def main():
parser = argparse.ArgumentParser()
parser.add_argument('--C', type=float, default=1.0, help='inverse of L1 / L2 regularization')
parser.add_argument('--l1', dest='l2', action='store_false')
parser.add_argument('--l2', dest='l2', action='store_true')
parser.set_defaults(l2=True)
parser.add_argument('--period', type=str, default='all', help='specifies which period extract features from',
choices=['first4days', 'first8days', 'last12hours', 'first25percent', 'first50percent', 'all'])
parser.add_argument('--features', type=str, default='all', help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
parser.add_argument('--data', type=str, help='Path to the data of in-hospital mortality task',
default=os.path.join(os.path.dirname(__file__), '../../../data/in-hospital-mortality/'))
parser.add_argument('--output_dir', type=str, help='Directory relative which all output files are stored',
default='.')
args = parser.parse_args()
print(args)
print('Reading data and extracting features ...')
train_X, train_y, train_names, val_X, val_y, val_names, test_X, test_y, test_names = \
load_data_logistic_regression(args)
penalty = ('l2' if args.l2 else 'l1')
file_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty, args.C)
logreg = LogisticRegression(penalty=penalty, C=args.C, random_state=42)
logreg.fit(train_X, train_y)
result_dir = os.path.join(args.output_dir, 'results')
common_utils.create_directory(result_dir)
with open(os.path.join(result_dir, 'train_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k : float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join(result_dir, 'val_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join(result_dir, 'test_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_binary(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(test_names, prediction, test_y,
os.path.join(args.output_dir, 'predictions', file_name + '.csv'))
def save_embeddings(names, embeddings, path):
common_utils.create_directory(os.path.dirname(path))
with open(path, 'w') as f:
header = ["stay"]
header += ["emb_{}".format(x) for x in range(len(embeddings[0]))]
header = ",".join(header)
f.write(header + '\n')
for name, emb in zip(names, embeddings):
line = [name]
#emb_str = ["%.10f" % x for x in emb]
emb_str = [str(x) for x in emb]
line += emb_str
line = ",".join(line)
f.write(line + '\n')
def save_results(names, ts, predictions, labels, path):
n_tasks = 25
common_utils.create_directory(os.path.dirname(path))
with open(path, 'w') as f:
header = ["stay", "period_length"]
header += ["pred_{}".format(x) for x in range(1, n_tasks + 1)]
header += ["label_{}".format(x) for x in range(1, n_tasks + 1)]
header = ",".join(header)
f.write(header + '\n')
for name, t, pred, y in zip(names, ts, predictions, labels):
line = [name]
line += ["{:.6f}".format(t)]
line += ["{:.6f}".format(a) for a in pred]
line += [str(a) for a in y]
line = ",".join(line)
f.write(line + '\n')
def save_results(names, ts, predictions, labels, path):
n_tasks = 25
common_utils.create_directory(os.path.dirname(path))
with open(path, 'w') as f:
header = ["stay", "period_length"]
header += ["pred_{}".format(x) for x in range(1, n_tasks + 1)]
header += ["label_{}".format(x) for x in range(1, n_tasks + 1)]
header = ",".join(header)
f.write(header + '\n')
for name, t, pred, y in zip(names, ts, predictions, labels):
line = [name]
line += ["{:.6f}".format(t)]
line += ["{:.6f}".format(a) for a in pred]
line += [str(a) for a in y]
line = ",".join(line)
f.write(line + '\n')
def save_results(names, pred, y_true, path, stochastic=False):
common_utils.create_directory(os.path.dirname(path))
with open(path, 'w') as f:
if stochastic:
aleatoric = np.mean(pred * (1. - pred), axis=1)
epistemic = np.var(pred, axis=1)
pred = np.mean(pred, axis=1)
uncertainty = aleatoric + epistemic
f.write("stay,prediction,y_true,aleatoric,epistemic,uncertainty\n")
for (name, x, y, a, e, u) in zip(names, pred, y_true, aleatoric,
epistemic, uncertainty):
f.write("{},{:.6f},{},{:.6f},{:.6f},{:.6f}\n".format(
name, x, y, a, e, u))
else:
f.write("stay,prediction,y_true\n")
for (name, x, y) in zip(names, pred, y_true):
f.write("{},{:.6f},{}\n".format(name, x, y))
def save_results(names,
ts,
pred,
y_true,
path,
aleatoric=None,
epistemic=None):
common_utils.create_directory(os.path.dirname(path))
with open(path, 'w') as f:
if aleatoric is not None and epistemic is not None:
f.write(
"stay,period_length,prediction,y_true,epistemic,aleatoric,uncertainty\n"
)
for (name, t, x, y, e, a) in zip(names, ts, pred, y_true,
epistemic, aleatoric):
f.write(
"{},{:.6f},{:.6f},{:.6f},{:.6f},{:.6f},{:.6f}\n".format(
name, t, x, y, e, a, e + a))
else:
f.write("stay,period_length,prediction,y_true\n")
for (name, t, x, y) in zip(names, ts, pred, y_true):
f.write("{},{:.6f},{:.6f},{:.6f}\n".format(name, t, x, y))
def save_results(names, ts, predictions, labels, path, stochastic=False):
n_tasks = 25
common_utils.create_directory(os.path.dirname(path))
with open(path, 'w') as f:
header = ["stay", "period_length"]
header += ["pred_{}".format(x) for x in range(1, n_tasks + 1)]
header += ["label_{}".format(x) for x in range(1, n_tasks + 1)]
if stochastic:
header += ["epistemic_{}".format(x) for x in range(1, n_tasks + 1)]
header += ["aleatoric_{}".format(x) for x in range(1, n_tasks + 1)]
header = ",".join(header)
