-
--------------Target factor information:---------------
{{ factor_information_str }}
@@ -154,7 +117,6 @@ evolving_strategy_factor_implementation_v2_user: |-
{% endfor %}
{% endif %}
evolving_strategy_error_summary_v2_system: |-
You are doing the following task:
{{factor_information_str}}
@@ -183,14 +145,10 @@ evolving_strategy_error_summary_v2_user: |-
select_implementable_factor_system: |-
User is trying to implement some factors in quant investment using Python code, You are an assistant who helps the user select the easiest-to-implement factors and some factors may be difficult to implement due to a lack of information or excessive complexity..
The user will provide the number of factor you should pick and information about the factors, including their descriptions, formulas, and variable explanations.
At the same time, user will provide with your former attempt to implement the factor and the feedback to the implementation.You need to carefully review your previous attempts. Some factors have been repeatedly tried without success. You should consider discarding these factors.
Here is the source data that user will use to implement the factors:
{{ data_info }}
User is trying to implement some factors in the following scenario:
{{ scenario }}
Your job is to help the user select the easiest-to-implement factors. Some factors may be difficult to implement due to a lack of information or excessive complexity. The user will provide the number of factors you should pick and information about the factors, including their descriptions, formulas, and variable explanations.
User will provide you the former attempt to implement the factor and the feedback to the implementation. You need to carefully review your previous attempts. Some factors have been repeatedly tried without success. You should consider discarding these factors.
Please analyze the difficulties of the each factors and provide the reason and response the indices of selected implementable factor in the json format. Here is an example structure for the JSON output:
{
"Analysis": "Analyze the difficulties of the each factors and provide the reason why the factor can be implemented or not."
@@ -226,4 +184,42 @@ analyze_component_prompt_v1_system: |-
Please response the critic in the json format. Here is an example structure for the JSON output, please strictly follow the format:
{
"component_no_list": the list containing indices of components.
}
}
evaluator_output_format_system: |-
User is trying to implement some factors in the following scenario:
{{ scenario }}
User will provide you the format of the output. Please help to check whether the output is align with the format.
Please respond in the JSON format. Here is an example structure for the JSON output:
{
"output_format_decision": True,
"output_format_feedback": "The output format is correct."
}
evaluator_final_decision_v1_system: |-
User is trying to implement some factors in the following scenario:
{{ scenario }}
User has finished evaluation and got some feedback from the evaluator.
The evaluator run the code and get the factor value dataframe and provide several feedback regarding user's code and code output. You should analyze the feedback and considering the scenario and factor description to give a final decision about the evaluation result. The final decision concludes whether the factor is implemented correctly and if not, detail feedback containing reason and suggestion if the final decision is False.
The implementation final decision is considered in the following logic:
1. If the value and the ground truth value are exactly the same under a small tolerance, the implementation is considered correct.
2. If the value and the ground truth value have a high correlation on ic or rank ic, the implementation is considered correct.
3. If no ground truth value is not provided, the implementation is considered correct if the code execution is successful and the code feedback is align with the scenario and factor description.
Please response the critic in the json format. Here is an example structure for the JSON output, please strictly follow the format:
{
"final_decision": True,
"final_feedback": "The final feedback message",
}
evaluator_final_decision_v1_user: |-
--------------Factor information:---------------
{{ factor_information }}
--------------Execution feedback:---------------
{{ execution_feedback }}
--------------Code feedback:---------------
{{ code_feedback }}
--------------Factor value feedback:---------------
{{ factor_value_feedback }}
rdagent/components/task_implementation/factor_implementation/utils.py → rdagent/components/coder/factor_coder/utils.py
+1 -3
View File
@@ -5,9 +5,7 @@ import pandas as pd
# render it with jinja
from jinja2 import Environment, StrictUndefined
from rdagent.components.task_implementation.factor_implementation.config import (
FACTOR_IMPLEMENT_SETTINGS,
)
from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
TPL = """
{{file_name}}
rdagent/components/task_implementation/model_implementation/benchmark/eval.py → rdagent/components/coder/model_coder/benchmark/eval.py
+1 -3
View File
@@ -1,9 +1,7 @@
# TODO: inherent from the benchmark base class
import torch
from rdagent.components.task_implementation.model_implementation.model import (
ModelImplementation,
)
from rdagent.components.coder.model_coder.model import ModelImplementation
def get_data_conf(init_val):
rdagent/components/task_implementation/model_implementation/benchmark/gt_code/A-DGN.py → rdagent/components/coder/model_coder/benchmark/gt_code/A-DGN.py
View File
rdagent/components/task_implementation/model_implementation/benchmark/gt_code/dirgnn.py → rdagent/components/coder/model_coder/benchmark/gt_code/dirgnn.py
View File
rdagent/components/task_implementation/model_implementation/benchmark/gt_code/gpsconv.py → rdagent/components/coder/model_coder/benchmark/gt_code/gpsconv.py
View File
rdagent/components/task_implementation/model_implementation/benchmark/gt_code/linkx.py → rdagent/components/coder/model_coder/benchmark/gt_code/linkx.py
+1
View File
@@ -166,6 +166,7 @@ class LINKX(torch.nn.Module):
f"out_channels={self.out_channels})"
)
model_cls = LINKX
if __name__ == "__main__":
rdagent/components/task_implementation/model_implementation/benchmark/gt_code/pmlp.py → rdagent/components/coder/model_coder/benchmark/gt_code/pmlp.py
+6 -1
View File
@@ -106,7 +106,12 @@ if __name__ == "__main__":
edge_index = torch.load("edge_index.pt")
# Model instantiation and forward pass
model = PMLP(in_channels=node_features.size(-1), hidden_channels=node_features.size(-1), out_channels=node_features.size(-1), num_layers=1)
model = PMLP(
in_channels=node_features.size(-1),
hidden_channels=node_features.size(-1),
out_channels=node_features.size(-1),
num_layers=1,
)
output = model(node_features, edge_index)
# Save output to a file
rdagent/components/task_implementation/model_implementation/benchmark/gt_code/visnet.py → rdagent/components/coder/model_coder/benchmark/gt_code/visnet.py
View File
rdagent/components/task_implementation/model_implementation/conf.py → rdagent/components/coder/model_coder/conf.py
View File
rdagent/components/task_implementation/model_implementation/evaluator.py → rdagent/components/coder/model_coder/evaluator.py
View File
rdagent/components/task_implementation/model_implementation/gt_code.py → rdagent/components/coder/model_coder/gt_code.py
View File
rdagent/components/task_implementation/model_implementation/main.py → rdagent/components/coder/model_coder/main.py
View File
rdagent/components/task_implementation/model_implementation/model.py → rdagent/components/coder/model_coder/model.py
+3 -4
View File
@@ -5,10 +5,8 @@ from typing import Dict, Optional, Sequence
import torch
from rdagent.components.coder.model_coder.conf import MODEL_IMPL_SETTINGS
from rdagent.components.loader.task_loader import ModelTaskLoader
from rdagent.components.task_implementation.model_implementation.conf import (
MODEL_IMPL_SETTINGS,
)
from rdagent.core.exception import CodeFormatException
from rdagent.core.experiment import Experiment, FBImplementation, ImpLoader, Task
from rdagent.utils import get_module_by_module_path
@@ -128,7 +126,8 @@ We'll import the model in the implementation in file `model.py` after setting th
"""
class ModelExperiment(Experiment[ModelTask, ModelImplementation]): ...
