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from __future__ import annotations
import os
import json
import sqlite3
from typing import Any, Dict
import pandas as pd
import matplotlib.pyplot as plt
from edgeeda.utils import ensure_dir
def export_trials(db_path: str) -> pd.DataFrame:
con = sqlite3.connect(db_path)
df = pd.read_sql_query("SELECT * FROM trials", con)
con.close()
return df
def make_plots(df: pd.DataFrame, out_dir: str) -> None:
ensure_dir(out_dir)
# Learning curve: best reward over time
df2 = df.copy()
df2["reward"] = pd.to_numeric(df2["reward"], errors="coerce")
df2 = df2.dropna(subset=["reward"]).sort_values("id")
if not df2.empty:
best = df2["reward"].cummax()
plt.figure()
plt.plot(df2["id"].values, best.values)
plt.xlabel("trial id")
plt.ylabel("best reward so far")
plt.tight_layout()
plt.savefig(os.path.join(out_dir, "learning_curve.png"), dpi=200)
plt.close()
# Pareto-ish scatter: area vs wns from metrics_json if available
areas, wnss = [], []
for _, r in df.iterrows():
mj = r.get("metrics_json")
if not isinstance(mj, str) or not mj:
continue
try:
m = json.loads(mj)
except Exception:
continue
# try common keys (flattened already stored by runner)
a = m.get("design__die__area") or m.get("finish__design__die__area")
w = m.get("timing__setup__wns") or m.get("finish__timing__setup__wns")
if a is None or w is None:
continue
try:
areas.append(float(a)); wnss.append(float(w))
except Exception:
pass
if areas:
plt.figure()
plt.scatter(areas, wnss)
plt.xlabel("die area")
plt.ylabel("WNS")
plt.tight_layout()
plt.savefig(os.path.join(out_dir, "area_vs_wns.png"), dpi=200)
plt.close()
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