diff --git a/CLAUDE.md b/CLAUDE.md index 25adbe706e1793775c1bca1c1d26c16127ccd40a..8cc2a8310ecdd061fe57cf8d931fc77838e75fe5 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -30,7 +30,9 @@ No test or lint frameworks are configured. ``` fev-leaderboard-app.py # Main entry point (Streamlit multi-page router) save_tables.py # Generates tables/ and src/vendor/ from a single fev commit (run at Docker build) -pages/ +benchmarks.py # Combined navigation of fev-bench and CREF, and the switcher between them +fev_pages/ # Not pages/: Streamlit would read that as a page folder and show + # "Page not found" on the first visit to /cref after a restart ├── fev_bench.py # Main leaderboard: computes leaderboard/pairwise, renders them └── about.py # Help page with links src/ @@ -38,6 +40,7 @@ src/ ├── strings.py # UI text, metric descriptions, paper citations ├── task_groups.py # Task groupings by frequency and domain └── vendor/ # GENERATED, gitignored: compute logic copied from the fev repo +cref/ # The CREF leaderboard (/cref pages); see cref/README.md └── fev_bench_compute.py tables/ # GENERATED by save_tables.py, gitignored ├── summaries.csv # Raw evaluation summaries (leaderboards are computed from these) diff --git a/benchmarks.py b/benchmarks.py new file mode 100644 index 0000000000000000000000000000000000000000..b28a5b87dab33cd0184a41e767e3fbd2459dcc39 --- /dev/null +++ b/benchmarks.py @@ -0,0 +1,55 @@ +import streamlit as st + +from cref import navigation + +SWITCHER_STYLE = """""" + +BENCHMARKS = [("fev-bench", "/"), ("CREF", "/cref")] + + +# Plain links, so a switch loads the other benchmark afresh instead of keeping fev's sidebar. +def switcher(current: str) -> str: + links = "".join( + f'{name}' + for name, href in BENCHMARKS + ) + return f'{SWITCHER_STYLE}
{current}
{links}
' + + +def run(fev: list[st.Page]) -> None: + cref = navigation.pages() + page = st.navigation(fev + cref, position="hidden") + # huggingface.co/spaces/autogluon/fev-bench?benchmark=cref: Hugging Face passes only the + # query string into the app, so this opens CREF on the first load. + if not st.session_state.get("routed"): + st.session_state["routed"] = True + if st.query_params.get("benchmark") == "cref" and page not in cref: + st.switch_page(cref[0]) + if page in cref: + navigation.page_config() + with st.container(horizontal=True, vertical_alignment="center", gap="large"): + st.html(switcher("CREF"), width="content") + for link in cref: + st.page_link(link) + else: + with st.sidebar: + st.html(switcher("fev-bench")) + for link in fev: + st.page_link(link) + page.run() diff --git a/cref/README.md b/cref/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d4c7b7f93799445af065d63fda4bdf47e5512fca --- /dev/null +++ b/cref/README.md @@ -0,0 +1,5 @@ +# CREF leaderboard + +The CREF leaderboard, served at `/cref`. It reads the published leaderboards from +`CREF_DATA_URL`, or from `cref/data/` when that is unset. Model names, types and links are +in `models.toml`; the tasks are in `tasks.csv`. diff --git a/cref/__init__.py b/cref/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/cref/charts.py b/cref/charts.py new file mode 100644 index 0000000000000000000000000000000000000000..3718b0340aee0c1e018913fdb6e674ab3b0d982d --- /dev/null +++ b/cref/charts.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +import altair as alt +import pandas as pd + +from cref import views +from cref.views import COLUMNS + +PALETTE = [ + "#4E79A7", "#F28E2B", "#59A14F", "#E15759", "#B07AA1", "#76B7B2", "#EDC948", "#9C755F", + "#FF9DA7", "#BAB0AC", "#A0CBE8", "#FFBE7D", "#8CD17D", "#FF9D9A", "#D4A6C8", "#86BCB6", + "#F1CE63", "#D7B5A6", "#499894", "#79706E", "#1F3A93", "#6B4226", "#2E7D32", "#8E24AA", +] # fmt: skip +LEGEND_COLUMNS = 3 +PAIRWISE = { + "win_rate": ("Win rate (%)", [0, 100], 50), + "skill_score": ("Skill score (%)", [-40, 40], 0), +} + + +def skill_chart(df: pd.DataFrame, models: views.Models) -> alt.LayerChart: + data = views.competitors(df.dropna(subset=["skill_score"]), models) + labels = views.unique_labels(zip(data["model_id"], data["model"], strict=True)) + data = pd.DataFrame( + { + "label": data["model_id"].map(labels), + "skill_score": data["skill_score"], + "type": [views.kind(m, models).label for m in data["model_id"]], + } + ) + kinds = [k for k in [*views.MODEL_TYPES.values(), views.OTHER] if k.label in set(data["type"])] + y = alt.Y("label:N", sort=None, title=None, axis=alt.Axis(labelLimit=320, ticks=False)) + x = alt.X("skill_score:Q", title=COLUMNS["skill_score"], axis=alt.Axis(grid=False)) + bars = ( + alt.Chart(data) + .mark_bar(cornerRadiusEnd=3) + .encode( + x=x, + y=y, + color=alt.Color( + "type:N", + scale=alt.Scale(domain=[k.label for k in kinds], range=[k.bar for k in kinds]), + title=None, + legend=alt.Legend(orient="top"), + ), + tooltip=[alt.Tooltip("skill_score:Q", title=COLUMNS["skill_score"], format=".1f")], + ) + ) + values = [ + alt.Chart(data) + .transform_filter(test) + .mark_text(align=align, dx=dx, fontSize=11, color="#414D5C") + .encode(x=x, y=y, text=alt.Text("skill_score:Q", format=".1f")) + for test, align, dx in [ + ("datum.skill_score >= 0", "left", 4), + ("datum.skill_score < 0", "right", -4), + ] + ] + return alt.layer(bars, *values).properties(height=max(120, 26 * len(data))) + + +def pairwise_frame( + df: pd.DataFrame, models: views.Models, pairwise: dict[str, Any], value: str +) -> tuple[pd.DataFrame, list[str]]: + shown = views.competitors(df, models) + labels = views.unique_labels(zip(shown["model_id"], shown["model"], strict=True)) + cells = [ + {"model": labels[a], "opponent": labels[b], "value": 100 * cell[value]} + for a in labels + for b, cell in pairwise.get(a, {}).items() + if b in labels and cell[value] is not None + ] + return pd.DataFrame(cells, columns=["model", "opponent", "value"]), list(labels.values()) + + +def pairwise_chart(cells: pd.DataFrame, order: Sequence[str], value: str) -> alt.LayerChart: + title, domain, mid = PAIRWISE[value] + far = (domain[1] - mid) * 0.6 + axis = {"labelLimit": 320, "ticks": False, "domain": False} + base = alt.Chart(cells).encode( + x=alt.X( + "opponent:N", + sort=list(order), + title=None, + axis=alt.Axis(orient="top", labelAngle=-90, **axis), + ), + y=alt.Y("model:N", sort=list(order), title=None, axis=alt.Axis(**axis)), + ) + heat = base.mark_rect().encode( + color=alt.Color( + "value:Q", + legend=None, + scale=alt.Scale(scheme="purplegreen", domain=domain, domainMid=mid, clamp=True), + ), + tooltip=[alt.Tooltip("value:Q", title=title, format=".1f")], + ) + text = base.mark_text(fontSize=9).encode( + text=alt.Text("value:Q", format=".0f"), + color=alt.condition( + f"abs(datum.value - {mid}) > {far}", alt.value("#FFFFFF"), alt.value("#31333F") + ), + ) + return alt.layer(heat, text).properties(height=alt.Step(22)) + + +def trend_frame(rows: list[dict[str, Any]], column: str, labels: dict[str, str]) -> pd.DataFrame: + df = pd.DataFrame(rows, columns=["date", "model_id", column]) + df = df[df["model_id"].isin(list(labels))].dropna(subset=[column]) + return pd.DataFrame( + { + "date": pd.to_datetime(df["date"]), + "value": 100 * df[column], + "model": df["model_id"].map(labels), + } + ) + + +def trend_chart(df: pd.DataFrame, order: Sequence[str], title: str) -> alt.Chart: + order = list(order) + picked = alt.selection_point(fields=["model"], bind="legend") + colors = [PALETTE[i % len(PALETTE)] for i in range(len(order))] + return ( + alt.Chart(df) + .mark_line(point=True) + .encode( + x=alt.X("date:T", title=None, axis=alt.Axis(format="%b %d")), + y=alt.Y("value:Q", title=title, scale=alt.Scale(zero=False)), + color=alt.Color( + "model:N", + scale=alt.Scale(domain=order, range=colors), + title=None, + legend=alt.Legend( + orient="bottom", columns=LEGEND_COLUMNS, symbolLimit=0, labelLimit=320 + ), + ), + opacity=alt.condition(picked, alt.value(1.0), alt.value(0.12)), + tooltip=[ + alt.Tooltip("date:T", title="Day"), + alt.Tooltip("value:Q", title=title, format=".1f"), + ], + ) + .add_params(picked) + # The height includes the legend, about 30 px a row. + .properties(height=460 + 30 * -(-len(order) // LEGEND_COLUMNS)) + ) diff --git a/cref/controls.py b/cref/controls.py new file mode 100644 index 0000000000000000000000000000000000000000..816d50a2d1620fc965362381431f9a55334ca036 --- /dev/null +++ b/cref/controls.py @@ -0,0 +1,73 @@ +from __future__ import annotations + +import streamlit as st + +from cref import source +from cref import views + + +@st.cache_resource(on_release=source.Source.close) +def data_source(variant: str = "release") -> source.Source: + return source.Source.from_env(variant) + + +@st.cache_resource(on_release=source.History.close) +def history(variant: str = "release") -> source.History: + return source.History.from_env(variant) + + +@st.cache_resource +def models() -> views.Models: + return views.load_models() + + +@st.cache_resource +def tasks(): + return views.load_tasks() + + +def metric_control() -> str: + return st.segmented_control( + "Metric", + list(views.METRICS), + default="mase", + required=True, + format_func=views.METRICS.get, + help=views.METRIC_HELP, + ) + + +def type_controls(models: views.Models) -> set[str]: + present = [t for t in views.MODEL_TYPES if any(i.get("type") == t for i in models.values())] + if not present: + return set(views.MODEL_TYPES) + picked = st.pills( + "Model types", + present, + selection_mode="multi", + default=present, + format_func=lambda t: views.MODEL_TYPES[t].label, + help="Hides the other models. The seasonal naive baseline is always shown.", + ) + return set(picked or ()) + + +def board_controls(models: views.Models) -> tuple[str, str, set[str]]: + variants = data_source("all").get().board is not None + cells = st.columns([1, 1.5, 3] if variants else [1, 4], vertical_alignment="bottom") + with cells[0]: + metric = metric_control() + variant = "release" + if variants: + with cells[1]: + variant = st.segmented_control( + "Pretrained samples counted", + list(views.VARIANTS), + default="release", + required=True, + format_func=views.VARIANTS.get, + help=views.VARIANT_HELP, + ) + with cells[-1]: + types = type_controls(models) + return metric, variant, types diff --git a/cref/data/v1/leaderboard/all_samples/history/2026-07-04.json b/cref/data/v1/leaderboard/all_samples/history/2026-07-04.json new file mode 100644 index 0000000000000000000000000000000000000000..d7ed71568f23d35491af4d0112bceb92b4e91264 --- /dev/null +++ b/cref/data/v1/leaderboard/all_samples/history/2026-07-04.json @@ -0,0 +1 @@ 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-0.013334, "win_rate": 0.479279}, "sql": {"marginal_win_rate": null, "skill_score": 0.529073, "win_rate": 0.822523}}, "missing_count": 0, "missing_samples": 0, "missing_weight": 0.0, "model_id": "naive"}, {"item_count": 1110, "metrics": {"mase": {"marginal_win_rate": null, "skill_score": -0.025799, "win_rate": 0.518018}, "sql": {"marginal_win_rate": null, "skill_score": 0.525451, "win_rate": 0.837838}}, "missing_count": 0, "missing_samples": 0, "missing_weight": 0.0, "model_id": "autoets"}], "sample_count": 1110, "tasks": {"wiki_world_economy": {"lookback": "120D", "sample_count": 1110}}}}, "timestamp": "2026-11-05T00:08:59.166823+00:00"} \ No newline at end of file diff --git a/cref/models.toml b/cref/models.toml new file mode 100644 index 0000000000000000000000000000000000000000..473acc9eb709963a45f0787a7a21d28c00b57b69 --- /dev/null +++ b/cref/models.toml @@ -0,0 +1,114 @@ +# By public model ID. type: statistical, pretrained, llm-based, agentic or closed-api. +# url: https:// only; without one, the model links to the paper or the About page. baseline: left out of the chart. + +[naive] +name = "Naive" +type = "statistical" +baseline = true + +[autoets] +name = "AutoETS" +type = "statistical" + +[autotheta] +name = "AutoTheta" +type = "statistical" + +[chronos-2] +name = "Chronos 2" +type = "pretrained" +url = "https://huggingface.co/amazon/chronos-2" + +[chronos-bolt-base] +name = "Chronos Bolt" +type = "pretrained" +url = "https://huggingface.co/amazon/chronos-bolt-base" + +[tirex] +name = "TiRex" +type = "pretrained" +url = "https://huggingface.co/NX-AI/TiRex" + +[timesfm-2-5] +name = "TimesFM 2.5" +type = "pretrained" +url = "https://huggingface.co/google/timesfm-2.5-200m-pytorch" + +[moirai-2-small] +name = "Moirai 2" +type = "pretrained" +url = "https://huggingface.co/Salesforce/moirai-2.0-R-small" + +[direct-prompt-opus-4-6] +name = "Direct Prompt (Opus 4.6)" +type = "llm-based" + +[direct-prompt-opus-4-8] +name = "Direct Prompt (Opus 4.8)" +type = "llm-based" + +[direct-prompt-sonnet-4-6] +name = "Direct Prompt (Sonnet 4.6)" +type = "llm-based" + +[direct-prompt-opus-5] +name = "Direct Prompt (Opus 5)" +type = "llm-based" + +[direct-prompt-sonnet-5] +name = "Direct Prompt (Sonnet 5)" +type = "llm-based" + +[direct-prompt-gpt-5-6-sol] +name = "Direct Prompt (GPT-5.6 Sol)" +type = "llm-based" + +[dp-chronos2-sonnet-4-6] +name = "DP + Chronos2 (Sonnet 4.6)" +type = "llm-based" + +[dp-chronos2-opus-5] +name = "DP + Chronos2 (Opus 5)" +type = "llm-based" + +[dp-chronos2-gpt-5-6-sol] +name = "DP + Chronos2 (GPT-5.6 Sol)" +type = "llm-based" + +[agentic-kiro-cli] +name = "Agentic (Opus 4.x)" +type = "agentic" + +[agentic-kiro-cli-opus-5] +name = "Agentic (Opus 5)" +type = "agentic" + +[tirex-2] +name = "TiRex 2" +type = "pretrained" +url = "https://huggingface.co/NX-AI/TiRex-2" + +[toto-2-1b] +name = "Toto 2 (1B)" +type = "pretrained" +url = "https://huggingface.co/Datadog/Toto-2.0-1B" + +[timesfm-3] +name = "TimesFM 3" +type = "pretrained" +url = "https://huggingface.co/google/timesfm-3.0-pytorch" + +[t0-gamma] +name = "t0-gamma" +type = "pretrained" +url = "https://huggingface.co/theforecastingcompany/t0-gamma" + +[tabpfn-ts-3] +name = "TabPFN-TS 3" +type = "pretrained" +url = "https://huggingface.co/Prior-Labs/tabpfn_3" + +[flowstate-r1-1] +name = "FlowState r1.1" +type = "pretrained" +url = "https://huggingface.co/ibm-research/flowstate/tree/r1.1" diff --git a/cref/navigation.py b/cref/navigation.py new file mode 100644 index 0000000000000000000000000000000000000000..8bd0a7e6133f1afcfc18f1ef3ec40b1cbbb80e71 --- /dev/null +++ b/cref/navigation.py @@ -0,0 +1,25 @@ +import streamlit as st + +PAGES = [ + ("leaderboard", "Leaderboard", ":material/leaderboard:"), + ("breakdown", "Breakdown", ":material/grid_view:"), + ("trends", "Trends", ":material/show_chart:"), + ("about", "About", ":material/info:"), + ("join", "Join the competition", ":material/group_add:"), +] + + +def pages() -> list[st.Page]: + return [ + st.Page( + f"cref/pages/{name}.py", + title=title, + icon=icon, + url_path="cref" if name == "leaderboard" else f"cref-{name}", + ) + for name, title, icon in PAGES + ] + + +def page_config() -> None: + st.set_page_config(page_title="CREF leaderboard", page_icon="📈", layout="wide") diff --git a/cref/pages/about.py b/cref/pages/about.py new file mode 100644 index 0000000000000000000000000000000000000000..fc3d6a83bcc14a38b309273a548dc80d88a6cebd --- /dev/null +++ b/cref/pages/about.py @@ -0,0 +1,45 @@ +import streamlit