class ModelExperiment(Experiment[ModelTask, ModelImplementation]):
...
class ModelTaskLoaderJson(ModelTaskLoader):
rdagent/components/task_implementation/model_implementation/one_shot/__init__.py → rdagent/components/coder/model_coder/one_shot/__init__.py
+1 -3
View File
@@ -1,13 +1,11 @@
import re
from pathlib import Path
from typing import Sequence
from jinja2 import Environment, StrictUndefined
from rdagent.components.task_implementation.model_implementation.model import (
from rdagent.components.coder.model_coder.model import (
ModelExperiment,
ModelImplementation,
ModelTask,
)
from rdagent.core.prompts import Prompts
from rdagent.core.task_generator import TaskGenerator
rdagent/components/task_implementation/model_implementation/one_shot/prompt.yaml → rdagent/components/coder/model_coder/one_shot/prompt.yaml
View File
rdagent/components/task_implementation/model_implementation/prompts.yaml → rdagent/components/coder/model_coder/prompts.yaml
View File
rdagent/components/task_implementation/model_implementation/task_loader.py → rdagent/components/coder/model_coder/task_loader.py
+4 -8
View File
@@ -4,15 +4,11 @@ import json
import re
from pathlib import Path
from rdagent.components.coder.model_coder.model import ModelExperiment, ModelTask
from rdagent.components.document_reader.document_reader import (
load_and_process_pdfs_by_langchain,
)
from rdagent.components.loader.task_loader import ModelTaskLoader
from rdagent.components.task_implementation.model_implementation.model import (
ModelExperiment,
ModelImplementationTaskLoaderFromDict,
ModelTask,
)
from rdagent.core.log import RDAgentLog
from rdagent.core.prompts import Prompts
from rdagent.oai.llm_utils import APIBackend
@@ -99,7 +95,7 @@ def extract_model_from_docs(docs_dict):
return model_dict
class ModelImplementationExperimentLoaderFromDict(ModelTaskLoader):
class ModelExperimentLoaderFromDict(ModelTaskLoader):
def load(self, model_dict: dict) -> list:
"""Load data from a dict."""
task_l = []
@@ -115,7 +111,7 @@ class ModelImplementationExperimentLoaderFromDict(ModelTaskLoader):
return ModelExperiment(sub_tasks=task_l)
class ModelImplementationExperimentLoaderFromPDFfiles(ModelTaskLoader):
class ModelExperimentLoaderFromPDFfiles(ModelTaskLoader):
def load(self, file_or_folder_path: Path) -> dict:
docs_dict = load_and_process_pdfs_by_langchain(Path(file_or_folder_path)) # dict{file_path:content}
model_dict = extract_model_from_docs(
@@ -124,7 +120,7 @@ class ModelImplementationExperimentLoaderFromPDFfiles(ModelTaskLoader):
model_dict = merge_file_to_model_dict_to_model_dict(
model_dict
) # dict {model_name: dict{description, formulation, variables}}
return ModelImplementationExperimentLoaderFromDict().load(model_dict)
return ModelExperimentLoaderFromDict().load(model_dict)
def main(path="../test_doc"):
rdagent/components/loader/experiment_loader.py
+1 -3
View File
@@ -1,6 +1,4 @@
from rdagent.components.task_implementation.factor_implementation.factor import (
FactorExperiment,
)
from rdagent.components.coder.factor_coder.factor import FactorExperiment
from rdagent.core.experiment import Loader
rdagent/components/loader/task_loader.py
+2 -4
View File
@@ -1,7 +1,5 @@
from rdagent.components.task_implementation.factor_implementation.factor import (
FactorTask,
)
from rdagent.components.task_implementation.model_implementation.model import ModelTask
from rdagent.components.coder.factor_coder.factor import FactorTask
from rdagent.components.coder.model_coder.model import ModelTask
from rdagent.core.experiment import Loader
rdagent/components/idea_proposal/factor_proposal.py → rdagent/components/proposal/factor_proposal.py
+9 -7
View File
@@ -4,9 +4,7 @@ from typing import Tuple
from jinja2 import Environment, StrictUndefined
from rdagent.components.task_implementation.factor_implementation.factor import (
FactorExperiment,
)
from rdagent.components.coder.factor_coder.factor import FactorExperiment
from rdagent.core.prompts import Prompts
from rdagent.core.proposal import (
Hypothesis,
@@ -30,10 +28,12 @@ class FactorHypothesisGen(HypothesisGen):
# The following methods are scenario related so they should be implemented in the subclass
@abstractmethod
def prepare_context(self, trace: Trace) -> Tuple[dict, bool]: ...
def prepare_context(self, trace: Trace) -> Tuple[dict, bool]:
...
@abstractmethod
def convert_response(self, response: str) -> FactorHypothesis: ...
def convert_response(self, response: str) -> FactorHypothesis:
...
def gen(self, trace: Trace) -> FactorHypothesis:
context_dict, json_flag = self.prepare_context(trace)
@@ -67,10 +67,12 @@ class FactorHypothesis2Experiment(Hypothesis2Experiment[FactorExperiment]):
super().__init__()
@abstractmethod
def prepare_context(self, hs: HypothesisSet) -> Tuple[dict, bool]: ...
def prepare_context(self, hs: HypothesisSet) -> Tuple[dict, bool]:
...