f.write(header + '\n')
if stochastic:
epistemic = np.var(predictions, axis=1)
aleatoric = np.mean(predictions * (1. - predictions), axis=1)
predictions = np.mean(predictions, axis=1)
for name, t, pred, y, epis, alea in zip(names, ts, predictions,
labels, epistemic,
aleatoric):
line = [name]
line += ["{:.6f}".format(t)]
line += ["{:.6f}".format(a) for a in pred]
line += [str(a) for a in y]
line += ["{:.6f}".format(e) for e in epis]
line += ["{:.6f}".format(a) for a in alea]
line = ",".join(line)
f.write(line + '\n')
else:
for name, t, pred, y in zip(names, ts, predictions, labels):
line = [name]
line += ["{:.6f}".format(t)]
line += ["{:.6f}".format(a) for a in pred]
line += [str(a) for a in y]
line = ",".join(line)
f.write(line + '\n')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--C',
type=float,
default=1.0,
help='inverse of L1 / L2 regularization')
parser.add_argument('--l1', dest='l2', action='store_false')
parser.add_argument('--l2', dest='l2', action='store_true')
parser.set_defaults(l2=True)
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len', 'mean_and_sd'])
parser.add_argument('--data',
type=str,
help='Path to the data of in-hospital mortality task',
default=os.path.join(
os.path.dirname(__file__),
'../../../data/in-hospital-mortality/'))
parser.add_argument(
'--output_dir',
type=str,
help='Directory relative which all output files are stored',
default='.')
args = parser.parse_args()
print(args)
train_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'),
period_length=48.0)
val_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'),
period_length=48.0)
test_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'test'),
listfile=os.path.join(args.data, 'test_listfile.csv'),
period_length=48.0)
print('Reading data and extracting features ...')
# read_and_extract removes some highly implausible values according to plausible_values.json
print('Remove implausible values ...')
(train_X, train_y,
train_names) = read_and_extract_features(train_reader, args.period,
args.features)
(val_X, val_y,
val_names) = read_and_extract_features(val_reader, args.period,
args.features)
(test_X, test_y,
test_names) = read_and_extract_features(test_reader, args.period,
args.features)
print(' train data shape = {}'.format(train_X.shape))
print(' validation data shape = {}'.format(val_X.shape))
print(' test data shape = {}'.format(test_X.shape))
# print('Imputing missing values ...')
# imputer = Imputer(missing_values=np.nan, strategy='mean', axis=0, verbose=0, copy=True)
# imputer.fit(train_X)
# train_X = np.array(imputer.transform(train_X), dtype=np.float32)
# val_X = np.array(imputer.transform(val_X), dtype=np.float32)
# test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Imputing missing values with -1.')
# Verified that all values are greater or equal than zero via np.nanmin()
train_X[np.isnan(train_X)] = -1.
val_X[np.isnan(val_X)] = -1.
test_X[np.isnan(test_X)] = -1.
train_X = np.array(train_X, dtype=np.float32)
val_X = np.array(val_X, dtype=np.float32)
test_X = np.array(test_X, dtype=np.float32)
# # print('Normalizing the data to have zero mean and unit variance ...')
# scaler = StandardScaler()
# scaler.fit(train_X)
# train_X = scaler.transform(train_X)
# val_X = scaler.transform(val_X)
# test_X = scaler.transform(test_X)
print('Export features along with target as csv files ...')
train_file = os.path.join(args.output_dir,
'in-hospital-mortality-train.csv')
val_file = os.path.join(args.output_dir, 'in-hospital-mortality-val.csv')
test_file = os.path.join(args.output_dir, 'in-hospital-mortality-test.csv')
np.savetxt(train_file,
np.concatenate((train_X, (np.array([train_y])).T), axis=1),
delimiter='\t')
np.savetxt(val_file,
np.concatenate((val_X, (np.array([val_y])).T), axis=1),
delimiter='\t')
np.savetxt(test_file,
np.concatenate((test_X, (np.array([test_y])).T), axis=1),
delimiter='\t')
penalty = ('l2' if args.l2 else 'l1')
file_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty,
args.C)
logreg = LogisticRegression(penalty=penalty, C=args.C, random_state=42)
logreg.fit(train_X, train_y)
result_dir = os.path.join(args.output_dir, 'results')
common_utils.create_directory(result_dir)
with open(os.path.join(result_dir, 'train_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join(result_dir, 'val_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join(result_dir, 'test_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(
test_names, prediction, test_y,
os.path.join(args.output_dir, 'predictions', file_name + '.csv'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
args = parser.parse_args()
print(args)
# penalties = ['l2', 'l2', 'l2', 'l2', 'l2', 'l2', 'l1', 'l1', 'l1', 'l1', 'l1']
# Cs = [1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001, 1.0, 0.1, 0.01, 0.001, 0.0001]
penalties = ['l2']
Cs = [0.00001]
train_reader = LengthOfStayReader(
dataset_dir='../../../data/length-of-stay/train/',
listfile='../../../data/length-of-stay/train_listfile.csv')
val_reader = LengthOfStayReader(
dataset_dir='../../../data/length-of-stay/train/',
listfile='../../../data/length-of-stay/val_listfile.csv')
test_reader = LengthOfStayReader(
dataset_dir='../../../data/length-of-stay/test/',
listfile='../../../data/length-of-stay/test_listfile.csv')
print('Reading data and extracting features ...')
