def get_all_features_data(labs_df,
labs_data_dict,
vitals_df,
vitals_data_dict,
demographics_df,
demographics_data_dict,
medications_df,
medications_data_dict,
include_medications=True):
'''Returns the merged labs, vitals and demographics features into a single table and the data dict'''
time_col = parse_time_col(vitals_data_dict)
id_cols = parse_id_cols(vitals_data_dict)
# merge the labs, vitals and medications
if include_medications:
highfreq_df = pd.merge(pd.merge(vitals_df,
labs_df,
on=id_cols + [time_col],
how='outer'),
medications_df,
on=id_cols + [time_col],
how='outer')
# forward fill medications because the patient is/is not on medication on new time points created by outer join
medication_features = parse_feature_cols(medications_data_dict)
highfreq_df[id_cols + medication_features] = highfreq_df[
id_cols + medication_features].groupby(id_cols).apply(
lambda x: x.fillna(method='pad')).copy()
highfreq_df[id_cols + medication_features] = highfreq_df[
id_cols + medication_features].fillna(0)
highfreq_data_dict = merge_data_dicts(
[labs_data_dict, vitals_data_dict, medications_data_dict])
else:
highfreq_df = pd.merge(vitals_df,
labs_df,
on=id_cols + [time_col],
how='outer')
highfreq_data_dict = merge_data_dicts(
[labs_data_dict, vitals_data_dict])
highfreq_data_dict['fields'] = highfreq_data_dict['schema']['fields']
cols_to_keep = parse_id_cols(highfreq_data_dict) + [
parse_time_col(highfreq_data_dict)
] + parse_feature_cols(highfreq_data_dict)
highfreq_df = highfreq_df[cols_to_keep].copy()
# merge the highfrequency features with the static features
features_df = pd.merge(highfreq_df,
demographics_df,
on=id_cols,
how='inner')
features_data_dict = merge_data_dicts(
[highfreq_data_dict, demographics_data_dict])
features_data_dict['fields'] = features_data_dict['schema']['fields']
return features_df, features_data_dict
def update_data_dict_mews(args):
data_dict = args.data_dict
id_cols = parse_id_cols(args.data_dict)
feature_cols = parse_feature_cols(args.data_dict)
new_fields = []
for name in id_cols:
for col in data_dict['fields']:
if col['name'] == name:
new_fields.append(col)
new_fields.append({
'name': 'mews_score',
'role': 'feature',
'type': 'numeric',
'description': 'Modified Early Warning Score',
'units': 'NONE',
'constraints': {
'required': 'FALSE',
'minimum': '0',
'maximum': 'INF'
}
})
new_data_dict = copy.deepcopy(data_dict)
if 'schema' in new_data_dict:
new_data_dict['schema']['fields'] = new_fields
del new_data_dict['fields']
else:
new_data_dict['fields'] = new_fields
return new_data_dict
def main():
parser = argparse.ArgumentParser(
description='PyTorch RNN with variable-length numeric sequences wrapper'
)
parser.add_argument('--outcome_col_name', type=str, required=True)
parser.add_argument('--train_csv_files', type=str, required=True)
parser.add_argument('--test_csv_files', type=str, required=True)
parser.add_argument('--data_dict_files', type=str, required=True)
parser.add_argument('--batch_size',
type=int,
default=1024,
help='Number of sequences per minibatch')
parser.add_argument('--epochs',
type=int,
default=50,
help='Number of epochs')
parser.add_argument('--hidden_units',
type=int,
default=32,
help='Number of hidden units')
parser.add_argument('--hidden_layers',
type=int,
default=1,
help='Number of hidden layers')
parser.add_argument('--lr',
type=float,
default=0.0005,
help='Learning rate for the optimizer')
parser.add_argument('--dropout',
type=float,
default=0,
help='dropout for optimizer')
parser.add_argument('--weight_decay',
type=float,
default=0.0001,
help='weight decay for optimizer')
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--validation_size',
type=float,
default=0.15,
help='validation split size')
parser.add_argument(
'--is_data_simulated',
type=bool,
default=False,
help='boolean to check if data is simulated or from mimic')
parser.add_argument(
'--simulated_data_dir',
type=str,
default='simulated_data/2-state/',
help=
'dir in which to simulated data is saved.Must be provide if is_data_simulated = True'
)
parser.add_argument(
'--output_dir',
type=str,
default=None,
help=
'directory where trained model and loss curves over epochs are saved')
parser.add_argument(
'--output_filename_prefix',
type=str,
default=None,
help='prefix for the training history jsons and trained classifier')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = 'cpu'
x_train_csv_filename, y_train_csv_filename = args.train_csv_files.split(
',')
x_test_csv_filename, y_test_csv_filename = args.test_csv_files.split(',')
x_dict, y_dict = args.data_dict_files.split(',')
x_data_dict = load_data_dict_json(x_dict)
# get the id and feature columns
id_cols = parse_id_cols(x_data_dict)
feature_cols = parse_feature_cols(x_data_dict)
# extract data
train_vitals = TidySequentialDataCSVLoader(
x_csv_path=x_train_csv_filename,
y_csv_path=y_train_csv_filename,
x_col_names=feature_cols,
idx_col_names=id_cols,
y_col_name=args.outcome_col_name,
y_label_type='per_sequence')
test_vitals = TidySequentialDataCSVLoader(x_csv_path=x_test_csv_filename,
y_csv_path=y_test_csv_filename,
x_col_names=feature_cols,
idx_col_names=id_cols,
y_col_name=args.outcome_col_name,
y_label_type='per_sequence')