@abstractmethod
def convert_response(self, response: str) -> FactorExperiment: ...
def convert_response(self, response: str) -> FactorExperiment:
...
def convert(self, hs: HypothesisSet) -> FactorExperiment:
context, json_flag = self.prepare_context(hs)
rdagent/components/idea_proposal/model_proposal.py → rdagent/components/proposal/model_proposal.py
View File
rdagent/components/idea_proposal/prompts.yaml → rdagent/components/proposal/prompts.yaml
View File
rdagent/components/task_implementation/factor_implementation/evolving/evaluators.py
-750
View File
@@ -1,750 +0,0 @@
import json
import re
from abc import abstractmethod
from pathlib import Path
from typing import List, Tuple
import pandas as pd
from jinja2 import Environment, StrictUndefined
from rdagent.components.task_implementation.factor_implementation.config import (
FACTOR_IMPLEMENT_SETTINGS,
)
from rdagent.components.task_implementation.factor_implementation.evolving.evolvable_subjects import (
FactorEvolvingItem,
)
from rdagent.components.task_implementation.factor_implementation.evolving.evolving_strategy import (
FactorTask,
)
from rdagent.core.conf import RD_AGENT_SETTINGS
from rdagent.core.evaluation import Evaluator
from rdagent.core.evolving_framework import Feedback, QueriedKnowledge
from rdagent.core.experiment import Implementation
from rdagent.core.log import RDAgentLog
from rdagent.core.prompts import Prompts
from rdagent.core.utils import multiprocessing_wrapper
from rdagent.oai.llm_utils import APIBackend
evaluate_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
class FactorImplementationEvaluator(Evaluator):
# TODO:
# I think we should have unified interface for all evaluates, for examples.
# So we should adjust the interface of other factors
@abstractmethod
def evaluate(
self,
gt: Implementation,
gen: Implementation,
) -> Tuple[str, object]:
"""You can get the dataframe by
.. code-block:: python
_, gt_df = gt.execute()
_, gen_df = gen.execute()
Returns
-------
Tuple[str, object]
- str: the text-based description of the evaluation result
- object: a comparable metric (bool, integer, float ...)
"""
raise NotImplementedError("Please implement the `evaluator` method")
def _get_df(self, gt: Implementation, gen: Implementation):
_, gt_df = gt.execute()
_, gen_df = gen.execute()
if isinstance(gen_df, pd.Series):
gen_df = gen_df.to_frame("source_factor")
if isinstance(gt_df, pd.Series):
gt_df = gt_df.to_frame("gt_factor")
return gt_df, gen_df
class FactorImplementationCodeEvaluator(Evaluator):
def evaluate(
self,
target_task: FactorTask,
implementation: Implementation,
execution_feedback: str,
factor_value_feedback: str = "",
gt_implementation: Implementation = None,
**kwargs,
):
factor_information = target_task.get_factor_information()
code = implementation.code
system_prompt = evaluate_prompts["evaluator_code_feedback_v1_system"]
execution_feedback_to_render = execution_feedback
user_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
evaluate_prompts["evaluator_code_feedback_v1_user"],
)
.render(
factor_information=factor_information,
code=code,
execution_feedback=execution_feedback_to_render,
factor_value_feedback=factor_value_feedback,
gt_code=gt_implementation.code if gt_implementation else None,
)
)
while (
APIBackend().build_messages_and_calculate_token(
user_prompt=user_prompt,
system_prompt=system_prompt,
former_messages=[],
)
> RD_AGENT_SETTINGS.chat_token_limit
):
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
user_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
evaluate_prompts["evaluator_code_feedback_v1_user"],
)
.render(
factor_information=factor_information,
code=code,
execution_feedback=execution_feedback_to_render,
factor_value_feedback=factor_value_feedback,
gt_code=gt_implementation.code if gt_implementation else None,
)
)
critic_response = APIBackend().build_messages_and_create_chat_completion(
user_prompt=user_prompt,
system_prompt=system_prompt,
json_mode=False,
)
return critic_response
class FactorImplementationSingleColumnEvaluator(FactorImplementationEvaluator):
def evaluate(
self,
gt: Implementation,
gen: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt, gen)
if len(gen_df.columns) == 1 and len(gt_df.columns) == 1:
return "Both dataframes have only one column.", True
elif len(gen_df.columns) != 1:
gen_df = gen_df.iloc(axis=1)[
[
0,
]
]
return (
"The source dataframe has more than one column. Please check the implementation. We only evaluate the first column.",
False,
)
return "", False
def __str__(self) -> str:
return self.__class__.__name__
class FactorImplementationIndexFormatEvaluator(FactorImplementationEvaluator):
def evaluate(
self,
gt: Implementation,
gen: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt, gen)
idx_name_right = gen_df.index.names == ("datetime", "instrument")
if idx_name_right:
return (
'The index of the dataframe is ("datetime", "instrument") and align with the predefined format.',
True,
)
else:
return (
'The index of the dataframe is not ("datetime", "instrument"). Please check the implementation.',
False,
)
def __str__(self) -> str:
return self.__class__.__name__
class FactorImplementationRowCountEvaluator(FactorImplementationEvaluator):
def evaluate(
self,
gt: Implementation,
gen: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt, gen)
if gen_df.shape[0] == gt_df.shape[0]:
return "Both dataframes have the same rows count.", True
else:
return (
f"The source dataframe and the ground truth dataframe have different rows count. The source dataframe has {gen_df.shape[0]} rows, while the ground truth dataframe has {gt_df.shape[0]} rows. Please check the implementation.",
False,
)
def __str__(self) -> str:
return self.__class__.__name__
class FactorImplementationIndexEvaluator(FactorImplementationEvaluator):
def evaluate(
self,
gt: Implementation,
gen: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt, gen)
if gen_df.index.equals(gt_df.index):
return "Both dataframes have the same index.", True
else:
return (
"The source dataframe and the ground truth dataframe have different index. Please check the implementation.",
False,
)
def __str__(self) -> str:
return self.__class__.__name__
class FactorImplementationMissingValuesEvaluator(FactorImplementationEvaluator):
def evaluate(
self,
gt: Implementation,
gen: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt, gen)
if gen_df.isna().sum().sum() == gt_df.isna().sum().sum():
return "Both dataframes have the same missing values.", True
else:
return (
f"The dataframes do not have the same missing values. The source dataframe has {gen_df.isna().sum().sum()} missing values, while the ground truth dataframe has {gt_df.isna().sum().sum()} missing values. Please check the implementation.",
False,
)
def __str__(self) -> str:
return self.__class__.__name__
class FactorImplementationValuesEvaluator(FactorImplementationEvaluator):
def evaluate(
self,
gt: Implementation,
gen: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt, gen)
try:
close_values = gen_df.sub(gt_df).abs().lt(1e-6)
result_int = close_values.astype(int)
pos_num = result_int.sum().sum()
acc_rate = pos_num / close_values.size
except:
close_values = gen_df
if close_values.all().iloc[0]:
return (
"All values in the dataframes are equal within the tolerance of 1e-6.",
acc_rate,
)
else:
return (
"Some values differ by more than the tolerance of 1e-6. Check for rounding errors or differences in the calculation methods.",