n_train = min(100000, train_reader.get_number_of_examples())
n_val = min(100000, val_reader.get_number_of_examples())
(train_X, train_y, train_actual, train_names,
train_ts) = read_and_extract_features(train_reader, n_train, args.period,
args.features)
(val_X, val_y, val_actual, val_names,
val_ts) = read_and_extract_features(val_reader, n_val, args.period,
args.features)
(test_X, test_y, test_actual, test_names,
test_ts) = read_and_extract_features(test_reader,
test_reader.get_number_of_examples(),
args.period, args.features)
print("train set shape: {}".format(train_X.shape))
print("validation set shape: {}".format(val_X.shape))
print("test set shape: {}".format(test_X.shape))
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan,
strategy='mean',
axis=0,
verbose=0,
copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
common_utils.create_directory('cf_results')
for (penalty, C) in zip(penalties, Cs):
model_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty,
C)
train_activations = np.zeros(shape=train_y.shape, dtype=float)
val_activations = np.zeros(shape=val_y.shape, dtype=float)
test_activations = np.zeros(shape=test_y.shape, dtype=float)
for task_id in range(n_bins):
logreg = LogisticRegression(penalty=penalty, C=C, random_state=42)
logreg.fit(train_X, train_y[:, task_id])
train_preds = logreg.predict_proba(train_X)
train_activations[:, task_id] = train_preds[:, 1]
val_preds = logreg.predict_proba(val_X)
val_activations[:, task_id] = val_preds[:, 1]
test_preds = logreg.predict_proba(test_X)
test_activations[:, task_id] = test_preds[:, 1]
train_predictions = np.array([
metrics.get_estimate_custom(x, n_bins) for x in train_activations
])
val_predictions = np.array(
[metrics.get_estimate_custom(x, n_bins) for x in val_activations])
test_predictions = np.array(
[metrics.get_estimate_custom(x, n_bins) for x in test_activations])
with open(
os.path.join('cf_results', 'train_{}.json'.format(model_name)),
'w') as f:
ret = metrics.print_metrics_custom_bins(train_actual,
train_predictions)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, f)
with open(os.path.join('cf_results', 'val_{}.json'.format(model_name)),
'w') as f:
ret = metrics.print_metrics_custom_bins(val_actual,
val_predictions)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, f)
with open(
os.path.join('cf_results', 'test_{}.json'.format(model_name)),
'w') as f:
ret = metrics.print_metrics_custom_bins(test_actual,
test_predictions)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, f)
save_results(test_names, test_ts, test_predictions, test_actual,
os.path.join('cf_predictions', model_name + '.csv'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--C',
type=float,
default=1.0,
help='inverse of L1 / L2 regularization')
parser.add_argument('--l1', dest='l2', action='store_false')
parser.add_argument('--l2', dest='l2', action='store_true')
parser.set_defaults(l2=True)
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
parser.add_argument('--data',
type=str,
help='Path to the data of in-hospital mortality task',
default=os.path.join(
os.path.dirname(__file__),
'../../../data/in-hospital-mortality/'))
parser.add_argument(
'--output_dir',
type=str,
help='Directory relative which all output files are stored',
default='.')
parser.add_argument('--generate-data-only',
dest='generate_data_only',
action="store_true")
parser.set_defaults(generate_data_only=False)
args = parser.parse_args()
print(args)
train_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'),
period_length=48.0)
val_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'),
period_length=48.0)
test_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'test'),
listfile=os.path.join(args.data, 'test_listfile.csv'),
period_length=48.0)
print('Reading data and extracting features ...')
(train_X, train_y,
train_names) = read_and_extract_features(train_reader, args.period,
args.features)
(val_X, val_y,
val_names) = read_and_extract_features(val_reader, args.period,
args.features)
(test_X, test_y,
test_names) = read_and_extract_features(test_reader, args.period,
args.features)
print(' train data shape = {}'.format(train_X.shape))
print(' validation data shape = {}'.format(val_X.shape))
print(' test data shape = {}'.format(test_X.shape))
if args.generate_data_only:
data_path = os.path.join(args.output_dir,
"mimic3_benchmark_data_logistic.csv")
dataset = create_frame(train_X, train_y).append(
create_frame(test_X, test_y)).append(create_frame(val_X, val_y))
dataset.to_csv(data_path)
print("Generated and saved the data at: %s" % data_path)
return
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan,
strategy='mean',
axis=0,
verbose=0,
copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
penalty = ('l2' if args.l2 else 'l1')
file_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty,
args.C)
logreg = LogisticRegression(penalty=penalty, C=args.C, random_state=42)
logreg.fit(train_X, train_y)
result_dir = os.path.join(args.output_dir, 'results')
common_utils.create_directory(result_dir)
with open(os.path.join(result_dir, 'train_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join(result_dir, 'val_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join(result_dir, 'test_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(
test_names, prediction, test_y,
os.path.join(args.output_dir, 'predictions', file_name + '.csv'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--period', type=str, default='all', help='specifies which period extract features from',
choices=['first4days', 'first8days', 'last12hours', 'first25percent', 'first50percent', 'all'])
parser.add_argument('--features', type=str, default='all', help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
args = parser.parse_args()
print(args)
# penalties = ['l2', 'l2', 'l2', 'l2', 'l2', 'l2', 'l1', 'l1', 'l1', 'l1', 'l1']
# Cs = [1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001, 1.0, 0.1, 0.01, 0.001, 0.0001]
penalties = ['l2']
Cs = [0.001]
train_reader = DecompensationReader(dataset_dir='../../../data/decompensation/train/',
listfile='../../../data/decompensation/train_listfile.csv')
val_reader = DecompensationReader(dataset_dir='../../../data/decompensation/train/',
listfile='../../../data/decompensation/val_listfile.csv')
test_reader = DecompensationReader(dataset_dir='../../../data/decompensation/test/',
listfile='../../../data/decompensation/test_listfile.csv')
print('Reading data and extracting features ...')
