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import os
import math
import matplotlib.pyplot as plt
files = [f"models/{x}" for x in os.listdir("models") if x.endswith(".csv")]
train_loss = {}
eval_loss = {}
def process_lines(lines):
global train_loss
global eval_loss
name = fp.split("/")[1]
vals = [x.split(",") for x in lines]
train_loss[name] = (
[int(x[0]) for x in vals],
[math.log(float(x[1])) for x in vals],
)
if len(vals[0]) >= 3:
eval_loss[name] = (
[int(x[0]) for x in vals],
[math.log(float(x[2])) for x in vals],
)
# https://stackoverflow.com/a/49357445
def smooth(scalars, weight):
last = scalars[0]
smoothed = list()
for point in scalars:
smoothed_val = last * weight + (1 - weight) * point
smoothed.append(smoothed_val)
last = smoothed_val
return smoothed
def plot(data, fname):
fig, ax = plt.subplots()
ax.grid()
for name, val in data.items():
ax.plot(val[0], smooth(val[1], 0.9), label=name)
plt.legend(loc="upper right")
plt.savefig(fname, dpi=300, bbox_inches='tight')
for fp in files:
with open(fp) as f:
lines = f.readlines()
process_lines(lines)
plot(train_loss, "loss.png")
plot(eval_loss, "loss-eval.png")
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