import math import pickle import re from collections import deque from datetime import datetime, timedelta from pathlib import Path import pandas as pd import plotly.express as px import plotly.graph_objects as go import streamlit as st from streamlit import session_state as state from rdagent.log.mle_summary import extract_mle_json from rdagent.log.ui.conf import UI_SETTING from rdagent.log.ui.ds_trace import load_times from rdagent.log.ui.utils import ALL, HIGH, LITE, MEDIUM from rdagent.scenarios.kaggle.kaggle_crawler import leaderboard_scores def get_metric_direction(competition: str): leaderboard = leaderboard_scores(competition) return float(leaderboard[0]) > float(leaderboard[-1]) def get_script_time(stdout_p: Path): with stdout_p.open("r") as f: first_line = next(f).strip() last_line = deque(f, maxlen=1).pop().strip() # Extract timestamps from the lines first_time_match = re.search(r"(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\+\d{2}:\d{2})", first_line) last_time_match = re.search(r"(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\+\d{2}:\d{2})", last_line) if first_time_match and last_time_match: first_time = datetime.fromisoformat(first_time_match.group(1)) last_time = datetime.fromisoformat(last_time_match.group(1)) return pd.Timedelta(last_time - first_time) return None @st.cache_data(persist=True) def get_final_sota_exp(log_path: Path): sota_exp_paths = [i for i in log_path.rglob(f"**/SOTA experiment/**/*.pkl")] if len(sota_exp_paths) == 0: return None final_sota_exp_path = max(sota_exp_paths, key=lambda x: int(re.match(r".*Loop_(\d+).*", str(x))[1])) with final_sota_exp_path.open("rb") as f: final_sota_exp = pickle.load(f) return final_sota_exp @st.cache_data(persist=True) def get_sota_exp_stat(log_path: Path): trace_paths = [i for i in log_path.rglob(f"**/trace/**/*.pkl")] if len(trace_paths) == 0: return None final_trace_path = max(trace_paths, key=lambda x: int(re.match(r".*Loop_(\d+).*", str(x))[1])) with final_trace_path.open("rb") as f: final_trace = pickle.load(f) if hasattr(final_trace, "sota_exp_to_submit"): st.toast("Using sota_exp_to_submit") sota_exp = final_trace.sota_exp_to_submit else: sota_exp = final_trace.sota_experiment() if sota_exp is None: return None sota_loop_id = None for i, ef in enumerate(final_trace.hist): if ef[0] == sota_exp: sota_loop_id = i break sota_mle_score_paths = [i for i in log_path.rglob(f"Loop_{sota_loop_id}/running/mle_score/**/*.pkl")] if len(sota_mle_score_paths) == 0: # sota exp is not evaluated by mle_score return None with sota_mle_score_paths[0].open("rb") as f: sota_mle_score = extract_mle_json(pickle.load(f)) sota_exp_stat = None if sota_mle_score: # sota exp's grade output if sota_mle_score["gold_medal"]: sota_exp_stat = "gold" elif sota_mle_score["silver_medal"]: sota_exp_stat = "silver" elif sota_mle_score["bronze_medal"]: sota_exp_stat = "bronze" elif sota_mle_score["above_median"]: sota_exp_stat = "above_median" elif sota_mle_score["valid_submission"]: sota_exp_stat = "valid_submission" elif sota_mle_score["submission_exists"]: sota_exp_stat = "made_submission" return sota_exp_stat @st.cache_data(persist=True) def get_summary_df(log_folders: list[str]) -> tuple[dict, pd.DataFrame]: summarys = {} sn = "summary.pkl" for lf in log_folders: if (Path(lf) / sn).exists(): summarys[lf] = pd.read_pickle(Path(lf) / sn) if len(summarys) == 0: return {}, pd.DataFrame() summary = {} for lf, s in summarys.items(): for k, v in s.items(): stdout_p = Path(lf) / f"{k}.stdout" if stdout_p.exists(): v["script_time"] = get_script_time(stdout_p) else: v["script_time"] = None exp_gen_time = timedelta() coding_time = timedelta() running_time = timedelta() all_time = timedelta() times_info = load_times(Path(lf) / k) for time_info in times_info.values(): all_time += sum((ti.end - ti.start for ti in time_info), timedelta()) exp_gen_time += time_info[0].end - time_info[0].start if len(time_info) > 1: coding_time += time_info[1].end - time_info[1].start if len(time_info) > 2: running_time += time_info[2].end - time_info[2].start v["exec_time"] = str(all_time).split(".")[0] v["exp_gen_time"] = str(exp_gen_time).split(".")[0] v["coding_time"] = str(coding_time).split(".")[0] v["running_time"] = str(running_time).split(".")