mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
732361bb90
- Path injection (B614): centralized safe_resolve_path in core/utils.py, refactored 6 UI modules to use it with safe_root validation - B701: added explicit autoescape=select_autoescape() to Jinja2 Environment() calls in 3 files - B101: replaced assert statements with proper if/raise patterns in 12+ files (partial) - B112: added logger.warning() to bare except:continue blocks in 5 files
72 lines
3.0 KiB
Python
72 lines
3.0 KiB
Python
import json
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from typing import Dict, List
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from jinja2 import Environment, StrictUndefined
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from rdagent.components.coder.factor_coder import FactorCoSTEER
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from rdagent.components.coder.model_coder import ModelCoSTEER
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from rdagent.core.developer import Developer
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from rdagent.oai.llm_utils import APIBackend
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from rdagent.scenarios.kaggle.experiment.kaggle_experiment import (
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KG_SELECT_MAPPING,
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KGModelExperiment,
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)
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KGModelCoSTEER = ModelCoSTEER
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KGFactorCoSTEER = FactorCoSTEER
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from rdagent.utils.agent.tpl import T
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DEFAULT_SELECTION_CODE = """
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import pandas as pd
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def select(X: pd.DataFrame) -> pd.DataFrame:
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\"""
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Select relevant features. To be used in fit & predict function.
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\"""
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if X.columns.nlevels == 1:
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return X
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{% if feature_index_list is not none %}
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X = X.loc[:, X.columns.levels[0][{{feature_index_list}}].tolist()]
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{% endif %}
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X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
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return X
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"""
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class KGModelFeatureSelectionCoder(Developer[KGModelExperiment]):
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def develop(self, exp: KGModelExperiment) -> KGModelExperiment:
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target_model_type = exp.sub_tasks[0].model_type
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if target_model_type not in KG_SELECT_MAPPING:
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raise ValueError(f"target_model_type {target_model_type} not in KG_SELECT_MAPPING")
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if len(exp.experiment_workspace.data_description) == 1:
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code = (
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Environment(undefined=StrictUndefined) # nosec B701 — renders Python code templates, not HTML; autoescape would corrupt code
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.from_string(DEFAULT_SELECTION_CODE)
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.render(feature_index_list=None)
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)
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else:
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system_prompt = T("scenarios.kaggle.prompts:model_feature_selection.system").r(
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scenario=exp.scen.get_scenario_all_desc(),
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model_type=exp.sub_tasks[0].model_type,
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)
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user_prompt = T("scenarios.kaggle.prompts:model_feature_selection.user").r(
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feature_groups=[desc[0] for desc in exp.experiment_workspace.data_description]
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)
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chosen_index = json.loads(
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APIBackend().build_messages_and_create_chat_completion(
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user_prompt=user_prompt,
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system_prompt=system_prompt,
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json_mode=True,
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json_target_type=Dict[str, List[int]],
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)
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).get("Selected Group Index", [i + 1 for i in range(len(exp.experiment_workspace.data_description))])
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chosen_index_to_list_index = [i - 1 for i in chosen_index]
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code = (
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Environment(undefined=StrictUndefined) # nosec B701 — renders Python code templates, not HTML; autoescape would corrupt code
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.from_string(DEFAULT_SELECTION_CODE)
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.render(feature_index_list=chosen_index_to_list_index)
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)
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exp.experiment_workspace.inject_files(**{KG_SELECT_MAPPING[target_model_type]: code})
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return exp
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