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do_plots.py
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do_plots.py
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import numpy as np
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
import matplotlib.pyplot as plt
import argparse
import re
import sys
import os
import collections
def find_event_files(root_dir):
p = re.compile('events\\.out\\.tfevents.*')
tfevent_files = []
for path, subdirs, files in os.walk(root_dir, followlinks=False):
for filename in files:
m = p.match(filename)
if m:
tfevent_files.append(os.path.join(path, filename))
return tfevent_files
def find_ordo_file(root_dir):
for path, subdirs, files in os.walk(root_dir, followlinks=False):
for filename in files:
if filename == 'ordo.out':
return os.path.join(path, filename)
return None
def get_list_aggregator(aggregation_mode='avg'):
if aggregation_mode == 'min':
return lambda x: min(x)
elif aggregation_mode == 'max':
return lambda x: max(x)
elif aggregation_mode == 'avg':
return lambda x: sum(x) / len(x)
else:
raise Exception('Invalid aggregation_mode {}'.format(aggregation_mode))
def aggregate_dict(values, aggregation_mode='avg'):
'''
values must be a dict of lists
each list is aggregated to a single scalar
based on the aggregation_mode
can be one of 'min', 'max', 'avg'
'''
aggregate_list = get_list_aggregator(aggregation_mode)
res = dict()
for k, v in values.items():
res[k] = aggregate_list(v)
return res
def dict_to_xy(d):
x = []
y = []
for k, v in sorted(d.items()):
x.append(k)
y.append(v)
return x, y
def parse_ordo_file(filename, label):
p = re.compile('.*nn-epoch(\\d*)\\.nnue')
with open(filename, 'r') as ordo_file:
rows = []
lines = ordo_file.readlines()
for line in lines:
if 'nn-epoch' in line and label in line:
fields = line.split()
net = fields[1]
epoch = int(p.match(net)[1])
rating = float(fields[3])
error = float(fields[4])
rows.append((net, epoch, rating, error))
return rows
def transpose_list_of_tuples(l):
return list(map(list, zip(*l)))
def do_plots(out_filename, root_dirs, elo_range, loss_range, split):
'''
1. Find tfevents files for each root directory
2. Look for metrics
2.1. Look for 'val_loss'
3. Look for ordo.out
3.1. Parse elo from ordo.
4. Do plots.
'''
tf_size_guidance = {
'compressedHistograms': 10,
'images': 0,
'scalars': 0,
'histograms': 1
}
fig = plt.figure()
fig.set_size_inches(18, 10)
ax_train_loss = fig.add_subplot(311)
ax_val_loss = fig.add_subplot(312)
ax_elo = None
ax_val_loss.set_xlabel('step')
ax_val_loss.set_ylabel('val_loss')
ax_train_loss.set_xlabel('step')
ax_train_loss.set_ylabel('train_loss')
for user_root_dir in root_dirs:
# if asked to split we split the roto dir into a number of user root dirs,
# i.e. all direct subdirectories containing tfevent files.
# we use the ordo file in the root dir, but split the content.
split_root_dirs = [user_root_dir]
if split:
split_root_dirs = []
for item in os.listdir(user_root_dir):
if os.path.isdir(os.path.join(user_root_dir, item)):
root_dir = os.path.join(user_root_dir, item)
if len(find_event_files(root_dir)) > 0:
split_root_dirs.append(root_dir)
split_root_dirs.sort()
for root_dir in split_root_dirs:
print('Processing root_dir {}'.format(root_dir))
tfevents_files = find_event_files(root_dir)
print('Found {} tfevents files.'.format(len(tfevents_files)))
val_losses = collections.defaultdict(lambda: [])
train_losses = collections.defaultdict(lambda: [])
for i, tfevents_file in enumerate(tfevents_files):
print('Processing tfevents file {}/{}: {}'.format(i+1, len(tfevents_files), tfevents_file))
events_acc = EventAccumulator(tfevents_file, tf_size_guidance)
events_acc.Reload()
vv = events_acc.Scalars('val_loss')
print('Found {} val_loss entries.'.format(len(vv)))
minloss = min([v[2] for v in vv])
for v in vv:
if v[2] < minloss + loss_range:
step = v[1]
val_losses[step].append(v[2])
vv = events_acc.Scalars('train_loss')
minloss = min([v[2] for v in vv])
print('Found {} train_loss entries.'.format(len(vv)))
for v in vv:
if v[2] < minloss + loss_range:
step = v[1]
train_losses[step].append(v[2])
print('Aggregating data...')
val_loss = aggregate_dict(val_losses, 'min')
x, y = dict_to_xy(val_loss)
ax_val_loss.plot(x, y, label=root_dir)
train_loss = aggregate_dict(train_losses, 'min')
x, y = dict_to_xy(train_loss)
ax_train_loss.plot(x, y, label=root_dir)
print('Finished aggregating data.')
ordo_file = find_ordo_file(user_root_dir)
if ordo_file:
print('Found ordo file {}'.format(ordo_file))
if ax_elo is None:
ax_elo = fig.add_subplot(313)
ax_elo.set_xlabel('epoch')
ax_elo.set_ylabel('Elo')
for root_dir in split_root_dirs:
rows = parse_ordo_file(ordo_file, root_dir if split else "nnue")
if len(rows) == 0:
continue
rows = sorted(rows, key=lambda x:x[1])
epochs = []
elos = []
errors = []
maxelo = max([row[2] for row in rows])
for row in rows:
epoch = row[1]
elo = row[2]
error = row[3]
if not epoch in epochs:
if elo > maxelo - elo_range:
epochs.append(epoch)
elos.append(elo)
errors.append(error)
print('Found ordo data for {} epochs'.format(len(epochs)))
ax_elo.errorbar(epochs, elos, yerr=errors, label=root_dir)
else:
print('Did not find ordo file. Skipping.')
ax_val_loss.legend()
ax_train_loss.legend()
if ax_elo:
ax_elo.legend()
print('Saving plot at {}'.format(out_filename))
#plt.show()
plt.savefig(out_filename, dpi=300)
def main():
#do_plots('test_plot_out.png', ['../nnue-pytorch-training/experiment_10', '../nnue-pytorch-training/experiment_11'])
parser = argparse.ArgumentParser(
description="Generate plots of losses and Elo for experiments run",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"root_dirs",
type=str,
nargs='+',
help="multiple root directories (containing ordo.out and tensorflow event files)"
)
parser.add_argument(
"--output",
type=str,
default="experiment_loss_Elo.png",
help="Filename of the plot generated",
)
parser.add_argument(
"--elo_range",
type=float,
default=50.0,
help="Limit Elo data shown to the best result - elo_range",
)
parser.add_argument(
"--loss_range",
type=float,
default=0.004,
help="Limit loss data shown to the best result + loss_range",
)
parser.add_argument("--split",
action='store_true',
help="Split the root dirs provided, assumes the ordo file is still at the root, and nets in that ordo file match root_dir/sub_dir/",
)
args = parser.parse_args()
print(args.root_dirs)
do_plots(args.output, args.root_dirs, elo_range = args.elo_range, loss_range = args.loss_range, split = args.split)
if __name__ == '__main__':
main()