add gpu examples

examples/learning_models/extract_data_run_before_learning.py → examples/learning_models/00_extract_data_run_before_learning.py

@@ -33,4 +33,13 @@
         # extracting all the files
         print('Extracting mimic demo files now...')
         zip.extractall()
+        print('Done!')
+
+    with ZipFile(os.path.join(data_dir, 'image.zip'), 'r') as zip:
+        # # printing all the contents of the zip file
+        # zip.printdir()
+
+        # extracting all the files
+        print('Extracting image files now...')
+        zip.extractall()
         print('Done!')

examples/learning_models/example_sequence_cpu_phenotyping.py

@@ -0,0 +1,60 @@
+# -*- coding: utf-8 -*-
+"""Example of using GRU on MIMIC demo mortality prediction
+"""
+# License: BSD 2 clause
+
+
+# environment setting
+import os
+import sys
+
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
+root_dir = os.path.abspath(os.path.join(__file__, "../../.."))
+os.chdir(root_dir)
+
+sys.path.append(root_dir)
+
+### May choose any of these models
+from pyhealth.models.sequence.dipole import Dipole
+# from pyhealth.models.sequence.lstm import LSTM as model
+# from pyhealth.models.sequence.gru import GRU as GRU
+# from pyhealth.models.sequence.embedgru import EmbedGRU as model
+# from pyhealth.models.sequence.retain import Retain as model
+# from pyhealth.models.sequence.raim import RAIM as model
+# from pyhealth.models.sequence.tlstm import tLSTM as model
+# from pyhealth.models.sequence.xgboost import XGBoost as model
+# from pyhealth.models.sequence.rf import RandomForest as model
+
+from pyhealth.data.expdata_generator import sequencedata as expdata_generator
+from pyhealth.evaluation.evaluator import func
+
+if __name__ == "__main__":
+    # override here to specify where the data locates
+    # root_dir = ''
+    # root_dir = os.path.abspath(os.path.join(__file__, "../../.."))
+    data_dir = os.path.join(root_dir, 'datasets', 'mimic')
+
+    expdata_id = '2020.0811.data.phenotyping.test.v2'
+
+    # set up the datasets
+    cur_dataset = expdata_generator(expdata_id, root_dir=root_dir)
+    cur_dataset.get_exp_data(sel_task='phenotyping', data_root=data_dir)
+    cur_dataset.load_exp_data()
+    # cur_dataset.show_data()
+
+    # initialize the model for training
+    expmodel_id = '2020.0811.model.phenotyping.test.v2'
+    clf = Dipole(expmodel_id=expmodel_id, n_batchsize=20, use_gpu=False,
+                 n_epoch=30)
+    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)
+
+    # evaluate the model
+    r = func(pred_results['hat_y'], pred_results['y'])
+    print(r)

examples/learning_models/example_sequence_gpu_mortality.py

@@ -0,0 +1,58 @@
+from __future__ import division
+from __future__ import print_function
+
+# environment setting
+import os
+import sys
+
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
+root_dir = os.path.abspath(os.path.join(__file__, "../../.."))
+os.chdir(root_dir)
+
+sys.path.append(root_dir)
+
+### May choose any of these models
+# from pyhealth.models.sequence.dipole import Dipole as model
+from pyhealth.models.sequence.lstm import LSTM as model
+# from pyhealth.models.sequence.gru import GRU as GRU
+# from pyhealth.models.sequence.embedgru import EmbedGRU as model
+# from pyhealth.models.sequence.retain import Retain as model
+# from pyhealth.models.sequence.raim import RAIM as model
+# from pyhealth.models.sequence.tlstm import tLSTM as model
+# from pyhealth.models.sequence.xgboost import XGBoost as model
+# from pyhealth.models.sequence.rf import RandomForest as model
+
+from pyhealth.data.expdata_generator import sequencedata as expdata_generator
+from pyhealth.evaluation.evaluator import func
+
+if __name__ == "__main__":
+    # override here to specify where the data locates
+    # root_dir = ''
+    # 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)