n_train = min(100000, train_reader.get_number_of_examples())
n_val = min(100000, val_reader.get_number_of_examples())
(train_X, train_y, train_names, train_ts) = read_and_extract_features(
train_reader, n_train, args.period, args.features)
(val_X, val_y, val_names, val_ts) = read_and_extract_features(
val_reader, n_val, args.period, args.features)
(test_X, test_y, test_names, test_ts) = read_and_extract_features(
test_reader, test_reader.get_number_of_examples(), args.period, args.features)
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan, strategy='mean', axis=0, verbose=0, copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
common_utils.create_directory('results')
for (penalty, C) in zip(penalties, Cs):
file_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty, C)
logreg = LogisticRegression(penalty=penalty, C=C, random_state=42)
logreg.fit(train_X, train_y)
with open(os.path.join('results', 'train_{}.json'.format(file_name)), "w") as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join('results', 'val_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join('results', 'test_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_binary(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(test_names, test_ts, prediction, test_y, os.path.join('predictions', file_name + '.csv'))
def save_results(names, pred, y_true, path):
common_utils.create_directory(os.path.dirname(path))
with open(path, 'w') as f:
f.write("stay,prediction,y_true\n")
for (name, x, y) in zip(names, pred, y_true):
f.write("{},{:.6f},{}\n".format(name, x, y))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
args = parser.parse_args()
print(args)
# penalties = ['l2', 'l2', 'l2', 'l2', 'l2', 'l2', 'l1', 'l1', 'l1', 'l1', 'l1']
# Cs = [1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001, 1.0, 0.1, 0.01, 0.001, 0.0001]
penalties = ['l2']
Cs = [0.001]
train_reader = DecompensationReader(
dataset_dir='../../../data/decompensation/train/',
listfile='../../../data/decompensation/train_listfile.csv')
val_reader = DecompensationReader(
dataset_dir='../../../data/decompensation/train/',
listfile='../../../data/decompensation/val_listfile.csv')
test_reader = DecompensationReader(
dataset_dir='../../../data/decompensation/test/',
listfile='../../../data/decompensation/test_listfile.csv')
print('Reading data and extracting features ...')
n_train = min(100000, train_reader.get_number_of_examples())
n_val = min(100000, val_reader.get_number_of_examples())
(train_X, train_y, train_names,
train_ts) = read_and_extract_features(train_reader, n_train, args.period,
args.features)
(val_X, val_y, val_names,
val_ts) = read_and_extract_features(val_reader, n_val, args.period,
args.features)
(test_X, test_y, test_names,
test_ts) = read_and_extract_features(test_reader,
test_reader.get_number_of_examples(),
args.period, args.features)
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan,
strategy='mean',
axis=0,
verbose=0,
copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
common_utils.create_directory('results')
for (penalty, C) in zip(penalties, Cs):
file_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty,
C)
logreg = LogisticRegression(penalty=penalty, C=C, random_state=42)
logreg.fit(train_X, train_y)
with open(os.path.join('results', 'train_{}.json'.format(file_name)),
"w") as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join('results', 'val_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join('results', 'test_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(test_names, test_ts, prediction, test_y,
os.path.join('predictions', file_name + '.csv'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
parser.add_argument('--data',
type=str,
help='Path to the data of length-of-stay task',
default=os.path.join(os.path.dirname(__file__),
'../../../data/length-of-stay/'))
parser.add_argument(
'--output_dir',
type=str,
help='Directory relative which all output files are stored',
default='.')
args = parser.parse_args()
print(args)
train_reader = LengthOfStayReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'))
val_reader = LengthOfStayReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'))
test_reader = LengthOfStayReader(
dataset_dir=os.path.join(args.data, 'test'),
listfile=os.path.join(args.data, 'test_listfile.csv'))
print('Reading data and extracting features ...')