X_train, y_train = train_vitals.get_batch_data(batch_id=0)
X_test, y_test = test_vitals.get_batch_data(batch_id=0)
_, T, F = X_train.shape
print('number of time points : %s\n number of features : %s\n' % (T, F))
# set class weights as 1/(number of samples in class) for each class to handle class imbalance
class_weights = torch.tensor(
[1 / (y_train == 0).sum(), 1 / (y_train == 1).sum()]).double()
# scale features
# X_train = standard_scaler_3d(X_train)
# X_test = standard_scaler_3d(X_test)
# callback to compute gradient norm
compute_grad_norm = ComputeGradientNorm(norm_type=2)
# LSTM
if args.output_filename_prefix == None:
output_filename_prefix = (
'hiddens=%s-layers=%s-lr=%s-dropout=%s-weight_decay=%s' %
(args.hidden_units, args.hidden_layers, args.lr, args.dropout,
args.weight_decay))
else:
output_filename_prefix = args.output_filename_prefix
print('RNN parameters : ' + output_filename_prefix)
# # from IPython import embed; embed()
rnn = RNNBinaryClassifier(
max_epochs=50,
batch_size=args.batch_size,
device=device,
lr=args.lr,
callbacks=[
EpochScoring('roc_auc',
lower_is_better=False,
on_train=True,
name='aucroc_score_train'),
EpochScoring('roc_auc',
lower_is_better=False,
on_train=False,
name='aucroc_score_valid'),
EarlyStopping(monitor='aucroc_score_valid',
patience=20,
threshold=0.002,
threshold_mode='rel',
lower_is_better=False),
LRScheduler(policy=ReduceLROnPlateau,
mode='max',
monitor='aucroc_score_valid',
patience=10),
compute_grad_norm,
GradientNormClipping(gradient_clip_value=0.3,
gradient_clip_norm_type=2),
Checkpoint(monitor='aucroc_score_valid',
f_history=os.path.join(
args.output_dir, output_filename_prefix + '.json')),
TrainEndCheckpoint(dirname=args.output_dir,
fn_prefix=output_filename_prefix),
],
criterion=torch.nn.CrossEntropyLoss,
criterion__weight=class_weights,
train_split=skorch.dataset.CVSplit(args.validation_size),
module__rnn_type='LSTM',
module__n_layers=args.hidden_layers,
module__n_hiddens=args.hidden_units,
module__n_inputs=X_train.shape[-1],
module__dropout_proba=args.dropout,
optimizer=torch.optim.Adam,
optimizer__weight_decay=args.weight_decay)
clf = rnn.fit(X_train, y_train)
y_pred_proba = clf.predict_proba(X_train)
y_pred_proba_neg, y_pred_proba_pos = zip(*y_pred_proba)
auroc_train_final = roc_auc_score(y_train, y_pred_proba_pos)
print('AUROC with LSTM (Train) : %.2f' % auroc_train_final)
y_pred_proba = clf.predict_proba(X_test)
y_pred_proba_neg, y_pred_proba_pos = zip(*y_pred_proba)
auroc_test_final = roc_auc_score(y_test, y_pred_proba_pos)
print('AUROC with LSTM (Test) : %.2f' % auroc_test_final)
parser.add_argument('--merge_x_y',
default=True,
type=lambda x: (str(x).lower() == 'true'),
required=False)
args = parser.parse_args()
# read the data dictionaries
print('Reading train-test data...')
# read the data dict JSONs and parse the feature and outcome columns
x_data_dict_file, y_data_dict_file = args.data_dict_files.split(',')
x_data_dict = load_data_dict_json(x_data_dict_file)
y_data_dict = load_data_dict_json(y_data_dict_file)
feature_cols = parse_feature_cols(x_data_dict)
key_cols = parse_id_cols(x_data_dict)
df_by_split = dict()
for split_name, csv_files in [('train', args.train_csv_files.split(',')),
('test', args.test_csv_files.split(','))]:
cur_df = None
for csv_file in csv_files:
# TODO use json data dict to load specific columns as desired types
more_df = pd.read_csv(csv_file)
if cur_df is None:
cur_df = more_df
else:
if args.merge_x_y:
cur_df = cur_df.merge(more_df, on=key_cols)
else:
print(
'Merging collapsed vitals, collapsed labs, demographics and outcomes data dicts into a single features data dict and a single outcomes data dict...'
)
features_dict_merged = collapsed_labs_data_dict['schema'][
'fields'] + collapsed_vitals_data_dict['schema'][
'fields'] + demographics_data_dict['schema']['fields']
for feat_dict in features_dict_merged:
if feat_dict['name'] not in feat_names:
features_data_dict['schema']['fields'].append(feat_dict)
feat_names.append(feat_dict['name'])
# convert the features to numpy float 32 to avoid memory issues
feature_cols = parse_feature_cols(features_data_dict['schema'])
feature_type_dict = dict.fromkeys(feature_cols)
for k in feature_type_dict.keys():
feature_type_dict[k] = np.float32
features_df_all_slices = features_df_all_slices.astype(feature_type_dict)
# save to disk
features_csv = os.path.join(args.output_dir, 'features.csv.gz')
outcomes_csv = os.path.join(args.output_dir, 'outcomes.csv.gz')
mews_csv = os.path.join(args.output_dir, 'mews.csv.gz')
features_json = os.path.join(args.output_dir, 'Spec_features.json')
outcomes_json = os.path.join(args.output_dir, 'Spec_outcomes.json')
mews_json = os.path.join(args.output_dir, 'Spec_mews.json')
print('saving features and outcomes to :\n%s\n%s\n%s' %
(features_csv, outcomes_csv, mews_csv))
type=str,
default=2,
help=
'''Slice of data to be extracted. If tslice is provided with a % sign (for eg. 20%),
then the script extracts the first tslice% data from the stay. If tslice is an int (for eg. 5),
the the script extracts the first tslice hrs of data. If tslice is negative (for eg. -5), then
the script extracts the data until tslice hours before deterioration/discharge.'''
