#from pyhealth.models.ecg.rcrnet import RCRNet as model #from pyhealth.models.ecg.rf import RandomForest as model #from pyhealth.models.ecg.xgboost import XGBoost as model from pyhealth.evaluation.evaluator import func data_dir = os.path.join(root_dir, 'datasets', 'ecg') expdata_id = '2020.1104.data.diagnose.ecg' # set up the datasets cur_dataset = expdata_generator(expdata_id, root_dir=root_dir) cur_dataset.get_exp_data(sel_task='diagnose', data_root=data_dir) cur_dataset.load_exp_data() cur_dataset.show_data() # initialize the model for training expmodel_id = '2020.1104.ecg.diagnose.' clf = model(expmodel_id=expmodel_id, n_epoch=10, use_gpu=True) clf.fit(cur_dataset.train, cur_dataset.valid) # load the best model for inference clf.load_model() clf.inference(cur_dataset.test) results = clf.get_results() print(results) # evaluate the model r = func(results['hat_y'], results['y']) print(r)
# root_dir = os.path.abspath(os.path.join(__file__, "../../.."))
data_dir = os.path.join(root_dir, 'datasets', 'cms')
expdata_id = '2020.0810.data.mortality.mimic'
# set up the datasets
cur_dataset = expdata_generator(expdata_id, root_dir=root_dir)
cur_dataset.get_exp_data(sel_task='mortality', data_root=data_dir)
cur_dataset.load_exp_data()
cur_dataset.show_data()
# initialize the model for training
# turn on GPU by setting use_gpu to True
expmodel_id = '2020.0810.gru.data.mortality.mimic.gpu'
clf = model(expmodel_id=expmodel_id,
n_batchsize=20,
use_gpu=True,
n_epoch=100,
gpu_ids='0,1')
clf.fit(cur_dataset.train, cur_dataset.valid)
# load the best model for inference
clf.load_model()
clf.inference(cur_dataset.test)
pred_results = clf.get_results()
print(pred_results['hat_y'])
# evaluate the model
r = func(pred_results['hat_y'], pred_results['y'])
print(r)