n_train = min(100000, train_reader.get_number_of_examples())
n_val = min(100000, val_reader.get_number_of_examples())
(train_X, train_y, train_names,
train_ts) = read_and_extract_features(train_reader, n_train, args.period,
args.features)
(val_X, val_y, val_names,
val_ts) = read_and_extract_features(val_reader, n_val, args.period,
args.features)
(test_X, test_y, test_names,
test_ts) = read_and_extract_features(test_reader,
test_reader.get_number_of_examples(),
args.period, args.features)
print(train_X.shape)
assert False
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan,
strategy='mean',
axis=0,
verbose=0,
copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
file_name = "{}.{}".format(args.period, args.features)
linreg = LinearRegression()
linreg.fit(train_X, train_y)
result_dir = os.path.join(args.output_dir, 'results')
common_utils.create_directory(result_dir)
with open(os.path.join(result_dir, 'train_{}.json'.format(file_name)),
"w") as res_file:
ret = print_metrics_regression(train_y, linreg.predict(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join(result_dir, 'val_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_regression(val_y, linreg.predict(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = linreg.predict(test_X)
with open(os.path.join(result_dir, 'test_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_regression(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(
test_names, test_ts, prediction, test_y,
os.path.join(args.output_dir, 'predictions', file_name + '.csv'))
test_X = scaler.transform(features_test)
test_X = [np.hstack([X, d]) for (X, d) in zip(test_X, diseases_embedding_test)]
test_X = [np.hstack([X, d]) for (X, d) in zip(test_X, idx_features_test)]
test_X = [np.hstack([X, d]) for (X, d) in zip(test_X, mask_test)]
#=========SVM====================
penalty = ('l2')
#file_name = '{}.{}.{}.C{}'.format(penalty, 0.001)
logreg = SVC(probability=True)
logreg.fit(train_X, train_y)
#-----------------
common_utils.create_directory('svm_results')
common_utils.create_directory('svm_predictions')
with open(os.path.join('svm_results', 'train.json'), 'w') as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join('svm_results', 'val.json'), 'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join('svm_results', 'test.json'), 'w') as res_file:
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
parser.add_argument('--grid-search',
dest='grid_search',
action='store_true')
parser.add_argument('--no-grid-search',
dest='grid_search',
action='store_false')
parser.set_defaults(grid_search=False)
parser.add_argument('--data',
type=str,
help='Path to the data of phenotyping task',
default=os.path.join(os.path.dirname(__file__),
'../../../data/phenotyping/'))
parser.add_argument(
'--output_dir',
type=str,
help='Directory relative which all output files are stored',
default='.')
args = parser.parse_args()
print(args)
if args.grid_search:
penalties = [
'l2', 'l2', 'l2', 'l2', 'l2', 'l2', 'l1', 'l1', 'l1', 'l1', 'l1'
]
coefs = [
1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001, 1.0, 0.1, 0.01, 0.001,
0.0001
]
else:
penalties = ['l1']
coefs = [0.1]
train_reader = PhenotypingReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'))
val_reader = PhenotypingReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'))
test_reader = PhenotypingReader(
dataset_dir=os.path.join(args.data, 'test'),
listfile=os.path.join(args.data, 'test_listfile.csv'))
print('Reading data and extracting features ...')
(train_X, train_y, train_names,
train_ts) = read_and_extract_features(train_reader, args.period,
args.features)
train_y = np.array(train_y)
(val_X, val_y, val_names,
val_ts) = read_and_extract_features(val_reader, args.period,
args.features)
val_y = np.array(val_y)
(test_X, test_y, test_names,
test_ts) = read_and_extract_features(test_reader, args.period,
args.features)
test_y = np.array(test_y)
print("train set shape: {}".format(train_X.shape))
print("validation set shape: {}".format(val_X.shape))
print("test set shape: {}".format(test_X.shape))
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan,
strategy='mean',
axis=0,
verbose=0,
copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
n_tasks = 25
result_dir = os.path.join(args.output_dir, 'results')
common_utils.create_directory(result_dir)
for (penalty, C) in zip(penalties, coefs):
model_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty,
C)
train_activations = np.zeros(shape=train_y.shape, dtype=float)
val_activations = np.zeros(shape=val_y.shape, dtype=float)
test_activations = np.zeros(shape=test_y.shape, dtype=float)
for task_id in range(n_tasks):
print('Starting task {}'.format(task_id))
logreg = LogisticRegression(penalty=penalty, C=C, random_state=42)
logreg.fit(train_X, train_y[:, task_id])
train_preds = logreg.predict_proba(train_X)
train_activations[:, task_id] = train_preds[:, 1]
val_preds = logreg.predict_proba(val_X)
val_activations[:, task_id] = val_preds[:, 1]
test_preds = logreg.predict_proba(test_X)
test_activations[:, task_id] = test_preds[:, 1]
with open(os.path.join(result_dir, 'train_{}.json'.format(model_name)),
'w') as f:
ret = metrics.print_metrics_multilabel(train_y, train_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
with open(os.path.join(result_dir, 'val_{}.json'.format(model_name)),
'w') as f:
ret = metrics.print_metrics_multilabel(val_y, val_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
with open(os.path.join(result_dir, 'test_{}.json'.format(model_name)),
'w') as f:
ret = metrics.print_metrics_multilabel(test_y, test_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
save_results(
test_names, test_ts, test_activations, test_y,
os.path.join(args.output_dir, 'predictions', model_name + '.csv'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--C',
type=float,
default=1.0,
help='inverse of L1 / L2 regularization')
parser.add_argument('--l1', dest='l2', action='store_false')
parser.add_argument('--l2', dest='l2', action='store_true')
parser.set_defaults(l2=True)
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
parser.add_argument('--method',
type=str,
default='logistic',
choices=['gridsearch', 'lgbm', 'logistic'])
args = parser.parse_args()
print(args)
import os, pickle
data_cache = '../../../data/in-hospital-mortality/lr_cache.pickle'
if os.path.exists(data_cache):
print('Loading data cache ...')
with open(data_cache, 'rb') as f:
(train_X, train_y,
train_names), (val_X, val_y,
val_names), (test_X, test_y,
test_names) = pickle.load(f)
else:
train_reader = InHospitalMortalityReader(
dataset_dir='../../../data/in-hospital-mortality/train/',
listfile='../../../data/in-hospital-mortality/train_listfile.csv',
period_length=48.0)
val_reader = InHospitalMortalityReader(
dataset_dir='../../../data/in-hospital-mortality/train/',
listfile='../../../data/in-hospital-mortality/val_listfile.csv',
period_length=48.0)
test_reader = InHospitalMortalityReader(
dataset_dir='../../../data/in-hospital-mortality/test/',
listfile='../../../data/in-hospital-mortality/test_listfile.csv',
period_length=48.0)
print('Reading data and extracting features ...')
