mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-01 01:27:42 +00:00
XianBW
730 lines
29 KiB
Python
730 lines
29 KiB
Python
import math
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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 pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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import streamlit as st
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from streamlit import session_state as state
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from rdagent.app.data_science.loop import DataScienceRDLoop
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from rdagent.log.mle_summary import extract_mle_json, is_valid_session
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from rdagent.log.storage import FileStorage
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from rdagent.log.ui.conf import UI_SETTING
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from rdagent.utils import remove_ansi_codes
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st.set_page_config(layout="wide", page_title="RD-Agent", page_icon="🎓", initial_sidebar_state="expanded")
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# 设置主日志路径
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if "log_folder" not in state:
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state.log_folder = Path("./log")
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if "log_folders" not in state:
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state.log_folders = UI_SETTING.default_log_folders
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if "log_path" not in state:
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state.log_path = None
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if "show_all_summary" not in state:
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state.show_all_summary = True
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if "show_stdout" not in state:
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state.show_stdout = False
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def load_stdout():
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# FIXME: TODO: 使用配置项来指定stdout文件名
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stdout_path = state.log_folder / f"{state.log_path}.stdout"
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if stdout_path.exists():
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stdout = stdout_path.read_text()
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else:
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stdout = f"Please Set: {stdout_path}"
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return stdout
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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 extract_evoid(tag):
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"""提取 EVO ID"""
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match = re.search(r"\.evo_loop_(\d+)\.", tag)
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return match.group(1) if match else None
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# @st.cache_data
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def load_data(log_path: Path):
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state.data = defaultdict(lambda: defaultdict(dict))
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state.times = defaultdict(lambda: defaultdict(dict))
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for msg in FileStorage(log_path).iter_msg():
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if msg.tag and "llm" not in msg.tag and "session" not in msg.tag:
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if msg.tag == "competition":
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state.data["competition"] = msg.content
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continue
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li, fn = extract_loopid_func_name(msg.tag)
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li = int(li)
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# read times
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loop_obj_path = log_path / "__session__" / f"{li}" / "4_record"
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if loop_obj_path.exists():
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try:
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state.times[li] = DataScienceRDLoop.load(loop_obj_path, do_truncate=False).loop_trace[li]
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except Exception as e:
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pass
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ei = extract_evoid(msg.tag)
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msg.tag = re.sub(r"\.evo_loop_\d+", "", msg.tag)
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msg.tag = re.sub(r"Loop_\d+\.[^.]+\.?", "", msg.tag)
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msg.tag = msg.tag.strip()
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if ei:
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if int(ei) not in state.data[li][fn]:
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state.data[li][fn][int(ei)] = {}
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state.data[li][fn][int(ei)][msg.tag] = msg.content
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else:
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if msg.tag:
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state.data[li][fn][msg.tag] = msg.content
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else:
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if not isinstance(msg.content, str):
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state.data[li][fn]["no_tag"] = msg.content
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# @st.cache_data
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def get_folders_sorted(log_path):
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"""缓存并返回排序后的文件夹列表，并加入进度打印"""
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if not log_path.exists():
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st.toast(f"Path {log_path} does not exist!")
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return []
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with st.spinner("正在加载文件夹列表..."):
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folders = sorted(
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(folder for folder in log_path.iterdir() if is_valid_session(folder)),
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key=lambda folder: folder.stat().st_mtime,
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reverse=True,
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)
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st.write(f"找到 {len(folders)} 个文件夹")
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return [folder.name for folder in folders]
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# UI - Sidebar
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with st.sidebar:
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log_folder_str = st.text_area(
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"**Log Folders**(split by ';')", placeholder=state.log_folder, value=";".join(state.log_folders)
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)
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state.log_folders = [folder.strip() for folder in log_folder_str.split(";") if folder.strip()]
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# # TODO: 只是临时的功能
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day_map = {"srv": "最近(srv)", "srv2": "上一批(srv2)", "srv3": "上上批(srv3)"}
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day_srv = st.radio("选择批次", ["srv", "srv2", "srv3"], format_func=lambda x: day_map[x], horizontal=True)
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if day_srv == "srv":
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state.log_folders = [re.sub(r"log\.srv\d*", "log.srv", folder) for folder in state.log_folders]
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elif day_srv == "srv2":
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state.log_folders = [re.sub(r"log\.srv\d*", "log.srv2", folder) for folder in state.log_folders]
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elif day_srv == "srv3":
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state.log_folders = [re.sub(r"log\.srv\d*", "log.srv3", folder) for folder in state.log_folders]
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state.log_folder = Path(st.radio(f"Select :blue[**one log folder**]", state.log_folders))
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if not state.log_folder.exists():
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st.warning(f"Path {state.log_folder} does not exist!")
