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import subprocess
import os
import time
import pandas as pd
import matplotlib.pyplot as plt
import torch
from runner import Runner
class Experiment:
def __init__(self, file, prefix):
self.make_dir(prefix)
self.copy_config(file)
self.metrics = ExperimentMetrics()
self.runner = Runner(file, self.metrics)
def run(self):
self.runner.run()
def save_results(self):
data = self.metrics.get_dataframe()
data.to_csv(self.dir_path('metrics.csv'))
plt.plot(data['train_losses'], label='train loss')
plt.plot(data['test_losses'], label='test loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.savefig(self.dir_path('losses.png'))
plt.clf()
plt.plot(data['test_accuracies'], label='test accuracy')
plt.xlabel('Epoch')
plt.ylabel('% correct')
plt.legend()
plt.savefig(self.dir_path('accuracies.png'))
torch.save(self.runner.net.state_dict(), self.dir_path('net.pt'))
def dir_path(self, file):
return '{}/{}'.format(self.dirname, file)
def make_dir(self, prefix):
time_string = time.strftime('%Y%m%d%H%M%S')
prefix = '' if prefix == '' else '{}-'.format(prefix)
dirname = 'outputs/{}{}'.format(prefix, time_string)
self.dirname = dirname
os.mkdir(dirname)
def copy_config(self, file):
subprocess.run(['cp', file, '{}/config.yaml'.format(self.dirname)])
class ExperimentMetrics:
def __init__(self):
self.train_losses = []
self.test_losses = []
self.test_accuracies = []
def add_train_loss(self, loss):
self.train_losses.append(round(loss.tolist(), 3))
def add_test_metrics(self, loss, accuracy):
self.test_losses.append(round(loss.tolist(), 3))
self.test_accuracies.append(accuracy)
def get_dataframe(self):
return pd.DataFrame({
'train_losses': self.train_losses,
'test_losses': self.test_losses,
'test_accuracies': self.test_accuracies,
})
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