- quant.py: guard against empty orch_factors, move strategy_name before try block
- quant_proposal.py: fix __init__ return type Tuple[dict,bool] -> None
- strategy_orchestrator.py: remove dead rdagent_logger import shadowed by getLogger
- factor.py: replace unusual 'not x is None' with idiomatic 'x is not None'
- workflow/loop.py: withdraw_loop(0) raises RuntimeError instead of looking for folder -1
- workflow/tracking.py: replace crash-prone AssertionError with logger.warning + skip
- factor_from_report.py: fix misleading comment about loop_n/step_n dual use
- 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
- Add vbt_backtest.py as single source of truth for all metric formulas
(Sharpe, drawdown, IC, transaction costs) — backtest_engine.py and
strategy_orchestrator.py now delegate to it
- Add LLMUnavailableError to exception.py; rd_loop.py catches it at the
proposal stage and raises LoopResumeError to avoid corrupting trace
history with None hypotheses
- Guard record() against None exp/hypothesis so loop resets leave
trace.hist in a consistent state
- Refactor strategy_orchestrator and optuna_optimizer to use unified
backtest path; remove duplicate metric calculation code
- Add predix_rebacktest_unified.py script for offline re-evaluation
- Update tests and README
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Problem:
- All 50 parallel runs failed with: AttributeError: 'QuantTrace' object has no attribute 'get_factor_count'
- The _build_strategies_with_ai() method calls self.trace.get_factor_count()
- This method didn't exist in the QuantTrace class
Fix:
- Add get_factor_count() method to QuantTrace class
- Add increment_factor_count() method to track factor generation
- Call increment_factor_count() in running() step when factors are generated
- _build_strategies_with_ai() is already wrapped in try/except for safety
Now the parallel runs will work correctly.
Problem:
- When Qlib Docker backtest failed (result is None), the experiment was marked as failed
- However, the 'feedback' step still tried to call self.factor_summarizer.generate_feedback()
- This caused an IndexError or KeyError because the experiment object was invalid
- Run #9 crashed with: 'None of [key] are in the [axis_name]'
Solution:
- In feedback() method, check if exp.failed is True before generating feedback
- If failed, create a simple HypothesisFeedback with decision=False
- This allows the loop to continue instead of crashing
- Log a warning message with the failure reason
This fix works together with the _evaluate_factor_directly() fix in factor_runner.py
to ensure that even when Docker fails, the loop continues smoothly.
- Add predix_parallel.py: Run multiple factor experiments concurrently
* python predix_parallel.py --runs 5 --api-keys 2 -m openrouter
* Round-robin API key distribution across available keys
* Rich live dashboard with per-run status, elapsed time, exit codes
* Graceful shutdown (Ctrl+C kills all children cleanly)
- Add --run-id parameter to predix.py for isolated single runs
* Separate log files: fin_quant_run{N}.log
* Separate results: results/runs/run{N}/
* Separate workspace: RD-Agent_workspace_run{N}/
* Separate databases per run
- Modify CoSTEER and FactorRunner for PARALLEL_RUN_ID isolation
* _save_intermediate_results uses run-specific directories
* _save_result_to_database and _write_run_log isolated per run
* _ensure_results_dirs creates run-specific paths
- Reduce max_loop from 10 to 3 for faster iterations
- Add docs/parallel_runs.md with full documentation
Tests: 103 passed
- Remove duplicate DB save from quant.py (keep only in factor_runner)
- Add explicit DB path creation with mkdir -p
- Add JSON factor summaries to results/factors/
- Add debug logging for result structure
- Fix logger.debug -> logger.info (RDAgentLog compatibility)
- Update tests to match new architecture (240/240 passing)
- Enhance extract_results.py with progress indicators
* update rdagent cmd
* fix log error message
* use multiProcessing.Process instead of subprocess.Popen
* add traces to gitignore
* add user interactor in RDLoop (finance scenarios)
* add interactor (feedback, hypothesis) for quant scens
* fix the test_end in qlib conf
* add features init config, general instruction to qlib scenarios
* set base features for based exp