acc_rate,
)
def __str__(self) -> str:
return self.__class__.__name__
class FactorImplementationCorrelationEvaluator(FactorImplementationEvaluator):
def __init__(self, hard_check: bool) -> None:
self.hard_check = hard_check
def evaluate(
self,
gt: Implementation,
gen: Implementation,
) -> Tuple[str, object]:
gt_df, gen_df = self._get_df(gt, gen)
concat_df = pd.concat([gen_df, gt_df], axis=1)
concat_df.columns = ["source", "gt"]
ic = concat_df.groupby("datetime").apply(lambda df: df["source"].corr(df["gt"])).dropna().mean()
ric = (
concat_df.groupby("datetime")
.apply(lambda df: df["source"].corr(df["gt"], method="spearman"))
.dropna()
.mean()
)
if self.hard_check:
if ic > 0.99 and ric > 0.99:
return (
f"The dataframes are highly correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}.",
True,
)
else:
return (
f"The dataframes are not sufficiently high correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}. Investigate the factors that might be causing the discrepancies and ensure that the logic of the factor calculation is consistent.",
False,
)
else:
return f"The ic is ({ic:.6f}) and the rankic is ({ric:.6f}).", ic
def __str__(self) -> str:
return self.__class__.__name__
class FactorImplementationValEvaluator(FactorImplementationEvaluator):
def evaluate(self, gt: Implementation, gen: Implementation):
_, gt_df = gt.execute()
_, gen_df = gen.execute()
# FIXME: refactor the two classes
fiv = FactorImplementationValueEvaluator()
return fiv.evaluate(source_df=gen_df, gt_df=gt_df)
def __str__(self) -> str:
return self.__class__.__name__
class FactorImplementationValueEvaluator(Evaluator):
# TODO: let's discuss the about the interface of the evaluator
def evaluate(
self,
source_df: pd.DataFrame,
gt_df: pd.DataFrame,
**kwargs,
) -> Tuple:
conclusions = []
if isinstance(source_df, pd.Series):
source_df = source_df.to_frame("source_factor")
conclusions.append(
"The source dataframe is a series, better convert it to a dataframe.",
)
if gt_df is not None and isinstance(gt_df, pd.Series):
gt_df = gt_df.to_frame("gt_factor")
conclusions.append(
"The ground truth dataframe is a series, convert it to a dataframe.",
)
# Check if both dataframe has only one columns
if len(source_df.columns) == 1:
conclusions.append("The source dataframe has only one column which is correct.")
else:
conclusions.append(
"The source dataframe has more than one column. Please check the implementation. We only evaluate the first column.",
)
source_df = source_df.iloc(axis=1)[
[
0,
]
]
if list(source_df.index.names) != ["datetime", "instrument"]:
conclusions.append(
rf"The index of the dataframe is not (\"datetime\", \"instrument\"), instead is {source_df.index.names}. Please check the implementation.",
)
else:
conclusions.append(
'The index of the dataframe is ("datetime", "instrument") and align with the predefined format.',
)
# Check if both dataframe have the same rows count
if gt_df is not None:
if source_df.shape[0] == gt_df.shape[0]:
conclusions.append("Both dataframes have the same rows count.")
same_row_count_result = True
else:
conclusions.append(
f"The source dataframe and the ground truth dataframe have different rows count. The source dataframe has {source_df.shape[0]} rows, while the ground truth dataframe has {gt_df.shape[0]} rows. Please check the implementation.",
)
same_row_count_result = False
# Check whether both dataframe has the same index
if source_df.index.equals(gt_df.index):
conclusions.append("Both dataframes have the same index.")
same_index_result = True
else:
conclusions.append(
"The source dataframe and the ground truth dataframe have different index. Please check the implementation.",
)
same_index_result = False
# Check for the same missing values (NaN)
if source_df.isna().sum().sum() == gt_df.isna().sum().sum():
conclusions.append("Both dataframes have the same missing values.")
same_missing_values_result = True
else:
conclusions.append(
f"The dataframes do not have the same missing values. The source dataframe has {source_df.isna().sum().sum()} missing values, while the ground truth dataframe has {gt_df.isna().sum().sum()} missing values. Please check the implementation.",
)
same_missing_values_result = False
# Check if the values are the same within a small tolerance
if not same_index_result:
conclusions.append(
"The source dataframe and the ground truth dataframe have different index. Give up comparing the values and correlation because it's useless",
)
same_values_result = False
high_correlation_result = False
else:
close_values = source_df.sub(gt_df).abs().lt(1e-6)
if close_values.all().iloc[0]:
conclusions.append(
"All values in the dataframes are equal within the tolerance of 1e-6.",
)
same_values_result = True
else:
conclusions.append(
"Some values differ by more than the tolerance of 1e-6. Check for rounding errors or differences in the calculation methods.",
)
same_values_result = False
# Check the ic and rankic between the two dataframes
concat_df = pd.concat([source_df, gt_df], axis=1)
concat_df.columns = ["source", "gt"]
try:
ic = concat_df.groupby("datetime").apply(lambda df: df["source"].corr(df["gt"])).dropna().mean()
ric = (
concat_df.groupby("datetime")
.apply(lambda df: df["source"].corr(df["gt"], method="spearman"))
.dropna()
.mean()
)
if ic > 0.99 and ric > 0.99:
conclusions.append(
f"The dataframes are highly correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}.",
)
high_correlation_result = True
else:
conclusions.append(
f"The dataframes are not sufficiently high correlated. The ic is {ic:.6f} and the rankic is {ric:.6f}. Investigate the factors that might be causing the discrepancies and ensure that the logic of the factor calculation is consistent.",
)
high_correlation_result = False
# Check for shifted alignments only in the "datetime" index
max_shift_days = 2
for shift in range(-max_shift_days, max_shift_days + 1):
if shift == 0:
continue # Skip the case where there is no shift
shifted_source_df = source_df.groupby(level="instrument").shift(shift)
concat_df = pd.concat([shifted_source_df, gt_df], axis=1)
concat_df.columns = ["source", "gt"]
shifted_ric = (
concat_df.groupby("datetime")
.apply(lambda df: df["source"].corr(df["gt"], method="spearman"))
.dropna()
.mean()
)
if shifted_ric > 0.99:
conclusions.append(
f"The dataframes are highly correlated with a shift of {max_shift_days} days in the 'date' index. Shifted rankic: {shifted_ric:.6f}.",
)
break
else:
conclusions.append(
f"No sufficient correlation found when shifting up to {max_shift_days} days in the 'date' index. Investigate the factors that might be causing discrepancies.",
)
except Exception as e:
RDAgentLog().warning(f"Error occurred when calculating the correlation: {str(e)}")
conclusions.append(
f"Some error occurred when calculating the correlation. Investigate the factors that might be causing the discrepancies and ensure that the logic of the factor calculation is consistent. Error: {e}",
)
high_correlation_result = False
# Combine all conclusions into a single string
conclusion_str = "\n".join(conclusions)
final_result = (same_values_result or high_correlation_result) if gt_df is not None else False
return conclusion_str, final_result
# TODO:
def shorten_prompt(tpl: str, render_kwargs: dict, shorten_key: str, max_trail: int = 10) -> str:
"""When the prompt is too long. We have to shorten it.
