mirror of
https://github.com/NicolasBohn/NexQuant.git
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Tim
f06e6986c8
1. Remove potential <code> tags from the generated code. 2. Use return codes in data_loader, feature, and model. 3. Configure the debug timeout.
243 lines
9.7 KiB
Python
243 lines
9.7 KiB
Python
import json
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import re
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from collections import defaultdict
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from datetime import timedelta
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from pathlib import Path
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import fire
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import pandas as pd
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from rdagent.app.data_science.conf import DS_RD_SETTING
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from rdagent.components.coder.data_science.conf import get_ds_env
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from rdagent.core.experiment import FBWorkspace
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from rdagent.core.proposal import ExperimentFeedback
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from rdagent.log.storage import FileStorage
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from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
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from rdagent.scenarios.kaggle.kaggle_crawler import score_rank
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from rdagent.utils.env import DockerEnv, MLEBDockerConf
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de = get_ds_env(conf_type="mlebench", extra_volumes={f"{DS_RD_SETTING.local_data_path}/zip_files": "/mle/data"})
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de.prepare()
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def extract_mle_json(log_content: str) -> dict | None:
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match = re.search(r"\{.*\}", log_content, re.DOTALL)
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if match:
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return json.loads(match.group(0))
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return None
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def extract_loopid_func_name(tag):
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"""提取 Loop ID 和函数名称"""
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match = re.search(r"Loop_(\d+)\.([^.]+)", tag)
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return match.groups() if match else (None, None)
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def save_grade_info(log_trace_path: Path):
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trace_storage = FileStorage(log_trace_path)
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for msg in trace_storage.iter_msg():
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if "competition" in msg.tag:
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competition = msg.content
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if "running" in msg.tag:
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if isinstance(msg.content, DSExperiment):
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mle_score_str = msg.content.experiment_workspace.execute(
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env=de,
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entry=f"mlebench grade-sample submission.csv {competition} --data-dir /mle/data | tee mle_score.txt",
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)
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msg.content.experiment_workspace.execute(env=de, entry="chmod 777 mle_score.txt")
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trace_storage.log(
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mle_score_str, name=f"{msg.tag}.mle_score.pid", save_type="pkl", timestamp=msg.timestamp
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)
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def is_valid_session(p: Path) -> bool:
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return p.is_dir() and p.joinpath("__session__").exists()
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def save_all_grade_info(log_folder):
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for log_trace_path in log_folder.iterdir():
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if is_valid_session(log_trace_path):
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save_grade_info(log_trace_path)
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def summarize_folder(log_folder: Path, hours: int | None = None):
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"""
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Summarize the log folder and save the summary as a pickle file.
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Args:
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log_folder (Path): The path to the log folder (contains many log traces).
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hours (int | None): The number of hours to stat. If None, stat all.
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"""
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log_folder = Path(log_folder)
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stat = defaultdict(dict)
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for log_trace_path in log_folder.iterdir(): # One log trace
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if not is_valid_session(log_trace_path):
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continue
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loop_num = 0
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made_submission_num = 0
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valid_submission_num = 0
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above_median_num = 0
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get_medal_num = 0
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bronze_num = 0
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silver_num = 0
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gold_num = 0
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test_scores = {}
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test_ranks = {}
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valid_scores = {}
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bronze_threshold = 0.0
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silver_threshold = 0.0
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gold_threshold = 0.0
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median_threshold = 0.0
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success_loop_num = 0
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sota_exp_stat = ""
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sota_exp_score = None
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sota_exp_rank = None
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grade_output = None
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start_time = None
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for msg in FileStorage(log_trace_path).iter_msg(): # messages in log trace
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if start_time and hours and msg.timestamp > start_time + timedelta(hours=hours):
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break
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if msg.tag and "llm" not in msg.tag and "session" not in msg.tag:
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if "competition" in msg.tag:
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stat[log_trace_path.name]["competition"] = msg.content
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start_time = msg.timestamp
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# get threshold scores
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workflowexp = FBWorkspace()
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stdout = workflowexp.execute(
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env=de,
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entry=f"mlebench grade-sample None {stat[log_trace_path.name]['competition']} --data-dir /mle/data",
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)
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grade_output = extract_mle_json(stdout)
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if grade_output:
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bronze_threshold = grade_output["bronze_threshold"]
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silver_threshold = grade_output["silver_threshold"]
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gold_threshold = grade_output["gold_threshold"]
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median_threshold = grade_output["median_threshold"]
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if "direct_exp_gen" in msg.tag and isinstance(msg.content, DSExperiment):
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loop_num += 1