* fix bug when combine factors
* move traces folder to git_ignore_folder
* fix bug in features init
* fix quant interact bug
* fix logger warning error
* bug fixes
* modify rdagent logger, now it can set file output
* adjust cli functions and fix logger bug
* fix server port transport problem
* update server_ui in cli
* add web code
* fix CI problem
* black fix
* update web ui README
* update README
* update readme
* refine prompt
* small update
* fix a small bug
* remove debug config after execution
* fix: only remove <think> at start
* feat: support creating dataset & multi-eval frame (#1302)
* feat: add iterative evolve and evaluation support with partial chain stop
* feat: add FTDataEvaluator and support multiple implement functions in finetune
* feat: data implement for pre-proposal and proposal and add datasets (#1303)
* feat:(1) support for multi layer dataset extraction (2) add category.json for dataset in datasets/
* fix: fix bug for generate category.json
* feat: add get_dataset_folder_desc
* init data proposal and merge qzli/ft
* update data proposal prompts and add max_position_embeddings and resolve confilcts
* remove sample counts in data proposal
* turn data and train to unified hypo_gen
* refine prompts
* remove category.json and add it to dataset_info
* fix jinja problem and proposal done
* lint
* add ai-generated description and raw readme into dataset_info.json
* update prompt for description
* add datasets
* initial fix for proposal of data
* final version for data proposal
* lint
* feat: add stats in dataset_info, and enable data coder (#1306)
* refactor(dataset): add stats into dataset_info.json, and remove dataset from gitignore_folder
* feat: enable data coder and run data process
* feat: Merge data coder (#1307)
* feat: implement finetune data coding, evaluation, and config improvements
* fix: deepspeed config path
* fix: dataset info columns
---------
Co-authored-by: Young <afe.young@gmail.com>
* replace str length with token_limit
* add readme to dataset_info and remove useless blank lines in scenario description
* feat: dataset prepare
* fix: extract prams script name
* feat: add loss&predictions samples to feedback
* remove duplicate envs and and add llm_api_preferences and enhance reasoning token limits
* feat: network for ft_env
* fix: remove gpt-4o, which has low quota
* feat: a simple ui
* feat: merge data and train task type (#1309)
* feat: filter redundant prams of lf
* fix: ui bug caused by removing task_type
* fix: force agent to use high concurrency, and remove redundant prompt
* feat: extract info from llama factory log, and check data exists before download
* fix: add compatibility rules
* feat: llm evaluator for data coder
* feat: openai package in ft docker, and refine prompt
* feat: refine ft ui, add more info
* feat: add raw logs
* refine data coder prompt(for feedback debug)
* feat: select dataset in scen init
* fix: ui for docker log seperately
* feat: sync log through blob
* improve ui, and add llm feedback in Runner&Exp2FB (#1312)
* fix: ui bug to visualize docker log, and lint
* feat: unified docker log for ft env, and some refactor
* fix bugs and improve ui
* feat: save log of evaluator(single feedback)
* feat: add evaluator, set cleanup docker log
* feat: call llm in RunnerEvaluator and Feedback
* fix: extract structured error message in RunnerEvaluator
* feat: feedback improve, and fix some bugs
* feat: feedback improve when runner fails
* small update
* feat(UI): add running info and benchmark metric in loop expander
* feat(UI): add render markdown toggle
* feat: refine prompts and add error type in exp2fb
* feat: add filterd params reason, set default benchmark timeout to infinite, and refine train loss express
* recover dataset deepscaler
* feat: set timeout in .env
* refactor: unifiied ft_env timeout
* feat: debug mode for data coder
* feat: deliver data_stats after generate debug_data
* feat: use gpt-5.1 as judge model, set judge_retry, and refine debug mode prompt
* refine prompt
* refactor: llama factory manager logic, and refine data processing prompt
* feat(DockerEnv): support GPU selection via CUDA_VISIBLE_DEVICES
* feat: set api concurrency via .env
* fix: ft env timeout bug
* feat: enable CondaEnv run
* fix: can't update bin path in first run, and path bug in lf manager
