def pd_prediction(dataio, patient_info, patient_array, params):
### Task Setting
task = params['task'][0] # disease prediction
split_method = 'cross-validation'
kfold = 5 # 5-fold validation
### Initialization
auc = np.zeros(kfold, dtype='float32') # evaluation metrics
ap = np.zeros(kfold, dtype='float32')
n_feature = dataio.feature.feature_len
param_w = np.zeros([n_feature, kfold], dtype='float32')
print ('-----')
split = Splitter(task, patient_array, kfold, split_method)
for k in range(kfold): # each fold, k is the index of test set
### Data Splitting
train_data, test_data = split.get_splitter(k)
### Model Training
pd_pred = PDPredictor(k, patient_info, train_data, params['result_path'])
model, y_pred = pd_pred.train_model()
param_w[:,k], _ = pd_pred.get_param()
### Evaluating
pd_eval = Evaluator(model, test_data, patient_info, task, pd_pred)
auc[k], ap[k] = pd_eval.compute_accuracy()
print ('-----')
print ('AUC of the %s task: %f' %(task, np.sum(auc)/kfold))
print ('Average Precision of the %s task: %f' %(task, np.sum(ap)/kfold))
### Displaying Feature (selected by prediction model)
feature = dataio.feature
feature.get_pred_feature(param_w, kfold, 'pd')
def hy_prediction(dataio, patient_info, patient_array, params):
### Task Setting
task = params['task'][1]
split_method = 'ratio'
ratio = 0.8 # provide the ratio
krun = 10 # run 5 times then average the result
### Initialization
acc = np.zeros(krun, dtype='float32') # evaluation metric
n_feature = dataio.feature.feature_len
param_w = np.zeros([n_feature, krun], dtype='float32') # weights parameter
### H&Y Reading
feature = dataio.feature
patient_info = feature.get_hy_stage(patient_info, patient_array)
print ('-----')
# split = Splitter(task, patient_array, ratio, split_method, patient_info)
split = Splitter(task, patient_array, ratio, split_method)
for k in range(krun):
### Data Splitting
train_data, test_data = split.get_splitter(k)
### Model Training
hy_pred = HYPredictor(k, patient_info, train_data, params['result_path'])
model, y_pred = hy_pred.train_model()
param_w[:,k], _ = hy_pred.get_param()
### Evaluating
hy_eval = Evaluator(model, test_data, patient_info, task, hy_pred)
acc[k] = hy_eval.compute_accuracy()
print ('-----')
print ('Accuracy of the %s task: %f' %(task, np.sum(acc)/krun))
### Displaying Feature (selected by prediction model)
feature = dataio.feature
feature.get_pred_feature(param_w, krun, 'yh')
def moca_prediction(dataio, patient_info, patient_array, params):
### Task Setting
task = params['task'][2]
split_method = 'ratio'
ratio = 0.8 # provide the ratio
krun = 5 # run 5 times then average the result
### Initialization
rmse = np.zeros(krun, dtype='float32') # evaluation metric
n_feature = dataio.feature.feature_len
param_w = np.zeros([n_feature, krun], dtype='float32')
### MoCA Reading
feature = dataio.feature
patient_info = feature.get_moca_score(patient_info, patient_array)
print ('-----')
split = Splitter(task, patient_array, ratio, split_method)
for k in range(krun):
### Data Splitting
train_data, test_data = split.get_splitter(k)
### Model Training
moca_pred = MoCAPredictor(k, patient_info, train_data, params['result_path'])
model, y_pred = moca_pred.train_model()
param_w[:,k] = moca_pred.get_param()
### Evaluating
pd_eval = Evaluator(model, test_data, patient_info, task, moca_pred)
rmse[k] = pd_eval.compute_accuracy()
print ('-----')
print ('RMSE of the %s task: %f' %(task, np.sum(rmse)/krun))
### Displaying Feature (selected by prediction model)
feature = dataio.feature
feature.get_pred_feature(param_w, krun, 'moca')
