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feat: Kaggle loop update (Feature & Model) (#241)

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* Init todo

* Evaluation & dataset

* Generate new data

* dataset generation

* add the result

* Analysis

* Factor update

* Updates

* Reformat analysis.py

* CI fix

* Revised Preprocessing & Supported Random Forest

* Revised to support three models with feature

* Further revised prompts

* Slight Revision

* docs: update contributors (#230)

* Revised to support three models with feature

* Further revised prompts

* Slight Revision

* feat: kaggle model and feature (#238)

* update first version code

* make hypothesis_gen and experiment_builder fit for both feature and model

* feat: continue kaggle feature and model coder (#239)

* use qlib docker to run qlib models

* feature coder ready

* model coder ready

* fix CI

* finish the first round of runner (#240)

* Optimized the factor scenario and added the front-end.

* fix a small bug

* fix a typo

* update the kaggle scenario

* delete model_template folder

* use experiment to run data preprocess script

* add source data to scenarios

* minor fix

* minor bug fix

* train.py debug

* fixed a bug in train.py and added some TODOs

* For Debugging

* fix two small bugs in based_exp

* fix some bugs

* update preprocess

* fix a bug in preprocess

* fix a bug in train.py

* reformat

* Follow-up

* fix a bug in train.py

* fix a bug in workspace

* fix a bug in feature duplication

* fix a bug in feedback

* fix a bug in preprocessed data

* fix a bug om feature engineering

* fix a ci error

* Debugged & Connected

* Fixed error on feedback & added other fixes

* fix CI errors

* fix a CI bug

* fix: fix_dotenv_error (#257)

* fix_dotenv_error

* format with isort

* Update rdagent/app/cli.py

---------

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* chore(main): release 0.2.1 (#249)