(train_X, train_y,
train_names) = read_and_extract_features(train_reader, args.period,
args.features)
(val_X, val_y,
val_names) = read_and_extract_features(val_reader, args.period,
args.features)
(test_X, test_y,
test_names) = read_and_extract_features(test_reader, args.period,
args.features)
print(' train data shape = {}'.format(train_X.shape))
print(' validation data shape = {}'.format(val_X.shape))
print(' test data shape = {}'.format(test_X.shape))
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan,
strategy='mean',
axis=0,
verbose=0,
copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
with open(data_cache, 'wb') as f:
pickle.dump([(train_X, train_y, train_names),
(val_X, val_y, val_names),
(test_X, test_y, test_names)], f,
pickle.HIGHEST_PROTOCOL)
penalty = ('l2' if args.l2 else 'l1')
file_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty,
args.C)
print("use {} to fit".format(args.method))
if args.method == "gridsearch":
param_test1 = {'n_estimators': range(10, 200, 20)}
gsearch1 = GridSearchCV(estimator=GradientBoostingClassifier(),
param_grid=param_test1)
gsearch1.fit(train_X, train_y)
print("gridsearch best result: ", gsearch1.best_params_,
gsearch1.best_score_)
logreg = GradientBoostingClassifier(
n_estimators=gsearch1.best_params_['n_estimators'])
elif args.method == "lgbm":
logreg = lgb.LGBMClassifier(objective='binary',
num_leaves=31,
learning_rate=0.05,
n_estimators=20)
elif args.method == "logistic":
logreg = LogisticRegression(penalty=penalty, C=args.C, random_state=42)
logreg.fit(train_X, train_y)
common_utils.create_directory('results')
with open(os.path.join('results', 'train_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join('results', 'val_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join('results', 'test_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(test_names, prediction, test_y,
os.path.join('predictions', file_name + '.csv'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--C',
type=float,
default=1.0,
help='inverse of L1 / L2 regularization')
parser.add_argument('--l1', dest='l2', action='store_false')
parser.add_argument('--l2', dest='l2', action='store_true')
parser.set_defaults(l2=True)
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
parser.add_argument('--data',
type=str,
help='Path to the data of in-hospital mortality task',
default=os.path.join(
os.path.dirname(__file__),
'../../../data/in-hospital-mortality/'))
parser.add_argument(
'--output_dir',
type=str,
help='Directory relative which all output files are stored',
default='.')
args = parser.parse_args()
print(args)
train_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'),
period_length=48.0)
val_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'),
period_length=48.0)
test_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'test'),
listfile=os.path.join(args.data, 'test_listfile.csv'),
period_length=48.0)
# Extract feature names
if args.features == "all" and args.period == "all":
reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'),
period_length=48.0)
feature_names = []
header = reader.read_next()["header"]
for item in header[1:]: # First item is 'hours'
for sub_period in [
"full-series", "first-10%", "first-25%", "first-50%",
"last-10%", "last-25%", "last-50%"
]:
for function in ["min", "max", "mean", "std", "skew", "count"]:
feature_names.append(f"{item}->{sub_period}->{function}")
with open(os.path.join(args.output_dir, "feature_names.pkl"),
"wb") as feature_names_file:
pickle.dump(feature_names, feature_names_file)
print('Reading data and extracting features ...')
(train_X, train_y,
train_names) = read_and_extract_features(train_reader, args.period,
args.features)
(val_X, val_y,
val_names) = read_and_extract_features(val_reader, args.period,
args.features)
(test_X, test_y,
test_names) = read_and_extract_features(test_reader, args.period,
args.features)
print(' train data shape = {}'.format(train_X.shape))
print(' validation data shape = {}'.format(val_X.shape))
print(' test data shape = {}'.format(test_X.shape))
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan,
strategy='mean',
axis=0,
verbose=0,
copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
print('Writing data ...')