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else:
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folders = get_folders_sorted(state.log_folder)
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st.selectbox(f"Select from :blue[**{state.log_folder.absolute()}**]", folders, key="log_path")
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if st.button("Refresh Data"):
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if state.log_path is None:
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st.toast("Please select a log path first!", icon="🟡")
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st.stop()
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load_data(state.log_folder / state.log_path)
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st.toggle("One Trace / Log Folder Summary", key="show_all_summary")
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st.toggle("Show stdout", key="show_stdout")
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# UI windows
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def task_win(data):
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with st.container(border=True):
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st.markdown(f"**:violet[{data.name}]**")
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st.markdown(data.description)
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if hasattr(data, "architecture"): # model task
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st.markdown(
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f"""
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| Model_type | Architecture | hyperparameters |
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|------------|--------------|-----------------|
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| {data.model_type} | {data.architecture} | {data.hyperparameters} |
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"""
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)
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def workspace_win(data):
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show_files = {k: v for k, v in data.file_dict.items() if not "test" in k}
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if len(show_files) > 0:
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with st.expander(f"Files in :blue[{replace_ep_path(data.workspace_path)}]"):
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code_tabs = st.tabs(show_files.keys())
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for ct, codename in zip(code_tabs, show_files.keys()):
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with ct:
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st.code(
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show_files[codename],
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language=("python" if codename.endswith(".py") else "markdown"),
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wrap_lines=True,
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line_numbers=True,
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)
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else:
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st.markdown(f"No files in :blue[{replace_ep_path(data.workspace_path)}]")
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def hypothesis_win(data):
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st.code(str(data).replace("\n", "\n\n"), wrap_lines=True)
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def exp_gen_win(data):
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st.header("Exp Gen", divider="blue", anchor="exp-gen")
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st.subheader("Hypothesis")
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hypothesis_win(data["no_tag"].hypothesis)
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st.subheader("pending_tasks")
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for tasks in data["no_tag"].pending_tasks_list:
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task_win(tasks[0])
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st.subheader("Exp Workspace")
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workspace_win(data["no_tag"].experiment_workspace)
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def evolving_win(data, key):
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with st.container(border=True):
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if len(data) > 1:
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evo_id = st.slider("Evolving", 0, len(data) - 1, 0, key=key)
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elif len(data) == 1:
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evo_id = 0
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else:
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st.markdown("No evolving.")
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return
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if evo_id in data:
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if data[evo_id]["evolving code"][0] is not None:
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st.subheader("codes")
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workspace_win(data[evo_id]["evolving code"][0])
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fb = data[evo_id]["evolving feedback"][0]
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st.subheader("evolving feedback" + ("✅" if bool(fb) else "❌"))
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f1, f2, f3 = st.tabs(["execution", "return_checking", "code"])
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f1.code(fb.execution, wrap_lines=True)
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f2.code(fb.return_checking, wrap_lines=True)
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f3.code(fb.code, wrap_lines=True)
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else:
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st.write("data[evo_id]['evolving code'][0] is None.")
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st.write(data[evo_id])
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else:
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st.markdown("No evolving.")