)
parser.add_argument('--output_dir', type=str)
args = parser.parse_args()
labs_df, labs_data_dict, vitals_df, vitals_data_dict, \
demographics_df, demographics_data_dict, outcomes_df, outcomes_data_dict = get_preprocessed_data(args.preproc_data_dir)
id_cols = parse_id_cols(vitals_data_dict)
labs_feature_cols = parse_feature_cols(labs_data_dict)
vitals_feature_cols = parse_feature_cols(vitals_data_dict)
# get lengths of stay for each admission
vitals_df_with_stay_lengths = pd.merge(vitals_df,
outcomes_df[id_cols +
['stay_length']],
on=id_cols,
how='inner')
labs_df_with_stay_lengths = pd.merge(labs_df,
outcomes_df[id_cols +
['stay_length']],
on=id_cols,
how='inner')
#demographics_df_with_stay_lengths = pd.merge(demographics_df, outcomes_df[id_cols + ['stay_length']], on=id_cols, how='inner')
def main():
parser = argparse.ArgumentParser(
description='PyTorch RNN with variable-length numeric sequences wrapper'
)
parser.add_argument('--outcome_col_name', type=str, required=True)
parser.add_argument('--train_csv_files', type=str, required=True)
parser.add_argument('--test_csv_files', type=str, required=True)
parser.add_argument('--data_dict_files', type=str, required=True)
parser.add_argument('--batch_size',
type=int,
default=1024,
help='Number of sequences per minibatch')
parser.add_argument('--epochs',
type=int,
default=50,
help='Number of epochs')
parser.add_argument('--n_filters',
type=int,
default=32,
help='Number of filters')
parser.add_argument('--kernel_size',
type=int,
default=1,
help='size of eack kernel')
parser.add_argument('--n_conv_layers',
type=int,
default=1,
help='number of convolutional layers')
parser.add_argument('--stride', type=int, default=1, help='stride')
parser.add_argument('--pool_size',
type=int,
default=4,
help='max pool size')
parser.add_argument('--dense_units',
type=int,
default=128,
help='number of units in fully connected layer')
parser.add_argument('--lr',
type=float,
default=0.0005,
help='Learning rate for the optimizer')
parser.add_argument('--dropout',
type=float,
default=0,
help='dropout for optimizer')
parser.add_argument('--weight_decay',
type=float,
default=0.0001,
help='weight decay for optimizer')
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--validation_size',
type=float,
default=0.15,
help='validation split size')
parser.add_argument(
'--output_dir',
type=str,
default=None,
help=
'directory where trained model and loss curves over epochs are saved')
parser.add_argument(
'--output_filename_prefix',
type=str,
default=None,
help='prefix for the training history jsons and trained classifier')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = 'cpu'
x_train_csv_filename, y_train_csv_filename = args.train_csv_files.split(
',')
x_test_csv_filename, y_test_csv_filename = args.test_csv_files.split(',')
x_dict, y_dict = args.data_dict_files.split(',')
x_data_dict = load_data_dict_json(x_dict)
# get the id and feature columns
id_cols = parse_id_cols(x_data_dict)
feature_cols = parse_feature_cols(x_data_dict)
# extract data
train_vitals = TidySequentialDataCSVLoader(
x_csv_path=x_train_csv_filename,
y_csv_path=y_train_csv_filename,
x_col_names=feature_cols,
idx_col_names=id_cols,
y_col_name=args.outcome_col_name,
y_label_type='per_sequence')
test_vitals = TidySequentialDataCSVLoader(x_csv_path=x_test_csv_filename,
y_csv_path=y_test_csv_filename,
x_col_names=feature_cols,
idx_col_names=id_cols,
y_col_name=args.outcome_col_name,
y_label_type='per_sequence')
X_train, y_train = train_vitals.get_batch_data(batch_id=0)
X_test, y_test = test_vitals.get_batch_data(batch_id=0)
N, T, F = X_train.shape
# add class weights
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
# class_weights = dict(zip(range(len(class_weights)), class_weights))
# convert y_train to categorical
y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)
X_train, X_val, y_train, y_val = train_test_split(
X_train, y_train, test_size=args.validation_size, random_state=213)
print('number of time points : %s\nnumber of features : %s\n' % (T, F))
set_random_seed(args.seed)
model = keras.Sequential()
for i in range(args.n_conv_layers):
model.add(
keras.layers.Conv1D(filters=args.n_filters,
kernel_size=args.kernel_size,
activation='relu',
strides=args.stride))
model.add(keras.layers.Dropout(args.dropout))
model.add(keras.layers.MaxPooling1D(pool_size=args.pool_size))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(args.dense_units, activation='relu'))
model.add(keras.layers.Dense(2, activation='softmax'))
# set optimizer
opt = keras.optimizers.Adam(learning_rate=args.lr)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy', keras.metrics.AUC()])
