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43604b6 33a95b3 43604b6 33a95b3 43604b6 33a95b3 43604b6 33a95b3 0817e0a 43604b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | from pathlib import Path
import sys
import streamlit as st
try:
from data_store import load_metric_store, BENCHMARK_DIMS
from common.leaderboard import render_leaderboard
except ModuleNotFoundError:
sys.path.append(str(Path(__file__).resolve().parents[1]))
from data_store import load_metric_store, BENCHMARK_DIMS
from common.leaderboard import render_leaderboard
st.set_page_config(page_title="Optimization Leaderboard", page_icon="📊", layout="wide")
# Sidebar navigation
st.sidebar.title("Navigation")
st.sidebar.page_link("streamlit_app.py", label="Home", icon="🏠")
st.sidebar.page_link("pages/OptimizationLeaderboard.py", label="Optimization Leaderboard", icon="📊")
st.sidebar.page_link("pages/UQLeaderboard.py", label="UQ Leaderboard", icon="🎯")
st.sidebar.page_link("pages/MethodDetails.py", label="Methods", icon="📘")
st.sidebar.page_link("pages/RawData.py", label="Get Data", icon="🧾")
st.markdown(r"**Optimization target.** A run succeeds when the normalized weighted distance from the ensemble-mean forward-model output to the true data falls at or below the (selected in the target controls) RMSE target level $\tau$:")
st.latex(r"\frac{1}{N_y}\bigl(y - G(\bar{\theta})\bigr)^\top \Gamma^{-1}\bigl(y - G(\bar{\theta})\bigr) \leq \tau")
st.markdown(r"where $y$ is the true observation, $G(\bar{\theta})$ is the forward-model output at the final ensemble mean $\bar{\theta}$, and $\Gamma$ is the observation noise covariance, and $N_y = \dim(y)$.")
render_leaderboard(
load_metric_store(),
target_col="rmse_target",
target_label="RMSE Target Level",
title="Optimization Leaderboard",
state_prefix="opt",
default_target=1.1,
raw_page="pages/RawData.py",
benchmark_dims=BENCHMARK_DIMS,
show_failure_panel=True,
)
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