But we should not truncate the prompt directly, so we should find the key we want to shorten and then shorten it.
"""
# TODO: this should replace most of code in
# - FactorImplementationFinalDecisionEvaluator.evaluate
# - FactorImplementationCodeEvaluator.evaluate
class FactorImplementationFinalDecisionEvaluator(Evaluator):
def evaluate(
self,
target_task: FactorTask,
execution_feedback: str,
value_feedback: str,
code_feedback: str,
**kwargs,
) -> Tuple:
system_prompt = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")[
"evaluator_final_decision_v1_system"
]
execution_feedback_to_render = execution_feedback
user_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
evaluate_prompts["evaluator_final_decision_v1_user"],
)
.render(
factor_information=target_task.get_factor_information(),
execution_feedback=execution_feedback_to_render,
code_feedback=code_feedback,
factor_value_feedback=(
value_feedback
if value_feedback is not None
else "No Ground Truth Value provided, so no evaluation on value is performed."
),
)
)
while (
APIBackend().build_messages_and_calculate_token(
user_prompt=user_prompt,
system_prompt=system_prompt,
former_messages=[],
)
> RD_AGENT_SETTINGS.chat_token_limit
):
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
user_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
evaluate_prompts["evaluator_final_decision_v1_user"],
)
.render(
factor_information=target_task.get_factor_information(),
execution_feedback=execution_feedback_to_render,
code_feedback=code_feedback,
factor_value_feedback=(
value_feedback
if value_feedback is not None
else "No Ground Truth Value provided, so no evaluation on value is performed."
),
)
)
final_evaluation_dict = json.loads(
APIBackend().build_messages_and_create_chat_completion(
user_prompt=user_prompt,
system_prompt=system_prompt,
json_mode=True,
),
)
return (
final_evaluation_dict["final_decision"],
final_evaluation_dict["final_feedback"],
)
class FactorImplementationSingleFeedback:
"""This class is a feedback to single implementation which is generated from an evaluator."""
def __init__(
self,
execution_feedback: str = None,
value_generated_flag: bool = False,
code_feedback: str = None,
factor_value_feedback: str = None,
final_decision: bool = None,
final_feedback: str = None,
final_decision_based_on_gt: bool = None,
) -> None:
self.execution_feedback = execution_feedback
self.value_generated_flag = value_generated_flag
self.code_feedback = code_feedback
self.factor_value_feedback = factor_value_feedback
self.final_decision = final_decision
self.final_feedback = final_feedback
self.final_decision_based_on_gt = final_decision_based_on_gt
def __str__(self) -> str:
return f"""------------------Factor Execution Feedback------------------
{self.execution_feedback}
------------------Factor Code Feedback------------------
{self.code_feedback}
------------------Factor Value Feedback------------------
{self.factor_value_feedback}
------------------Factor Final Feedback------------------
{self.final_feedback}
------------------Factor Final Decision------------------
This implementation is {'SUCCESS' if self.final_decision else 'FAIL'}.
"""
class FactorImplementationsMultiFeedback(
Feedback,
List[FactorImplementationSingleFeedback],
):
"""Feedback contains a list, each element is the corresponding feedback for each factor implementation."""
class FactorImplementationEvaluatorV1(FactorImplementationEvaluator):
"""This class is the v1 version of evaluator for a single factor implementation.
It calls several evaluators in share modules to evaluate the factor implementation.
"""
def __init__(self) -> None:
self.code_evaluator = FactorImplementationCodeEvaluator()
self.value_evaluator = FactorImplementationValueEvaluator()
self.final_decision_evaluator = FactorImplementationFinalDecisionEvaluator()
def evaluate(
self,
target_task: FactorTask,
implementation: Implementation,
gt_implementation: Implementation = None,
queried_knowledge: QueriedKnowledge = None,
**kwargs,
) -> FactorImplementationSingleFeedback:
if implementation is None:
return None
target_task_information = target_task.get_factor_information()
if (
queried_knowledge is not None
and target_task_information in queried_knowledge.success_task_to_knowledge_dict
):
return queried_knowledge.success_task_to_knowledge_dict[target_task_information].feedback
elif queried_knowledge is not None and target_task_information in queried_knowledge.failed_task_info_set:
return FactorImplementationSingleFeedback(
execution_feedback="This task has failed too many times, skip implementation.",
value_generated_flag=False,
code_feedback="This task has failed too many times, skip code evaluation.",
factor_value_feedback="This task has failed too many times, skip value evaluation.",
final_decision=False,
final_feedback="This task has failed too many times, skip final decision evaluation.",
final_decision_based_on_gt=False,
)
else:
factor_feedback = FactorImplementationSingleFeedback()
(
factor_feedback.execution_feedback,
source_df,
) = implementation.execute()
# Remove the long list of numbers in the feedback
pattern = r"(?<=\D)(,\s+-?\d+\.\d+){50,}(?=\D)"
factor_feedback.execution_feedback = re.sub(pattern, ", ", factor_feedback.execution_feedback)
execution_feedback_lines = [
line for line in factor_feedback.execution_feedback.split("\n") if "warning" not in line.lower()
]
factor_feedback.execution_feedback = "\n".join(execution_feedback_lines)
if source_df is None:
factor_feedback.factor_value_feedback = "No factor value generated, skip value evaluation."
factor_feedback.value_generated_flag = False
value_decision = None
else:
factor_feedback.value_generated_flag = True
if gt_implementation is not None:
_, gt_df = gt_implementation.execute(store_result=True)
else:
gt_df = None
try:
source_df = source_df.sort_index()
if gt_df is not None:
gt_df = gt_df.sort_index()
(
factor_feedback.factor_value_feedback,
value_decision,
) = self.value_evaluator.evaluate(source_df=source_df, gt_df=gt_df)
except Exception as e:
RDAgentLog().warning("Value evaluation failed with exception: %s", e)
factor_feedback.factor_value_feedback = "Value evaluation failed."
value_decision = False
factor_feedback.final_decision_based_on_gt = gt_implementation is not None
if value_decision is not None and value_decision is True:
# To avoid confusion, when value_decision is True, we do not need code feedback
factor_feedback.code_feedback = "Final decision is True and there are no code critics."