[0] final_sota_exp = get_final_sota_exp(Path(lf) / k) if final_sota_exp is not None and final_sota_exp.result is not None: v["sota_exp_score_valid"] = final_sota_exp.result.loc["ensemble"].iloc[0] else: v["sota_exp_score_valid"] = None v["sota_exp_stat_new"] = get_sota_exp_stat(Path(lf) / k) # 调整实验名字 if "amlt" in lf: summary[f"{lf[lf.rfind('amlt')+5:].split('/')[0]} - {k}"] = v elif "ep" in lf: summary[f"{lf[lf.rfind('ep'):]} - {k}"] = v else: summary[f"{lf} - {k}"] = v summary = {k: v for k, v in summary.items() if "competition" in v} base_df = pd.DataFrame( columns=[ "Competition", "Script Time", "Exec Time", "Exp Gen", "Coding", "Running", "Total Loops", "Successful Final Decision", "Made Submission", "Valid Submission", "V/M", "Above Median", "Bronze", "Silver", "Gold", "Any Medal", "Best Result", "SOTA Exp", "SOTA Exp (_to_submit)", "SOTA Exp Score (valid)", "SOTA Exp Score", "Baseline Score", "Ours - Base", "Ours vs Base", "Ours vs Bronze", "Ours vs Silver", "Ours vs Gold", "Bronze Threshold", "Silver Threshold", "Gold Threshold", "Medium Threshold", ], 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) def compare_score(s1, s2): if s1 is None or s2 is None: return None try: c_value = math.exp(abs(math.log(s1 / s2))) except Exception as e: c_value = None return c_value for k, v in summary.items(): loop_num = v["loop_num"] base_df.loc[k, "Competition"] = v["competition"] base_df.loc[k, "Script Time"] = v["script_time"] base_df.loc[k, "Exec Time"] = v["exec_time"] base_df.loc[k, "Exp Gen"] = v["exp_gen_time"] base_df.loc[k, "Coding"] = v["coding_time"] base_df.loc[k, "Running"] = v["running_time"] 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"] if v["made_submission_num"] > 0: base_df.loc[k, "Best Result"] = "made_submission" base_df.loc[k, "Valid Submission"] = v["valid_submission_num"] if v["valid_submission_num"] > 0: base_df.loc[k, "Best Result"] = "valid_submission" base_df.loc[k, "Above Median"] = v["above_median_num"] if v["above_median_num"] > 0: base_df.loc[k, "Best Result"] = "above_median" base_df.loc[k, "Bronze"] = v["bronze_num"] if v["bronze_num"] > 0: base_df.loc[k, "Best Result"] = "bronze" base_df.loc[k, "Silver"] = v["silver_num"] if v["silver_num"] > 0: base_df.loc[k, "Best Result"] = "silver" base_df.loc[k, "Gold"] = v["gold_num"] if v["gold_num"] > 0: base_df.loc[k, "Best Result"] = "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) base_df.loc[k, "SOTA Exp (_to_submit)"] = v["sota_exp_stat_new"] 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 base_df.loc[k, "Ours vs Base"] = compare_score(v["sota_exp_score"], baseline_score) base_df.loc[k, "Ours vs Bronze"] = compare_score(v["sota_exp_score"], v.get("bronze_threshold", None)) base_df.loc[k, "Ours vs Silver"] = compare_score(v["sota_exp_score"], v.get("silver_threshold", None)) base_df.loc[k, "Ours vs Gold"] = compare_score(v["sota_exp_score"], v.get("gold_threshold", None)) base_df.loc[k, "SOTA Exp Score"] = v.get("sota_exp_score", None) base_df.loc[k, "SOTA Exp Score (valid)"] = v.get("sota_exp_score_valid", 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["SOTA Exp"] = base_df["SOTA Exp"].replace("", pd.NA) base_df["SOTA Exp Score (valid)"] = ( base_df["SOTA Exp Score (valid)"].replace("Not Calculated", 0).replace("Not Computed", 0) ) 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, "SOTA Exp Score (valid)": 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: num = int(num) total = int(total) 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.copy(deep=True) # Convert columns to object dtype so we can store strings like "14 (53.85%)" without warnings columns_to_convert = [ "Successful Final Decision", "Made Submission", "Valid Submission", "Above Median", "Bronze", "Silver", "Gold", "Any Medal", ] base_df[columns_to_convert] = base_df[columns_to_convert].astype(object) 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 ) for lf in selected_folders: if not (Path(lf) / "summary.pkl").exists(): st.warning( f"summary.pkl not found in **{lf}**\n\nRun:`dotenv run -- python rdagent/log/mle_summary.py grade_summary --log_folder={lf} --hours=<>`" ) summary, base_df = get_summary_df(selected_folders) if not summary: return base_df = percent_df(base_df) base_df.insert(0, "Select", True) bt1, bt2 = st.columns(2) select_lite_level = bt2.selectbox( "Select MLE-Bench Competitions Level", options=["ALL", "HIGH", "MEDIUM", "LITE"], index=0, key="select_lite_level", ) if select_lite_level != "ALL": if select_lite_level == "HIGH": lite_set = set(HIGH) elif select_lite_level == "MEDIUM": lite_set = set(MEDIUM) elif select_lite_level == "LITE": lite_set = set(LITE) else: lite_set = set() base_df["Select"] = base_df["Competition"].isin(lite_set) else: base_df["Select"] = True # select all if ALL is chosen if bt1.toggle("Select Best", key="select_best"): def apply_func(cdf: pd.DataFrame): cp = cdf["Competition"].values[0] md = get_metric_direction(cp) # If SOTA Exp Score (valid) column is empty, return the first index 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()