as st + +from cref import controls +from cref import texts + +tasks = controls.tasks() +st.title("About CREF") +st.markdown(texts.ABOUT) +st.markdown( + "**" + + texts.SCOPE.format( + tasks=len(tasks), sources=tasks["provider"].nunique(), domains=tasks["domain"].nunique() + ) + + "**" +) + +paper, data, join = st.columns(3) +with paper.container(border=True, height="stretch"): + st.subheader(":material/article: Paper") + st.markdown(texts.PAPER) + if texts.PAPER_URL: + st.link_button("Read the paper", texts.PAPER_URL, type="primary") + else: + st.caption("The link follows soon.") +with data.container(border=True, height="stretch"): + st.subheader(":material/database: Data") + st.markdown(texts.DATA) + st.link_button("Open on Harvard Dataverse", texts.DATA_URL, type="primary") +with join.container(border=True, height="stretch"): + st.subheader(":material/group_add: Join the competition") + st.markdown(texts.JOIN_TEASER) + if st.button("How to join", type="primary"): + st.switch_page("cref/pages/join.py") + +st.subheader("Citation") +st.code(texts.CITATION, language="bibtex", wrap_lines=True) + +st.subheader("How the scores are computed") +for cards in (texts.STEPS, texts.SCORE_DEFINITIONS[:2], texts.SCORE_DEFINITIONS[2:]): + for cell, (title, formula, text) in zip(st.columns(len(cards)), cards, strict=True): + with cell.container(border=True, height="stretch"): + st.markdown(f"**{title}**") + if formula: + st.latex(formula) + st.markdown(text) \ No newline at end of file diff --git a/cref/pages/breakdown.py b/cref/pages/breakdown.py new file mode 100644 index 0000000000000000000000000000000000000000..228e11e06590b9b565b4e02907fc8e9186267cd4 --- /dev/null +++ b/cref/pages/breakdown.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +import streamlit as st + +from cref import controls +from cref import views + +st.title("Breakdown") +st.markdown( + "The skill score of each model by domain, frequency and task." + "Bottom: Overview of tasks with their data sources." +) + +updated = st.empty() +models = controls.models() +metric, variant, types = controls.board_controls(models) +board = controls.data_source(variant).get().board +if board is None: + st.info("The leaderboard data is not available right now. Please try again later.") + st.stop() +updated.markdown(f"Last updated: {views.utc(board['timestamp'])}") +ranked = views.table(views.shown(views.overall(board)["rankings"], models, types), metric, models) +ranked = views.competitors(ranked, models) +columns = views.unique_labels(zip(ranked["model_id"], ranked["model"], strict=True)) +tasks = controls.tasks() +names = dict(zip(tasks["task"], tasks["name"], strict=True)) + +frames = { + title: df + for group_type, title in views.BREAKDOWNS.items() + if not (df := views.breakdown(board, group_type, metric, columns, names)).empty +} +if not frames: + st.info("No scores by domain, frequency or task yet.") +for title, df in frames.items(): + st.subheader(title) + st.dataframe(views.breakdown_style(df), width="stretch", height=views.table_height(len(df))) +if frames: + st.caption( + "Skill score (%); higher is better. Gold, silver " + "and bronze mark the best three in each row." + ) + +st.subheader("Tasks") +st.dataframe( + tasks[list(views.TASK_COLUMNS)], + hide_index=True, + width="stretch", + height=views.table_height(len(tasks)), + column_config={ + **{c: st.column_config.TextColumn(label) for c, label in views.TASK_COLUMNS.items()}, + "horizon": st.column_config.NumberColumn(views.TASK_COLUMNS["horizon"]), + "source": st.column_config.LinkColumn( + views.TASK_COLUMNS["source"], display_text=views.LINK_TEXT + ), + }, +) diff --git a/cref/pages/join.py b/cref/pages/join.py new file mode 100644 index 0000000000000000000000000000000000000000..9d43b2dfe1a4a3847f91fa29f69fee6889fac8d1 --- /dev/null +++ b/cref/pages/join.py @@ -0,0 +1,17 @@ +import streamlit as st + +from cref import texts + +st.title("Join the competition") +st.markdown(texts.JOIN) +st.info(texts.JOIN_STATUS, icon=":material/hourglass_top:") + +st.subheader("How it works") +for cell, (title, text) in zip(st.columns(len(texts.JOIN_STEPS)), texts.JOIN_STEPS, strict=True): + with cell.container(border=True, height="stretch"): + st.markdown(f"**{title}**") + st.markdown(text) + +with st.container(border=True): + st.markdown(f"**Interested?** Write to {texts.CONTACT}.") + st.link_button("Email us", f"mailto:{texts.CONTACT}", type="primary", icon=":material/mail:") diff --git a/cref/pages/leaderboard.py b/cref/pages/leaderboard.py new file mode 100644 index 0000000000000000000000000000000000000000..bbf852aed2664aae4363e18b244c6e8954b0e2e5 --- /dev/null +++ b/cref/pages/leaderboard.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +import streamlit as st + +from cref import charts +from cref import controls +from cref import texts +from cref import views + +st.title("CREF leaderboard") +updated = st.empty() + +models = controls.models() +metric, variant, types = controls.board_controls(models) +snapshot = controls.data_source(variant).get() +board = snapshot.board +if board is None: + st.info("The leaderboard data is not available right now. Please try again later.") + st.stop() +overall = views.overall(board) +updated.markdown(f"Last updated: {views.utc(board['timestamp'])}") + +if snapshot.stale: + st.warning("The data could not be refreshed. This is the last leaderboard that could be read.") + +st.space("small") +rows = views.shown(overall["rankings"], models, types) +df = views.table( + rows, metric, models, texts.PAPER_URL, overall.get("pairwise"), overall["sample_count"] +) +if df.empty: + st.info("No models to show.") + st.stop() + +percent = {"format": "%.1f"} +st.dataframe( + views.style(df), + hide_index=True, + width="stretch", + height=views.table_height(len(df)), + column_order=[c for c in views.COLUMNS if c in df.columns], + column_config={ + "link": st.column_config.LinkColumn( + views.COLUMNS["link"], display_text=views.LINK_TEXT, pinned=True, width=280 + ), + "skill_score": st.column_config.NumberColumn( + views.COLUMNS["skill_score"], + width=130, + help="Relative to the seasonal naive baseline; higher is better.", + **percent, + ), + "win_rate": st.column_config.NumberColumn( + views.COLUMNS["win_rate"], + width=120, + help="Share of items with a lower error than the seasonal naive baseline.", + **percent, + ), + "marginal_win_rate": st.column_config.NumberColumn( + views.COLUMNS["marginal_win_rate"], + width=170, + help="Average win rate against each other model shown.", + **percent, + ), + "coverage_samples": st.column_config.NumberColumn( + views.COLUMNS["coverage_samples"], + width=110, + format="%.0f", + help="Share of the samples with valid model forecasts.", + ), + "coverage_weight": st.column_config.NumberColumn( + views.COLUMNS["coverage_weight"], + width=110, + format="%.0f", + help="Share of the score's weight with valid model forecasts; every task weighs the " + "same. The rest counts as the seasonal naive baseline.", + ), + "type": st.column_config.MultiselectColumn( + views.COLUMNS["type"], + width=130, + options=[k.label for k in views.MODEL_TYPES.values()], + color=[k.tag for k in views.MODEL_TYPES.values()], + ), + }, +) + +if views.competitors(df, models)["skill_score"].notna().any(): + st.space("large") + st.subheader("Skill score") + st.altair_chart(charts.skill_chart(df, models), width="stretch") + +pairwise = (overall.get("pairwise") or {}).get(metric) or {} +frames = {value: charts.pairwise_frame(df, models, pairwise, value) for value in charts.PAIRWISE} +if len(frames["win_rate"][1]) > 1 and not frames["win_rate"][0].empty: + st.space("large") + st.subheader("Pairwise comparison") + st.caption( + f"Pariwise comparision, on {views.METRICS[metric]}: the share " + "of items where the row's model has the lower error, and its skill score with the " + "column's model as the reference." + ) + for value, (cells, order) in frames.items(): + st.markdown(f"**{charts.PAIRWISE[value][0]}**") + st.altair_chart(charts.pairwise_chart(cells, order, value), width="stretch") +st.page_link("cref/pages/about.py", label="How the scores are computed", icon=":material/info:") diff --git a/cref/pages/trends.py b/cref/pages/trends.