# set early stopping
early_stopping = EarlyStopping(monitor='val_auc',
patience=20,
mode='max',
verbose=1)
model.fit(X_train,
y_train,
epochs=100,
validation_data=(X_val, y_val),
callbacks=[early_stopping],
class_weight=class_weights,
batch_size=args.batch_size)
y_score_val = model.predict_proba(X_val)
val_auc = roc_auc_score(y_val, y_score_val)
print('AUC on val set : %.4f' % val_auc)
y_score_test = model.predict_proba(X_test)
test_auc = roc_auc_score(y_test, y_score_test)
print('AUC on val set : %.4f' % test_auc)
# save the model history
training_hist_df = pd.DataFrame(model.history.history)
training_hist_df.loc[:, 'test_auc'] = test_auc
training_hist_csv = os.path.join(args.output_dir,
args.output_filename_prefix + '.csv')
training_hist_df.to_csv(training_hist_csv, index=False)
# save the model
model_file = os.path.join(args.output_dir,
args.output_filename_prefix + '.model')
model.save(model_file)
args = parser.parse_args()
# get the train test features
x_train_csv = os.path.join(args.train_test_split_dir, 'x_train.csv.gz')
x_valid_csv = os.path.join(args.train_test_split_dir, 'x_valid.csv.gz')
x_test_csv = os.path.join(args.train_test_split_dir, 'x_test.csv.gz')
x_dict_json = os.path.join(args.train_test_split_dir, 'x_dict.json')
# impute values by carry forward and then pop mean on train and test sets separately
x_data_dict = load_data_dict_json(x_dict_json)
x_train_df = pd.read_csv(x_train_csv)
x_valid_df = pd.read_csv(x_valid_csv)
x_test_df = pd.read_csv(x_test_csv)
id_cols = parse_id_cols(x_data_dict)
feature_cols = parse_feature_cols(x_data_dict)
time_col = parse_time_col(x_data_dict)
# add mask features
non_medication_feature_cols = [
feature_col for feature_col in feature_cols
if 'medication' not in feature_col
]
medication_feature_cols = [
feature_col for feature_col in feature_cols
if 'medication' in feature_col
]
print('Adding missing values mask as features...')
for feature_col in non_medication_feature_cols:
x_train_df.loc[:, 'mask_' +
def get_all_features_data(labs_df, labs_data_dict, vitals_df, vitals_data_dict, demographics_df, demographics_data_dict):
'''Returns the merged labs, vitals and demographics features into a single table and the data dict'''
time_col = parse_time_col(vitals_data_dict)
id_cols = parse_id_cols(vitals_data_dict)
# merge the labs and vitals
highfreq_df = pd.merge(vitals_df, labs_df, on=id_cols +[time_col], how='outer')
highfreq_data_dict = merge_data_dicts([labs_data_dict, vitals_data_dict])
highfreq_data_dict['fields'] = highfreq_data_dict['schema']['fields']
cols_to_keep = parse_id_cols(highfreq_data_dict) + [parse_time_col(highfreq_data_dict)] + parse_feature_cols(highfreq_data_dict)
highfreq_df = highfreq_df[cols_to_keep].copy()
# merge the highfrequency features with the static features
features_df = pd.merge(highfreq_df, demographics_df, on=id_cols, how='inner')
features_data_dict = merge_data_dicts([highfreq_data_dict, demographics_data_dict])
features_data_dict['fields'] = features_data_dict['schema']['fields']
return features_df, features_data_dict
def main():
parser = argparse.ArgumentParser(
description='PyTorch RNN with variable-length numeric sequences wrapper'
)
parser.add_argument('--outcome_col_name', type=str, required=True)
parser.add_argument('--train_csv_files', type=str, required=True)
parser.add_argument('--valid_csv_files', type=str, required=True)
parser.add_argument('--test_csv_files', type=str, required=True)
parser.add_argument('--data_dict_files', type=str, required=True)
parser.add_argument('--batch_size',
type=int,
default=1024,
help='Number of sequences per minibatch')
parser.add_argument('--epochs',
type=int,
default=50,
help='Number of epochs')
parser.add_argument('--hidden_units',
type=int,
default=32,
help='Number of hidden units')
parser.add_argument('--hidden_layers',
type=int,
default=1,
help='Number of hidden layers')
parser.add_argument('--lr',
type=float,
default=0.0005,
help='Learning rate for the optimizer')
parser.add_argument('--dropout',
type=float,
default=0,
help='dropout for optimizer')
parser.add_argument('--weight_decay',
type=float,
default=0.0001,
help='weight decay for optimizer')
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--validation_size',
type=float,
default=0.15,
help='validation split size')
parser.add_argument(
'--is_data_simulated',
type=bool,
default=False,
help='boolean to check if data is simulated or from mimic')
parser.add_argument(
'--output_dir',
type=str,
default=None,
help=