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def coding_win(data):
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st.header("Coding", divider="blue", anchor="coding")
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evolving_data = {k: v for k, v in data.items() if isinstance(k, int)}
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task_set = set()
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for v in evolving_data.values():
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for t in v:
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if "Task" in t.split(".")[0]:
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task_set.add(t.split(".")[0])
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if task_set:
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# 新版存Task tag的Trace
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for task in task_set:
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st.subheader(task)
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task_data = {k: {a.split(".")[1]: b for a, b in v.items() if task in a} for k, v in evolving_data.items()}
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evolving_win(task_data, key=task)
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else:
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# 旧版未存Task tag的Trace
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evolving_win(evolving_data, key="coding")
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if "no_tag" in data:
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st.subheader("Exp Workspace (coding final)")
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workspace_win(data["no_tag"].experiment_workspace)
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def running_win(data, mle_score):
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st.header("Running", divider="blue", anchor="running")
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evolving_win({k: v for k, v in data.items() if isinstance(k, int)}, key="running")
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if "no_tag" in data:
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st.subheader("Exp Workspace (running final)")
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workspace_win(data["no_tag"].experiment_workspace)
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st.subheader("Result")
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st.write(data["no_tag"].result)
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st.subheader("MLE Submission Score" + ("✅" if (isinstance(mle_score, dict) and mle_score["score"]) else "❌"))
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if isinstance(mle_score, dict):
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st.json(mle_score)
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else:
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st.code(mle_score, wrap_lines=True)
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def feedback_win(data):
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data = data["no_tag"]
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st.header("Feedback" + ("✅" if bool(data) else "❌"), divider="orange", anchor="feedback")
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st.code(str(data).replace("\n", "\n\n"), wrap_lines=True)
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if data.exception is not None:
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st.markdown(f"**:red[Exception]**: {data.exception}")
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def sota_win(data):
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st.header("SOTA Experiment", divider="rainbow", anchor="sota-exp")
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if data:
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st.markdown(f"**SOTA Exp Hypothesis**")
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hypothesis_win(data.hypothesis)
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st.markdown("**Exp Workspace**")
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workspace_win(data.experiment_workspace)
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else:
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st.markdown("No SOTA experiment.")
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def main_win(data):
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exp_gen_win(data["direct_exp_gen"])
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if "coding" in data:
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coding_win(data["coding"])