* feat(ui): set log path through .env
* refactor(ui): wrap_lines, remove css
* feat(coder): retry when parse code-block fail
* fix: refine single-fb in ui, and fix path bug(not allow proposal to decide path)
* fix: opencompass CondaEnv torch compatible with vllm
* fix: refine error text in coding
* feat: deepspeed config for CondaEnv
* feat: memory estimator
* fix: deepspeed package for condaenv
* fix: use `client.chat.completions.create()` only
* feat: flash attention for condaenv
* feat: strong and weak models interface
* fix: condaenv package dependency
* use multi round conversation in llm finetune proposal
* refine prompt for data processing
* enable evolving in data coder
* maximize output token size
* fix: refine ui
* fix: optional packages for llama factory
* fix: torch denpendency for b200
* fix: opencompass dependency
* update cot prompts
* skip the sub implement
* skip conda preparation if env exists
* update chemcot datasets
* fix: unify docker to use litellm
* update readme and instructions
* fix: set CUDA_VISIBLE_DEVICES for CondaEnv
* feat: add panorama dataset, refactor dataset interface
* feat: calculate token using tiktoken, and ndarray bug
* fix: download subtasks of chemcotdataset seperately
* feat: customized prepare func for datasets
* feat: update new benchmarks
* add datasets package
* docs: readme for llm finetune
* feat: download raw data directly, with post-process function
* feat: analyze raw dataset
* suppress litellm debug info
* feat(ui): summary page
* feat: run multi-jobs
* feat: improve ui
* feat: add path and checkout options to LLM finetune loop entrypoint
* feat: add FinanceIQ_ppl benchmark with auto-download and dataset desc rendering
* refactor: remove unused imports and dead code, fix session folder logging
* feat: enable tablebench and tableInstruct dataset
* refine dataset readme, and coder prompt
* refine proposal and coder prompt
* fix: ui path (default log path)
* feat: add automatic LoRA model merging for benchmarking with vLLM
* refactor: reorganize finetune benchmark and merge modules under benchmark dir
* refactor: modularize benchmark config and error extraction for finetune scenario
* fix: update benchmark import paths and disable env cache for device info
* refactor docke&conda env and fix import bugs
* modify init python file
* feat: add FinanceIQ dataset split utility and integrate with pipeline
* feat: set weak and strong model by env, distribute workload across models
* feat: sample dataset and rm params for tensorboard, wandb
* update script to run jobs
* refine proposal prompt, remove specific dataset name
* fix(ui): auto switch log folder
* fix: estimate the processed full data after sample
* feat: filter raw data more aggressively, and lower data_eval standard
* feat: sync workspace to blob
* feat: rdkit for chemcotbench
* update qwen2.5&llama3.1 context
* fix: force failure on validation error and remove try/except in validator
* feat: unified error sample extraction (with test scripts)
* feat: set conda cache with .env
* feat: skip data eval if data pass in last evo
* fix: rm redundant param
* fix ui bug
* refactor: centralize assign_code_list_to_evo in MultiProcessEvolvingStrategy
* feat: add test_params.yaml generation and workspace cleanup improvements for finetune
* refactor: replace get_clear_ws_cmd with clear_workspace and update prompts for hard check criteria
* add bioprobench dataset
* fix: handle commas in training config extraction and refactor prompt includes
* bioprobench description
* add bioprobench readme
* feat: merge lora adapter for blackwell gpu
* feat: support for multi benchmarks in one job
* change dfficult aware content for training
* update difficulty-aware and logging principles
* fix: resolve variable name conflict in FTRunnerEvaluator
* set job id accuracy to minute
* feat(ui): display one selected metric per benchmark
* feat: store sota exp, and fix ws_ckp bug
* fix: truncate data.json in feedback
* fix: opencompass data for conda env
* fix: save only the last model
* feat: set log path and ws path
* fix: set overwrite_cache to avoid lock contention(through injecting params)
* feat: redirect stdout to file in localenv
* add pickle cache to dataset desc
* fix CI
* fix: remove redundant wrapper
* feat: set python_unbuffered
