id_cols, time_col, sequence_feature_cols)
# impute missing values in the test features
curr_sequence_features_df = curr_sequence_features_df.groupby(id_cols).apply(lambda x: x.fillna(method='pad')).copy()
for feature_col in sequence_feature_cols:
curr_sequence_features_df[feature_col].fillna(curr_sequence_features_df[feature_col].mean(), inplace=True)
for feature_col in sequence_feature_cols:
curr_sequence_features_df[feature_col].fillna(x_train_df[feature_col].mean(), inplace=True)
# load test data with TidySequentialDataLoader
test_vitals = TidySequentialDataCSVLoader(
x_csv_path=curr_sequence_features_df,
y_csv_path=chosen_stay_outcomes_df,
x_col_names=feature_cols_with_mask_features,
idx_col_names=id_cols,
y_col_name=args.outcome_column_name,
y_label_type='per_sequence'
)
# predict on test data
x_test, y_test = test_vitals.get_batch_data(batch_id=0)
per_feature_scaling = np.load(os.path.join(args.rnn_models_dir, 'per_feature_scaling.npy'))
for f in range(x_test.shape[2]):
x_test[:,:,f] = x_test[:,:,f]/per_feature_scaling[f]
mask_feature_cols = [i for i in feature_cols_with_mask_features if 'mask' in i]
total_missing_features_over_time[q]=(curr_sequence_features_df[mask_feature_cols]==0).sum().sum()
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)
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)
type=int,
default=10,
metavar='N',
help='number of epochs')
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--save',
type=str,
default='model.pt',
help='path to save the final model')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = 'cpu'
dataset = TidySequentialDataCSVLoader('my_dataset.csv')
X, y = dataset.get_batch_data(batch_id=0)
rnn = RNNBinaryClassifier(
max_epochs=args.epochs,
batch_size=args.batch_size,
device=device,
callbacks=[
#skorch.callbacks.Checkpoint(),
skorch.callbacks.ProgressBar(),
],
module__rnn_type='ELMAN+relu',
module__n_inputs=X.shape[-1],
module__n_hiddens=10,
module__n_layers=1,
optimizer=torch.optim.SGD,
metavar='N',
help='number of epochs')
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--save',
type=str,
default='model.pt',
help='path to save the final model')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = 'cpu'
dataset = TidySequentialDataCSVLoader(
per_tstep_csv_path='eeg_rnn_data/eeg_train_balanced.csv',
idx_col_names='chunk_id',
x_col_names=['eeg_signal'],
y_col_name='seizure_binary_label',
y_label_type='per_tstep')
X, y = dataset.get_batch_data(batch_id=0)
rnn = RNNBinaryClassifier(
max_epochs=args.epochs,
batch_size=args.batch_size,
device=device,
callbacks=[
#skorch.callbacks.Checkpoint(),
skorch.callbacks.ProgressBar(),
],
module__rnn_type='ELMAN+relu',
#module__rnn_type='LSTM',
module__n_inputs=X.shape[-1],
def main():
parser = argparse.ArgumentParser(
description='PyTorch RNN with variable-length numeric sequences wrapper'
)
parser.add_argument('--train_vitals_csv',
type=str,
help='Location of vitals data for training')
parser.add_argument('--test_vitals_csv',
type=str,
help='Location of vitals data for testing')
parser.add_argument('--metadata_csv',
type=str,
help='Location of metadata for testing and training')
parser.add_argument('--data_dict', type=str)
parser.add_argument('--batch_size',
type=int,
default=256,
help='Number of sequences per minibatch')
parser.add_argument('--epochs',
type=int,
default=100000,
help='Number of epochs')
parser.add_argument('--hidden_units',
type=int,
default=10,
help='Number of hidden units')
parser.add_argument('--lr',
type=float,
default=1e-2,
help='Learning rate for the optimizer')
parser.add_argument('--dropout',
type=float,
default=0.3,
help='dropout for optimizer')
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--save',
type=str,
default='RNNmodel.pt',
help='path to save the final model')
parser.add_argument('--report_dir',
type=str,
default='html',
help='dir in which to save results report')
parser.add_argument('--simulated_data_dir',
type=str,
default='simulated_data/2-state/',
help='dir in which to simulated data is saved')
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_filename_prefix',
type=str,
default='current_config',
help='file to save the loss and validation over epochs')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = 'cpu'
# hyperparameter space
# learning_rate = [1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1]
# hyperparameters = dict(lr=learning_rate)
# extract data
if not (args.is_data_simulated):
#------------------------Loaded from TidySequentialDataCSVLoader--------------------#
train_vitals = TidySequentialDataCSVLoader(
per_tstep_csv_path=args.train_vitals_csv,
per_seq_csv_path=args.metadata_csv,
idx_col_names=['subject_id', 'episode_id'],
x_col_names='__all__',
y_col_name='inhospital_mortality',
y_label_type='per_tstep')
test_vitals = TidySequentialDataCSVLoader(
per_tstep_csv_path=args.test_vitals_csv,
per_seq_csv_path=args.metadata_csv,