data_dir = os.path.join(args.output_dir, 'data')
common_utils.create_directory(data_dir)
common_utils.write_data(data_dir, train_X, val_X, test_X, train_y, val_y,
test_y)
penalty = ('l2' if args.l2 else 'l1')
file_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty,
args.C)
logreg = LogisticRegression(penalty=penalty, C=args.C, random_state=42)
logreg.fit(train_X, train_y)
result_dir = os.path.join(args.output_dir, 'results')
common_utils.create_directory(result_dir)
with open(os.path.join(result_dir, 'train_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join(result_dir, 'val_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join(result_dir, 'test_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(
test_names, prediction, test_y,
os.path.join(args.output_dir, 'predictions', file_name + '.csv'))
joblib.dump(logreg, os.path.join(args.output_dir,
"lr.joblib")) # Save model
# Generate ranked list of features
if args.features == "all" and args.period == "all":
coefs = logreg.coef_.reshape((714, ))
features = list(zip(feature_names, coefs))
ranked = sorted(features, key=lambda pair: abs(pair[1]), reverse=True)
with open(os.path.join(args.output_dir, "ranked_features.csv"),
"w") as ranked_features_file:
writer = csv.writer(ranked_features_file)
_ = writer.writerow(("Feature Name", "Coefficient Magnitude"))
for pair in ranked:
_ = writer.writerow(pair)
def save_results(names, pred, y_true, path):
common_utils.create_directory(os.path.dirname(path))
with open(path, 'w') as f:
f.write("stay,prediction,y_true\n")
for (name, x, y) in zip(names, pred, y_true):
f.write("{},{:.6f},{}\n".format(name, x, y))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--C', type=float, default=1.0, help='inverse of L1 / L2 regularization')
parser.add_argument('--l1', dest='l2', action='store_false')
parser.add_argument('--l2', dest='l2', action='store_true')
parser.set_defaults(l2=True)
parser.add_argument('--period', type=str, default='all', help='specifies which period extract features from',
choices=['first4days', 'first8days', 'last12hours', 'first25percent', 'first50percent', 'all'])
parser.add_argument('--features', type=str, default='all', help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
args = parser.parse_args()
print(args)
train_reader = InHospitalMortalityReader(dataset_dir='../../../data/in-hospital-mortality/train/',
listfile='../../../data/in-hospital-mortality/train_listfile.csv',
period_length=48.0)
val_reader = InHospitalMortalityReader(dataset_dir='../../../data/in-hospital-mortality/train/',
listfile='../../../data/in-hospital-mortality/val_listfile.csv',
period_length=48.0)
test_reader = InHospitalMortalityReader(dataset_dir='../../../data/in-hospital-mortality/test/',
listfile='../../../data/in-hospital-mortality/test_listfile.csv',
period_length=48.0)
print('Reading data and extracting features ...')
(train_X, train_y, train_names) = read_and_extract_features(train_reader, args.period, args.features)
(val_X, val_y, val_names) = read_and_extract_features(val_reader, args.period, args.features)
(test_X, test_y, test_names) = read_and_extract_features(test_reader, args.period, args.features)
print(' train data shape = {}'.format(train_X.shape))
print(' validation data shape = {}'.format(val_X.shape))
print(' test data shape = {}'.format(test_X.shape))
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan, strategy='mean', axis=0, verbose=0, copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
penalty = ('l2' if args.l2 else 'l1')
file_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty, args.C)
logreg = LogisticRegression(penalty=penalty, C=args.C, random_state=42)
logreg.fit(train_X, train_y)
common_utils.create_directory('results')
with open(os.path.join('results', 'train_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k : float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join('results', 'val_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join('results', 'test_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_binary(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(test_names, prediction, test_y, os.path.join('predictions', file_name + '.csv'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--period', type=str, default='all', help='specifies which period extract features from',
choices=['first4days', 'first8days', 'last12hours', 'first25percent', 'first50percent', 'all'])
parser.add_argument('--features', type=str, default='all', help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
args = parser.parse_args()
print(args)
train_reader = LengthOfStayReader(dataset_dir='../../../data/length-of-stay/train/',
listfile='../../../data/length-of-stay/train_listfile.csv')
val_reader = LengthOfStayReader(dataset_dir='../../../data/length-of-stay/train/',
listfile='../../../data/length-of-stay/val_listfile.csv')
test_reader = LengthOfStayReader(dataset_dir='../../../data/length-of-stay/test/',
listfile='../../../data/length-of-stay/test_listfile.csv')
print('Reading data and extracting features ...')
n_train = min(100000, train_reader.get_number_of_examples())
n_val = min(100000, val_reader.get_number_of_examples())
(train_X, train_y, train_names, train_ts) = read_and_extract_features(
train_reader, n_train, args.period, args.features)
(val_X, val_y, val_names, val_ts) = read_and_extract_features(
val_reader, n_val, args.period, args.features)
(test_X, test_y, test_names, test_ts) = read_and_extract_features(
test_reader, test_reader.get_number_of_examples(), args.period, args.features)
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan, strategy='mean', axis=0, verbose=0, copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
file_name = "{}.{}".format(args.period, args.features)
linreg = LinearRegression()
linreg.fit(train_X, train_y)
common_utils.create_directory('results')
with open(os.path.join("results", 'train_{}.json'.format(file_name)), "w") as res_file:
ret = print_metrics_regression(train_y, linreg.predict(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join('results', 'val_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_regression(val_y, linreg.predict(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = linreg.predict(test_X)
with open(os.path.join('results', 'test_{}.json'.format(file_name)), 'w') as res_file:
ret = print_metrics_regression(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(test_names, test_ts, prediction, test_y, os.path.join('predictions', file_name + '.csv'))
def save_results(names, ts, pred, y_true, path):
common_utils.create_directory(path)
with open(path, 'w') as f:
f.write("stay,period_length,prediction,y_true\n")
for (name, t, x, y) in zip(names, ts, pred, y_true):
f.write("{},{:.6f},{:.6f},{:.6f}\n".format(name, t, x, y))
imputer = Imputer(missing_values=np.nan, strategy='mean', axis=0, verbose=0, copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
n_tasks = 25
result_dir = os.path.join(args.output_dir, 'results')
common_utils.create_directory(result_dir)
for (penalty, C) in zip(penalties, coefs):
model_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty, C)
train_activations = np.zeros(shape=train_y.shape, dtype=float)
val_activations = np.zeros(shape=val_y.shape, dtype=float)
test_activations = np.zeros(shape=test_y.shape, dtype=float)
for task_id in range(n_tasks):
print('Starting task {}'.format(task_id))
logreg = LogisticRegression(penalty=penalty, C=C, random_state=42)
logreg.fit(train_X, train_y[:, task_id])
train_preds = logreg.predict_proba(train_X)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--C',
type=float,
default=1.0,
help='inverse of L1 / L2 regularization')
parser.add_argument('--l1', dest='l2', action='store_false')
parser.add_argument('--l2', dest='l2', action='store_true')
parser.set_defaults(l2=True)
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
args = parser.parse_args()
print(args)
train_reader = InHospitalMortalityReader(
dataset_dir='../../../data/in-hospital-mortality/train/',
listfile='../../../data/in-hospital-mortality/train_listfile.csv',
period_length=48.0)
val_reader = InHospitalMortalityReader(
dataset_dir='../../../data/in-hospital-mortality/train/',
listfile='../../../data/in-hospital-mortality/val_listfile.csv',
period_length=48.0)
test_reader = InHospitalMortalityReader(
dataset_dir='../../../data/in-hospital-mortality/test/',
listfile='../../../data/in-hospital-mortality/test_listfile.csv',
period_length=48.0)
print('Reading data and extracting features ...')
