mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
XianBW
624 lines
25 KiB
Python
624 lines
25 KiB
Python
import math
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import pickle
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import re
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from collections import deque
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from datetime import datetime, 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.log.mle_summary import extract_mle_json
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from rdagent.log.ui.conf import UI_SETTING
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from rdagent.log.ui.ds_trace import load_times
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from rdagent.scenarios.kaggle.kaggle_crawler import leaderboard_scores
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def get_metric_direction(competition: str):
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leaderboard = leaderboard_scores(competition)
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return float(leaderboard[0]) > float(leaderboard[-1])
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def get_script_time(stdout_p: Path):
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with stdout_p.open("r") as f:
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first_line = next(f).strip()
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last_line = deque(f, maxlen=1).pop().strip()
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# Extract timestamps from the lines
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first_time_match = re.search(r"(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\+\d{2}:\d{2})", first_line)
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last_time_match = re.search(r"(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\+\d{2}:\d{2})", last_line)
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if first_time_match and last_time_match:
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first_time = datetime.fromisoformat(first_time_match.group(1))
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last_time = datetime.fromisoformat(last_time_match.group(1))
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return pd.Timedelta(last_time - first_time)
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return None
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@st.cache_data(persist=True)
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def get_final_sota_exp(log_path: Path):
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sota_exp_paths = [i for i in log_path.rglob(f"**/SOTA experiment/**/*.pkl")]
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if len(sota_exp_paths) == 0:
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return None
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final_sota_exp_path = max(sota_exp_paths, key=lambda x: int(re.match(r".*Loop_(\d+).*", str(x))[1]))
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with final_sota_exp_path.open("rb") as f:
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final_sota_exp = pickle.load(f)
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return final_sota_exp
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# @st.cache_data(persist=True)
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def get_sota_exp_stat(log_path: Path):
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trace_paths = [i for i in log_path.rglob(f"**/trace/**/*.pkl")]
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if len(trace_paths) == 0:
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return None
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final_trace_path = max(trace_paths, key=lambda x: int(re.match(r".*Loop_(\d+).*", str(x))[1]))
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with final_trace_path.open("rb") as f:
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final_trace = pickle.load(f)
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if hasattr(final_trace, "sota_exp_to_submit"):
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st.toast("Using sota_exp_to_submit")
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sota_exp = final_trace.sota_exp_to_submit
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else:
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sota_exp = final_trace.sota_experiment()
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if sota_exp is None:
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return None
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sota_loop_id = None
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for i, ef in enumerate(final_trace.hist):
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if ef[0] == sota_exp:
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sota_loop_id = i
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break
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sota_mle_score_paths = [i for i in log_path.rglob(f"Loop_{sota_loop_id}/running/mle_score/**/*.pkl")]
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if len(sota_mle_score_paths) == 0:
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# sota exp is not evaluated by mle_score
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return None
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with sota_mle_score_paths[0].open("rb") as f:
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sota_mle_score = extract_mle_json(pickle.load(f))
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sota_exp_stat = None
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if sota_mle_score: # sota exp's grade output
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if sota_mle_score["gold_medal"]:
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sota_exp_stat = "gold"
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elif sota_mle_score["silver_medal"]:
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sota_exp_stat = "silver"
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elif sota_mle_score["bronze_medal"]:
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sota_exp_stat = "bronze"
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elif sota_mle_score["above_median"]:
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sota_exp_stat = "above_median"
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elif sota_mle_score["valid_submission"]:
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sota_exp_stat = "valid_submission"
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elif sota_mle_score["submission_exists"]:
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sota_exp_stat = "made_submission"
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return sota_exp_stat
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# @st.cache_data(persist=True)
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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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with st.sidebar:
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if st.toggle("show 24h summary", key="show_hours_summary"):
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sn = "summary_24h.pkl"
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else:
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sn = "summary.pkl"
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for lf in log_folders:
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if not (Path(lf) / sn).exists():
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st.warning(
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f"{sn} not found in **{lf}**\n\nRun:`dotenv run -- python rdagent/log/mle_summary.py grade_summary --log_folder={lf} --hours=<>`"
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)
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else:
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summarys[lf] = pd.read_pickle(Path(lf) / sn)
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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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if stdout_p.exists():
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v["script_time"] = get_script_time(stdout_p)
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else:
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v["script_time"] = None
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exp_gen_time = timedelta()
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coding_time = timedelta()
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running_time = timedelta()
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all_time = timedelta()
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times_info = load_times(Path(lf) / k)
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for time_info in times_info.values():
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all_time += sum((ti.end - ti.start for ti in time_info), timedelta())
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exp_gen_time += time_info[0].end - time_info[0].start
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if len(time_info) > 1:
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coding_time += time_info[1].end - time_info[1].start
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if len(time_info) > 2:
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running_time += time_info[2].end - time_info[2].start
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v["exec_time"] = str(all_time).split(".")[0]
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v["exp_gen_time"] = str(exp_gen_time).split(".")[0]
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v["coding_time"] = str(coding_time).split(".")[0]
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v["running_time"] = str(running_time).split(".")[0]
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final_sota_exp = get_final_sota_exp(Path(lf) / k)
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if final_sota_exp is not None and final_sota_exp.result is not None:
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v["sota_exp_score_valid"] = final_sota_exp.result.loc["ensemble"].iloc[0]
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else:
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v["sota_exp_score_valid"] = None
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v["sota_exp_stat_new"] = get_sota_exp_stat(Path(lf) / k)
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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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"Script Time",
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"Exec Time",
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"Exp Gen",
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"Coding",
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"Running",
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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",
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"Above Median",
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"Bronze",
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"Silver",
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"Gold",
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"Any Medal",
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"Best Result",
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"SOTA Exp",
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"SOTA Exp (_to_submit)",
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"SOTA Exp Score (valid)",
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"SOTA Exp Score",
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"Baseline Score",
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"Ours - Base",
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"Ours vs Base",
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"Ours vs Bronze",
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"Ours vs Silver",
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"Ours vs Gold",
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"Bronze Threshold",
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"Silver Threshold",
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"Gold Threshold",
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"Medium Threshold",
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],
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index=summary.keys(),
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)
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# Read baseline results
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baseline_result_path = UI_SETTING.baseline_result_path
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if Path(baseline_result_path).exists():
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baseline_df = pd.read_csv(baseline_result_path)
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def compare_score(s1, s2):
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if s1 is None or s2 is None:
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return None
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try:
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c_value = math.exp(abs(math.log(s1 / s2)))
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except Exception as e:
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c_value = None
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return c_value
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for k, v in summary.items():
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loop_num = v["loop_num"]
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base_df.loc[k, "Competition"] = v["competition"]
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base_df.loc[k, "Script Time"] = v["script_time"]
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base_df.loc[k, "Exec Time"] = v["exec_time"]
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base_df.loc[k, "Exp Gen"] = v["exp_gen_time"]
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base_df.loc[k, "Coding"] = v["coding_time"]