'directory where trained model and loss curves over epochs are saved')
parser.add_argument(
'--output_filename_prefix',
type=str,
default=None,
help='prefix for the training history jsons and trained classifier')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = 'cpu'
x_train_csv_filename, y_train_csv_filename = args.train_csv_files.split(
',')
x_valid_csv_filename, y_valid_csv_filename = args.valid_csv_files.split(
',')
x_test_csv_filename, y_test_csv_filename = args.test_csv_files.split(',')
x_dict, y_dict = args.data_dict_files.split(',')
x_data_dict = load_data_dict_json(x_dict)
# get the id and feature columns
id_cols = parse_id_cols(x_data_dict)
feature_cols = parse_feature_cols(x_data_dict)
# extract data
train_vitals = TidySequentialDataCSVLoader(
x_csv_path=x_train_csv_filename,
y_csv_path=y_train_csv_filename,
x_col_names=feature_cols,
idx_col_names=id_cols,
y_col_name=args.outcome_col_name,
y_label_type='per_tstep')
valid_vitals = TidySequentialDataCSVLoader(
x_csv_path=x_valid_csv_filename,
y_csv_path=y_valid_csv_filename,
x_col_names=feature_cols,
idx_col_names=id_cols,
y_col_name=args.outcome_col_name,
y_label_type='per_tstep')
test_vitals = TidySequentialDataCSVLoader(x_csv_path=x_test_csv_filename,
y_csv_path=y_test_csv_filename,
x_col_names=feature_cols,
idx_col_names=id_cols,
y_col_name=args.outcome_col_name,
y_label_type='per_tstep')
X_train, y_train = train_vitals.get_batch_data(batch_id=0)
X_valid, y_valid = valid_vitals.get_batch_data(batch_id=0)
X_test, y_test = test_vitals.get_batch_data(batch_id=0)
N, T, F = X_train.shape
# from IPython import embed; embed()
# X_train = (X_train - np.min(X_train))/(np.max(X_train)-np.min(X_train))
# X_valid = (X_valid - np.min(X_train))/(np.max(X_train)-np.min(X_train))
# X_test = (X_test - np.min(X_train))/(np.max(X_train)-np.min(X_train))
valid_ds = Dataset(X_valid, y_valid)
print('number of time points : %s\nnumber of features : %s\n' % (T, F))
# set class weights as 1/(number of samples in class) for each class to handle class imbalance
class_weights = torch.tensor(
[1 / (y_train == 0).sum(), 1 / (y_train == 1).sum()]).float()
print('Number of training sequences : %s' % N)
print('Number of test sequences : %s' % X_test.shape[0])
print('Ratio positive in train : %.2f' %
((y_train == 1).sum() / len(y_train)))
print('Ratio positive in test : %.2f' %
((y_test == 1).sum() / len(y_test)))
# callback to compute gradient norm
compute_grad_norm = ComputeGradientNorm(norm_type=2)
# LSTM
if args.output_filename_prefix == None:
output_filename_prefix = (
'hiddens=%s-layers=%s-lr=%s-dropout=%s-weight_decay=%s' %
(args.hidden_units, args.hidden_layers, args.lr, args.dropout,
args.weight_decay))
else:
output_filename_prefix = args.output_filename_prefix
print('RNN parameters : ' + output_filename_prefix)
loss_early_stopping_cp = EarlyStopping(monitor='valid_loss',
patience=15,
threshold=0.002,
threshold_mode='rel',
lower_is_better=True)
rnn = RNNPerTStepBinaryClassifier(
max_epochs=250,
batch_size=args.batch_size,
device=device,
lr=args.lr,
callbacks=[
EpochScoring(calc_auprc,
lower_is_better=False,
on_train=True,
name='auprc_train'),
EpochScoring(calc_auprc,
lower_is_better=False,
on_train=False,
name='auprc_valid'),
EpochScoring(calc_auroc,
lower_is_better=False,
on_train=True,
name='auroc_train'),
EpochScoring(calc_auroc,
lower_is_better=False,
on_train=False,
name='auroc_valid'),
# EpochScoring(calc_precision, lower_is_better=False, on_train=True, name='precision_train'),
# EpochScoring(calc_precision, lower_is_better=False, on_train=False, name='precision_valid'),
# EpochScoring(calc_recall, lower_is_better=False, on_train=True, name='recall_train'),
# EpochScoring(calc_recall, lower_is_better=False, on_train=False, name='recall_valid'),
# EpochScoring('roc_auc', lower_is_better=False, on_train=True, name='aucroc_score_train'),
# EpochScoring('roc_auc', lower_is_better=False, on_train=False, name='aucroc_score_valid'),
# EarlyStopping(monitor='auprc_valid', patience=5, threshold=0.002, threshold_mode='rel',
# lower_is_better=False),
# LRScheduler(policy=ReduceLROnPlateau, mode='max', monitor='aucroc_score_valid', patience=10),
# compute_grad_norm,
# GradientNormClipping(gradient_clip_value=0.5, gradient_clip_norm_type=2),
loss_early_stopping_cp,
Checkpoint(monitor='auprc_valid',
f_history=os.path.join(
args.output_dir, output_filename_prefix + '.json')),
TrainEndCheckpoint(dirname=args.output_dir,
fn_prefix=output_filename_prefix),
],
# criterion=torch.nn.CrossEntropyLoss,
# criterion__weight=class_weights,
train_split=predefined_split(valid_ds),
module__rnn_type='GRU',