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if "running" in msg.tag:
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if isinstance(msg.content, DSExperiment):
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submission_path = msg.content.experiment_workspace.workspace_path / "submission.csv"
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if submission_path.exists():
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made_submission_num += 1
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scores_path = msg.content.experiment_workspace.workspace_path / "scores.csv"
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valid_scores[loop_num - 1] = pd.read_csv(scores_path, index_col=0)
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elif "mle_score" in msg.tag:
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loop_id, _ = extract_loopid_func_name(msg.tag)
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loop_id = int(loop_id)
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grade_output = extract_mle_json(msg.content)
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if grade_output:
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if grade_output["score"] is not None:
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test_scores[loop_id + 1] = grade_output["score"]
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_, test_ranks[loop_id + 1] = score_rank(
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stat[log_trace_path.name]["competition"], grade_output["score"]
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)
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if grade_output["valid_submission"]:
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valid_submission_num += 1
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if grade_output["above_median"]:
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above_median_num += 1
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if grade_output["any_medal"]:
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get_medal_num += 1
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if grade_output["bronze_medal"]:
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bronze_num += 1
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if grade_output["silver_medal"]:
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silver_num += 1
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if grade_output["gold_medal"]:
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gold_num += 1
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if "feedback" in msg.tag and "evolving" not in msg.tag:
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if isinstance(msg.content, ExperimentFeedback) and bool(msg.content):
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success_loop_num += 1
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if grade_output: # sota exp's grade output
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if grade_output["gold_medal"]:
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sota_exp_stat = "gold"
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elif grade_output["silver_medal"]:
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sota_exp_stat = "silver"
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elif grade_output["bronze_medal"]:
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sota_exp_stat = "bronze"
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elif grade_output["above_median"]:
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sota_exp_stat = "above_median"
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elif grade_output["valid_submission"]:
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sota_exp_stat = "valid_submission"
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elif grade_output["submission_exists"]:
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sota_exp_stat = "made_submission"
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if grade_output["score"] is not None:
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sota_exp_score = grade_output["score"]
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_, sota_exp_rank = score_rank(
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stat[log_trace_path.name]["competition"], grade_output["score"]
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)
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stat[log_trace_path.name].update(
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{
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"loop_num": loop_num,
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"made_submission_num": made_submission_num,
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"valid_submission_num": valid_submission_num,
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"above_median_num": above_median_num,
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"get_medal_num": get_medal_num,
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"bronze_num": bronze_num,
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"silver_num": silver_num,
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"gold_num": gold_num,
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"test_scores": test_scores,
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"test_ranks": test_ranks,
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"valid_scores": valid_scores,
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"success_loop_num": success_loop_num,
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"sota_exp_stat": sota_exp_stat,
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"sota_exp_score": sota_exp_score,
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"sota_exp_rank": sota_exp_rank,
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"bronze_threshold": bronze_threshold,
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"silver_threshold": silver_threshold,
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"gold_threshold": gold_threshold,
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"median_threshold": median_threshold,
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}
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)
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# Save the summary
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save_name = f"summary_{hours}h.pkl" if hours else "summary.pkl"
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save_p = log_folder / save_name
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if save_p.exists():
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save_p.unlink()
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print(f"Old {save_name} removed.")
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pd.to_pickle(stat, save_p)
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# {
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# "competition_id": "stanford-covid-vaccine",
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# "score": null,
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# "gold_threshold": 0.34728,
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# "silver_threshold": 0.35175,
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# "bronze_threshold": 0.3534,
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# "median_threshold": 0.363095,
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# "any_medal": false,
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# "gold_medal": false,
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# "silver_medal": false,
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# "bronze_medal": false,
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# "above_median": false,
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# "submission_exists": true,
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# "valid_submission": false,
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# "is_lower_better": true,
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# "created_at": "2025-01-21T11:59:33.788201",
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# "submission_path": "submission.csv"
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# }
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def grade_summary(log_folder):
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log_folder = Path(log_folder)
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save_all_grade_info(log_folder)
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summarize_folder(log_folder)
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if __name__ == "__main__":
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fire.Fire(
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{
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"grade": save_all_grade_info,
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"summary": summarize_folder,
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"grade_summary": grade_summary,
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}
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)
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