update

util.py

@@ -271,6 +271,55 @@ def read_data_physionet_4(window_size=3000, stride=500):
 
     return X_train, X_test, Y_train, Y_test, pid_test
 
+def read_data_physionet_4_with_val(window_size=3000, stride=500):
+
+    # read pkl
+    with open('../data/challenge2017/challenge2017.pkl', 'rb') as fin:
+        res = pickle.load(fin)
+    ## scale data
+    all_data = res['data']
+    for i in range(len(all_data)):
+        tmp_data = all_data[i]
+        tmp_std = np.std(tmp_data)
+        tmp_mean = np.mean(tmp_data)
+        all_data[i] = (tmp_data - tmp_mean) / tmp_std
+    ## encode label
+    all_label = []
+    for i in res['label']:
+        if i == 'N':
+            all_label.append(0)
+        elif i == 'A':
+            all_label.append(1)
+        elif i == 'O':
+            all_label.append(2)
+        elif i == '~':
+            all_label.append(3)
+    all_label = np.array(all_label)
+
+    # split train val test
+    X_train, X_test, Y_train, Y_test = train_test_split(all_data, all_label, test_size=0.2, random_state=0)
+    X_val, X_test, Y_val, Y_test = train_test_split(X_test, Y_test, test_size=0.5, random_state=0)
+
+    # slide and cut
+    print('before: ')
+    print(Counter(Y_train), Counter(Y_val), Counter(Y_test))
+    X_train, Y_train = slide_and_cut(X_train, Y_train, window_size=window_size, stride=stride)
+    X_val, Y_val, pid_val = slide_and_cut(X_val, Y_val, window_size=window_size, stride=stride, output_pid=True)
+    X_test, Y_test, pid_test = slide_and_cut(X_test, Y_test, window_size=window_size, stride=stride, output_pid=True)
+    print('after: ')
+    print(Counter(Y_train), Counter(Y_val), Counter(Y_test))
+
+    # shuffle train
+    shuffle_pid = np.random.permutation(Y_train.shape[0])
+    X_train = X_train[shuffle_pid]
+    Y_train = Y_train[shuffle_pid]
+
+    X_train = np.expand_dims(X_train, 1)
+    X_val = np.expand_dims(X_val, 1)
+    X_test = np.expand_dims(X_test, 1)
+
+    return X_train, X_val, X_test, Y_train, Y_val, Y_test, pid_val, pid_test
+
 
 def read_data_generated(n_samples, n_length, n_channel, n_classes, verbose=False):
     """