Thesis/visualization_util.py
2023-06-24 04:11:17 +00:00

74 lines
3.0 KiB
Python

import argparse
import re
import os
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
def extract_learning_curves(args):
paths = args.log_path.split(',')
if len(paths) == 1 and os.path.isdir(paths[0]):
paths = [os.path.join(paths[0], f) for f in os.listdir(paths[0]) if os.path.isfile(os.path.join(paths[0], f))]
learning_curves = {}
print(paths)
for path in paths:
print(path)
learning_curve = []
lines = open(path, 'r').readlines()
last_epoch = -1
stacked_epoch = -1
max_epoch = -1
for line in lines:
matched = re.match(r'[0-9\- :,]*\[INFO\] - \[Epoch ([0-9]+)\].*Valid MRR: ([0-9\.]+).*', line)
# matched = re.match(r'\tMRR: Tail : [0-9\.]+, Head : [0-9\.]+, Avg : ([0-9\.]+)', line)
if matched:
this_epoch = int(matched.group(1))
if (this_epoch > max_epoch):
learning_curve.append(float(matched.group(2)))
max_epoch = this_epoch
stacked_epoch = this_epoch
elif (this_epoch < max_epoch and this_epoch > last_epoch):
last_epoch = this_epoch
max_epoch = stacked_epoch + 1 + this_epoch
learning_curve.append(float(matched.group(2)))
if max_epoch >= args.num_epochs:
break
# if matched:
# max_epoch += 1
# learning_curve.append(float(matched.group(1)))
# if max_epoch >= args.num_epochs:
# break
while len(learning_curve) < args.num_epochs:
learning_curve.append(learning_curve[-1])
learning_curves[os.path.basename(path)] = learning_curve
return learning_curves
def draw_learning_curves(args, learning_curves):
for name in learning_curves.keys():
epochs = np.arange(len(learning_curves[name]))
matched = re.match(r'(.*)\..*', name)
if matched:
label = matched.group(1)
else:
label = name
plt.plot(epochs, learning_curves[name], label = label)
plt.xlabel("Epochs")
plt.ylabel("Best Valid MRR")
plt.legend(title=args.legend_title)
plt.savefig(os.path.join(args.out_path, str(round(datetime.utcnow().timestamp() * 1000)) + '.' + args.fig_filetype))
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Parser For Arguments", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--task', default = None, type=str)
parser.add_argument('--log_path', type=str, default=None)
parser.add_argument('--out_path', type=str, default=None)
parser.add_argument('--num_epochs', type=int, default=200)
parser.add_argument('--legend_title', type=str, default="Learning rate")
parser.add_argument('--fig_filetype', type=str, default="svg")
args = parser.parse_args()
if (args.task == 'learning_curve'):
draw_learning_curves(args, extract_learning_curves(args))