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import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
TASK = "sudoku"
batch_size = 32
BASE_DIR = f"../{TASK}_results_{batch_size}"
PLOT_PATH=os.path.join(BASE_DIR, "figures")
os.makedirs(PLOT_PATH, exist_ok=True)
# METHODS = [
# "cvxpylayer",
# "ffoqp_eq",
# "ffocp_eq",
# "lpgd"
# ]
METHODS = [
"lpgd"
]
def load_results(base_dir=BASE_DIR, methods=METHODS):
dfs = []
for m in methods:
pattern = os.path.join(base_dir, m, "*.csv")
for fp in sorted(glob.glob(pattern)):
df = pd.read_csv(fp)
df["method"] = m
fname = os.path.basename(fp)
def grab(pat, cast=float):
mo = re.search(pat, fname)
return cast(mo.group(1)) if mo else np.nan
df["seed"] = grab(r"_seed(\d+)", int)
df["n"] = grab(r"n(\d+)", int)
df["lr"] = grab(r"lr([0-9eE\.\-]+)", float)
#df["eps"] = grab(r"eps([0-9eE\.\-]+)", float)
dfs.append(df)
print("method: ", m)
# print(df)
if not dfs:
raise FileNotFoundError(f"No CSVs found under {base_dir}.")
return pd.concat(dfs, ignore_index=True, sort=False)
def plot_metric_curve(df, metric_type, fig_path=PLOT_PATH):
sns.set_theme(style="whitegrid", context="talk")
assert(metric_type in ["error", "loss"])
fig_name=f"{metric_type}_curve.png"
metric_cols = [c for c in [f"test_{metric_type}", f"train_{metric_type}"] if c in df.columns]
id_vars = [c for c in ["epoch","method","seed","ydim"] if c in df.columns]
## collapse metric columns into a value column
long_curves = df.melt(
id_vars=id_vars,
value_vars=metric_cols,
var_name="metric", value_name="value"
).dropna(subset=["value","epoch","method"])
## for each metric (col) and each dimensionality (ydim), create a plot of value vs epoch for different methods
g = sns.relplot(
data=long_curves, x="epoch", y="value",
hue="method", style="method",
markers=True, dashes=True,
kind="line", ci=None,
linewidth=1.5, alpha=0.9,
col="metric", col_wrap=2, height=4, aspect=1.3,
facet_kws=dict(sharey=False)
)
# g = sns.relplot(
# data=long_curves, x="epoch", y="value",
# hue="method", style="method",
# markers=True, dashes=True,
# kind="line", ci=None,
# linewidth=1.5, alpha=0.9,
# col="metric", row='ydim', # remove col_wrap
# height=4, aspect=1.3,
# facet_kws=dict(sharey=False)
# )
g.set_titles("{col_name}")
g.set_xlabels("Epoch"); g.set_ylabels("Loss")
plt.suptitle("Loss vs Epoch by Method", y=1.02)
## reset legend
handles, labels = g.axes.flat[0].get_legend_handles_labels()
if g._legend is not None:
g._legend.remove()
g.figure.legend(
handles, labels,
loc="lower center",
bbox_to_anchor=(0.5, -0.10),
ncol=max(1, len(labels)),
frameon=False
)
g.figure.subplots_adjust(bottom=0.50)
plt.tight_layout()
plt.savefig(os.path.join(fig_path, fig_name), dpi=300, bbox_inches="tight")
def plot_final_metric(df, metric_type, fig_path=PLOT_PATH):
keys = ["method","seed"] if "seed" in df.columns else ["method"]
assert(metric_type in ["loss", "error"])
fig_name=f"{metric_type}_final.png"
if "epoch" in df.columns and df["epoch"].notna().any():
last_rows = df.sort_values("epoch").groupby(keys, dropna=False).tail(1)
else:
last_rows = df.groupby(keys, dropna=False).tail(1)
#print(last_rows)
loss_final = last_rows.melt(
id_vars=["method","seed","ydim"],
value_vars=[f"train_{metric_type}",f"test_{metric_type}"],
var_name="metric", value_name="value"
).dropna(subset=["value","method"])
#print(loss_final)
plt.figure(figsize=(10,5))
ax = sns.barplot(data=loss_final, x="method", y="value", hue="metric", errorbar=("ci",95))
ax.set_title("Final Loss by Method")
ax.set_xlabel(""); ax.set_ylabel("Loss")
ax.tick_params(axis="x", rotation=20)
# g = sns.catplot(
# data=loss_final,
# x="method", y="value", hue="metric",
# col="ydim", kind="bar", ci=95,
# height=4, aspect=1.2
# )
# g.set_titles("ydim = {col_name}")
# g.set_axis_labels("", "Loss")
# g.set_xticklabels(rotation=20)
plt.tight_layout()
plt.savefig(os.path.join(fig_path, fig_name), dpi=300, bbox_inches="tight")
def plot_forward_backward_time(df, fig_path=PLOT_PATH, fig_name="forward_backward_time.png"):
keys = ["method","seed"] if "seed" in df.columns else ["method"]
time_median = (
df.groupby(keys, dropna=False)[["forward_time","backward_time"]]
.median().reset_index()
)
# print(time_median)
time_long = time_median.melt(id_vars=["method","seed"], value_vars=["forward_time","backward_time"],
var_name="phase", value_name="seconds").dropna(subset=["seconds","method"])
# print(time_long)
# method_order_all = [
# "cvxpylayer","qpth","ffocp_eq","lpgd"
# ]
method_order_all = METHODS
method_order = [m for m in method_order_all if m in time_long["method"].unique()]
phase_order = ["forward_time", "backward_time"]
time_long["method"] = pd.Categorical(time_long["method"],
categories=method_order, ordered=True)
# print(time_long)
plt.figure(figsize=(10,5))
ax = sns.barplot(
data=time_long,
x="method", y="seconds",
hue="phase",
order=method_order,
hue_order=phase_order,
errorbar=("ci",95)
)
ax.set_title("Per-Epoch Time by Method")
ax.set_xlabel(""); ax.set_ylabel("Seconds")
ax.tick_params(axis="x", rotation=20)
plt.tight_layout()
plt.savefig(os.path.join(fig_path, fig_name), dpi=300, bbox_inches="tight")
if __name__=="__main__":
df = load_results()
df = df.rename(columns=lambda c: c.strip() if isinstance(c, str) else c)
df['ydim'] = df['n'] ** 6
print(df)
plot_metric_curve(df, metric_type="loss")
plot_metric_curve(df, metric_type="error")
plot_final_metric(df, metric_type="loss")
plot_final_metric(df, metric_type="error")
plot_forward_backward_time(df)
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