Release-As: 0.2.1

* init a scenario for kaggle feature engineering

* delete error codes

* Delete rdagent/app/kaggle_feature/conf.py

---------

Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: Taozhi Wang <taozhi.mark.wang@gmail.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
Co-authored-by: cyncyw <47289405+taozhiwang@users.noreply.github.com>
Co-authored-by: Xisen-Wang <xisen_application@163.com>
Co-authored-by: Haotian Chen <113661982+Hytn@users.noreply.github.com>
Co-authored-by: WinstonLiye <1957922024@qq.com>
Co-authored-by: WinstonLiyt <104308117+WinstonLiyt@users.noreply.github.com>
Co-authored-by: Linlang <30293408+SunsetWolf@users.noreply.github.com>
This commit is contained in:
Xu Yang
2024-09-11 15:26:52 +08:00
committed by GitHub
parent 11fb03ef62
commit dbe2cf12bb
42 changed files with 1513 additions and 681 deletions
Download Patch File Download Diff File Expand all files Collapse all files
rdagent/components/coder/factor_coder/CoSTEER/evaluators.py
+17 -11
View File
@@ -161,7 +161,7 @@ class FactorOutputFormatEvaluator(FactorEvaluator):
)
buffer = io.StringIO()
gen_df.info(buf=buffer)
gen_df_info_str = buffer.getvalue()
gen_df_info_str = f"The use is currently working on a feature related task.\nThe output dataframe info is:\n{buffer.getvalue()}"
system_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
@@ -378,6 +378,7 @@ class FactorValueEvaluator(FactorEvaluator):
self,
implementation: Workspace,
gt_implementation: Workspace,
version: int = 1, # 1 for qlib factors and 2 for kaggle factors
**kwargs,
) -> Tuple:
conclusions = []
@@ -389,18 +390,21 @@ class FactorValueEvaluator(FactorEvaluator):
equal_value_ratio_result = 0
high_correlation_result = False
# Check if both dataframe has only one columns
feedback_str, _ = FactorSingleColumnEvaluator(self.scen).evaluate(implementation, gt_implementation)
conclusions.append(feedback_str)
# Check if both dataframe has only one columns Mute this since factor task might generate more than one columns now
if version == 1:
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)
feedback_str, daily_check_result = FactorDatetimeDailyEvaluator(self.scen).evaluate(
implementation, gt_implementation
)
conclusions.append(feedback_str)
if version == 1:
feedback_str, daily_check_result = FactorDatetimeDailyEvaluator(self.scen).evaluate(
implementation, gt_implementation
)
conclusions.append(feedback_str)
else:
daily_check_result = None
# Check if both dataframe have the same rows count
if gt_implementation is not None:
@@ -627,7 +631,9 @@ class FactorEvaluatorForCoder(FactorEvaluator):
(
factor_feedback.factor_value_feedback,
decision_from_value_check,
) = self.value_evaluator.evaluate(implementation=implementation, gt_implementation=gt_implementation)
) = self.value_evaluator.evaluate(
implementation=implementation, gt_implementation=gt_implementation, version=target_task.version
)
factor_feedback.final_decision_based_on_gt = gt_implementation is not None
@@ -647,7 +653,7 @@ class FactorEvaluatorForCoder(FactorEvaluator):
target_task=target_task,
implementation=implementation,
execution_feedback=factor_feedback.execution_feedback,
value_feedback=factor_feedback.factor_value_feedback,
factor_value_feedback=factor_feedback.factor_value_feedback,
gt_implementation=gt_implementation,
)
(
rdagent/components/coder/factor_coder/factor.py
+30 -11
View File
@@ -24,9 +24,11 @@ class FactorTask(Task):
factor_name,
factor_description,
factor_formulation,
*args,
variables: dict = {},
resource: str = None,
factor_implementation: bool = False,
**kwargs,
) -> None:
self.factor_name = factor_name
self.factor_description = factor_description
@@ -34,6 +36,7 @@ class FactorTask(Task):
self.variables = variables
self.factor_resources = resource
self.factor_implementation = factor_implementation
super().__init__(*args, **kwargs)
def get_task_information(self):
return f"""factor_name: {self.factor_name}
@@ -75,8 +78,8 @@ class FactorFBWorkspace(FBWorkspace):
def __init__(
self,
*args,
executed_factor_value_dataframe=None,
raise_exception=False,
executed_factor_value_dataframe: pd.DataFrame = None,
raise_exception: bool = False,
**kwargs,
) -> None:
super().__init__(*args, **kwargs)
@@ -102,7 +105,10 @@ class FactorFBWorkspace(FBWorkspace):
1. make the directory in workspace path
2. write the code to the file in the workspace path
3. link all the source data to the workspace path folder
4. execute the code
if call_factor_py is True:
4. execute the code
else:
4. generate a script from template to import the factor.py dump get the factor value to result.h5
5. read the factor value from the output file in the workspace path folder
returns the execution feedback as a string and the factor value as a pandas dataframe
@@ -130,15 +136,21 @@ class FactorFBWorkspace(FBWorkspace):
if self.executed_factor_value_dataframe is not None:
return self.FB_FROM_CACHE, self.executed_factor_value_dataframe
source_data_path = (
Path(
FACTOR_IMPLEMENT_SETTINGS.data_folder_debug,
if self.target_task.version == 1:
source_data_path = (
Path(
FACTOR_IMPLEMENT_SETTINGS.data_folder_debug,
)
if data_type == "Debug"
else Path(
FACTOR_IMPLEMENT_SETTINGS.data_folder,
)
)
if data_type == "Debug"
else Path(
elif self.target_task.version == 2:
# TODO you can change the name of the data folder for a better understanding
source_data_path = Path(
FACTOR_IMPLEMENT_SETTINGS.data_folder,
)
)
source_data_path.mkdir(exist_ok=True, parents=True)
code_path = self.workspace_path / f"factor.py"
@@ -147,9 +159,16 @@ class FactorFBWorkspace(FBWorkspace):
execution_feedback = self.FB_EXECUTION_SUCCEEDED
execution_success = False
if self.target_task.version == 1:
execution_code_path = code_path
elif self.target_task.version == 2:
execution_code_path = self.workspace_path / f"{uuid.uuid4()}.py"
execution_code_path.write_text((Path(__file__).parent / "factor_execution_template.txt").read_text())
try:
subprocess.check_output(
f"{FACTOR_IMPLEMENT_SETTINGS.python_bin} {code_path}",
f"{FACTOR_IMPLEMENT_SETTINGS.python_bin} {execution_code_path}",
shell=True,
cwd=self.workspace_path,
stderr=subprocess.STDOUT,
@@ -161,7 +180,7 @@ class FactorFBWorkspace(FBWorkspace):
execution_feedback = (
e.output.decode()
.replace(str(code_path.parent.absolute()), r"/path/to")
.replace(str(execution_code_path.parent.absolute()), r"/path/to")
.replace(str(site.getsitepackages()[0]), r"/path/to/site-packages")
)
if len(execution_feedback) > 2000:
rdagent/components/coder/factor_coder/factor_execution_template.txt
+13
View File
@@ -0,0 +1,13 @@
import os
import numpy as np
import pandas as pd
from factor import feat_eng
if os.path.exists("valid.pkl"):
valid_df = pd.read_pickle("valid.pkl")
else:
raise FileNotFoundError("No valid data found.")
new_feat = feat_eng(valid_df)
new_feat.to_hdf("result.h5", key="data", mode="w")