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if "running" in data:
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running_win(data["running"], data["mle_score"])
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if "feedback" in data:
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feedback_win(data["feedback"])
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if "record" in data and "SOTA experiment" in data["record"]:
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sota_win(data["record"]["SOTA experiment"])
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with st.sidebar:
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st.markdown(
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f"""
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- [Exp Gen](#exp-gen)
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- [Coding](#coding)
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- [Running](#running)
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- [Feedback](#feedback)
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- [SOTA Experiment](#sota-exp)
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"""
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)
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def replace_ep_path(p: Path):
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# 替换workspace path为对应ep机器mount在ep03的path
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# TODO: FIXME: 使用配置项来处理
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match = re.search(r"ep\d+", str(state.log_folder))
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if match:
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ep = match.group(0)
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return Path(
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str(p).replace("repos/RD-Agent-Exp", f"repos/batch_ctrl/all_projects/{ep}").replace("/Data", "/data")
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)
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return p
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def summarize_data():
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st.header("Summary", divider="rainbow")
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df = pd.DataFrame(
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columns=["Component", "Running Score", "Feedback", "e-loops", "Time", "Start Time (UTC+8)", "End Time (UTC+8)"],
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index=range(len(state.data) - 1),
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)
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for loop in range(len(state.data) - 1):
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loop_data = state.data[loop]
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df.loc[loop, "Component"] = loop_data["direct_exp_gen"]["no_tag"].hypothesis.component
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if state.times[loop]:
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df.loc[loop, "Time"] = str(sum((i.end - i.start for i in state.times[loop]), timedelta())).split(".")[0]
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df.loc[loop, "Start Time (UTC+8)"] = state.times[loop][0].start + timedelta(hours=8)
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df.loc[loop, "End Time (UTC+8)"] = state.times[loop][-1].end + timedelta(hours=8)
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if "running" in loop_data and "no_tag" in loop_data["running"]:
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if "mle_score" not in state.data[loop]:
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if "mle_score" in loop_data["running"]:
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mle_score_txt = loop_data["running"]["mle_score"]
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state.data[loop]["mle_score"] = extract_mle_json(mle_score_txt)
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if state.data[loop]["mle_score"]["score"] is not None:
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df.loc[loop, "Running Score"] = str(state.data[loop]["mle_score"]["score"])
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else:
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state.data[loop]["mle_score"] = mle_score_txt
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df.loc[loop, "Running Score"] = "❌"
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else:
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mle_score_path = (
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replace_ep_path(loop_data["running"]["no_tag"].experiment_workspace.workspace_path)