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dd1b01ea4889b27da58279e81005fc5ebb8d5e --- /dev/null +++ b/cref/pages/trends.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +import streamlit as st + +from cref import charts +from cref import controls +from cref import views + +st.title("Trends") +updated = st.empty() + +models = controls.models() +metric, variant, types = controls.board_controls(models) +board = controls.data_source(variant).get().board +history = controls.history(variant).rows() +if board is None or not history: + st.info("The history is not available yet. It grows by one leaderboard a day.") + st.stop() +updated.markdown(f"Last updated: {views.utc(board['timestamp'])}") +rows = views.shown(views.overall(board)["rankings"], models, types) +current = views.competitors(views.table(rows, metric, models), models) + +labels = views.unique_labels(zip(current["model_id"], current["model"], strict=True)) +value = st.segmented_control( + "Show", + views.TREND_VALUES, + default="skill_score", + required=True, + format_func=views.COLUMNS.get, + help="Skill score and win rate are for the selected metric; coverage is the same for both.", +) +column = f"{metric}_{value}" if value in ("skill_score", "win_rate") else value +df = charts.trend_frame(history, column, labels) +if df.empty: + st.info("No history for this selection yet.") + st.stop() +st.altair_chart( + charts.trend_chart(df, list(labels.values()), views.COLUMNS[value]), width="stretch" +) +st.caption( + "Each point is the leaderboard at the end of that day (UTC). The legend is in leaderboard " + "order; click a model to highlight it." +) diff --git a/cref/schemas/v1/leaderboard.schema.json b/cref/schemas/v1/leaderboard.schema.json new file mode 100644 index 0000000000000000000000000000000000000000..dfe7017a635b0c175080f4335971d703be9d2224 --- /dev/null +++ b/cref/schemas/v1/leaderboard.schema.json @@ -0,0 +1,309 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "additionalProperties": false, + "properties": { + "boards": { + "additionalProperties": { + "additionalProperties": false, + "properties": { + "pairwise": { + "additionalProperties": false, + "properties": { + "mase": { + "additionalProperties": { + "additionalProperties": { + "additionalProperties": false, + "properties": { + "skill_score": { + "type": [ + "number", + "null" + ] + }, + "win_rate": { + "type": [ + "number", + "null" + ] + } + }, + "required": [ + "win_rate", + "skill_score" + ], + "type": "object" + }, + "maxProperties": 1000, + "propertyNames": { + 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+ "type": "string" + }, + { + "type": "null" + } + ] + }, + "sample_count": { + "minimum": 0, + "type": "integer" + } + }, + "required": [ + "sample_count", + "lookback" + ], + "type": "object" + }, + "maxProperties": 1000, + "propertyNames": { + "pattern": "^[a-z0-9_]{1,64}$", + "type": "string" + }, + "type": "object" + } + }, + "required": [ + "sample_count", + "tasks", + "rankings" + ], + "type": "object" + }, + "maxProperties": 1000, + "propertyNames": { + "pattern": "^(overall|(domain|freq|task)/[a-z0-9_]{1,64})$", + "type": "string" + }, + "type": "object" + }, + "timestamp": { + "format": "date-time", + "pattern": "^[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}:[0-9]{2}:[0-9]{2}(\\.[0-9]{1,9})?(Z|[+-][0-9]{2}:[0-9]{2})$", + "type": "string" + } + }, + "required": [ + "timestamp", + "boards" + ], + "title": "CREF leaderboard", + "type": "object", + "x-cref-schema": "v1/leaderboard" +} diff --git a/cref/source.py b/cref/source.py new file mode 100644 index 0000000000000000000000000000000000000000..2f7a33535f91846d89b3ee6bb27939b5330fc119 --- /dev/null +++ b/cref/source.py @@ -0,0 +1,314 @@ +from __future__ import annotations + +import functools +import json +import logging +import math +import os +import sys +import threading +import time +import urllib.request +from collections.abc import Callable +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass +from datetime import UTC, date, datetime, timedelta +from pathlib import Path +from typing import Any + +from jsonschema import Draft202012Validator, FormatChecker + +ROOT = Path(__file__).parent +SCHEMAS = ROOT / "schemas" / "v1" +LEADERBOARDS = {"release": "v1/leaderboard", "all": "v1/leaderboard/all_samples"} +TTL_SECONDS = 300 +HISTORY_TTL_SECONDS = 3600 +SOCKET_TIMEOUT_SECONDS = 10 +READ_DEADLINE_SECONDS = 30 +MAX_BYTES = 25_000_000 +HISTORY_START = date(2026, 7, 1) + +log = logging.getLogger(__name__) + + +class InvalidData(ValueError): + pass + + +_FORMATS = FormatChecker(formats=()) + + +@_FORMATS.checks("date-time", raises=ValueError) +def _instant(value: object) -> bool: + return not isinstance(value, str) or 2000 <= datetime.fromisoformat(value).year <= 2200 + + +@functools.cache +def _validator() -> Draft202012Validator: + doc = json.loads((SCHEMAS / "leaderboard.schema.json").read_text(encoding="utf-8")) + Draft202012Validator.check_schema(doc) + return Draft202012Validator(doc, format_checker=_FORMATS) + + +def _finite(text: str) -> str: + if not math.isfinite(float(text)): + raise InvalidData(f"number out of range: {text[:40]}") + return text + + +def overall_board(doc: Any) -> Any: + if isinstance(doc, dict) and isinstance(doc.get("boards"), dict) and "overall" in doc["boards"]: + return {**doc, "boards": {"overall": doc["boards"]["overall"]}} + return doc + + +def parse(data: bytes, select: Callable[[Any], Any] | None = None) -> dict[str, Any]: + if len(data) > MAX_BYTES: + raise InvalidData(f"larger than {MAX_BYTES} bytes") + try: + doc = json.loads( + data.decode("utf-8"), + parse_constant=_finite, + parse_float=lambda text: float(_finite(text)), + parse_int=lambda text: int(_finite(text)), + ) + except InvalidData: + raise + except (ValueError, RecursionError) as exc: + raise InvalidData(str(exc)[:200]) from exc + if select: + doc = select(doc) + error = next(_validator().iter_errors(doc), None) + if error is not None: + raise InvalidData(f"{error.json_path}: {error.message[:200]}") + return doc + + +class _NoRedirect(urllib.request.HTTPRedirectHandler): + def redirect_request(self, *args: Any, **kwargs: Any) -> None: + return None + + +_OPENER = urllib.request.build_opener(_NoRedirect) + + +def read_url(url: str, max_bytes: int, deadline: float = READ_DEADLINE_SECONDS) -> bytes: + request = urllib.request.Request( + url, headers={"Accept": "application/json", "User-Agent": "cref-leaderboard"} + ) + end = time.monotonic() + deadline + chunks, size = [], 0 + with _OPENER.open(request, timeout=SOCKET_TIMEOUT_SECONDS) as response: + while size <= max_bytes and (chunk := response.read1(65_536)): + if time.monotonic() > end: + raise TimeoutError(f"{url}: not read within {deadline} s") + chunks.append(chunk) + size += len(chunk) + return b"".join(chunks) + + +def read(base: str, path: str, max_bytes: int = MAX_BYTES) -> bytes: + if base.startswith("https://"): + return read_url(f"{base.rstrip('/')}/{path}", max_bytes) + with open(Path(base) / path, "rb") as file: + return file.read(max_bytes + 1) + + +def check_base(base: str) -> str: + if "://" in base and not base.startswith("https://"): + raise ValueError(f"CREF_DATA_URL must start with https://, got {base[:80]!r}") + return base + + +def base_from_env() -> str: + return check_base(os.environ.get("CREF_DATA_URL", "").strip() or str(ROOT / "data")) + + +class Schedule: + def __init__(self) -> None: + self._lock = threading.Lock() + self._wake = threading.Event() + self._jobs: dict[object, list[Any]] = {} + self._thread: threading.Thread | None = None + + def add(self, seconds: float, update: Callable[[], None]) -> Callable[[], None]: + key = object() + with self._lock: + self._jobs[key] = [time.monotonic() + seconds, seconds, update] + self._wake.set() + self.ensure_running() + + def remove() -> None: + with self._lock: + self._jobs.pop(key, None) + + return remove + + # Called on every page view: cheap while the thread runs, and restarts it if it has died. + def ensure_running(self) -> None: + if self._thread is not None and self._thread.is_alive(): + return + with self._lock: + if self._thread is None or not self._thread.is_alive(): + self._thread = threading.Thread(target=self._run, name="cref-reads", daemon=True) + self._thread.start() + + def _run(self) -> None: + while True: + with self._lock: + jobs = list(self._jobs.values()) + for job in jobs: + if job[0] <= time.monotonic(): + job[0] = time.monotonic() + job[1] + try: + job[2]() + except Exception: + log.exception("scheduled read failed") + due = min((job[0] for job in jobs), default=None) + self._wake.wait(None if due is None else max(0, due - time.monotonic())) + self._wake.clear() + + +SCHEDULE = Schedule() + + +@dataclass(frozen=True) +class Snapshot: + board: dict[str, Any] | None + stale: bool + + +class Source: + def __init__( + self, base: str, prefix: str = LEADERBOARDS["release"], ttl: float = TTL_SECONDS + ) -> None: + self.base = check_base(base) + self._path = f"{prefix}/latest.json" + self._ttl = ttl + self._lock = threading.Lock() + self._snapshot = Snapshot(None, stale=False) + self._remove: Callable[[], None] | None = None + + @classmethod + def from_env(cls, variant: str = "release") -> Source: + return cls(base_from_env(), LEADERBOARDS[variant]) + + def get(self) -> Snapshot: + # Only the first read waits for a page view; later ones run on the schedule, never on demand. + if self._remove is None: + with self._lock: + if self._remove is None: + self._refresh() + self._remove = SCHEDULE.add(self._ttl, self._refresh) + SCHEDULE.ensure_running() + return self._snapshot + + def close(self) -> None: + if self._remove is not None: + self._remove() + + def _refresh(self) -> None: + try: + self._snapshot = Snapshot(parse(read(self.base, self._path)), stale=False) + except Exception as exc: + # Logged once per outage, not on every scheduled read. + report = log.debug if self._snapshot.stale else log.warning + report("leaderboard read failed: %s: %s", type(exc).__name__, exc) + self._snapshot = Snapshot(self._snapshot.board, stale=True) + + +class History: + def __init__( + self, + base: str, + start: date, + prefix: str = LEADERBOARDS["release"], + ttl: float = HISTORY_TTL_SECONDS, + today: Callable[[], date] = lambda: datetime.now(UTC).date(), + ) -> None: + self.base = check_base(base) + self._start = start + self._prefix = prefix + self._ttl = ttl + self._today = today + self._lock = threading.Lock() + self._days: dict[str, list[dict[str, Any]]] = {} + self._remove: Callable[[], None] | None = None + + @classmethod + def from_env(cls, variant: str = "release") -> History: + return cls(base_from_env(), HISTORY_START, LEADERBOARDS[variant]) + + def _read(self, day: str) -> list[dict[str, Any]] | None: + try: + data = read(self.base, f"{self._prefix}/history/{day}.json") + board = parse(data, select=overall_board) + except InvalidData as exc: + log.warning("history %s rejected: %s", day, exc) + return None + except Exception as exc: + log.debug("history %s not available: %s", day, type(exc).__name__) + return None + overall = board["boards"].get("overall", {"sample_count": 0, "rankings": []}) + samples = overall["sample_count"] + return [ + { + "date": day, + "model_id": row["model_id"], + **{ + f"{m}_{k}": row["metrics"][m][k] + for m in ("mase", "sql") + for k in ("skill_score", "win_rate") + }, + "coverage_samples": 1 - row["missing_samples"] / samples if samples else None, + "coverage_weight": None + if row["missing_weight"] is None + else 1 - row["missing_weight"], + } + for row in overall["rankings"] + ] + + def rows(self) -> list[dict[str, Any]]: + if self._remove is None: + with self._lock: + if self._remove is None: + self._update() + self._remove = SCHEDULE.add(self._ttl, self._update) + SCHEDULE.ensure_running() + days = self._days + return [row for day in sorted(days) for row in days[day]] + + def close(self) -> None: + if self._remove is not None: + self._remove() + + def _update(self) -> None: + days = range((self._today() - self._start).days + 1) + due = [ + d + for d in ((self._start + timedelta(n)).isoformat() for n in days) + if d not in self._days + ] + if not due: + return + with ThreadPoolExecutor(16) as pool: + read = zip(due, pool.map(self._read, due), strict=True) + found = {day: rows for day, rows in read if rows is not None} + self._days = {**self._days, **found} + + +def main(paths: list[str]) -> int: + status = 0 + for path in paths: + try: + board = parse(Path(path).read_bytes()) + except (OSError, InvalidData) as exc: + print(f"{path}: INVALID: {exc}") + status = 1 + else: + print(f"{path}: OK, {board['timestamp']}, {len(board['boards'])} boards") + return status + + +if __name__ == "__main__": + sys.exit(main(sys.argv[1:])) diff --git a/cref/tasks.csv b/cref/tasks.csv new file mode 100644 index 0000000000000000000000000000000000000000..d749a0aaaa39d1246951ef6bd14122006235365e --- /dev/null +++ b/cref/tasks.csv @@ -0,0 +1,46 @@ +task,name,domain,frequency,horizon,provider,url,license +airnow_aqi,US Air Quality (PM2.5),Environment,Hourly,24,EPA AirNow,https://www.airnowapi.org/,US Public Domain +austin_311,Austin 311 Requests,Gov. Services,Hourly,24,City of Austin Open Data,https://data.austintexas.gov/,Public Domain (City of Austin) +bls_employment_sectors,US Employment by Sector,Economics,Monthly,4,U.S. Bureau of Labor Statistics (BLS),https://api.bls.gov/publicAPI/v2/,US Public Domain +bls_inflation,US Consumer Prices (CPI),Economics,Monthly,4,U.S. Bureau of Labor Statistics (BLS),https://api.bls.gov/publicAPI/v2/,US Public Domain +bls_labor_market,US Labor Market,Economics,Monthly,4,U.S. Bureau of Labor Statistics (BLS),https://api.bls.gov/publicAPI/v2/,US Public Domain +bls_producer_prices,US Producer Prices (PPI),Economics,Monthly,4,U.S. Bureau of Labor Statistics (BLS),https://api.bls.gov/publicAPI/v2/,US Public Domain +canada_grain,Canada Grain Stocks,Agriculture,Weekly,4,Canadian Grain Commission,https://www.grainscanada.gc.ca/,OGL-Canada 2.0 +cdc_nhsn_capacity,CDC Hospital Capacity,Health,Weekly,4,CDC NHSN (data.cdc.gov),https://data.cdc.gov/,US Public Domain +cdc_nhsn_pathogens,CDC Respiratory Pathogens,Health,Weekly,4,CDC NHSN (data.cdc.gov),https://data.cdc.gov/,US Public Domain +chicago_311,Chicago 311 Requests,Gov. Services,Hourly,24,City of Chicago Data Portal,https://data.cityofchicago.org/,"City of Chicago Data Terms (disclaimer, no