(train_X, train_y,
train_names) = read_and_extract_features(train_reader, args.period,
args.features)
(val_X, val_y,
val_names) = read_and_extract_features(val_reader, args.period,
args.features)
(test_X, test_y,
test_names) = read_and_extract_features(test_reader, args.period,
args.features)
print(' train data shape = {}'.format(train_X.shape))
print(' validation data shape = {}'.format(val_X.shape))
print(' test data shape = {}'.format(test_X.shape))
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan,
strategy='mean',
axis=0,
verbose=0,
copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
penalty = ('l2' if args.l2 else 'l1')
file_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty,
args.C)
logreg = LogisticRegression(penalty=penalty, C=args.C, random_state=42)
logreg.fit(train_X, train_y)
common_utils.create_directory('results')
with open(os.path.join('results', 'train_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(train_y, logreg.predict_proba(train_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
with open(os.path.join('results', 'val_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(val_y, logreg.predict_proba(val_X))
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
prediction = logreg.predict_proba(test_X)[:, 1]
with open(os.path.join('results', 'test_{}.json'.format(file_name)),
'w') as res_file:
ret = print_metrics_binary(test_y, prediction)
ret = {k: float(v) for k, v in ret.items()}
json.dump(ret, res_file)
save_results(test_names, prediction, test_y,
os.path.join('predictions', file_name + '.csv'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--period', type=str, default='all', help='specifies which period extract features from',
choices=['first4days', 'first8days', 'last12hours', 'first25percent', 'first50percent', 'all'])
parser.add_argument('--features', type=str, default='all', help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
args = parser.parse_args()
print(args)
# penalties = ['l2', 'l2', 'l2', 'l2', 'l2', 'l2', 'l1', 'l1', 'l1', 'l1', 'l1']
# Cs = [1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001, 1.0, 0.1, 0.01, 0.001, 0.0001]
penalties = ['l1']
Cs = [0.1]
train_reader = PhenotypingReader(dataset_dir='../../../data/phenotyping/train/',
listfile='../../../data/phenotyping/train_listfile.csv')
val_reader = PhenotypingReader(dataset_dir='../../../data/phenotyping/train/',
listfile='../../../data/phenotyping/val_listfile.csv')
test_reader = PhenotypingReader(dataset_dir='../../../data/phenotyping/test/',
listfile='../../../data/phenotyping/test_listfile.csv')
print('Reading data and extracting features ...')
(train_X, train_y, train_names, train_ts) = read_and_extract_features(train_reader, args.period, args.features)
train_y = np.array(train_y)
(val_X, val_y, val_names, val_ts) = read_and_extract_features(val_reader, args.period, args.features)
val_y = np.array(val_y)
(test_X, test_y, test_names, test_ts) = read_and_extract_features(test_reader, args.period, args.features)
test_y = np.array(test_y)
print("train set shape: {}".format(train_X.shape))
print("validation set shape: {}".format(val_X.shape))
print("test set shape: {}".format(test_X.shape))
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan, strategy='mean', axis=0, verbose=0, copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
n_tasks = 25
common_utils.create_directory('results')
for (penalty, C) in zip(penalties, Cs):
model_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty, C)
train_activations = np.zeros(shape=train_y.shape, dtype=float)
val_activations = np.zeros(shape=val_y.shape, dtype=float)
test_activations = np.zeros(shape=test_y.shape, dtype=float)
for task_id in range(n_tasks):
print('Starting task {}'.format(task_id))
logreg = LogisticRegression(penalty=penalty, C=C, random_state=42)
logreg.fit(train_X, train_y[:, task_id])
train_preds = logreg.predict_proba(train_X)
train_activations[:, task_id] = train_preds[:, 1]
val_preds = logreg.predict_proba(val_X)
val_activations[:, task_id] = val_preds[:, 1]
test_preds = logreg.predict_proba(test_X)
test_activations[:, task_id] = test_preds[:, 1]
with open(os.path.join('results', 'train_{}.json'.format(model_name)), 'w') as f:
ret = metrics.print_metrics_multilabel(train_y, train_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
with open(os.path.join('results', 'val_{}.json'.format(model_name)), 'w') as f:
ret = metrics.print_metrics_multilabel(val_y, val_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
with open(os.path.join('results', 'test_{}.json'.format(model_name)), 'w') as f:
ret = metrics.print_metrics_multilabel(test_y, test_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
save_results(test_names, test_ts, test_activations, test_y, os.path.join('predictions', model_name + '.csv'))