factor_feedback.final_decision = value_decision
factor_feedback.final_feedback = "Value evaluation passed, skip final decision evaluation."
else:
factor_feedback.code_feedback = self.code_evaluator.evaluate(
target_task=target_task,
implementation=implementation,
execution_feedback=factor_feedback.execution_feedback,
value_feedback=factor_feedback.factor_value_feedback,
gt_implementation=gt_implementation,
)
(
factor_feedback.final_decision,
factor_feedback.final_feedback,
) = self.final_decision_evaluator.evaluate(
target_task=target_task,
execution_feedback=factor_feedback.execution_feedback,
value_feedback=factor_feedback.factor_value_feedback,
code_feedback=factor_feedback.code_feedback,
)
return factor_feedback
class FactorImplementationsMultiEvaluator(Evaluator):
def __init__(self, single_evaluator=FactorImplementationEvaluatorV1()) -> None:
super().__init__()
self.single_factor_implementation_evaluator = single_evaluator
def evaluate(
self,
evo: FactorEvolvingItem,
queried_knowledge: QueriedKnowledge = None,
**kwargs,
) -> FactorImplementationsMultiFeedback:
multi_implementation_feedback = FactorImplementationsMultiFeedback()
# for index in range(len(evo.sub_tasks)):
# corresponding_implementation = evo.sub_implementations[index]
# corresponding_gt_implementation = (
# evo.sub_gt_implementations[index]
# if evo.sub_gt_implementations is not None
# else None
# )
# multi_implementation_feedback.append(
# self.single_factor_implementation_evaluator.evaluate(
# target_task=evo.sub_tasks[index],
# implementation=corresponding_implementation,
# gt_implementation=corresponding_gt_implementation,
# queried_knowledge=queried_knowledge,
# )
# )
calls = []
for index in range(len(evo.sub_tasks)):
corresponding_implementation = evo.sub_implementations[index]
corresponding_gt_implementation = (
evo.sub_gt_implementations[index] if evo.sub_gt_implementations is not None else None
)
calls.append(
(
self.single_factor_implementation_evaluator.evaluate,
(
evo.sub_tasks[index],
corresponding_implementation,
corresponding_gt_implementation,
queried_knowledge,
),
),
)
multi_implementation_feedback = multiprocessing_wrapper(calls, n=FACTOR_IMPLEMENT_SETTINGS.evo_multi_proc_n)
final_decision = [
None if single_feedback is None else single_feedback.final_decision
for single_feedback in multi_implementation_feedback
]
RDAgentLog().info(f"Final decisions: {final_decision} True count: {final_decision.count(True)}")
return multi_implementation_feedback
rdagent/core/evaluation.py
+12 -1
View File
@@ -1,9 +1,20 @@
from abc import ABC, abstractmethod
from rdagent.core.experiment import Task, Implementation
from rdagent.core.experiment import Implementation, Task
from rdagent.core.scenario import Scenario
class Feedback:
pass
class Evaluator(ABC):
def __init__(
self,
scen: Scenario,
) -> None:
self.scen = scen
@abstractmethod
def evaluate(
self,
rdagent/core/evolving_framework.py
+5 -3
View File
@@ -5,9 +5,8 @@ from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any
class Feedback:
pass
from rdagent.core.evaluation import Feedback
from rdagent.core.scenario import Scenario
class Knowledge:
@@ -54,6 +53,9 @@ class EvoStep:
class EvolvingStrategy(ABC):
def __init__(self, scen: Scenario) -> None:
self.scen = scen
@abstractmethod
def evolve(
self,
rdagent/core/exception.py
+1 -1
View File
@@ -1,7 +1,7 @@
class ImplementRunException(Exception):
"""
Exceptions raised when Implementing and running code.
- start: FactorImplementationTask => FactorGenerator
- start: FactorTask => FactorGenerator
- end: Get dataframe after execution
The more detailed evaluation in dataframe values are managed by the evaluator.
rdagent/core/proposal.py
+3 -29
View File
@@ -5,8 +5,9 @@
from abc import ABC, abstractmethod
from typing import Dict, Generic, List, Tuple, TypeVar
from rdagent.core.evolving_framework import Feedback
from rdagent.core.experiment import Experiment, Implementation, Loader, Task
from rdagent.core.evaluation import Feedback
from rdagent.core.experiment import Experiment
from rdagent.core.scenario import Scenario
# class data_ana: XXX
@@ -29,33 +30,6 @@ class Hypothesis:
# Origin(path of repo/data/feedback) => view/summarization => generated Hypothesis
class Scenario(ABC):
@property
@abstractmethod
def background(self):
"""Background information"""
@property
@abstractmethod
def source_data(self):
"""Source data description"""
@property
@abstractmethod
def interface(self):
"""Interface description about how to run the code"""
@property
@abstractmethod
def simulator(self):
"""Simulator description"""
@abstractmethod
def get_scenario_all_desc(self) -> str:
"""Combine all the description together"""
class HypothesisFeedback(Feedback): ...
rdagent/core/scenario.py
+32
View File
@@ -0,0 +1,32 @@
from abc import ABC, abstractmethod
class Scenario(ABC):
@property
@abstractmethod
def background(self):
"""Background information"""
@property
@abstractmethod
def source_data(self):
"""Source data description"""
@property
@abstractmethod
def interface(self):
"""Interface description about how to run the code"""
@property
@abstractmethod
def output_format(self):
"""Output format description"""
@property
@abstractmethod
def simulator(self):
"""Simulator description"""
@abstractmethod
def get_scenario_all_desc(self) -> str:
"""Combine all the description together"""
rdagent/core/task_generator.py
+4
View File
@@ -2,11 +2,15 @@ from abc import ABC, abstractmethod
from typing import Generic, List, Sequence, TypeVar
from rdagent.core.experiment import Experiment
from rdagent.core.scenario import Scenario
ASpecificExp = TypeVar("ASpecificExp", bound=Experiment)
class TaskGenerator(ABC, Generic[ASpecificExp]):
def __init__(self, scen: Scenario) -> None:
self.scen: Scenario = scen
@abstractmethod
def generate(self, exp: ASpecificExp) -> ASpecificExp:
"""
rdagent/scenarios/qlib/experiment/factor_experiment.py
+9 -7
View File
@@ -1,13 +1,9 @@
from pathlib import Path
from rdagent.components.task_implementation.factor_implementation.factor import (
FactorExperiment,
)
from rdagent.components.task_implementation.factor_implementation.utils import (
get_data_folder_intro,
)
from rdagent.components.coder.factor_coder.factor import FactorExperiment
from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
from rdagent.core.prompts import Prompts
from rdagent.core.proposal import Scenario
from rdagent.core.scenario import Scenario
prompt_dict = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
@@ -23,6 +19,10 @@ class QlibFactorScenario(Scenario):
def source_data(self) -> str:
return get_data_folder_intro()
@property
def output_format(self) -> str:
return prompt_dict["qlib_factor_output_format"]
@property
def interface(self) -> str:
return prompt_dict["qlib_factor_interface"]
@@ -38,6 +38,8 @@ The source data you can use:
{self.source_data}
The interface you should follow to write the runnable code:
{self.interface}
The output of your code should be in the format:
{self.output_format}
The simulator user can use to test your factor:
{self.simulator}
"""
rdagent/scenarios/qlib/experiment/prompts.yaml
+25 -3
View File
@@ -11,9 +11,31 @@ qlib_factor_background: |-
Please specifically give all the hyperparameter in the factors like the window size, look back period, and so on. One factor should statically defines one output with a static source data. For example, last 10 days momentum and last 20 days momentum should be two different factors.