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base_df.loc[k, "Running"] = v["running_time"]
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base_df.loc[k, "Total Loops"] = loop_num
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if loop_num == 0:
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base_df.loc[k] = "N/A"
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else:
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base_df.loc[k, "Successful Final Decision"] = v["success_loop_num"]
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base_df.loc[k, "Made Submission"] = v["made_submission_num"]
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if v["made_submission_num"] > 0:
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base_df.loc[k, "Best Result"] = "made_submission"
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base_df.loc[k, "Valid Submission"] = v["valid_submission_num"]
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if v["valid_submission_num"] > 0:
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base_df.loc[k, "Best Result"] = "valid_submission"
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base_df.loc[k, "Above Median"] = v["above_median_num"]
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if v["above_median_num"] > 0:
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base_df.loc[k, "Best Result"] = "above_median"
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base_df.loc[k, "Bronze"] = v["bronze_num"]
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if v["bronze_num"] > 0:
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base_df.loc[k, "Best Result"] = "bronze"
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base_df.loc[k, "Silver"] = v["silver_num"]
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if v["silver_num"] > 0:
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base_df.loc[k, "Best Result"] = "silver"
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base_df.loc[k, "Gold"] = v["gold_num"]
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if v["gold_num"] > 0:
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base_df.loc[k, "Best Result"] = "gold"
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base_df.loc[k, "Any Medal"] = v["get_medal_num"]
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baseline_score = None
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if Path(baseline_result_path).exists():
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baseline_score = baseline_df.loc[baseline_df["competition_id"] == v["competition"], "score"].item()
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base_df.loc[k, "SOTA Exp"] = v.get("sota_exp_stat", None)
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base_df.loc[k, "SOTA Exp (_to_submit)"] = v["sota_exp_stat_new"]
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if baseline_score is not None and v.get("sota_exp_score", None) is not None:
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base_df.loc[k, "Ours - Base"] = v["sota_exp_score"] - baseline_score
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base_df.loc[k, "Ours vs Base"] = compare_score(v["sota_exp_score"], baseline_score)
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base_df.loc[k, "Ours vs Bronze"] = compare_score(v["sota_exp_score"], v.get("bronze_threshold", None))
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base_df.loc[k, "Ours vs Silver"] = compare_score(v["sota_exp_score"], v.get("silver_threshold", None))
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base_df.loc[k, "Ours vs Gold"] = compare_score(v["sota_exp_score"], v.get("gold_threshold", None))
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base_df.loc[k, "SOTA Exp Score"] = v.get("sota_exp_score", None)
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base_df.loc[k, "SOTA Exp Score (valid)"] = v.get("sota_exp_score_valid", None)
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base_df.loc[k, "Baseline Score"] = baseline_score
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base_df.loc[k, "Bronze Threshold"] = v.get("bronze_threshold", None)
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base_df.loc[k, "Silver Threshold"] = v.get("silver_threshold", None)
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base_df.loc[k, "Gold Threshold"] = v.get("gold_threshold", None)
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base_df.loc[k, "Medium Threshold"] = v.get("median_threshold", None)
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base_df["SOTA Exp"] = base_df["SOTA Exp"].replace("", pd.NA)
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base_df["SOTA Exp Score (valid)"] = base_df["SOTA Exp Score (valid)"].replace("Not Calculated", 0)
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base_df = base_df.astype(
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{
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"Total Loops": int,
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"Successful Final Decision": int,
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"Made Submission": int,
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"Valid Submission": int,
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"Above Median": int,
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"Bronze": int,
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"Silver": int,
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"Gold": int,
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"Any Medal": int,
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"Ours - Base": float,
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"Ours vs Base": float,
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"SOTA Exp Score": float,
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"SOTA Exp Score (valid)": float,
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"Baseline Score": float,
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"Bronze Threshold": float,
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"Silver Threshold": float,
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"Gold Threshold": float,
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"Medium Threshold": float,
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}
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)
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return summary, base_df
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def num2percent(num: int, total: int, show_origin=True) -> str:
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num = int(num)
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total = int(total)
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if show_origin:
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return f"{num} ({round(num / total * 100, 2)}%)"
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return f"{round(num / total * 100, 2)}%"
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def percent_df(df: pd.DataFrame, show_origin=True) -> pd.DataFrame:
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base_df = df.copy(deep=True)
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# Convert columns to object dtype so we can store strings like "14 (53.85%)" without warnings
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columns_to_convert = [
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"Successful Final Decision",
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"Made Submission",
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"Valid Submission",
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"Above Median",
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"Bronze",
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"Silver",
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"Gold",
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"Any Medal",
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]
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base_df[columns_to_convert] = base_df[columns_to_convert].astype(object)
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for k in base_df.index:
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loop_num = int(base_df.loc[k, "Total Loops"])
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if loop_num != 0:
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base_df.loc[k, "Successful Final Decision"] = num2percent(
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base_df.loc[k, "Successful Final Decision"], loop_num, show_origin
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)
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if base_df.loc[k, "Made Submission"] != 0:
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base_df.loc[k, "V/M"] = (
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f"{round(base_df.loc[k, 'Valid Submission'] / base_df.loc[k, 'Made Submission'] * 100, 2)}%"
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)
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else:
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base_df.loc[k, "V/M"] = "N/A"
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base_df.loc[k, "Made Submission"] = num2percent(base_df.loc[k, "Made Submission"], loop_num, show_origin)
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base_df.loc[k, "Valid Submission"] = num2percent(base_df.loc[k, "Valid Submission"], loop_num, show_origin)
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base_df.loc[k, "Above Median"] = num2percent(base_df.loc[k, "Above Median"], loop_num, show_origin)
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base_df.loc[k, "Bronze"] = num2percent(base_df.loc[k, "Bronze"], loop_num, show_origin)
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base_df.loc[k, "Silver"] = num2percent(base_df.loc[k, "Silver"], loop_num, show_origin)
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base_df.loc[k, "Gold"] = num2percent(base_df.loc[k, "Gold"], loop_num, show_origin)
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base_df.loc[k, "Any Medal"] = num2percent(base_df.loc[k, "Any Medal"], loop_num, show_origin)
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return base_df
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def days_summarize_win():
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lfs1 = [re.sub(r"log\.srv\d*", "log.srv", folder) for folder in state.log_folders]
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lfs2 = [re.sub(r"log\.srv\d*", "log.srv2", folder) for folder in state.log_folders]
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lfs3 = [re.sub(r"log\.srv\d*", "log.srv3", folder) for folder in state.log_folders]
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_, df1 = get_summary_df(lfs1)
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_, df2 = get_summary_df(lfs2)
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_, df3 = get_summary_df(lfs3)
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df = pd.concat([df1, df2, df3], axis=0)
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def mean_func(x: pd.DataFrame):
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numeric_cols = x.select_dtypes(include=["int", "float"]).mean()
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string_cols = x.select_dtypes(include=["object"]).agg(lambda col: ", ".join(col.fillna("none").astype(str)))
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return pd.concat([numeric_cols, string_cols], axis=0).reindex(x.columns).drop("Competition")
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df = df.groupby("Competition").apply(mean_func)
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if st.toggle("Show Percent", key="show_percent"):
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st.dataframe(percent_df(df, show_origin=False))
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else:
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st.dataframe(df)
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LITE = [
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"aerial-cactus-identification",
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"aptos2019-blindness-detection",
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"denoising-dirty-documents",
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"detecting-insults-in-social-commentary",
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"dog-breed-identification",
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"dogs-vs-cats-redux-kernels-edition",
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"histopathologic-cancer-detection",
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"jigsaw-toxic-comment-classification-challenge",
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"leaf-classification",
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"mlsp-2013-birds",
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"new-york-city-taxi-fare-prediction",
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"nomad2018-predict-transparent-conductors",
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"plant-pathology-2020-fgvc7",
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"random-acts-of-pizza",
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"ranzcr-clip-catheter-line-classification",
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"siim-isic-melanoma-classification",
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"spooky-author-identification",
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"tabular-playground-series-dec-2021",
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"tabular-playground-series-may-2022",
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"text-normalization-challenge-english-language",
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"text-normalization-challenge-russian-language",
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"the-icml-2013-whale-challenge-right-whale-redux",
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]
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def all_summarize_win():
