def main(n_images, n_tissues, n_patches, patch_size, model_file_id):
logger.info('Initializing cluster_classify script')
dataset = Dataset(n_tissues=n_tissues, n_images=n_images)
data = dataset.sample_data(patch_size, n_patches)
patches, GTEx_IDs = data
image_objs = [Image(x) for x in GTEx_IDs]
dataset_name = ''.join([s for s in str(dataset) if s.isalnum()])
features_ID = dataset_name + f'_{n_patches}_{patch_size}_{n_images}' \
+ model_file_id
features = generate_features(features_ID, patches, model_file_id)
a_features, a_image_objs = aggregate_features(dataset_name, features,
image_objs, 'GTEx_IDs',
np.mean)
a_features, a_image_objs = aggregated_features['GTEx_factor_IDs'][
'np.mean']
lung_features, lung_image_objs = subselect_tissue(dataset_name, 'Lung',
features, image_objs)
train_classifiers(dataset_name,
features_ID,
lung_features,
lung_image_objs,
'GTEx_IDs',
retrain=True)
def main(n_tissues, n_images, n_patches, patch_size, model_type, param_string):
np.random.seed(42)
os.makedirs('data/images', exist_ok=True)
dataset = Dataset(n_tissues=n_tissues, n_images=n_images)
logger.debug('Initializing download script')
params = extract_params(param_string)
params['patch_size'] = patch_size
N = dataset.n_tissues * dataset.n_images * params['batch_size']
data = dataset.sample_data(patch_size, int(n_patches))
patches_data, imageIDs_data = data
if model_type == 'concrete_vae':
from dependencies.vae_concrete.vae_concrete import VAE
m = VAE(latent_cont_dim=256)
m.fit(patches_data, num_epochs=20)
else:
Model = eval(model_type)
m = Model(inner_dim=params['inner_dim'])
N = patches_data.shape[0]
assert N == imageIDs_data.shape[0]
p = np.random.permutation(N)
patches_data, imageIDs_data = patches_data[p], imageIDs_data[p]
m.train_on_data(patches_data, params)
m.save()
def main():
logger.info('Initializing debug script')
dataset = Dataset(n_tissues=6, n_images=10)
data = dataset.sample_data(128, 50)
patches_data, imageIDs_data = data
for i in tqdm(range(len(imageIDs_data))):
GTEx_ID = imageIDs_data[i]
idx = i % 50
scipy.misc.imsave(
f'data/cellprofiler/patches/{i:04d}_{GTEx_ID}_{idx}.png',
255 - patches_data[i])
def main(n_tissues, n_images, n_patches, patch_size, model_file):
logger.info('Initializing inspect script')
dataset = Dataset(n_tissues=n_tissues, n_images=n_images)
data = dataset.sample_data(patch_size, 15)
patches_data, imageIDs_data = data
K = 5
N = patches_data.shape[0]
idx = np.random.choice(range(N), K)
patches = patches_data[idx]
if model_file:
# fig, ax = plt.subplots(
# 2, K, figsize=(8, 3)
# )
fig = plt.figure()
figsize = 128
figure = np.zeros((figsize * 2, figsize * K, 3))
model = load_model(MODEL_PATH + f'{model_file}.pkl')
decoded_patches = model.predict(patches)
fig.suptitle(model_file, fontsize=10)
for i in range(K):
figure[0 * figsize:(0 + 1) * figsize,
i * figsize:(i + 1) * figsize, :] = deprocess(patches[i])
figure[1 * figsize:(1 + 1) * figsize, i * figsize:(i + 1) *
figsize, :] = deprocess(decoded_patches[i])
# ax[0][i].imshow(deprocess(patches[i]))
# ax[0][i].axis('off')
# ax[1][i].imshow(deprocess(decoded_patches[i]))
# ax[1][i].axis('off')
plt.imshow(figure)
fig.savefig(f'figures/{model_file}.png', bbox_inches='tight')
else:
model_files = sorted(os.listdir(MODEL_PATH))
n = len(model_files)
fig, ax = plt.subplots(2 * n, K, figsize=(8, 4 * n))
for (k, model_file) in enumerate(model_files):
model_name = model_file.replace('.pkl', '')
model = load_model(MODEL_PATH + f'{model_name}.pkl')
logger.debug(f'Generating decodings for {model_file}')
decoded_patches = model.predict(patches)
for i in range(K):
ax[2 * k][i].imshow(deprocess(patches[i]))
ax[2 * k][i].axis('off')
if i == int(K / 2):
ax[2 * k][i].set_title(model_file)
ax[2 * k + 1][i].imshow(deprocess(decoded_patches[i]))
ax[2 * k + 1][i].axis('off')
plt.savefig(f'figures/all_models.png')