module__n_layers=args.hidden_layers,
module__n_hiddens=args.hidden_units,
module__n_inputs=X_train.shape[-1],
module__dropout_proba=args.dropout,
optimizer=torch.optim.Adam,
optimizer__weight_decay=args.weight_decay)
# N=len(X_train)
# X_train = X_train[:N]
# y_train = y_train[:N]
clf = rnn.fit(X_train, y_train)
# get threshold with max recall at fixed precision
fixed_precision = 0.1
# get predict probas for y=1 on validation set
keep_inds_va = torch.logical_not(
torch.all(torch.isnan(torch.FloatTensor(X_valid)), dim=-1))
y_va_pred_proba = clf.predict_proba(
X_valid)[keep_inds_va][:, 1].detach().numpy()
unique_probas = np.unique(y_va_pred_proba)
thr_grid_G = np.linspace(np.percentile(unique_probas, 1),
max(unique_probas), 100)
precision_scores_G, recall_scores_G = [
np.zeros(thr_grid_G.size),
np.zeros(thr_grid_G.size)
]
for gg, thr in enumerate(thr_grid_G):
# logistic_clf.module_.linear_transform_layer.bias.data = torch.tensor(thr_grid[gg]).double()
curr_thr_y_preds = clf.predict_proba(
torch.FloatTensor(X_valid))[keep_inds_va][:, 1] >= thr_grid_G[gg]
precision_scores_G[gg] = precision_score(y_valid[keep_inds_va],
curr_thr_y_preds)
recall_scores_G[gg] = recall_score(y_valid[keep_inds_va],
curr_thr_y_preds)
keep_inds = precision_scores_G >= fixed_precision
if keep_inds.sum() > 0:
print('Choosing threshold with precision >= %.3f' % fixed_precision)
else:
fixed_precision_old = fixed_precision
fixed_precision = np.percentile(precision_scores_G, 99)
keep_inds = precision_scores_G >= fixed_precision
print(
'Could not find threshold with precision >= %.3f \n Choosing threshold to maximize recall at precision %.3f'
% (fixed_precision_old, fixed_precision))
thr_grid_G = thr_grid_G[keep_inds]
precision_scores_G = precision_scores_G[keep_inds]
recall_scores_G = recall_scores_G[keep_inds]
thr_perf_df = pd.DataFrame(
np.vstack([
thr_grid_G[np.newaxis, :], precision_scores_G[np.newaxis, :],
recall_scores_G[np.newaxis, :]
]).T,
columns=['thr', 'precision_score', 'recall_score'])
print(thr_perf_df)
best_ind = np.argmax(recall_scores_G)
best_thr = thr_grid_G[best_ind]
print('chosen threshold : %.3f' % best_thr)
splits = ['train', 'valid', 'test']
# data_splits = ((x_tr, y_tr), (x_va, y_va), (X_test, y_test))
auroc_per_split, auprc_per_split, precisions_per_split, recalls_per_split = [
np.zeros(len(splits)),
np.zeros(len(splits)),
np.zeros(len(splits)),
np.zeros(len(splits))
]
for ii, (X, y) in enumerate([(X_train, y_train), (X_valid, y_valid),
(X_test, y_test)]):
keep_inds = torch.logical_not(
torch.all(torch.isnan(torch.FloatTensor(X)), dim=-1))
y_pred_proba_pos = clf.predict_proba(X)[keep_inds][:,
1].detach().numpy()
# y_pred_proba_neg, y_pred_proba_pos = zip(*y_pred_proba)
auroc_per_split[ii] = roc_auc_score(y[keep_inds], y_pred_proba_pos)
# y_pred_proba_pos = np.asarray(y_pred_proba_pos)
auprc_per_split[ii] = average_precision_score(y[keep_inds],
y_pred_proba_pos)
y_pred = y_pred_proba_pos >= best_thr
precisions_per_split[ii] = precision_score(y[keep_inds], y_pred)
recalls_per_split[ii] = recall_score(y[keep_inds], y_pred)
auroc_train, auroc_valid, auroc_test = auroc_per_split
auprc_train, auprc_valid, auprc_test = auprc_per_split
precision_train, precision_valid, precision_test = precisions_per_split
recall_train, recall_valid, recall_test = recalls_per_split
# save performance
perf_dict = {
'auroc_train': auroc_train,
'auroc_valid': auroc_valid,
'auroc_test': auroc_test,
'auprc_train': auprc_train,
'auprc_valid': auprc_valid,
'auprc_test': auprc_test,
'precision_train': precision_train,
'precision_valid': precision_valid,
'precision_test': precision_test,
'recall_train': recall_train,
'recall_valid': recall_valid,
'recall_test': recall_test,
'threshold': best_thr
}
perf_df = pd.DataFrame([perf_dict])
perf_csv = os.path.join(args.output_dir, output_filename_prefix + '.csv')
print('Final performance on train, valid and test :\n')
print(perf_df)
print('Final performance saved to %s' % perf_csv)
perf_df.to_csv(perf_csv, index=False)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--preproc_data_dir', help = 'directory where the labs, vitals, demographics and outcomes are stored')
parser.add_argument('--tslice', type=str, default=2,
help='''Slice of data to be extracted. If tslice is provided with a % sign (for eg. 20%),
then the script extracts the first tslice% data from the stay. If tslice is an int (for eg. 5),
the the script extracts the first tslice hrs of data. If tslice is negative (for eg. -5), then
the script extracts the data until tslice hours before deterioration/discharge.''')