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/ "mle_score.txt"
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)
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try:
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mle_score_txt = mle_score_path.read_text()
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state.data[loop]["mle_score"] = extract_mle_json(mle_score_txt)
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if state.data[loop]["mle_score"]["score"] is not None:
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df.loc[loop, "Running Score"] = str(state.data[loop]["mle_score"]["score"])
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else:
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state.data[loop]["mle_score"] = mle_score_txt
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df.loc[loop, "Running Score"] = "❌"
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except Exception as e:
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state.data[loop]["mle_score"] = str(e)
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df.loc[loop, "Running Score"] = "❌"
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else:
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if isinstance(state.data[loop]["mle_score"], dict):
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df.loc[loop, "Running Score"] = str(state.data[loop]["mle_score"]["score"])
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else:
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df.loc[loop, "Running Score"] = "❌"
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else:
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df.loc[loop, "Running Score"] = "N/A"
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if "coding" in loop_data:
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df.loc[loop, "e-loops"] = max(i for i in loop_data["coding"].keys() if isinstance(i, int)) + 1
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if "feedback" in loop_data:
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df.loc[loop, "Feedback"] = "✅" if bool(loop_data["feedback"]["no_tag"]) else "❌"
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else:
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df.loc[loop, "Feedback"] = "N/A"
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stat_t0, stat_t1 = st.columns(2)
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stat_t0.dataframe(df)
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def comp_stat_func(x: pd.DataFrame):
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total_num = x.shape[0]
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valid_num = x[x["Running Score"] != "N/A"].shape[0]
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avg_e_loops = x["e-loops"].mean()
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return pd.Series(
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{
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"Total": total_num,
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"Valid": valid_num,
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"Valid Rate": round(valid_num / total_num * 100, 2),
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"Avg e-loops": round(avg_e_loops, 2),
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}
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)
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comp_df = df.loc[:, ["Component", "Running Score", "e-loops"]].groupby("Component").apply(comp_stat_func)
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comp_df.loc["Total"] = comp_df.sum()
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comp_df.loc["Total", "Valid Rate"] = round(comp_df.loc["Total", "Valid"] / comp_df.loc["Total", "Total"] * 100, 2)
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comp_df["Valid Rate"] = comp_df["Valid Rate"].apply(lambda x: f"{x}%")
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comp_df.loc["Total", "Avg e-loops"] = round(df["e-loops"].mean(), 2)
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stat_t1.dataframe(comp_df)
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def get_summary_df(log_folders: list[str]) -> tuple[dict, pd.DataFrame]:
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summarys = {}
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for lf in log_folders:
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if not (Path(lf) / "summary.pkl").exists():
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st.warning(