* move redirect stdout to env run
* fix a small bug
* move model folder
* feat(ui): display benchmark baseline
* fix: enrich scenario and benchmark description
* fix: rewrite runner eval to accept easier
* feat: compare with baseline when no SOTA
* update tablebench readme
* fix: switch back to single benchmark (for baseline)
* feat(ui): add ws path in ui
* refactor: update SOTA tracking to use DAG traversal and parent selection
* fix: prioritize local_selection in trace and refactor sibling retrieval logic
* refactor: unify error handling in feedback generation and update workspace injection
* feat: add skip_loop_error_stepname to control error skip step in LoopBase
* fix: set local_selection to NEW_ROOT for experiments without parent
* feat: set different ports for jobs
* feat: set different ports for jobs
* feat: add upper data size limit for LLM fine-tuning and update related prompts
* fix: replace get_truncated_stdout() with stdout for consistent output handling
* refactor: remove data.json from cache and workspace logic, focus on script-based reuse
* fix: rm target_scenario
* feat: add selective cache extraction and custom cache key for data processing
* fix(ui): bug when displaying tablebench
* fix: filter config in dataset_info.json
* feat: add test set, set valid set
* feat(ui): update test score, and set color for final decision
* feat: add test score for baseline and update ui
* fix: use [-100:] as test range
* feat: update data_stats in runner
* feat: wait for opencompass init when run multi jobs
* fix: adjust test&valid split
* feat: force to generate COT(with <think> token), and add answer format in scenarios.json
* feat: improve ui
* fix: unify benchmark volume mounts and set extra_volumes for conda env
* fix(ui): number color
* fix: update GPU memory handling to use total memory in GB and streamline code
* fix: set use_cot_postprocessor
* feat: add env_dict to config classes and merge env vars in Env run
* fix: let coder obey proposal
* fix(ui): direction bug and update chemcot core metirc
* fix: set consistent benchmark mount points and env vars for docker and conda
* fix: addintional target for LoRA
* feat: workspace dir log for benchmark running
* fix: tableInstruct path bug and update benchmark description
* feat: timeout for whole job
* fix: align FinanceIQ import to opencompass
* feat: use llm_judge for FinanceIQ
* feat: switch to turn on <think> or not
* feat: using scripts to redirect stdout, and run in different windows
* feat: sync litellm log
* fix: gpu memory format
* fix: escape special characters in benchmark desc
* fix: set data processing timeout to 1h
* feat: set valid_loss and save_best_model
* fix: inject timeout and stage
* fix: loss history extract logic
* feat: inject output dir
* feat: inject eval batch size
* feat: inject save_total_limit
* feat: update data prompt
* fix: escape shell special characters
* fix: tablebench visualization UI
* fix: move implementation validation to coder, and ignore injected params
* feat: README for FinanceIQ dataset
* fix: bioprobench desc error
* fix: remove task alignment when coder eval
* fix: FinanceIQ now extracts last capital as answer
* fix: stdout contains binary data
* feat: recover estimate full output and set eval setting automatically
* fix(ui): precision for summary table
* fix(ui): import error
* feat: try to use lora
* fix(api): fix litellm bug for code block
* fix: refine prompts to give agent more decision space
* chore(ci): fix mypy typing issues
* chore(ci): format code with black
* chore(ci): fix ruff lint violations
* chore(ci): sort imports with isort
* chore(ci): format code with black
* test: temporarily skip extract_parameters imports due to numpy pin
* fix: compatibility issues for qlib scenarios on finetune branch
* fix(fin_factor): skip to fb for coder error
* fix(loop): default skip to feedback step on skip_loop_error
When skip_loop_error exception happens and skip_loop_error_stepname is not
explicitly set, default to jumping to 'feedback' step if it exists,
otherwise fall back to the last step (record).
This prevents KeyError when record step tries to access feedback data that
doesn't exist because we skipped the feedback phase.
Also removed redundant skip_loop_error_stepname from finetune loop since
it's now the default behavior.