parser.add_argument('--output_dir', type=str)
args = parser.parse_args()
labs_df, labs_data_dict, vitals_df, vitals_data_dict, \
demographics_df, demographics_data_dict, medications_df, medications_data_dict, outcomes_df, outcomes_data_dict = get_preprocessed_data(args.preproc_data_dir)
id_cols = parse_id_cols(vitals_data_dict)
labs_feature_cols = parse_feature_cols(labs_data_dict)
vitals_feature_cols = parse_feature_cols(vitals_data_dict)
medications_feature_cols = parse_feature_cols(medications_data_dict)
# get lengths of stay for each admission
vitals_df_with_stay_lengths = pd.merge(vitals_df, outcomes_df[id_cols + ['stay_length']], on=id_cols, how='inner')
labs_df_with_stay_lengths = pd.merge(labs_df, outcomes_df[id_cols + ['stay_length']], on=id_cols, how='inner')
medications_df_with_stay_lengths = pd.merge(medications_df, outcomes_df[id_cols + ['stay_length']], on=id_cols, how='inner')
#demographics_df_with_stay_lengths = pd.merge(demographics_df, outcomes_df[id_cols + ['stay_length']], on=id_cols, how='inner')
# find stays that satisfy minimum stay length
censor_start=504
tstops_df = outcomes_df[id_cols].copy()
if ('%' in args.tslice):
min_stay_length = 0
print('Including EHR measured in first %s percent of patient stays having atleast %s hours of data'%(args.tslice, min_stay_length))
def featurize_stack_of_many_time_series(
ts_df=None,
ts_data_dict=None,
outcomes_df=None,
outcomes_data_dict=None,
summary_ops=['mean', 'min', 'max'],
percentile_slices_to_featurize=[(0, 100)],
outcome_col='clinical_deterioration_outcome',
outcome_seq_duration_col='stay_length',
start_time_of_each_sequence=-24.0,
max_time_of_each_sequence=504,
start_time_of_endpoints=0.0,
time_between_endpoints=12,
prediction_horizon=24,
verbose=True,
):
''' Featurize many patient stays slices and extract outcome for each slice
Args
----
ts_df : pandas DataFrame
Each row provides all measurements at one time of a single patient-stay
Must contain one column already converted to numerical time
ts_data_dict : dict
Provides specification for every column of ts_df
outcomes_df : pandas DataFrame
Each row provides outcome of a single patient stay
outcomes_data_dict : dict
Provides specification for each column of outcomes_df
summary_ops : list of strings
Identifies the summary functions we wish to apply to each variable's ts
percentile_slices_to_featurize : list of tuples
Indicates percentile range of all subwindows we will featurize
Example: [(0, 100), (0, 50)])
Returns
-------
all_feat_df : DataFrame
One row per featurized window of any patient-stay slice
Key columns: ids + ['start', 'stop']
Value columns: one per extracted feature
all_outcomes_df : DataFrame
One row per featurized window of any patient-stay slice
Key columns: ids + ['start', 'stop']
Value columns: just one, the outcome column
Examples
--------
>>> args = make_fake_input_data(n_seqs=25, n_features=10, max_duration=50.0)
>>> feat_df, outcome_df = featurize_stack_of_many_time_series(*args,
... summary_ops=['mean', 'slope'],
... start_time_of_each_sequence=0,
... start_time_of_endpoints=0.0,
... time_between_endpoints=12.0,
... verbose=False,
... );
>>> feat_df.shape
(95, 24)
'''
# Parse desired slices to featurize at each window
# This allows command-line specification of ranges as a string
if isinstance(percentile_slices_to_featurize, str):
percentile_slices_to_featurize = ast.literal_eval(
percentile_slices_to_featurize)
if isinstance(summary_ops, str):
summary_ops = summary_ops.split(' ')
# Parse provided data dictionary
# Recover specific columns for each of the different roles:
id_cols = parse_id_cols(ts_data_dict)
id_cols = remove_col_names_from_list_if_not_in_df(id_cols, ts_df)
feature_cols = parse_feature_cols(ts_data_dict)
feature_cols = remove_col_names_from_list_if_not_in_df(feature_cols, ts_df)
time_cols = parse_time_cols(ts_data_dict)
time_cols = remove_col_names_from_list_if_not_in_df(time_cols, ts_df)
if len(time_cols) == 0:
raise ValueError("Expected at least one variable with role='time'")
elif len(time_cols) > 1:
raise Warning("More than one time variable found. Choosing %s" %
time_cols[-1])
time_col = time_cols[-1]
# Obtain fenceposts delineating each individual sequence within big stack
# We assume that sequences changeover when *any* key differs
# We convert all keys to a numerical datatype to make this possible
keys_df = ts_df[id_cols].copy()
for col in id_cols:
if not pd.api.types.is_numeric_dtype(keys_df[col].dtype):
keys_df[col] = keys_df[col].astype('category')
keys_df[col] = keys_df[col].cat.codes
middle_fence_posts = 1 + np.flatnonzero(
np.diff(keys_df.values, axis=0).any(axis=1))
fp = np.hstack([0, middle_fence_posts, keys_df.shape[0]])
feat_arr_per_seq = list()
windows_per_seq = list()
outcomes_per_seq = list()
durations_per_seq = list()
missingness_density_per_seq = list()
ids_per_seq = list()