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f"No summary file found in **{lf}**\n\nRun:`dotenv run -- python rdagent/log/mle_summary.py grade_summary --log_folder={lf}`"
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)
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else:
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summarys[lf] = pd.read_pickle(Path(lf) / "summary.pkl")
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if len(summarys) == 0:
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return {}, pd.DataFrame()
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summary = {}
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for lf, s in summarys.items():
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for k, v in s.items():
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stdout_p = Path(lf) / f"{k}.stdout"
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v["stdout"] = []
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if stdout_p.exists():
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# stdout = stdout_p.read_text()
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stdout = ""
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if "Retrying" in stdout:
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v["stdout"].append("LLM Retry")
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if "Traceback (most recent call last):" in stdout[-10000:]:
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v["stdout"].append("Code Error")
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v["stdout"] = ", ".join([i for i in v["stdout"] if i])
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# 调整实验名字
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if "amlt" in lf:
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summary[f"{lf[lf.rfind('amlt')+5:].split('/')[0]} - {k}"] = v
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elif "ep" in lf:
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summary[f"{lf[lf.rfind('ep'):]} - {k}"] = v
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else:
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summary[f"{lf} - {k}"] = v
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summary = {k: v for k, v in summary.items() if "competition" in v}
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base_df = pd.DataFrame(
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columns=[
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"Competition",
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"Total Loops",
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"Successful Final Decision",
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"Made Submission",
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"Valid Submission",
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"V/M",
|
|
"Above Median",
|
|
"Bronze",
|
|
"Silver",
|
|
"Gold",
|
|
"Any Medal",
|
|
"Best Medal",
|
|
"SOTA Exp",
|
|
"Ours - Base",
|
|
"Ours vs Base",
|
|
"SOTA Exp Score",
|
|
"Baseline Score",
|
|
"Bronze Threshold",
|
|
"Silver Threshold",
|
|
"Gold Threshold",
|
|
"Medium Threshold",
|
|
"stdout",
|
|
],
|
|
index=summary.keys(),
|
|
)
|
|
|
|
# Read baseline results
|
|
baseline_result_path = UI_SETTING.baseline_result_path
|
|
if Path(baseline_result_path).exists():
|
|
baseline_df = pd.read_csv(baseline_result_path)
|
|
|
|
for k, v in summary.items():
|
|
loop_num = v["loop_num"]
|
|
base_df.loc[k, "Competition"] = v["competition"]
|
|
base_df.loc[k, "Total Loops"] = loop_num
|
|
if loop_num == 0:
|
|
base_df.loc[k] = "N/A"
|
|
else:
|
|
base_df.loc[k, "Successful Final Decision"] = v["success_loop_num"]
|
|
base_df.loc[k, "Made Submission"] = v["made_submission_num"]
|
|
base_df.loc[k, "Valid Submission"] = v["valid_submission_num"]
|
|
base_df.loc[k, "Above Median"] = v["above_median_num"]
|
|
base_df.loc[k, "Bronze"] = v["bronze_num"]
|
|
if v["bronze_num"] > 0:
|
|
base_df.loc[k, "Best Medal"] = "bronze"
|
|
base_df.loc[k, "Silver"] = v["silver_num"]
|
|
if v["silver_num"] > 0:
|
|
base_df.loc[k, "Best Medal"] = "silver"
|
|
base_df.loc[k, "Gold"] = v["gold_num"]
|
|
if v["gold_num"] > 0:
|
|
base_df.loc[k, "Best Medal"] = "gold"
|
|
base_df.loc[k, "Any Medal"] = v["get_medal_num"]
|
|
|
|
baseline_score = None
|
|
if Path(baseline_result_path).exists():
|
|
baseline_score = baseline_df.loc[baseline_df["competition_id"] == v["competition"], "score"].item()
|
|
|
|
base_df.loc[k, "SOTA Exp"] = v.get("sota_exp_stat", None)
|
|
if baseline_score is not None and v.get("sota_exp_score", None) is not None:
|
|
base_df.loc[k, "Ours - Base"] = v["sota_exp_score"] - baseline_score
|
|
try:
|
|
base_df.loc[k, "Ours vs Base"] = math.exp(