* add 'skip to record' to DS scenario like other scenarios
* fix 2 scenarios bug about rd_loop class
* fix: lint(mypy, ruff, black) error
* fix: mypy lint error
* fix data science scenario bug
---------
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
Co-authored-by: Qizheng Li <jenssenlee@163.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
Co-authored-by: amstrongzyf <201840057@smail.nju.edu.cn>
Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: amstrongzyf <amstrongzyf@126.com>
Co-authored-by: chelsea97 <zhuowbrown@gmail.com>
Co-authored-by: SunsetWolf <Lv.Linlang@hotmail.com>
* fix: prevent calendar index overflow when signal data ends early
* fix: make test_end optional to resolve Qlib backtest calendar misalignment
* fix: enhance GPU information output in get_gpu_info function
* fix: improve GPU information output in get_gpu_info function for better clarity
---------
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
* refactor: unify qlib experiment configs, runners, and templates
* fix: use PropSetting instances instead of class attributes in qlib runners
* docs: add configurable train/valid/test time segments for fintech scenarios
* update data science docs part1
* update data science docs part2
* update data science docs part3
* update data science docs part4
* update data science docs part5
* format with isort
* update data science docs part6
* add check environment scripts
* format with isort
* format with isort
* merge env_check to health_check
* format with isort
* format with black
* optimize code
* use boolean cli options
* replace fire with typer
* replace fire with typer
* optimizing parameter and variable naming
* ui server update
* update
* fix bugs
* updates
* use randomname
* fix
* fix bugs
* change time interval in debug server
* some change
* fix CI
* time interval change
* updates
* some changes
* fix
* fix curves
* one IP, one pointers to show msgs return progress
* enable /control
* fix bugs
* fix CI
* fix isort
* docs: document extra_volumes dict format in DockerConf
* feat: accept dict values in extra_volumes to specify bind and mode
* fix: skip invalid PDF reports to prevent infinite loop
* from break to raise self.LoopTerminationError
* format with black
---------
Co-authored-by: Young <afe.young@gmail.com>
* remove state.times in old ui
* remove "r" tag
* remove "d" tag
* remove "ef" tag
* remove "init" tag
* fix CI
* remove old tag in app UI
* fix bugs
* fix CI
* some updates
* filter tags
* refactor: split workflow into pkg, add WorkflowTracker & wait_retry
* feat: add async LoopBase with parallel workers and step semaphores
* fix: replace pickle with dill and run blocking tasks via joblib wrapper
* feat: add log format settings, dynamic parallelism & pickle-based snapshot
* fix: default step semaphore to 1 and avoid subprocess when single worker
* merge bowen's changes
* merge tim's changes
* refactor: extract component task mapping, add conditional logger setup
* lint
* refactor: add type hints and safer remain_time metric logging in workflow
* lint
* fix: allow BadRequestError to be pickled via custom copyreg reducer
* fix: stop loop when LoopTerminationError is raised in LoopBase
* lint
* refactor: make log tag context-local using ContextVar for thread safety
* feat: add subproc_step flag and helper to decide subprocess execution
* fix: use ./cache path and normalize relative volume bind paths
* fix: reset loop_idx to 0 on loop restart/resume to ensure correct flow
* fix: avoid chmod on cache and input dirs in Env timeout wrapper
* fix: skip chmod on 'cache' and 'input' dirs using find -prune
* fix: restrict chmod to immediate mount dirs excluding cache/input
* fix: chmod cache and input dirs alongside their contents after entry run
* fix: guard chmod with directory checks for cache and input
* fix: prefix mount_path in chmod command for cache/input dirs
* fix: drop quotes from find exclude patterns to ensure chmod executes
* fix: skip chmod on cache/input directories to avoid warning spam
* feat: support string volume mappings and poll subprocess stdout/stderr
* support remove symbolic link
* test: use dynamic home path and code volume in LocalEnv local_simple
* fix: skip trace and progress update when loop step is withdrawn
* refactor: add clean_workspace util and non-destructive workspace backup
* fix: preserve symlinks when backing up workspace with copytree
