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Commit 0522c77

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author
Satya Shukla
committed
fixed bugs
1 parent a0a73e7 commit 0522c77

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6 files changed

+27
-17
lines changed

6 files changed

+27
-17
lines changed

‎requirements.txt

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
numpy>=1.15.3
2-
sklearn>=0.20.0
3-
tensorflow>=1.0.0
1+
numpy
2+
sklearn
3+
tensorflow-gpu==1.15.0
44
keras==2.1.2

‎src/interpolation_layer.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -16,7 +16,7 @@ def __init__(self, ref_points, hours_look_ahead, **kwargs):
1616
def build(self, input_shape):
1717
#input_shape [batch, features, time_stamp]
1818
self.time_stamp = input_shape[2]
19-
self.d_dim = input_shape[1]/4
19+
self.d_dim = input_shape[1] // 4
2020
self.activation = activations.get('sigmoid')
2121
self.kernel = self.add_weight(
2222
name='kernel',
@@ -73,7 +73,7 @@ def __init__(self, **kwargs):
7373
super(cross_channel_interp, self).__init__(**kwargs)
7474

7575
def build(self, input_shape):
76-
self.d_dim = input_shape[1]/3
76+
self.d_dim = input_shape[1] // 3
7777
self.activation = activations.get('sigmoid')
7878
self.cross_channel_interp = self.add_weight(
7979
name='cross_channel_interp',

‎src/mimic_data_extraction.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -34,7 +34,7 @@
3434

3535
data = []
3636
for id in range(len(list_adm_id)):
37-
print id, list_adm_id[id][0]
37+
print(id, list_adm_id[id][0])
3838
vitals = []
3939

4040
# print("Sp02")

‎src/mimic_preprocessing.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -4,11 +4,11 @@
44

55

66
def load_data():
7-
print 'Loading files ...'
7+
print('Loading files ...')
88
vitals = pickle.load(open('vitals_records.p', 'rb'))
99
adm_info = pickle.load(
1010
open('adm_type_los_mortality.p', 'rb'))
11-
print 'Loading Done!'
11+
print('Loading Done!')
1212
adm_id = [record[0] for record in adm_info]
1313
adm_id_needed = [record[0] for record in adm_info if record[2] >= 48]
1414

@@ -18,7 +18,7 @@ def load_data():
1818

1919
vitals = [vitals_dict[x] for x in adm_id_needed]
2020
label = [rec[3] for x in adm_id_needed for rec in adm_info if x == rec[0]]
21-
print len(vitals), len(label)
21+
print(len(vitals), len(label))
2222
return vitals, label
2323

2424

‎src/multivariate_example.py

Lines changed: 11 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,8 @@
11
import argparse
22
import numpy as np
3+
import logging, os
4+
logging.disable(logging.WARNING)
5+
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
36
import tensorflow as tf
47
from sklearn.model_selection import StratifiedKFold
58
from sklearn.metrics import average_precision_score as auprc
@@ -10,6 +13,8 @@
1013
from keras.models import Model
1114
from interpolation_layer import single_channel_interp, cross_channel_interp
1215
from mimic_preprocessing import load_data, trim_los, fix_input_format
16+
import warnings
17+
warnings.filterwarnings("ignore")
1318

1419
np.random.seed(10)
1520
tf.set_random_seed(10)
@@ -76,8 +81,6 @@ def mean_imputation(vitals, mask):
7681
gpu_num = args["gpus"]
7782
epoch = args["epochs"]
7883
hid = args["hidden_units"]
79-
timestamp = x.shape[2]
80-
num_features = x.shape[1]/4
8184
ref_points = args["reference_points"]
8285
hours_look_ahead = args["hours_from_adm"]
8386
if gpu_num > 0:
@@ -103,7 +106,9 @@ def mean_imputation(vitals, mask):
103106
mean_imputation(x, m)
104107
x = np.concatenate((x, m, T, hold_out(m)), axis=1) # input format
105108
y = np.array(label)
106-
print x.shape, y.shape
109+
print(x.shape, y.shape)
110+
timestamp = x.shape[2]
111+
num_features = x.shape[1] // 4
107112

108113

109114
def customloss(ytrue, ypred):
@@ -160,7 +165,7 @@ def interp_net():
160165
model = multi_gpu_model(orig_model, gpus=gpu_num)
161166
else:
162167
model = orig_model
163-
print orig_model.summary()
168+
print(orig_model.summary())
164169
return model
165170

166171

@@ -173,7 +178,7 @@ def interp_net():
173178
i = 0
174179
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)
175180
for train, test in kfold.split(np.zeros(len(y)), y):
176-
print "Running Fold:", i+1
181+
print("Running Fold:", i+1)
177182
model = interp_net() # re-initializing every time
178183
model.compile(
179184
optimizer='adam',
@@ -198,5 +203,5 @@ def interp_net():
198203
results['acc'].append(acc)
199204
results['auc'].append(auc_score(y[test], y_pred))
200205
results['auprc'].append(auprc(y[test], y_pred))
201-
print results
206+
print(results)
202207
i += 1

‎src/univariate_example.py

Lines changed: 7 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1,12 +1,17 @@
11
import argparse
2-
import cPickle as pickle
2+
import pickle
33
import numpy as np
4+
import logging, os
5+
logging.disable(logging.WARNING)
6+
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
47
import tensorflow as tf
58
import keras
69
from keras.models import Model
710
from keras.layers import Input, Dense, GRU, Lambda, Permute
811
from sklearn.preprocessing import MultiLabelBinarizer
912
from interpolation_layer import single_channel_interp
13+
import warnings
14+
warnings.filterwarnings("ignore")
1015

1116
ap = argparse.ArgumentParser()
1217
ap.add_argument("-batch", "--batch_size", type=int, default=256,
@@ -29,7 +34,7 @@
2934

3035
# Loading Dataset
3136
with open('Dataset/UWaveGestureLibraryAll-10.pkl', 'rb') as f:
32-
x_train, y_train, x_test, y_test, l_train, l_test = pickle.load(f)
37+
x_train, y_train, x_test, y_test, l_train, l_test = pickle.load(f, encoding='latin1')
3338

3439
x_train = np.array(x_train)
3540
l_train = np.array(l_train)

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