ax.xaxis.set_ticklabels([])
ax.yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(human_format))
ax.tick_params(axis='both', labelsize=10)
if return_counts:
return counts
# %% Get data info
df = pd.read_csv(DATA_PATH + 'data_info.csv')
df = df.drop(df.columns[0], axis=1)
df = df[df.low_contrast == 0]
# %% split data
spliter = MURA.MURA_TrainValidTestSplitter(df,
train_frac=0.5,
ratio_known_normal=0.05,
ratio_known_abnormal=0.05,
random_state=42)
spliter.split_data(verbose=True)
train_df = spliter.get_subset('train')
valid_df = spliter.get_subset('valid')
test_df = spliter.get_subset('test')
# %% get counts
all_counts = get_patient_bodypart_counts(df, False)
train_counts = get_patient_bodypart_counts(train_df, False)
valid_counts = get_patient_bodypart_counts(valid_df, False)
test_counts = get_patient_bodypart_counts(test_df, False)
# %%
def main(replicat_i):
# final results file
results = {
'reconstruction': {
'train': {
'time': None,
'loss': None
},
'scores_threshold': None,
'valid': {
'time': None,
'auc': None,
'f1': None,
'scores': None
},
'test': {
'time': None,
'auc': None,
'f1': None,
'scores': None
}
},
'embedding': {
'train': {
'time': None,
'loss': None
},
'scores_threshold': None,
'valid': {
'time': None,
'auc': None,
'f1': None,
'scores': None
},
'test': {
'time': None,
'auc': None,
'f1': None,
'scores': None
}
}
}
# %% load the AE_results file and DeepSAD results file
with open(PATH_TO_OUTPUTS + f'results/AE_results_{replicat_i}.json') as f:
ae_results = json.load(f)
with open(PATH_TO_OUTPUTS +
f'results/DeepSAD_valid_results_{replicat_i}.json') as f:
sad_results_valid = json.load(f)
with open(PATH_TO_OUTPUTS +
f'results/DeepSAD_test_results_{replicat_i}.json') as f:
sad_results_test = json.load(f)
# %% Recover AE and SAD train time and train loss
results['embedding']['train']['time'] = sad_results_test['train_time']
results['embedding']['train']['loss'] = sad_results_test['train_loss']
results['reconstruction']['train']['time'] = ae_results['train_time']
results['reconstruction']['train']['loss'] = ae_results['train_loss']
# %% recover test and valid AUC and time
results['embedding']['valid']['time'] = sad_results_valid['test_time']
results['embedding']['valid']['auc'] = sad_results_valid['test_auc']
results['embedding']['valid']['scores'] = sad_results_valid['test_scores']
results['embedding']['test']['time'] = sad_results_test['test_time']
results['embedding']['test']['auc'] = sad_results_test['test_auc']
results['embedding']['test']['scores'] = sad_results_test['test_scores']
results['reconstruction']['valid']['time'] = ae_results['test_time']
results['reconstruction']['valid']['auc'] = ae_results['test_auc']
# %% compute f1-scores and threshold
scores_v = np.array(results['embedding']['valid']['scores'])[:, 2]
labels_v = np.array(results['embedding']['valid']['scores'])[:, 1]
scores_t = np.array(results['embedding']['test']['scores'])[:, 2]
labels_t = np.array(results['embedding']['test']['scores'])[:, 1]
thres, f1_valid = get_best_threshold(scores_v, labels_v, metric=f1_score)
f1_test = f1_score(np.where(scores_t > thres, 1, 0), labels_t)
results['embedding']['scores_threshold'] = thres
results['embedding']['valid']['f1'] = f1_valid
results['embedding']['test']['f1'] = f1_test
################################################################################
# %% get the datasets
df_info = pd.read_csv(DATA_INFO_PATH)
df_info = df_info.drop(df_info.columns[0], axis=1)
# remove low contrast images (all black)
df_info = df_info[df_info.low_contrast == 0]
# Train Validation Test Split
spliter = MURA.MURA_TrainValidTestSplitter(df_info,
train_frac=0.5,
ratio_known_normal=0.05,
ratio_known_abnormal=0.05,
random_state=42)
spliter.split_data(verbose=True)
valid_df = spliter.get_subset('valid')
test_df = spliter.get_subset('test')
# make datasets
valid_dataset = MURA.MURA_Dataset(valid_df,
data_path=DATA_PATH,
load_mask=True,
load_semilabels=True,
output_size=512)
test_dataset = MURA.MURA_Dataset(test_df,
data_path=DATA_PATH,
load_mask=True,
load_semilabels=True,
output_size=512)
################################################################################
# %% Load the AE model and compute valid and test scores of reconstruction
print('>>> initializing AE')
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
# define model
ae_net = AE_ResNet18(embed_dim=256, output_size=(1, 512, 512))
ae_net = ae_net.to(device)
# get the trained parameters
model_dict = torch.load(PATH_TO_OUTPUTS +
f'model/deepSAD_model_{replicat_i}.pt',
map_location=device)
ae_state_dict = model_dict['ae_net_dict']
ae_net.load_state_dict(ae_state_dict)
print('>>> AE initialized')
print('>>> Validating AE')
valid_time, valid_scores = test_AE(ae_net, valid_dataset, device=device)
print('>>> Testing AE')
test_time, test_scores = test_AE(ae_net, test_dataset, device=device)
print('>>> Computing scores and thresholds')
scores_v = np.array(valid_scores)[:, 2]
labels_v = np.array(valid_scores)[:, 1]
scores_t = np.array(test_scores)[:, 2]
labels_t = np.array(test_scores)[:, 1]
thres, f1_valid = get_best_threshold(scores_v, labels_v, metric=f1_score)
auc_valid = roc_auc_score(labels_v, scores_v)
f1_test = f1_score(np.where(scores_t > thres, 1, 0), labels_t)
auc_test = roc_auc_score(labels_t, scores_t)
print(f'>>> valid auc : {auc_valid}')
print(f'>>> valid f1 : {f1_valid}')
print(f'>>> test auc : {auc_test}')
print(f'>>> test f1 : {f1_test}')
results['reconstruction']['scores_threshold'] = thres
results['reconstruction']['valid']['time'] = valid_time
results['reconstruction']['valid']['auc'] = auc_valid
results['reconstruction']['valid']['f1'] = f1_valid
results['reconstruction']['valid']['scores'] = valid_scores
results['reconstruction']['test']['time'] = test_time
results['reconstruction']['test']['auc'] = auc_test
results['reconstruction']['test']['f1'] = f1_test
results['reconstruction']['test']['scores'] = test_scores
# save corrected results on disk
with open(PATH_TO_OUTPUTS + f'results/results_corrected_{replicat_i}.json',
'w') as f:
json.dump(results, f)
print('>>> results saved at' + PATH_TO_OUTPUTS +
f'results/results_corrected_{replicat_i}.json')