|
|
abs(math.log(v["sota_exp_score"] / baseline_score))
|
|
) # exp^|ln(a/b)|
|
|
except Exception as e:
|
|
base_df.loc[k, "Ours vs Base"] = None
|
|
base_df.loc[k, "SOTA Exp Score"] = v.get("sota_exp_score", None)
|
|
base_df.loc[k, "Baseline Score"] = baseline_score
|
|
base_df.loc[k, "Bronze Threshold"] = v.get("bronze_threshold", None)
|
|
base_df.loc[k, "Silver Threshold"] = v.get("silver_threshold", None)
|
|
base_df.loc[k, "Gold Threshold"] = v.get("gold_threshold", None)
|
|
base_df.loc[k, "Medium Threshold"] = v.get("median_threshold", None)
|
|
base_df.loc[k, "stdout"] = v["stdout"]
|
|
|
|
base_df["SOTA Exp"].replace("", pd.NA, inplace=True)
|
|
base_df = base_df.astype(
|
|
{
|
|
"Total Loops": int,
|
|
"Successful Final Decision": int,
|
|
"Made Submission": int,
|
|
"Valid Submission": int,
|
|
"Above Median": int,
|
|
"Bronze": int,
|
|
"Silver": int,
|
|
"Gold": int,
|
|
"Any Medal": int,
|
|
"Ours - Base": float,
|
|
"Ours vs Base": float,
|
|
"SOTA Exp Score": float,
|
|
"Baseline Score": float,
|
|
"Bronze Threshold": float,
|
|
"Silver Threshold": float,
|
|
"Gold Threshold": float,
|
|
"Medium Threshold": float,
|
|
}
|
|
)
|
|
return summary, base_df
|
|
|
|
|
|
def num2percent(num: int, total: int, show_origin=True) -> str:
|
|
if show_origin:
|
|
return f"{num} ({round(num / total * 100, 2)}%)"
|
|
return f"{round(num / total * 100, 2)}%"
|
|
|
|
|
|
def percent_df(df: pd.DataFrame, show_origin=True) -> pd.DataFrame:
|
|
base_df = df.astype("object", copy=True)
|
|
for k in base_df.index:
|
|
loop_num = int(base_df.loc[k, "Total Loops"])
|
|
if loop_num != 0:
|
|
base_df.loc[k, "Successful Final Decision"] = num2percent(
|
|
base_df.loc[k, "Successful Final Decision"], loop_num, show_origin
|
|
)
|
|
if base_df.loc[k, "Made Submission"] != 0:
|
|
base_df.loc[k, "V/M"] = (
|
|
f"{round(base_df.loc[k, 'Valid Submission'] / base_df.loc[k, 'Made Submission'] * 100, 2)}%"
|
|
)
|
|
else:
|
|
base_df.loc[k, "V/M"] = "N/A"
|
|
base_df.loc[k, "Made Submission"] = num2percent(base_df.loc[k, "Made Submission"], loop_num, show_origin)
|
|
base_df.loc[k, "Valid Submission"] = num2percent(base_df.loc[k, "Valid Submission"], loop_num, show_origin)
|
|
base_df.loc[k, "Above Median"] = num2percent(base_df.loc[k, "Above Median"], loop_num, show_origin)
|
|
base_df.loc[k, "Bronze"] = num2percent(base_df.loc[k, "Bronze"], loop_num, show_origin)
|
|
base_df.loc[k, "Silver"] = num2percent(base_df.loc[k, "Silver"], loop_num, show_origin)
|
|
base_df.loc[k, "Gold"] = num2percent(base_df.loc[k, "Gold"], loop_num, show_origin)
|
|
base_df.loc[k, "Any Medal"] = num2percent(base_df.loc[k, "Any Medal"], loop_num, show_origin)
|
|
return base_df
|
|
|
|
|
|
def days_summarize_win():
|
|
lfs1 = [re.sub(r"log\.srv\d*", "log.srv", folder) for folder in state.log_folders]
|
|
lfs2 = [re.sub(r"log\.srv\d*", "log.srv2", folder) for folder in state.log_folders]
|
|
lfs3 = [re.sub(r"log\.srv\d*", "log.srv3", folder) for folder in state.log_folders]
|
|
|
|
_, df1 = get_summary_df(lfs1)
|
|
_, df2 = get_summary_df(lfs2)
|
|
_, df3 = get_summary_df(lfs3)
|
|
|
|
df = pd.concat([df1, df2, df3], axis=0)
|
|
|
|
def mean_func(x: pd.DataFrame):
|
|
numeric_cols = x.select_dtypes(include=["int", "float"]).mean()
|
|
string_cols = x.select_dtypes(include=["object"]).agg(lambda col: ", ".join(col.fillna("none").astype(str)))
|
|
return pd.concat([numeric_cols, string_cols], axis=0).reindex(x.columns).drop("Competition")
|
|
|
|
df = df.groupby("Competition").apply(mean_func)
|
|
if st.toggle("Show Percent", key="show_percent"):
|
|
st.dataframe(percent_df(df, show_origin=False))
|
|
else:
|
|
st.dataframe(df)
|
|
|
|
|
|
def all_summarize_win():
|
|
def shorten_folder_name(folder: str) -> str:
|
|
if "amlt" in folder:
|
|
return folder[folder.rfind("amlt") + 5 :].split("/")[0]
|
|
if "ep" in folder:
|
|
return folder[folder.rfind("ep") :]
|
|
return folder
|
|
|
|
selected_folders = st.multiselect(
|
|
"Show these folders", state.log_folders, state.log_folders, format_func=shorten_folder_name
|
|
)
|
|
summary, base_df = get_summary_df(selected_folders)
|
|
if not summary:
|
|
return
|
|
|
|
base_df = percent_df(base_df)
|
|
st.dataframe(base_df)
|
|
st.markdown("Ours vs Base: `math.exp(abs(math.log(sota_exp_score / baseline_score)))`")
|
|
st.markdown(f"**统计的比赛数目: :red[{base_df.shape[0]}]**")
|
|
total_stat = (
|
|
base_df[
|
|
[
|
|
"Made Submission",
|
|
"Valid Submission",
|
|
"Above Median",
|
|
"Bronze",
|
|
"Silver",
|
|
"Gold",
|
|
"Any Medal",
|
|
]
|
|
]
|
|
!= "0 (0.0%)"
|
|
).sum()
|
|
total_stat.name = "总体统计(%)"
|
|
total_stat.loc["Bronze"] = base_df["Best Medal"].value_counts().get("bronze", 0)