# Total number of features we'll compute in each feature vector
F = len(percentile_slices_to_featurize) * len(feature_cols) * len(
summary_ops)
# Loop over each sequence in the tall tidy-format dataset
start_time_sec = time.time()
n_seqs = len(fp) - 1
pbar = ProgressBar()
for p in pbar(range(n_seqs)):
# Get features and times for the current fencepost
fp_start = fp[p]
fp_end = fp[p + 1]
# Get the current stay keys
cur_id_df = ts_df[id_cols].iloc[fp_start:fp_end].drop_duplicates(
subset=id_cols)
if outcomes_df is not None:
# Get the total duration of the current sequence
cur_outcomes_df = pd.merge(outcomes_df,
cur_id_df,
on=id_cols,
how='inner')
# Get the current sequence's finale outcome
cur_final_outcome = int(cur_outcomes_df[outcome_col].values[0])
cur_seq_duration = float(
cur_outcomes_df[outcome_seq_duration_col].values[0])
else:
cur_seq_duration = float(
ts_df[time_col].iloc[fp_start:fp_end].values[-1])
# Create windows at desired spacing
stop_time_of_cur_sequence = min(cur_seq_duration,
max_time_of_each_sequence)
window_ends = np.arange(
start_time_of_endpoints,
stop_time_of_cur_sequence + 0.01 * time_between_endpoints,
time_between_endpoints)
# Create a dictionary of times and values for each feature
time_arr_by_var = dict()
val_arr_by_var = dict()
times_U = ts_df[time_col].values[fp_start:fp_end]
for feature_col in feature_cols:
vals_U = ts_df[feature_col].values[fp_start:fp_end]
keep_mask_U = np.isfinite(vals_U)
if np.sum(keep_mask_U) > 0:
time_arr_by_var[feature_col] = times_U[keep_mask_U]
val_arr_by_var[feature_col] = vals_U[keep_mask_U]
cur_seq_missing_density = (
1.0 - len(val_arr_by_var.keys()) / float(len(feature_cols)))
# Deprecated code from preetish.... MCH couldn't get this to work.
'''
v = cur_fp_df.set_index(time_col).agg(lambda x: x.dropna().to_dict())
res = v[v.str.len() > 0].to_dict()
for feature_col in feature_cols:
if feature_col in res.keys():
time_arr_by_var[feature_col] = np.array(
list(res[feature_col].keys()), dtype=np.float64)
val_arr_by_var[feature_col] = np.array(
list(res[feature_col].values()), dtype=np.float64)
'''
W = len(window_ends)
window_features_WF = np.zeros([W, F], dtype=np.float32)
window_starts_stops_W2 = np.zeros([W, 2], dtype=np.float32)
if outcomes_df is not None:
window_outcomes_W1 = np.zeros([W, 1], dtype=np.int64)
for ww, window_end in enumerate(window_ends):
window_starts_stops_W2[ww, 0] = start_time_of_each_sequence
window_starts_stops_W2[ww, 1] = window_end
window_features_WF[ww, :], feat_names = featurize_ts(
time_arr_by_var,
val_arr_by_var,
var_cols=feature_cols,
var_spec_dict=ts_data_dict,
start_numerictime=start_time_of_each_sequence,
stop_numerictime=window_end,
summary_ops=summary_ops,
percentile_slices_to_featurize=percentile_slices_to_featurize)
if outcomes_df is not None:
# Determine the outcome for this window
# Set outcome as final outcome if within the provided horizon
# Otherwise, set to zero
if window_end >= cur_seq_duration - prediction_horizon:
window_outcomes_W1[ww] = cur_final_outcome
else:
window_outcomes_W1[ww] = 0
# Append all windows from this sequence to the big lists
feat_arr_per_seq.append(window_features_WF)
windows_per_seq.append(window_starts_stops_W2)
ids_per_seq.append(np.tile(cur_id_df.values[0], (W, 1)))
durations_per_seq.append(np.tile(cur_seq_duration, (W, 1)))
missingness_density_per_seq.append(cur_seq_missing_density)
if outcomes_df is not None:
outcomes_per_seq.append(window_outcomes_W1)
# Produce final data frames
features_df = pd.DataFrame(np.vstack(feat_arr_per_seq), columns=feat_names)
ids_df = pd.DataFrame(np.vstack(ids_per_seq), columns=id_cols)
windows_df = pd.DataFrame(np.vstack(windows_per_seq),
columns=['start', 'stop'])
all_features_df = pd.concat([ids_df, windows_df, features_df], axis=1)
if outcomes_df is not None:
durations_df = pd.DataFrame(np.vstack(durations_per_seq),
columns=[outcome_seq_duration_col])
outcomes_df = pd.DataFrame(np.vstack(outcomes_per_seq),
columns=[outcome_col])
all_outcomes_df = pd.concat(
[ids_df, windows_df, durations_df, outcomes_df], axis=1)
else:
durations_df = pd.DataFrame(np.vstack(durations_per_seq),
columns=[outcome_seq_duration_col])
all_outcomes_df = pd.concat([ids_df, windows_df, durations_df], axis=1)
seq_lengths = np.vstack([a[0] for a in durations_per_seq])
elapsed_time_sec = time.time() - start_time_sec
if verbose:
print('-----------------------------------------')
print('Processed %d sequences of duration %.1f-%.1f in %.1f sec' % (
n_seqs,
np.percentile(seq_lengths, 5),
np.percentile(seq_lengths, 95),
elapsed_time_sec,
))
print(' Total number of measured features: %d' % len(feature_cols))
print(
' Fraction of possible features NEVER seen in a seq. : %.2f-%.2f '
% (
np.percentile(missingness_density_per_seq, 5),
np.percentile(missingness_density_per_seq, 95),
))
print('-----------------------------------------')
return all_features_df, all_outcomes_df