qlib_factor_interface: |-
Your code should follow the interface to better interact with the user's system.
Your code should contain the following part: the import part, the function part, and the main part. You should write a main function name: "calculate_{function_name}" and call this function in "if __name__ == __main__" part. Don't write any try-except block in your code. The user will catch the exception message and provide the feedback to you.
User will write your code into a python file and execute the file directly with "python {your_file_name}.py". You should calculate the factor values and save the result into a HDF5(H5) file named "result.h5" in the same directory as your python file. The result file is a HDF5(H5) file containing a pandas dataframe. The index of the dataframe is the "datetime" and "instrument", and the single column name is the factor name,and the value is the factor value. The result file should be saved in the same directory as your python file.
Your python code should follow the interface to better interact with the user's system.
Your python code should contain the following part: the import part, the function part, and the main part. You should write a main function name: "calculate_{function_name}" and call this function in "if __name__ == __main__" part. Don't write any try-except block in your python code. The user will catch the exception message and provide the feedback to you.
User will write your python code into a python file and execute the file directly with "python {your_file_name}.py". You should calculate the factor values and save the result into a HDF5(H5) file named "result.h5" in the same directory as your python file. The result file is a HDF5(H5) file containing a pandas dataframe. The index of the dataframe is the "datetime" and "instrument", and the single column name is the factor name,and the value is the factor value. The result file should be saved in the same directory as your python file.
qlib_factor_output_format: |-
Your output should be a pandas dataframe similar to the following example information:
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 40914 entries, (Timestamp('2020-01-02 00:00:00'), 'SH600000') to (Timestamp('2021-12-31 00:00:00'), 'SZ300059')
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 your factor name 40914 non-null float64
dtypes: float64(1)
memory usage: <ignore>
None
One possible format of `result.h5` may be like following:
datetime instrument
2020-01-02 SZ000001 -0.001796
SZ000166 0.005780
SZ000686 0.004228
SZ000712 0.001298
SZ000728 0.005330
...
2021-12-31 SZ000750 0.000000
SZ000776 0.002459
qlib_factor_simulator: |-
The factors will be sent into Qlib to train a model to predict the next several days return based on the factor values of the previous days.
rdagent/scenarios/qlib/factor_experiment_loader/json_loader.py
+7 -7
View File
@@ -2,16 +2,16 @@ import json
from pathlib import Path
from rdagent.components.benchmark.eval_method import TestCase
from rdagent.components.loader.experiment_loader import FactorExperimentLoader
from rdagent.components.task_implementation.factor_implementation.factor import (
from rdagent.components.coder.factor_coder.factor import (
FactorExperiment,
FactorTask,
FileBasedFactorImplementation,
)
from rdagent.components.loader.experiment_loader import FactorExperimentLoader
from rdagent.core.experiment import Loader
class FactorImplementationExperimentLoaderFromDict(FactorExperimentLoader):
class FactorExperimentLoaderFromDict(FactorExperimentLoader):
def load(self, factor_dict: dict) -> list:
"""Load data from a dict."""
task_l = []
@@ -27,17 +27,17 @@ class FactorImplementationExperimentLoaderFromDict(FactorExperimentLoader):
return exp
class FactorImplementationExperimentLoaderFromJsonFile(FactorExperimentLoader):
class FactorExperimentLoaderFromJsonFile(FactorExperimentLoader):
def load(self, json_file_path: Path) -> list:
with open(json_file_path, "r") as file:
factor_dict = json.load(file)
return FactorImplementationExperimentLoaderFromDict().load(factor_dict)
return FactorExperimentLoaderFromDict().load(factor_dict)
class FactorImplementationExperimentLoaderFromJsonString(FactorExperimentLoader):
class FactorExperimentLoaderFromJsonString(FactorExperimentLoader):
def load(self, json_string: str) -> list:
factor_dict = json.loads(json_string)
return FactorImplementationExperimentLoaderFromDict().load(factor_dict)
return FactorExperimentLoaderFromDict().load(factor_dict)
# TODO loader only supports generic of task or experiment, testcase might cause CI error here
rdagent/scenarios/qlib/factor_experiment_loader/pdf_loader.py
+3 -3
View File
@@ -22,7 +22,7 @@ from rdagent.core.log import RDAgentLog
from rdagent.core.prompts import Prompts
from rdagent.oai.llm_utils import APIBackend, create_embedding_with_multiprocessing
from rdagent.scenarios.qlib.factor_experiment_loader.json_loader import (
FactorImplementationExperimentLoaderFromDict,
FactorExperimentLoaderFromDict,
)
document_process_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
@@ -579,7 +579,7 @@ def deduplicate_factors_by_llm( # noqa: C901, PLR0912
return llm_deduplicated_factor_dict, final_duplication_names_list
class FactorImplementationExperimentLoaderFromPDFfiles(FactorExperimentLoader):
class FactorExperimentLoaderFromPDFfiles(FactorExperimentLoader):
def load(self, file_or_folder_path: Path) -> dict:
docs_dict = load_and_process_pdfs_by_langchain(Path(file_or_folder_path))
@@ -589,4 +589,4 @@ class FactorImplementationExperimentLoaderFromPDFfiles(FactorExperimentLoader):
factor_viability = check_factor_viability(factor_dict)
factor_dict, duplication_names_list = deduplicate_factors_by_llm(factor_dict, factor_viability)
return FactorImplementationExperimentLoaderFromDict().load(factor_dict)
return FactorExperimentLoaderFromDict().load(factor_dict)
rdagent/scenarios/qlib/factor_proposal.py
+3 -8
View File
@@ -4,18 +4,13 @@ from typing import List, Tuple
from jinja2 import Environment, StrictUndefined
from rdagent.components.idea_proposal.factor_proposal import (
from rdagent.components.coder.factor_coder.factor import FactorExperiment, FactorTask
from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
from rdagent.components.proposal.factor_proposal import (
FactorHypothesis,
FactorHypothesis2Experiment,
FactorHypothesisGen,
)
from rdagent.components.task_implementation.factor_implementation.factor import (
FactorExperiment,
FactorTask,
)
from rdagent.components.task_implementation.factor_implementation.utils import (
get_data_folder_intro,
)
from rdagent.core.prompts import Prompts
from rdagent.core.proposal import HypothesisSet, Scenario, Trace
rdagent/scenarios/qlib/factor_task_implementation/__init__.py
+3 -4
View File
@@ -1,5 +1,4 @@
from rdagent.components.task_implementation.factor_implementation.CoSTEER import (
CoSTEERFG,
)
from rdagent.components.coder.factor_coder.CoSTEER import FactorCoSTEER
COSTEERFG_QUANT_FACTOR_IMPLEMENTATION = CoSTEERFG # TODO: This is a placeholder. We need to split the scenario part of the task implementation into this folder
QlibFactorCoSTEER = FactorCoSTEER
# TODO: This is a placeholder. We need to split the scenario part of the task implementation into this folder
rdagent/scenarios/qlib/model_task_implementation/__init__.py
+3 -4
View File
@@ -1,6 +1,5 @@
from rdagent.components.task_implementation.model_implementation.one_shot import (
ModelCodeWriter,
)
from rdagent.components.coder.model_coder.one_shot import ModelCodeWriter
class QlibModelCodeWriter(ModelCodeWriter): ...