|
|
total_stat.loc["Silver"] = base_df["Best Medal"].value_counts().get("silver", 0)
|
|
total_stat.loc["Gold"] = base_df["Best Medal"].value_counts().get("gold", 0)
|
|
total_stat = total_stat / base_df.shape[0] * 100
|
|
|
|
# SOTA Exp 统计
|
|
se_counts = base_df["SOTA Exp"].value_counts(dropna=True)
|
|
se_counts.loc["made_submission"] = se_counts.sum()
|
|
se_counts.loc["Any Medal"] = se_counts.get("gold", 0) + se_counts.get("silver", 0) + se_counts.get("bronze", 0)
|
|
se_counts.loc["above_median"] = se_counts.get("above_median", 0) + se_counts.get("Any Medal", 0)
|
|
se_counts.loc["valid_submission"] = se_counts.get("valid_submission", 0) + se_counts.get("above_median", 0)
|
|
|
|
sota_exp_stat = pd.Series(index=total_stat.index, dtype=int, name="SOTA Exp 统计(%)")
|
|
sota_exp_stat.loc["Made Submission"] = se_counts.get("made_submission", 0)
|
|
sota_exp_stat.loc["Valid Submission"] = se_counts.get("valid_submission", 0)
|
|
sota_exp_stat.loc["Above Median"] = se_counts.get("above_median", 0)
|
|
sota_exp_stat.loc["Bronze"] = se_counts.get("bronze", 0)
|
|
sota_exp_stat.loc["Silver"] = se_counts.get("silver", 0)
|
|
sota_exp_stat.loc["Gold"] = se_counts.get("gold", 0)
|
|
sota_exp_stat.loc["Any Medal"] = se_counts.get("Any Medal", 0)
|
|
sota_exp_stat = sota_exp_stat / base_df.shape[0] * 100
|
|
|
|
stat_df = pd.concat([total_stat, sota_exp_stat], axis=1)
|
|
stat_t0, stat_t1 = st.columns(2)
|
|
with stat_t0:
|
|
st.dataframe(stat_df.round(2))
|
|
markdown_table = f"""
|
|
| xxx | {stat_df.iloc[0,1]:.1f} | {stat_df.iloc[1,1]:.1f} | {stat_df.iloc[2,1]:.1f} | {stat_df.iloc[3,1]:.1f} | {stat_df.iloc[4,1]:.1f} | {stat_df.iloc[5,1]:.1f} | {stat_df.iloc[6,1]:.1f} |
|
|
"""
|
|
st.text(markdown_table)
|
|
with stat_t1:
|
|
Loop_counts = base_df["Total Loops"]
|
|
fig = px.histogram(Loop_counts, nbins=10, title="Total Loops Histogram (nbins=10)")
|
|
mean_value = Loop_counts.mean()
|
|
median_value = Loop_counts.median()
|
|
fig.add_vline(
|
|
x=mean_value, line_color="orange", annotation_text="Mean", annotation_position="top right", line_width=3
|
|
)
|
|
fig.add_vline(
|
|
x=median_value, line_color="red", annotation_text="Median", annotation_position="top right", line_width=3
|
|
)
|
|
st.plotly_chart(fig)
|
|
|
|
# write curve
|
|
for k, v in summary.items():
|
|
with st.container(border=True):
|
|
st.markdown(f"**:blue[{k}] - :violet[{v['competition']}]**")
|
|
fc1, fc2 = st.columns(2)
|
|
tscores = {f"loop {k-1}": v for k, v in v["test_scores"].items()}
|
|
tdf = pd.Series(tscores, name="score")
|
|
f2 = px.line(tdf, markers=True, title="Test scores")
|
|
fc2.plotly_chart(f2, key=k)
|
|
try:
|
|
vscores = {k: v.iloc[:, 0] for k, v in v["valid_scores"].items()}
|
|
|
|
if len(vscores) > 0:
|
|
metric_name = list(vscores.values())[0].name
|
|
else:
|
|
metric_name = "None"
|
|
|
|
vdf = pd.DataFrame(vscores)
|
|
vdf.columns = [f"loop {i}" for i in vdf.columns]
|
|
f1 = px.line(vdf.T, markers=True, title=f"Valid scores (metric: {metric_name})")
|
|
|
|
fc1.plotly_chart(f1, key=f"{k}_v")
|
|
except Exception as e:
|
|
import traceback
|
|
|
|
st.markdown("- Error: " + str(e))
|
|
st.code(traceback.format_exc())
|
|
st.markdown("- Valid Scores: ")
|
|
# st.write({k: type(v) for k, v in v["valid_scores"].items()})
|
|
st.json(v["valid_scores"])
|
|
|
|
|
|
def stdout_win(loop_id: int):
|
|
stdout = load_stdout()
|
|
if stdout.startswith("Please Set"):
|
|
st.toast(stdout, icon="🟡")
|
|
return
|
|
start_index = stdout.find(f"Start Loop {loop_id}")
|
|
end_index = stdout.find(f"Start Loop {loop_id + 1}")
|
|
loop_stdout = remove_ansi_codes(stdout[start_index:end_index])
|
|
with st.container(border=True):
|
|
st.subheader(f"Loop {loop_id} stdout")
|
|
pattern = f"Start Loop {loop_id}, " + r"Step \d+: \w+"
|
|
matches = re.finditer(pattern, loop_stdout)
|
|
step_stdouts = {}
|
|
for match in matches:
|
|
step = match.group(0)
|
|
si = match.start()
|
|
ei = loop_stdout.find(f"Start Loop {loop_id}", match.end())
|
|
step_stdouts[step] = loop_stdout[si:ei].strip()
|
|
|
|
for k, v in step_stdouts.items():
|
|
with st.expander(k, expanded=False):
|
|
st.code(v, language="log", wrap_lines=True)
|
|
|
|
|
|
# UI - Main
|
|
if state.show_all_summary:
|
|
with st.container(border=True):
|
|
if st.toggle("近3天平均", key="show_3days"):
|
|
days_summarize_win()
|
|
with st.container(border=True):
|
|
all_summarize_win()
|
|
elif "data" in state:
|
|
st.title(state.data["competition"])
|
|
summarize_data()
|
|
if len(state.data) > 2:
|
|
loop_id = st.slider("Loop", 0, len(state.data) - 2, 0)
|
|
else:
|
|
loop_id = 0
|
|
if state.show_stdout:
|
|
stdout_win(loop_id)
|
|
main_win(state.data[loop_id])
|