* fix: prevent AttributeError when _pbar not yet initialized in LoopBase
* perf: replace shutil.copytree with rsync for faster workspace backup
* fix: cast log directory Path to str in tar command of data science loop
* fix: use portable 'cp -r -P' instead of rsync for workspace backup
* fix: add retry and logging to workspace backup for robustness
* refactor: extract backup_folder helper and reuse in DataScienceRDLoop
* fix: propagate backup errors & default _pbar getattr to avoid error
* fix the division by zero bug
* refactor: execute RD loops via asyncio.run and add necessary imports
* lint
* lint
* lint
---------
Co-authored-by: Xu <v-xuminrui@microsoft.com>
* fix model input shape bug and costeer_model bug
* fix a bug
* fix a bug in docker result extraction
* a system-level optimization
* add a filter of stdout
* update
* add stdout to model
* model training_hyperparameters update
* quant scenario
* update some quant settings
* llm choose action
* Thompson Sampling Bandit for action choosing
* refine both scens
* add trace messages for quant scen
* fix some bugs
* fix some bugs
* update
* update
* update
* fix
* fix
* fix
* update for merge
* fix ci
* fix some bugs
* fix ci
* fix ci
* fix ci
* fix ci
* refactor
* default qlib4rdagent local env downloading
* fix ci
* fix ci
* fix a bug
* fix ci
* fix: align all prompts on template (#908)
* use template to render all prompts
* fix CI
---------
Co-authored-by: Xu Yang <xuyang1@microsoft.com>
* add fin_quant in cli
* fix a bug
* fix ci
* fix some bugs
* refactor
* remove the columns in hypothesis if no value generated in this column
* fix a bug
* fix ci
* fix conda env
* add qlib gitignore
* remove existed qlib folder & install torch in qlib conda
* fix workspace ui in feedback
* align model config in coder and runner in docker or conda
* fix CI
* fix CI
---------
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
Co-authored-by: Xu Yang <xuyang1@microsoft.com>
* move cache auto continue and retry to all api backend
* add type checker to json mode output
* fix CI
* feat: Add json_mode handling and streaming support in chat completion function
* lint
* fix a bug when returning a dict which value could contain int or bool
* remove litellm
---------
Co-authored-by: Xu Yang <xuyang1@microsoft.com>
Co-authored-by: Young <afe.young@gmail.com>
* refine ds modal for more cases: eval and es
* update model template
* prompts for model and ensemble
* fix a bug
* fix a bug
* init: ds workflow evovingstrategy
* Adding ensemble (#505)
* Initial Draft
* Updating logic for init
* Revising
* Successful Testing
* Updating to use the latest & right class
* bug: bug-fixing for testing
* data science loop changes
* data science loop base
* ds loop feedback
* fix
* remove measure_time because it's duplicated (in LoopBase)
* add the knowledge query for data_loader & feature
* edit ds workflow evaluator
* data_loader bug fix
* stop evolving when all tasks completed
* llm app change
* fix break all complete strategy
* Adding queried knowledge (#508)
Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com>
* fix loop bug
* ds workflow evaluator; test; refine prompts
* workflow spec
* fix ci
* feature task changes
* ds loop change
* fix a bug in feat
* add query knowledge for model and workflow
* llm_debug info(for show) using pickle instead of json
* remove NextLoopException
* loop change
* coder raise CoderError when all sub_tasks failed
* rename code_dict to file_dict in FBWorkspace
* add CoSTEER unittest
* now show self.version in Task.get_task_information(), simplify CoSTEER sub tasks definition
* remove some properties in ModelTask, add model_type in it.
* fix llm app bug
* llm web app bug fix
* ds loop bug fix
* fix: give component code to feature&ens eval
* loop catch error bug
* rename load_from_raw_data to load_data
* feat: Add debug data creation functionality for data science scenarios
* support local folder (#511)
* support local folder
* remove unnecessary random
* KaggleScen Subclass
* small fix
* use template for style description
* update default scen to kaggle
* update sample data script
* make sure frac < 1
* fix a bug
* feature spec changes
* fix
* changeimport order
* clear unnecessary std outputs
* fix a typo
* create sample folder after unzip kaggle data
* feature/model test script update
* Align the data types across modules.