class QlibModelCodeWriter(ModelCodeWriter):
...
rdagent/scenarios/qlib/task_generator/feedback.py
+2 -1
View File
@@ -4,4 +4,5 @@
from rdagent.core.proposal import Experiment2Feedback
class QlibFactorExperiment2Feedback(Experiment2Feedback): ...
class QlibFactorExperiment2Feedback(Experiment2Feedback):
...
rdagent/scenarios/qlib/task_generator/model.py
+2 -1
View File
@@ -1,7 +1,8 @@
from rdagent.components.coder.model_coder.model import ModelImplementation
from rdagent.core.task_generator import TaskGenerator
class QlibModelImplementation(TaskGenerator[QlibModelExperiment]):
class QlibModelImplementation(TaskGenerator[ModelImplementation]):
"""
Docker run
Everything in a folder
rdagent/utils/env.py
+15 -14
View File
@@ -6,11 +6,12 @@ Tries to create uniform environment for the agent to run;
"""
import os
import docker
from abc import abstractmethod
from pydantic import BaseModel
from typing import Generic, TypeVar
from pathlib import Path
from typing import Generic, TypeVar
import docker
from pydantic import BaseModel
ASpecificBaseModel = TypeVar("ASpecificBaseModel", bound=BaseModel)
@@ -21,6 +22,7 @@ class Env(Generic[ASpecificBaseModel]):
- It provides base typing and checking featurs.
- loading and dumping the information will be easier: for example, we can use package like `pydantic-yaml`
"""
conf: ASpecificBaseModel # different env have different conf.
def __init__(self, conf: ASpecificBaseModel):
@@ -33,10 +35,7 @@ class Env(Generic[ASpecificBaseModel]):
"""
@abstractmethod
def run(self,
entry: str | None,
local_path: str | None = None,
env: dict | None = None) -> str:
def run(self, entry: str | None, local_path: str | None = None, env: dict | None = None) -> str:
"""
Run the folder under the environment.
@@ -70,6 +69,7 @@ class LocalEnv(Env[LocalConf]):
"""
Sometimes local environment may be more convinient for testing
"""
conf: LocalConf
@@ -87,10 +87,12 @@ class DockerConf(BaseModel):
# So we just want to download it once.
QLIB_TORCH_IMAGE = DockerConf(image="linlanglv/qlib_image_nightly_pytorch:nightly",
mount_path="/workspace",
default_entry="qrun conf.yaml",
extra_volumes={Path("~/.qlib/").expanduser().resolve(): "/root/.qlib/"})
QLIB_TORCH_IMAGE = DockerConf(
image="linlanglv/qlib_image_nightly_pytorch:nightly",
mount_path="/workspace",
default_entry="qrun conf.yaml",
extra_volumes={Path("~/.qlib/").expanduser().resolve(): "/root/.qlib/"},
)
class DockerEnv(Env[DockerConf]):
@@ -109,7 +111,6 @@ class DockerEnv(Env[DockerConf]):
raise RuntimeError(f"Error while pulling the image: {e}")
def run(self, entry: str | None = None, local_path: str | None = None, env: dict | None = None):
if env is None:
env = {}
client = docker.from_env()
@@ -119,10 +120,10 @@ class DockerEnv(Env[DockerConf]):
volumns = {}
if local_path is not None:
local_path = os.path.abspath(local_path)
volumns[local_path] = {'bind': self.conf.mount_path, 'mode': 'rw'}
volumns[local_path] = {"bind": self.conf.mount_path, "mode": "rw"}
if self.conf.extra_volumes is not None:
for lp, rp in self.conf.extra_volumes.items():
volumns[lp] = {'bind': rp, 'mode': 'rw'}
volumns[lp] = {"bind": rp, "mode": "rw"}
log_output = ""
try:
test/utils/test_env.py
+5 -5
View File
@@ -1,24 +1,24 @@
import os
import sys
import subprocess
import sys
import unittest
from pathlib import Path
sys.path.append(str(Path(__file__).resolve().parent.parent))
from rdagent.utils.env import QTDockerEnv
import shutil
from rdagent.utils.env import QTDockerEnv
DIRNAME = Path(__file__).absolute().resolve().parent
class EnvUtils(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
# NOTE: For a docker file, the output are generated with root permission.
# mlrun_p = DIRNAME / "env_tpl" / "mlruns"
# mlrun_p = DIRNAME / "env_tpl" / "mlruns"
# if mlrun_p.exists():
# shutil.rmtree(mlrun_p)
...
@@ -34,7 +34,7 @@ class EnvUtils(unittest.TestCase):
# the stdout are returned as result
result = qtde.run(local_path=str(DIRNAME / "env_tpl"), entry="qrun conf.yaml")
mlrun_p = DIRNAME / "env_tpl" / "mlruns"
mlrun_p = DIRNAME / "env_tpl" / "mlruns"
self.assertTrue(mlrun_p.exists(), f"Expected output file {mlrun_p} not found")