args=parser.parse_args()
current_working_dir = os.getcwd()
print('current_working_dir: ', current_working_dir)
pre = Preprocessing('fer2013', root_dir=current_working_dir)
pre.load_data('train_reduced_norm.csv.gz', name='train')
pre.load_data('test_public_norm.csv.gz', name='val')
X = pre.get('val').drop(columns=['emotion'])
y = pre.get('val')['emotion']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)
val = pd.DataFrame(X_test)
val['emotion'] = y_test
pre.set(name='val', value=val)
print(pre.get(name='val').head())
train_pixels = pre.get(name='train').drop(columns=['emotion'])
val_pixels = pre.get(name='val').drop(columns=['emotion'])
print('data loaded')
img_conv = ImageConverter()
train_pixel_np = train_pixels.values
train_pixel_224_np = np.zeros(shape=[train_pixel_np.shape[0], 224 * 224])
for i in range(train_pixel_np.shape[0]):
image = train_pixel_np[i].reshape(48, 48)
newimg = img_conv.upscale(image)
X_train_encoded = trained_model.encoder(X_train)
X_test_encoded = trained_model.encoder(X_test)
X_test_decoded = trained_model.decoder(X_test_encoded)
X_train_encoded_df = pd.DataFrame(X_train_encoded.detach().numpy())
X_test_encoded_df = pd.DataFrame(X_test_encoded.detach().numpy())
cols = list(range(1, n_features_encoded + 1))
X_train_encoded_df.columns = cols
X_test_encoded_df.columns = cols
train_encoded_data = X_train_encoded_df.join(y_train_df)
test_encoded_data = X_test_encoded_df.join(y_test_df)
pre.set('train_encoded', train_encoded_data)
pre.set('test_encoded', test_encoded_data)
pre.save('train_encoded')
pre.save('test_encoded')
plt.figure(1, figsize=(30, 20))
for idx in range(30):
image = X_test[idx].detach().numpy().reshape(48, 48)
image2 = X_test_encoded[idx].detach().numpy().reshape(
int(math.sqrt(n_features_encoded)),
int(math.sqrt(n_features_encoded)))
image3 = X_test_decoded[idx].detach().numpy().reshape(48, 48)
# Call signature: subplot(nrows, ncols, index, **kwargs)
plt.subplot(10, 9, 1 + idx * 3)
plt.imshow(image, cmap='hot', interpolation='none')