* fix a bug in model eval
* show line number
* move sample entry point to app
* spec & model prompt changes
* Refine the competition specification to address the data type problem and the coherence issue.
* fix some bugs
* add file filter in FBworkspace.code property
* support non-binary prediction
* avoid too much warnings
* fix a bug in ensemble module
* filtered the knowledge query in all modules
* delete RAG in idea proposal
* refine the code in ensemble
* show exp workspace in llm_st
* exp_gen bug fix
* feedback bug fix
* use `feature` instead of `feat01`
* Trace & method of judging if exp is completed change
* fix a bug in package calling and execute ci
* fix code
* bug fix
* bug fix
* fix a bug
* fix some bugs
* fix a bug
* refactor: Enhance error handling and feedback in data science loop
* support different use_azure on chat and embedding models
* multi-model proposal logic
* fix a small syntax error
* loopBase and some changes
* ensemble scores change
* fbworkspace.code -> .all_codes
* use all model codes in workflow coder
* check scores.csv's keys(model_names)
* model name changes
* add a todo in ensemble test
* sota_exp changes
* give model info in exp gen
* add runner time limit
* config using debug data or not in evals
* exp to feedback base
* add feature code when writing model task
* small problem
* copying during sampling
* update
* refactor: Simplify code handling and improve workspace management
* model part output fix
* print model's execution time
* bug fix
* ensemble test fix
* ens small change
* ens_test bug fix
* Refine partial expansion logic to display only a few subfolders when their structure is uniform, improving readability in nested directories.
* several update on prompts
* sample subfolders
* Filter the stdout after code execution to remove irrelevant information e.g. progress bars, whitespace characters, excessive line breaks.
* Add some more prompts and comments
* several update on the first init rounds
* model timeout as error
* fix pattern of getting model codes in workspace
* small bux fix on model prompts
* remove get_code_with_key since we have regex pattern
* fix: Correct tqdm progress bar update logic in LoopBase class
* feat: Add diff generation and enhance feedback mechanism in data science loop
* update some fix to model and workflow prompts
* refine the logic of progress bar filter
* add last_successful_exp in exp_gen
* fix a one line bug
* add a hint in prompt
* fix data sample for bms
* fix data sample for bms
* hypothesis small fix
* crawler readme update
* fix component gen
* fix bug
* annotation change
* load description.md if it exists
* refactor: Simplify SOTA description handling in feedback and prompts
* refactor: Use shared templates for feedback and experiment descriptions
* change webapp for model codes changes
* update proposal
* add timeout message for docker run output
* fix
* refine the code in docker time processing
* use .shape instead of len() when do shape eval
* won't change size during iteration
* support bson sample
* sample support jsonl and bson
* add former_code to coder prompts
* a little speed us in debug data creating
* filter progress bar when eval ens and main
* avoid costeer makes no change to former code
* fix several log error
* add timeout judge threshold
* fix some bugs in the evaluation of component output shapes
* File structure for supporting litellm (#517)
Co-authored-by: Young <afe.young@gmail.com>
* ignore submission and show processing
* ignore submission and show processing
* add efficiency notice
* refactor: Enhance error message with detailed feedback summary
* refactor: Simplify component handling in DSExpGen class
* refactor: Update code structure and add docstring for clarity
* reserve one sample to each label in data sampling
* add Evaluation info
* refine costeer code to avoid giving same code twice
* use raw_description as plain text
* add a prompt hint to avoid same dict key
* model task name bug in first model exp gen
* fix a typo
* add some debug info in costeer tests
* task init change
* enhance data sampling
* refine the code in data_loader
* more reasonable loop
* fix a bug in data folder description
* add error msg & traceback to execution feedback
* fix llm error msg detection
* add task information to costeer eval & add cache to docker run(use zipfile to store the whole workspace)
* fix CI first round
* fix CI second round
* use txt to store test script to avoid pytest