restrictions)" +chicago_towed,Chicago Towed Vehicles,Transport,Daily,14,City of Chicago Data Portal,https://data.cityofchicago.org/,"City of Chicago Data Terms of Use (disclaimer, no restrictions)" +delphi_covid,Delphi Epidemiological,Health,Daily,14,CMU Delphi Epidata,https://api.delphi.cmu.edu/epidata/,CC BY 4.0 +dshield_cyber,DShield Cyber Threats,Digital,Daily,14,SANS Internet Storm Center (DShield),https://isc.sans.edu/api/,CC BY-NC-SA 4.0 (redistribution permitted by SANS ISC) +eia_electricity_demand,US Electricity Demand,Energy,Hourly,24,U.S. Energy Information Administration (EIA),https://api.eia.gov/v2/,US Public Domain +eia_electricity_generation,US Electricity Generation,Energy,Hourly,24,U.S. Energy Information Administration (EIA),https://api.eia.gov/v2/,US Public Domain +eia_electricity_interchange,US Grid Interchange,Energy,Hourly,24,U.S. Energy Information Administration (EIA),https://api.eia.gov/v2/,US Public Domain +entsoe_generation,EU Electricity Generation,Energy,15 min,96,ENTSO-E Transparency Platform,https://transparency.entsoe.eu/,CC BY 4.0 +entsoe_load,EU Electricity Load,Energy,15 min,96,ENTSO-E Transparency Platform,https://transparency.entsoe.eu/,CC BY 4.0 +gb_carbon_intensity,GB Carbon Intensity,Environment,30 min,48,NESO GB Carbon Intensity API,https://api.carbonintensity.org.uk/,CC BY 4.0 +gridstatus_caiso_fuel_mix,CAISO Fuel Mix,Energy,5 min,576,"California ISO (CAISO), via gridstatus",https://www.caiso.com/,"California Public Records Act (freely available, attribution required)" +gridstatus_caiso_storage,CAISO Battery Storage,Energy,5 min,288,"California ISO (CAISO), via gridstatus",https://www.caiso.com/,"California Public Records Act (freely available, attribution required)" +gridstatus_iso_load,US ISO Load,Energy,5 min,576,"California ISO (CAISO), via gridstatus",https://www.caiso.com/,CAISO: California PRA (widely redistributed in academic and commercial use) +gridstatus_nyiso_zonal,NYISO Zonal Load,Energy,5 min,288,"New York ISO (NYISO), via gridstatus",https://www.nyiso.com/,No explicit data license (widely redistributed in academic and commercial use) +melbourne_pedestrian,Melbourne Pedestrian Counts,Transport,Hourly,24,City of Melbourne Open Data,https://data.melbourne.vic.gov.au/,CC BY 4.0 +mempool_bitcoin,Bitcoin Mempool,Digital,30 min,48,mempool.space,https://mempool.space/api/,Public blockchain data +mempool_pools,Bitcoin Mining Pools,Digital,Weekly,4,mempool.space,https://mempool.space/api/,Public blockchain data +nasa_fires,NASA Active Fires,Environment,Daily,7,NASA FIRMS,https://firms.modaps.eosdis.nasa.gov/api/,US Public Domain +noaa_buoys,NOAA Ocean Buoys,Water,30 min,48,NOAA National Data Buoy Center,https://www.ndbc.noaa.gov/,US Public Domain +noaa_tides,NOAA Tide Gauges,Water,6 min,240,NOAA CO-OPS (tides and currents),https://api.tidesandcurrents.noaa.gov/api/prod/,US Public Domain +npm_sdks_multi,NPM SDK Downloads,Software,Daily,14,npm registry download counts,https://api.npmjs.org/,"npm Open Source Terms (aggregate statistics, widely republished)" +nyc_311,NYC 311 Requests,Gov. Services,Hourly,24,NYC Open Data,https://data.cityofnewyork.us/,NYC Open Data (No restrictions) +nyc_respiratory,NYC Respiratory Illness,Health,Weekly,4,NYC Department of Health and Mental Hygiene,https://github.com/nychealth/respiratory-illness-data,No license file in source repository (NYC DOH public GitHub; approved for redistribution) +nyc_traffic_speed_multi,NYC Traffic Speed,Transport,Hourly,24,NYC Open Data,https://data.cityofnewyork.us/,NYC Open Data (No restrictions) +paris_bike_counters,Paris Bike Counters,Transport,Hourly,24,Paris Open Data (Ville de Paris),https://opendata.paris.fr/,ODbL 1.0 (tasks have separate partition to preserve share-alike scope) +pypi_model_providers_multi,PyPI ML Provider Downloads,Software,Daily,14,PyPI download statistics (pypistats),https://pypistats.org/api/,PSF public dataset via BigQuery (widely republished) +route_views_bgp_multi,BGP Route Announcements,Digital,15 min,96,Route Views Project (University of Oregon),https://www.routeviews.org/,CC BY 4.0 +seattle_911_by_type,Seattle 911 Calls,Gov. Services,Hourly,24,Seattle Open Data,https://data.seattle.gov/,Public Domain (City of Seattle) +sf_311,SF 311 Requests,Gov. Services,Hourly,24,DataSF (San Francisco Open Data),https://data.sfgov.org/,PDDL 1.0 (DataSF) +sf_911,SF 911 Calls,Gov. Services,Hourly,24,DataSF (San Francisco Open Data),https://data.sfgov.org/,PDDL 1.0 (DataSF) +usda_crop_condition,USDA Crop Condition,Agriculture,Weekly,8,USDA NASS Quick Stats,https://quickstats.nass.usda.gov/api/,US Public Domain +usgs_streamflow,USGS Streamflow,Water,15 min,96,USGS Water Services,https://waterservices.usgs.gov/rest/IV-Service.html,US Public Domain +wiki_people_culture,Wikipedia People & Culture,Public Interest,Daily,14,Wikimedia pageviews,https://wikimedia.org/api/rest_v1/,CC0 +wiki_science_world,Wikipedia Science & World,Public Interest,Daily,14,Wikimedia pageviews,https://wikimedia.org/api/rest_v1/,CC0 +wiki_technology,Wikipedia Technology,Public Interest,Daily,14,Wikimedia pageviews,https://wikimedia.org/api/rest_v1/,CC0 +wiki_world_economy,Wikipedia World Economy,Public Interest,Daily,14,Wikimedia pageviews,https://wikimedia.org/api/rest_v1/,CC0 diff --git a/cref/texts.py b/cref/texts.py new file mode 100644 index 0000000000000000000000000000000000000000..db96bbd797aafbdc95d45e7cd6ecdcb8e4c39aab --- /dev/null +++ b/cref/texts.py @@ -0,0 +1,130 @@ +from __future__ import annotations + +PAPER_URL: str | None = None +CONTACT = "auerand@amazon.de" + +DATA_URL = "https://doi.org/10.7910/DVN/B4OOJK" + +ABOUT = """ +CREF (Continuous Real-Time Evaluation for Forecasting) evaluates forecasting models on +*actual forecasting*. Static benchmarks score forecasts against values that are already +known, and those values can leak into a model: through training data from the same period, +through what a language model remembers about that time, or through an agent that simply +looks the answer up. CREF takes every forecast before its forecast period begins and scores +it once the actual values have been observed, which rules out all of these by design. +""" + +SCOPE = ( + "{tasks} tasks from {sources} public data sources in {domains} domains, scored continuously." +) + +DATA = ( + "The public API for the live data is expected to open in November 2026. Until then, " + "a recent snapshot of the benchmark data is on Harvard Dataverse." +) + +JOIN_TEASER = ( + "Get your model scored and ranked live next to the others. Open to external teams soon." +) + +STEPS = [ + ( + ":material/target: Score each forecast", + None, + "The error $E_i$ of a model on **item** $i$, one target variable of one sample: MASE " + "for the point forecast and SQL (scaled quantile loss) for the quantile forecast, both " + "divided by the seasonal naive error on the sample's history. A missing or invalid " + "forecast gets the seasonal naive baseline's error, so no model gains from leaving out " + "a sample.", + ), + ( + ":material/date_range: Keep recent samples", + None, + "Only samples from a recent window count, and its length depends on how often the " + "data comes. After one window, a model that joined later is scored on exactly the same " + "samples as the others.