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Nice graph formatting. (from c0nn3r)
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import re | |
import os | |
import glob | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
from collections import OrderedDict | |
fig_size = [12, 9] | |
plt.rcParams["figure.figsize"] = fig_size | |
def parse_filepath(string): | |
head, tail = os.path.split(string) | |
return re.search(r'_(.*),', tail).group(1) | |
def load_csv_files(path, index_column='Step', wanted_columns=[1, 2], skip_rows=2): | |
expanded_path = os.path.abspath(path) | |
csv_filepaths = glob.glob(os.path.join(expanded_path, '*.csv')) | |
dataframe_from_each_file = (pd.read_csv(filepath, | |
index_col=index_column, | |
names=['Wall time', 'Step', parse_filepath(filepath)], | |
skiprows=skip_rows, | |
header=None, | |
usecols=wanted_columns) | |
for filepath in csv_filepaths) | |
concatenated_dataframe = pd.concat(dataframe_from_each_file, axis=1, join='inner') | |
return concatenated_dataframe | |
experiment_logs = load_csv_files('./experiment_results/mnist_losses') | |
def add_rolling_average(dataframe, window=6): | |
for column in dataframe: | |
dataframe[f'{column}_rolling_average'] = dataframe[column].rolling(window=window).mean() | |
add_rolling_average(experiment_logs) | |
colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'] | |
def graph_experiment_losses(dataframe, number_of_logs=4, filters=('average')): | |
fig = plt.figure() | |
ax = fig.add_subplot() | |
axes = experiment_logs.plot(ax=ax, color=colors[:number_of_logs], logy=True, sort_columns=True) | |
axes.set_xlabel('Step', fontsize=16) | |
axes.set_ylabel('Training Loss', fontsize=16) | |
axes.spines["top"].set_visible(False) | |
axes.spines["bottom"].set_visible(False) | |
axes.spines["right"].set_visible(False) | |
axes.spines["left"].set_visible(False) | |
plt.xticks(fontsize=12) | |
plt.yticks(fontsize=12) | |
for i in range(number_of_logs): | |
axes.lines[i].set_alpha(0.3) | |
axes.lines[i].set_alpha(0.3) | |
handles, labels = plt.gca().get_legend_handles_labels() | |
by_label = OrderedDict((label, handel) for label, handel in zip(labels, handles) if not label.endswith(filters)) | |
legend = plt.legend(by_label.values(),by_label.keys(), frameon=False, prop={'size': 12}) | |
for leg in legend.legendHandles: | |
leg.set_alpha(1) | |
graph_experiment_losses(experiment_logs) | |
plt.savefig('losses.png', format='png', bbox_inches='tight') |
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