* remove zipfile in requirements
* add azure.identity to requirements
* ignore debug web page
* component test changes
* remove redundent task_desc in model coder
* feat: Add APE module and prompts for automated prompt engineering
* fix: Update .gitignore and improve text formatting in eval.py
* refactor: Update print output and improve code comments and imports
* style: Fix string formatting and import order in ape.py and fmt.py
* exclude ape
* add a data folder notice
* reduce unnecessary output to stdout
* refine the code of describe_data_folder
* fix ci
* style: streamlit style update (#522)
* streamlit style update
* fix import
* fix format
* fix llm_st loop progress bar
* debugapp small change
* fix model str
* refine some prompts
* fix model str
* fix CI
* refine the logic associated with the data_folder
* fix ci
* small change
* set filter_progress_bar as default in execute
* model proposal with workflow
* add submission check in workflow eval
* fix bug
* small change
* fix CI
* fix CI
* refactor: Move generate_diff to utils and update DSExpGen logic
* more reasonable prompt describing metric direction
* fix a minor jinja2 bug
* quick fix exp_gen bugs
* fix the following bug
* fix
* fix some bugs
* remove workflow from model
* add pending_tasks_list in data science to enable coding model and workflow
* refine the code for handling JSON-formatted data descriptions
* assert with information
* ensure correct csv file name
* add logging to help record the output
* log competition
* add log tag for debug llm app
* test: Test ds refactor ll (#523)
* fix bugs to former scenario
* fix a bug because coding in rdloop changed
* fix the bug when feedback gets no hypothesis
* fix trace structure
* change all trace hist when merging hypothesis to experiments
* ignore some error in ruff
* fix kaggle scenario bugs
* refine one line
* another bug
* another small bug
* fix ui bugs
* chage kaggle train.py path
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Co-authored-by: Xu Yang <peteryang@vip.qq.com>
* fix CI
* Update rdagent/app/data_science/loop.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* add samplecsv into spec prompts
* fix CI
---------
Co-authored-by: TPLin22 <tplin2@163.com>
Co-authored-by: yuanteli <1957922024@qq.com>
Co-authored-by: Xisen Wang <118058822+xisen-w@users.noreply.github.com>
Co-authored-by: Bowen Xian <xianbowen@outlook.com>
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com>
Co-authored-by: Tim <illking@foxmail.com>
Co-authored-by: 炼金术师华华 <37462254+YeewahChan@users.noreply.github.com>
Co-authored-by: Linlang <30293408+SunsetWolf@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Use ExtendedBaseSettings to replace BaseSettings
* update a more general way to pass the default setting
* update all code
* fix CI
* fix CI
* fix qlib scenario
* fix CI
* fix CI
* fix CI & add data science interfaces
* remove redundant code
* abandon costeer knowledge base v1
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Co-authored-by: Xu Yang <xuyang1@microsoft.com>
Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com>
* Add runtime measurement for each step and loop in RDLoop.
* refine some codes
* refine the code (#276)
* show variables only when it exists (#277)
* fix: support seed and fix absolute path (#278)
* fix: support seed and fix absolute path
* Absolute path
* lint
* fix: improve_execution_time_in_kaggle_loop (#279)
* improve_execution_time_in_kaggle_loop
* fix CI
* fix CI
* fix CI
* fix: Update runner.py to fix a small bug (#282)
* fix: Update runner.py to fix a small bug
* fix CI
* refine the code
* Update loop.py
* Update rd_loop.py
* Update model_xgb.py
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Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
* init a scenario for kaggle feature engineering
* update the readme
* Delete rdagent/app/kaggle_feature/conf.py
* update some pictures
* Delete rdagent/app/kaggle_feature/model.py
* change a photo
* add pics to docs
* update the readme
* update the README
* for a try
* for another try
* change the style of the pictures in readme
* fix a small bug
* update the readme and the docs
* update the docs
* fix a typo
* change a website url
* change some styles
* fix a typo
* change the size of the logo
* change two urls
* Update README.md
* Update README.md
* Update README.md
* Update README.md
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Co-authored-by: Linlang <Lv.Linlang@hotmail.com>
TPTBusiness