\n\n" + "| Data | Window |\n|---|---|\n| Up to hourly | 30 days |\n| Daily | 120 days |\n" + "| Weekly | 365 days |\n| Monthly | 365 days |", + ), + ( + ":material/balance: Weigh every task the same", + r"w_i = \frac{1}{n_{t(i)}}", + "The weight $w_i$ of item $i$, where $n_{t(i)}$ is the number of items its task has in " + "the window: every task weighs 1 in total, so a task with many samples does not " + "dominate. The overall board aggregates all items; the domain, frequency and task " + "boards only their own. All boards are recomputed four times a day.", + ), +] + +SCORE_DEFINITIONS = [ + ( + "Skill score", + r"S = 1 - \exp\Big(\frac{\sum_i w_i \log r_i}{\sum_i w_i}\Big), \quad " + r"r_i = \mathrm{clip}\Big(\frac{E_i}{E_i^{\mathrm{baseline}}}, 0.1, 10\Big)", + "The error relative to the seasonal naive baseline. 0 % means as good as the baseline; " + "higher is better.", + ), + ( + "Win rate", + r"W = \frac{\sum_i w_i \big[\mathbb{1}(E_i < E_i^{\mathrm{baseline}}) " + r"+ \tfrac{1}{2}\,\mathbb{1}(E_i = E_i^{\mathrm{baseline}})\big]}{\sum_i w_i}", + "The weighted share of items on which the model beats the baseline; ties count half.", + ), + ( + "Marginal win rate", + r"\bar{W}_j = \frac{1}{M - 1} \sum_{k \neq j} W_{jk}", + "$W_{jk}$ is the win rate of model $j$ with model $k$ in place of the baseline, and $M$ " + "the number of models shown. Hiding a model type removes its models from the " + "comparison.", + ), + ( + "Coverage", + None, + "By samples: the share of samples the model forecast itself. By weight: the share of " + "the score's weight. The rest counts as the seasonal naive baseline.", + ), +] + +RELEASE = ( + "**After release** (the default): a pretrained model counts only on samples created after " + "its checkpoint was released, and as the baseline on earlier ones. **All samples** counts " + "every sample." +) + + +PAPER = ( + "**CREF: Forecasting Benchmarks for the Age of Agents.** NeurIPS 2026 " +) + +CITATION = """@inproceedings{auer2026cref, + title={{CREF}: Forecasting Benchmarks for the Age of Agents}, + author={Auer, Andreas and Ansari, Abdul Fatir and Shchur, Oleksandr and Zhang, Xiyuan and Han, Boran and Mercado, Pedro and Desai, Prateek Mutalik and Wang, Bernie and Bohlke-Schneider, Michael}, + booktitle={Advances in Neural Information Processing Systems}, + volume={39}, + year={2026}, +}""" + +JOIN = ( + "Get your model scored on data that does not exist yet, and ranked live next to the " + "others: time series foundation models, LLM-based forecasters and agents alike." +) + +JOIN_STATUS = "The competition is not open to new teams yet; this is coming soon." + +JOIN_STEPS = [ + ( + ":material/mail: Contact us", + "Tell us your team's name and about your model. We let you know when the competition " + "opens.", + ), + ( + ":material/schedule: Forecast every cycle", + "Submit point and, optionally, quantile forecasts before each sample's deadline. It " + "comes with the sample and is always at least an hour after the sample is published.", + ), + ( + ":material/leaderboard: Get ranked", + "Your forecasts are scored once the actual values are observed, and your model joins " + "the leaderboard.", + ), +] diff --git a/cref/views.py b/cref/views.py new file mode 100644 index 0000000000000000000000000000000000000000..fc7c4d3534b1864715c0c386b8eeb519206c10a1 --- /dev/null +++ b/cref/views.py @@ -0,0 +1,291 @@ +from __future__ import annotations + +import re +import tomllib +from collections import Counter +from collections.abc import Iterable +from datetime import UTC, datetime +from pathlib import Path +from typing import Any, NamedTuple +from urllib.parse import urlsplit + +import pandas as pd +from pandas.io.formats.style import Styler + +BASELINE = "__baseline__" +BASELINE_NAME = "Seasonal naive" +BREAKDOWNS = {"domain": "By domain", "freq": "By frequency", "task": "By task"} +FREQUENCIES = ("upto30min", "hourly", "daily", "weekly", "monthly") +GROUP_LABELS = { + "gov_services": "Gov. Services", + "public_interest": "Public Interest", + "upto30min": "≤ 30 min", +} +EMPTY_BOARD: dict[str, Any] = {"sample_count": 0, "tasks": {}, "rankings": []} +METRICS = {"mase": "MASE", "sql": "SQL"} +VARIANTS = {"release": "After release", "all": "All samples"} +VARIANT_HELP = ( + "A pretrained model can be trained on data published up to its release " + "To facilicate accelerated comparision but account for potential leakage two views exist:" + "**After release:** a pretrained model has only valid forecasts on samples created after its " + "checkpoint was published. This is guaranteed leak free." + "**All samples:** every model is scored on every sample (unless confirmed leakage)" +) +METRIC_HELP = ( + "MASE: Mean Absolute Scaled Error (point forecast) " + "SQL: Scaled Quantile Loss (probabilistic forecast)" +) + + +class Kind(NamedTuple): + label: str + tag: str + bar: str + + +MODEL_TYPES = { + "statistical": Kind("Statistical", "#ECE6DD", "#A08C6E"), + "pretrained": Kind("Pretrained", "#E9DDF5", "#8D5EB7"), + "llm-based": Kind("LLM-based", "#DCE6F2", "#4E79A7"), + "agentic": Kind("Agentic", "#DCEEE4", "#3E9C6E"), + "closed-api": Kind("Closed API", "#F3DEDE", "#C44E52"), +} +OTHER = Kind("Other", "#ECEDEF", "#9AA0A6") +TABLE_ROWS = 20 +ROW_HEIGHT = 35 +MEDALS = {1: "🥇", 2: "🥈", 3: "🥉"} +BASELINE_COLOR = "#7D8998" +RANK_COLORS = {1: "#F7D36B", 2: "#E5E7EB", 3: "#E6B089"} + +COLUMNS = { + "link": "Model", + "skill_score": "Skill score (%)", + "win_rate": "Win rate (%)", + "marginal_win_rate": "Marginal win rate (%)", + "coverage_samples": "Coverage, samples (%)", + "coverage_weight": "Coverage, weight (%)", + "type": "Type", +} +HIDDEN_COLUMNS = ("model", "model_id") +TREND_VALUES = ("skill_score", "win_rate", "coverage_samples", "coverage_weight") +# The Model column links each name; its text is the URL's fragment. +LINK_TEXT = r"#(.*)$" +ABOUT_PAGE = "cref-about" + +MODELS = Path(__file__).parent / "models.toml" +TASKS = Path(__file__).parent / "tasks.csv" +TASK_COLUMNS = { + "name": "Task", + "domain": "Domain", + "frequency": "Frequency", + "horizon": "Horizon (steps)", + "source": "Source", + "license": "License", +} +MODEL_FIELDS = {"name", "url", "type", "baseline"} +MODEL_ID = re.compile(rf"[a-z0-9](?:[a-z0-9-]{{0,62}}[a-z0-9])?|{BASELINE}") + +Models = dict[str, dict[str, Any]] + + +def is_https(url: object) -> bool: + if not isinstance(url, str) or any(c.isspace() or not c.isprintable() for c in url): + return False + parts = urlsplit(url) + return parts.scheme == "https" and bool(parts.netloc) + + +def load_models(path: Path = MODELS) -> Models: + models = tomllib.loads(path.read_text(encoding="utf-8")) + for model_id, info in models.items(): + where = f"{path.name}: [{model_id}]" + if not MODEL_ID.fullmatch(model_id) or not isinstance(info, dict): + raise ValueError(f"{where}: not a model ID") + if unknown := set(info) - MODEL_FIELDS: + raise ValueError(f"{where}: unknown fields {sorted(unknown)}") + if "url" in info and (not is_https(info["url"]) or "#" in info["url"]): + raise ValueError(f"{where}: url is not an https:// link without #") + if "baseline" in info and not isinstance(info["baseline"], bool): + raise ValueError(f"{where}: baseline must be true or false") + name = info.get("name", "x") + if not isinstance(name, str) or not 0 < len(name) <= 256 or not name.isprintable(): + raise ValueError(f"{where}: name must be 1 to 256 printable characters") + if "type" in info and info["type"] not in MODEL_TYPES: + raise ValueError(f"{where}: type must be one of {list(MODEL_TYPES)}") + return models + + +def load_tasks(path: Path = TASKS) -> pd.DataFrame: + df = pd.read_csv(path, dtype=str, keep_default_na=False) + if bad := [u for u in df["url"] if not is_https(u) or "#" in u]: + raise ValueError(f"{path.name}: not https:// links