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