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def shorten_folder_name(folder: str) -> str:
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if "amlt" in folder:
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return folder[folder.rfind("amlt") + 5 :].split("/")[0]
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if "ep" in folder:
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return folder[folder.rfind("ep") :]
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return folder
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selected_folders = st.multiselect(
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"Show these folders", state.log_folders, state.log_folders, format_func=shorten_folder_name
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)
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summary, base_df = get_summary_df(selected_folders)
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if not summary:
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return
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base_df = percent_df(base_df)
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base_df.insert(0, "Select", True)
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bt1, bt2 = st.columns(2)
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if bt2.toggle("Select Lite Competitions", key="select_lite"):
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base_df["Select"] = base_df["Competition"].isin(LITE)
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if bt1.toggle("Select Best", key="select_best"):
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def apply_func(cdf: pd.DataFrame):
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cp = cdf["Competition"].values[0]
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md = get_metric_direction(cp)
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# If SOTA Exp Score (valid) column is empty, return the first index
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if cdf["SOTA Exp Score (valid)"].dropna().empty:
|
|
return cdf.index[0]
|
|
if md:
|
|
best_idx = cdf["SOTA Exp Score (valid)"].idxmax()
|
|
else:
|
|
best_idx = cdf["SOTA Exp Score (valid)"].idxmin()
|
|
return best_idx
|
|
|
|
best_idxs = base_df.groupby("Competition").apply(apply_func)
|
|
base_df["Select"] = base_df.index.isin(best_idxs.values)
|
|
|
|
base_df = st.data_editor(
|
|
base_df.style.apply(
|
|
lambda col: col.map(lambda val: "background-color: #F0F8FF"),
|
|
subset=["Baseline Score", "Bronze Threshold", "Silver Threshold", "Gold Threshold", "Medium Threshold"],
|
|
axis=0,
|
|
)
|
|
.apply(
|
|
lambda col: col.map(lambda val: "background-color: #FFFFE0"),
|
|
subset=[
|
|
"Ours - Base",
|
|
"Ours vs Base",
|
|
"Ours vs Bronze",
|
|
"Ours vs Silver",
|
|
"Ours vs Gold",
|
|
],
|
|
axis=0,
|
|
)
|
|
.apply(
|
|
lambda col: col.map(lambda val: "background-color: #E6E6FA"),
|
|
subset=[
|
|
"Script Time",
|
|
"Exec Time",
|
|
"Exp Gen",
|
|
"Coding",
|
|
"Running",
|
|
],
|
|
axis=0,
|
|
)
|
|
.apply(
|
|
lambda col: col.map(lambda val: "background-color: #F0FFF0"),
|
|
subset=[
|
|
"Best Result",
|
|
"SOTA Exp",
|
|
"SOTA Exp (_to_submit)",
|
|
"SOTA Exp Score",
|
|
"SOTA Exp Score (valid)",
|
|
],
|
|
axis=0,
|
|
),
|
|
column_config={
|
|
"Select": st.column_config.CheckboxColumn("Select", help="Stat this trace.", disabled=False),
|
|
},
|
|
disabled=(col for col in base_df.columns if col not in ["Select"]),
|
|
)
|
|
st.markdown("Ours vs Base: `math.exp(abs(math.log(sota_exp_score / baseline_score)))`")
|
|
|
|
# 统计选择的比赛
|
|
base_df = base_df[base_df["Select"]]
|
|
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 Result"].value_counts().get("bronze", 0)
|
|
total_stat.loc["Silver"] = base_df["Best Result"].value_counts().get("silver", 0)
|
|
total_stat.loc["Gold"] = base_df["Best Result"].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
|
|
|
|
# SOTA Exp (trace.sota_exp_to_submit) 统计
|
|
se_counts_new = base_df["SOTA Exp (_to_submit)"].value_counts(dropna=True)
|
|
se_counts_new.loc["made_submission"] = se_counts_new.sum()
|
|
se_counts_new.loc["Any Medal"] = (
|
|
se_counts_new.get("gold", 0) + se_counts_new.get("silver", 0) + se_counts_new.get("bronze", 0)
|
|
)
|
|
se_counts_new.loc["above_median"] = se_counts_new.get("above_median", 0) + se_counts_new.get("Any Medal", 0)
|
|
se_counts_new.loc["valid_submission"] = se_counts_new.get("valid_submission", 0) + se_counts_new.get(
|
|
"above_median", 0
|
|
)
|
|
|
|
sota_exp_stat_new = pd.Series(index=total_stat.index, dtype=int, name="SOTA Exp (_to_submit) 统计(%)")
|
|
sota_exp_stat_new.loc["Made Submission"] = se_counts_new.get("made_submission", 0)
|
|
sota_exp_stat_new.loc["Valid Submission"] = se_counts_new.get("valid_submission", 0)
|
|
sota_exp_stat_new.loc["Above Median"] = se_counts_new.get("above_median", 0)
|
|
sota_exp_stat_new.loc["Bronze"] = se_counts_new.get("bronze", 0)
|
|
sota_exp_stat_new.loc["Silver"] = se_counts_new.get("silver", 0)
|
|
sota_exp_stat_new.loc["Gold"] = se_counts_new.get("gold", 0)
|
|
sota_exp_stat_new.loc["Any Medal"] = se_counts_new.get("Any Medal", 0)
|
|
sota_exp_stat_new = sota_exp_stat_new / base_df.shape[0] * 100
|
|
|
|
stat_df = pd.concat([total_stat, sota_exp_stat, sota_exp_stat_new], 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
|
|
st.subheader("Curves", divider="rainbow")
|
|
if st.toggle("Show Curves", key="show_curves"):
|
|
for k, v in summary.items():
|
|
with st.container(border=True):
|
|
st.markdown(f"**:blue[{k}] - :violet[{v['competition']}]**")
|
|
try:
|
|
tscores = {f"loop {k-1}": v for k, v in v["test_scores"].items()}
|
|
vscores = {}
|
|
for k, vs in v["valid_scores"].items():
|
|
if not vs.index.is_unique:
|
|
st.warning(
|
|
f"Loop {k}'s valid scores index are not unique, only the last one will be kept to show."
|
|
)
|
|
st.write(vs)
|
|
vscores[k] = vs[~vs.index.duplicated(keep="last")].iloc[:, 0]
|
|
|
|
if len(vscores) > 0:
|
|
metric_name = list(vscores.values())[0].name
|
|
else:
|
|
metric_name = "None"
|
|
|
|
tdf = pd.Series(tscores, name="score")
|
|
vdf = pd.DataFrame(vscores)
|
|
if "ensemble" in vdf.index:
|
|
ensemble_row = vdf.loc[["ensemble"]]
|
|
vdf = pd.concat([ensemble_row, vdf.drop("ensemble")])
|
|
vdf.columns = [f"loop {i}" for i in vdf.columns]
|
|
fig = go.Figure()
|
|
# Add test scores trace from tdf
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=tdf.index,
|
|
y=tdf,
|
|
mode="lines+markers",
|
|
name="Test scores",
|
|
marker=dict(symbol="diamond"),
|
|
line=dict(shape="linear", dash="dash"),
|
|
)
|
|
)
|
|
# Add valid score traces from vdf (transposed to have loops on x-axis)
|
|
for column in vdf.T.columns:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=vdf.T.index,
|
|
y=vdf.T[column],
|
|
mode="lines+markers",
|
|
name=f"{column}",
|
|
visible=("legendonly" if column != "ensemble" else None),
|
|
)
|
|
)
|
|
fig.update_layout(title=f"Test and Valid scores (metric: {metric_name})")
|
|
|
|
st.plotly_chart(fig)
|
|
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"])
|
|
|
|
|
|
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()
|