without #: {bad[:3]}") + df["horizon"] = df["horizon"].astype(int) + df["source"] = df["url"] + "#" + df["provider"] + return df.sort_values(["domain", "name"]) + + +def name(model_id: str, models: Models) -> str: + if model_id == BASELINE: + return BASELINE_NAME + return models.get(model_id, {}).get("name") or model_id + + +def link(model_id: str, models: Models, paper_url: str | None = None) -> str: + return models.get(model_id, {}).get("url") or paper_url or ABOUT_PAGE + + +def is_baseline(model_id: str, models: Models) -> bool: + return model_id == BASELINE or bool(models.get(model_id, {}).get("baseline")) + + +def kind(model_id: str, models: Models) -> Kind: + return MODEL_TYPES.get(models.get(model_id, {}).get("type"), OTHER) + + +def shown(rows: list[dict[str, Any]], models: Models, types: set[str]) -> list[dict[str, Any]]: + def keep(row: dict[str, Any]) -> bool: + kind = models.get(row["model_id"], {}).get("type") + return row["model_id"] == BASELINE or kind is None or kind in types + + return [row for row in rows if keep(row)] + + +def label(key: str) -> str: + return key.replace("_", " ") + + +def group_label(value: str) -> str: + return GROUP_LABELS.get(value) or label(value).capitalize() + + +def unique_labels(models: Iterable[tuple[str, str]]) -> dict[str, str]: + pairs = list(models) + counts = Counter(name for _, name in pairs) + return {mid: name if counts[name] == 1 else f"{name} ({mid})" for mid, name in pairs} + + +def utc(timestamp: str) -> str: + return f"{datetime.fromisoformat(timestamp).astimezone(UTC):%Y-%m-%d %H:%M} UTC" + + +def table_height(rows: int) -> int: + return (min(rows, TABLE_ROWS) + 1) * ROW_HEIGHT + 3 + + +def overall(board: dict[str, Any]) -> dict[str, Any]: + return board["boards"].get("overall", EMPTY_BOARD) + + +def group_values(board: dict[str, Any], group_type: str) -> list[str]: + prefix = f"{group_type}/" + values = [k.removeprefix(prefix) for k in board["boards"] if k.startswith(prefix)] + if group_type == "freq": + order = {f: i for i, f in enumerate(FREQUENCIES)} + return sorted(values, key=lambda v: (order.get(v, len(order)), v)) + return sorted(values) + + +def _percent(value: float | None) -> float | None: + return None if value is None else 100 * value + + +def marginal_win_rates( + rows: list[dict[str, Any]], metric: str, pairwise: dict[str, Any] | None +) -> dict[str, float | None]: + if not pairwise: + return {r["model_id"]: r["metrics"][metric]["marginal_win_rate"] for r in rows} + shown = {r["model_id"] for r in rows} + out = {} + for model_id in shown: + against = pairwise[metric].get(model_id, {}) + rates = [ + v["win_rate"] for o, v in against.items() if o in shown and v["win_rate"] is not None + ] + out[model_id] = sum(rates) / len(rates) if rates else None + return out + + +def table( + rows: list[dict[str, Any]], + metric: str, + models: Models | None = None, + paper_url: str | None = None, + pairwise: dict[str, Any] | None = None, + samples: int = 0, +) -> pd.DataFrame: + models = models or {} + marginal = marginal_win_rates(rows, metric, pairwise) + + def key(row: dict[str, Any]) -> tuple[bool, float]: + score = row["metrics"][metric]["skill_score"] + return score is not None, score or 0.0 + + records, place = [], 0 + for row in sorted(rows, key=key, reverse=True): + model_id = row["model_id"] + prefix = "" + if not is_baseline(model_id, models): + place += 1 + prefix = f"{MEDALS[place]} " if place in MEDALS else "" + records.append( + { + "model": (model := name(model_id, models)), + "link": f"{link(model_id, models, paper_url)}#{prefix}{model}", + "type": [MODEL_TYPES[t].label] + if (t := models.get(model_id, {}).get("type")) + else [], + "skill_score": _percent(row["metrics"][metric]["skill_score"]), + "win_rate": _percent(row["metrics"][metric]["win_rate"]), + "marginal_win_rate": _percent(marginal[model_id]), + "coverage_samples": 100 * (1 - row["missing_samples"] / samples) + if samples + else None, + "coverage_weight": _percent( + None if (w := row["missing_weight"]) is None else 1 - w + ), + "model_id": model_id, + } + ) + df = pd.DataFrame.from_records(records, columns=[*COLUMNS, *HIDDEN_COLUMNS]) + return df if any(df["type"].map(len)) else df.drop(columns="type") + + +def competitors(df: pd.DataFrame, models: Models) -> pd.DataFrame: + return df.loc[~df["model_id"].map(lambda m: is_baseline(m, models)).astype(bool)] + + +def style(df: pd.DataFrame) -> Styler: + def colors(row: pd.Series) -> list[str]: + color = f"color: {BASELINE_COLOR}" if row["model_id"] == BASELINE else "" + return [color] * len(row) + + return df.style.apply(colors, axis=1) + + +def breakdown( + board: dict[str, Any], + group_type: str, + metric: str, + columns: dict[str, str], + names: dict[str, str] | None = None, +) -> pd.DataFrame: + data = {} + for value in group_values(board, group_type): + rows = board["boards"][f"{group_type}/{value}"]["rankings"] + scores = {row["model_id"]: row["metrics"][metric]["skill_score"] for row in rows} + data[(names or {}).get(value) or group_label(value)] = [ + float("nan") if scores.get(m) is None else 100 * scores[m] for m in columns + ] + df = pd.DataFrame.from_dict(data, orient="index", columns=list(columns.values()), dtype=float) + return df.dropna(how="all") + + +def breakdown_style(df: pd.DataFrame) -> Styler: + def colors(row: pd.Series) -> list[str]: + ranks = row.rank(ascending=False, method="min") + return [ + f"background-color: {RANK_COLORS[int(r)]}" if pd.notna(r) and r <= 3 else "" + for r in ranks + ] + + return df.style.apply(colors, axis=1).format("{:.1f}", na_rep="–") diff --git a/fev-leaderboard-app.py b/fev-leaderboard-app.py index 5254daa3546fcd4274035c5b7dd89ba69642d72a..96ebf4894242d732a78132273a3c2d6b15322f31 100644 --- a/fev-leaderboard-app.py +++ b/fev-leaderboard-app.py @@ -1,9 +1,10 @@ import streamlit as st +import benchmarks + pages = [ - st.Page("pages/fev_bench.py", title="fev-bench", icon=":material/trophy:"), - st.Page("pages/about.py", title="About", icon=":material/info:"), + st.Page("fev_pages/fev_bench.py", title="Leaderboard", icon=":material/trophy:"), + st.Page("fev_pages/about.py", title="About", icon=":material/info:"), ] -page = st.navigation(pages) -page.run() +benchmarks.run(pages) diff --git a/pages/about.py b/fev_pages/about.py similarity index 100% rename from pages/about.py rename to fev_pages/about.py diff --git a/pages/fev_bench.py b/fev_pages/fev_bench.py similarity index 100% rename from pages/fev_bench.py rename to fev_pages/fev_bench.py diff --git a/pyproject.toml b/pyproject.toml index 5beb6eb822dfffd8e8ebc03e887ca03dbb6da316..1dd41fade2dba0c566a1699db88915494992150c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,5 +8,6 @@ dependencies = [ "numpy<2.2", "pyarrow<21", "scipy<1.15", - "streamlit>=1.53.1", + "jsonschema>=4.26.0", + "streamlit>=1.66.1", ] diff --git a/requirements.txt b/requirements.txt index 5c4cfbfd6c4310cbe99097b0d3467d9178920ab4..269fad6ab5a463527f1da03859d0fb3b11ef673e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,6 +2,7 @@ matplotlib numpy pandas requests -streamlit==1.53.1 +streamlit==1.66.1 +jsonschema==4.26.0 fev>=0.6.0 altair>=5.5.0 diff --git a/uv.lock b/uv.lock index 22d86b661d176fc5058fb9f5ca1effbf9dc10167..4014d540fef569ede66af27bf0fbf64f4f6f7229 100644 --- a/uv.lock +++ b/uv.lock @@ -1,15 +1,15 @@ version = 1 -revision = 3 +revision = 5 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform == 'emscripten'", "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", 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