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import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent))
import fev
import pandas as pd
import streamlit as st
from streamlit.elements.lib.column_types import ColumnConfig
from src.strings import (
CHRONOS_BENCHMARK_BASIC_INFO,
CHRONOS_BENCHMARK_DETAILS,
CITATION_CHRONOS,
CITATION_FEV,
CITATION_HEADER,
PAIRWISE_BENCHMARK_DETAILS,
get_pivot_legend,
)
from src.utils import (
construct_bar_chart,
construct_pairwise_chart,
construct_pivot_table,
format_leaderboard,
format_metric_name,
get_metric_description,
)
st.set_page_config(layout="wide", page_title="FEV Benchmark Leaderboard", page_icon=":material/trophy:")
TITLE = "<h1 style='text-align: center; font-size: 350%;'>Chronos Benchmark II</h1>"
BASELINE_MODEL = "seasonal_naive"
LEAKAGE_IMPUTATION_MODEL = "chronos_bolt_base"
SORT_COL = "win_rate"
N_RESAMPLES_FOR_CI = 1000
TOP_K_MODELS_TO_PLOT = 15
AVAILABLE_METRICS = ["WQL", "MASE"]
SUMMARY_URLS = [
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/auto_arima.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/auto_ets.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/auto_theta.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_base.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_large.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_mini.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_small.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_tiny.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_bolt_base.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_bolt_mini.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_bolt_small.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/chronos_bolt_tiny.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/moirai_base.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/moirai_large.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/moirai_small.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/seasonal_naive.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/timesfm.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/timesfm-2.0.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/ttm-r2.csv",
"https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/tirex.csv",
]
@st.cache_data()
def load_summaries():
summaries = []
for url in SUMMARY_URLS:
df = pd.read_csv(url)
summaries.append(df)
return pd.concat(summaries, ignore_index=True)
@st.cache_data()
def get_leaderboard(metric_name: str) -> pd.DataFrame:
summaries = load_summaries()
lb = fev.analysis.leaderboard(
summaries=summaries,
metric_column=metric_name,
missing_strategy="impute",
baseline_model=BASELINE_MODEL,
leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
)
lb = lb.astype("float64").reset_index()
lb["skill_score"] = lb["skill_score"] * 100
lb["win_rate"] = lb["win_rate"] * 100
lb["num_failures"] = lb["num_failures"] / summaries["task_name"].nunique() * 100
return lb
@st.cache_data()
def get_pairwise(metric_name: str, included_models: list[str]) -> pd.DataFrame:
if BASELINE_MODEL not in included_models:
included_models = included_models + [BASELINE_MODEL]
summaries = load_summaries()
return (
fev.analysis.pairwise_comparison(
summaries,
included_models=included_models,
metric_column=metric_name,
baseline_model=BASELINE_MODEL,
missing_strategy="impute",
n_resamples=N_RESAMPLES_FOR_CI,
leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
)
.round(3)
.reset_index()
)
with st.sidebar:
selected_metric = st.selectbox("Evaluation Metric", options=AVAILABLE_METRICS, format_func=format_metric_name)
st.caption(get_metric_description(selected_metric))
cols = st.columns(spec=[0.025, 0.95, 0.025])
with cols[1] as main_container:
st.markdown(TITLE, unsafe_allow_html=True)
metric_df = get_leaderboard(selected_metric).sort_values(by=SORT_COL, ascending=False)
top_k_models = metric_df.head(TOP_K_MODELS_TO_PLOT)["model_name"].tolist()
pairwise_df = get_pairwise(selected_metric, included_models=top_k_models)
st.markdown("## :material/trophy: Leaderboard", unsafe_allow_html=True)
st.markdown(CHRONOS_BENCHMARK_BASIC_INFO, unsafe_allow_html=True)
df_styled = format_leaderboard(metric_df)
st.dataframe(
df_styled,
use_container_width=True,
hide_index=True,
column_config={
"model_name": ColumnConfig(label="Model Name", alignment="left"),
"win_rate": st.column_config.NumberColumn(label="Avg. win rate (%)", format="%.1f"),
"skill_score": st.column_config.NumberColumn(label="Skill score (%)", format="%.1f"),
"median_inference_time_s": st.column_config.NumberColumn(label="Median runtime (s)", format="%.1f"),
"training_corpus_overlap": st.column_config.NumberColumn(label="Leakage (%)", format="%d"),
"num_failures": st.column_config.NumberColumn(label="Failed tasks (%)", format="%.0f"),
"zero_shot": ColumnConfig(label="Zero-shot", alignment="center"),
"org": ColumnConfig(label="Organization", alignment="left"),
"link": st.column_config.LinkColumn(label="Link", display_text=":material/open_in_new:"),
},
)
with st.expander("See details"):
st.markdown(CHRONOS_BENCHMARK_DETAILS, unsafe_allow_html=True)
st.markdown("## :material/bar_chart: Pairwise comparison", unsafe_allow_html=True)
chart_col_1, _, chart_col_2 = st.columns(spec=[0.45, 0.1, 0.45])
with chart_col_1:
st.altair_chart(
construct_pairwise_chart(pairwise_df, col="win_rate", metric_name=selected_metric),
use_container_width=True,
)
with chart_col_2:
st.altair_chart(
construct_pairwise_chart(pairwise_df, col="skill_score", metric_name=selected_metric),
use_container_width=True,
)
with st.expander("See details"):
st.markdown(PAIRWISE_BENCHMARK_DETAILS, unsafe_allow_html=True)
st.markdown("## :material/table_chart: Results for individual tasks", unsafe_allow_html=True)
with st.expander("Show detailed results"):
st.markdown(get_pivot_legend(BASELINE_MODEL, LEAKAGE_IMPUTATION_MODEL), unsafe_allow_html=True)
st.dataframe(
construct_pivot_table(
summaries=load_summaries(),
metric_name=selected_metric,
baseline_model=BASELINE_MODEL,
leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
)
)
st.divider()
st.markdown("### :material/format_quote: Citation", unsafe_allow_html=True)
st.markdown(CITATION_HEADER)
st.markdown(CITATION_FEV)
st.markdown(CITATION_CHRONOS)
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