idx_col_names=['subject_id', 'episode_id'],
x_col_names='__all__',
y_col_name='inhospital_mortality',
y_label_type='per_tstep')
X_train_with_time_appended, y_train = train_vitals.get_batch_data(
batch_id=0)
X_test_with_time_appended, y_test = test_vitals.get_batch_data(
batch_id=0)
_, T, F = X_train_with_time_appended.shape
if T > 1:
X_train = X_train_with_time_appended[:, :,
1:] # removing hours column
X_test = X_test_with_time_appended[:, :,
1:] # removing hours column
else: # account for collapsed features across time
X_train = X_train_with_time_appended
X_test = X_test_with_time_appended
# set class weights as (1-Beta)/(1-Beta^(number of training samples in class))
# beta = (len(y_train)-1)/len(y_train)
# class_weights = torch.tensor(np.asarray([(1-beta)/(1-beta**((y_train==0).sum())), (1-beta)/(1-beta**((y_train==1).sum()))]))
# 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()
# define a auc scorer function and pass it as callback of skorch to track training and validation AUROC
roc_auc_scorer = make_scorer(roc_auc_score,
greater_is_better=True,
needs_threshold=True)
# use only last time step as feature for LR debugging
# X_train = X_train[:,-1,:][:,np.newaxis,:]
# X_test = X_test[:,-1,:][:,np.newaxis,:]
# use time steps * features as vectorized feature into RNN for LR debugging
# X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]*X_train.shape[2]))
# X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]*X_test.shape[2]))
#---------------------------------------------------------------------#
# Pseudo LSTM (hand engineered features through LSTM, collapsed across time)
#---------------------------------------------------------------------#
# instantiate RNN
rnn = RNNBinaryClassifier(
max_epochs=args.epochs,
batch_size=args.batch_size,
device=device,
criterion=torch.nn.CrossEntropyLoss,
criterion__weight=class_weights,
train_split=skorch.dataset.CVSplit(4),
callbacks=[
skorch.callbacks.GradientNormClipping(gradient_clip_value=0.4,
gradient_clip_norm_type=2),
skorch.callbacks.EpochScoring(roc_auc_scorer,
lower_is_better=False,
on_train=True,
name='aucroc_score_train'),
skorch.callbacks.EpochScoring(roc_auc_scorer,
lower_is_better=False,
on_train=False,
name='aucroc_score_valid'),
ComputeGradientNorm(
norm_type=2,
f_history=args.report_dir +
'/%s_running_rnn_classifer_gradient_norm_history.csv' %
args.output_filename_prefix),
# LSTMtoLogReg(),# transformation to log reg for debugging
skorch.callbacks.EarlyStopping(monitor='aucroc_score_valid',
patience=1000,
threshold=1e-10,
threshold_mode='rel',
lower_is_better=False),
skorch.callbacks.Checkpoint(
monitor='train_loss',
f_history=args.report_dir +
'/%s_running_rnn_classifer_history.json' %
args.output_filename_prefix),
# skorch.callbacks.Checkpoint(monitor='aucroc_score_valid', f_pickle = args.report_dir + '/%s_running_rnn_classifer_model'%args.output_filename_prefix),
skorch.callbacks.PrintLog(floatfmt='.2f')
],
module__rnn_type='LSTM',
module__n_inputs=X_train.shape[-1],
module__n_hiddens=args.hidden_units,
module__n_layers=1,
# module__dropout_proba_non_recurrent=args.dropout,
# module__dropout_proba=args.dropout,
optimizer=torch.optim.SGD,
optimizer__weight_decay=1e-2,
# optimizer__momentum=0.9,
# optimizer=torch.optim.Adam,
lr=args.lr)
from IPython import embed
embed()
# scale input features
X_train = standard_scaler_3d(X_train)
X_test = standard_scaler_3d(X_test)
rnn.fit(X_train, y_train)
# get the training history
epochs, train_loss, validation_loss, aucroc_score_train, aucroc_score_valid = get_loss_plots_from_training_history(
rnn.history)
# plot the validation and training error plots and save
f = plt.figure()
plt.plot(epochs, train_loss, 'r-.', label='Train Loss')
plt.plot(epochs, validation_loss, 'b-.', label='Validation Loss')
plt.plot(epochs, aucroc_score_train, 'g-.', label='AUCROC score (Train)')
plt.plot(epochs, aucroc_score_valid, 'm-.', label='AUCROC score (Valid)')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.title('Training Performance (learning rate : %s, hidden units : %s)' %
(str(args.lr), str(args.hidden_units)))
f.savefig(args.report_dir + '/%s_training_performance_plots.png' %
args.output_filename_prefix)
plt.close()
# save the training and validation loss in a csv
train_perf_df = pd.DataFrame(
data=np.stack([epochs, train_loss, validation_loss]).T,
columns=['epochs', 'train_loss', 'validation_loss'])
train_perf_df.to_csv(args.report_dir +
'/%s_perf_metrics.csv' % args.output_filename_prefix)
# save classifier history to later evaluate early stopping for this model
dump(
rnn, args.report_dir +
'/%s_rnn_classifer.pkl' % args.output_filename_prefix)
y_pred_proba = rnn.predict_proba(X_test)
y_pred = convert_proba_to_binary(y_pred_proba)
y_pred_proba_neg, y_pred_proba_pos = zip(*y_pred_proba)
fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba_pos)
roc_area = roc_auc_score(y_test, y_pred_proba_pos)
from IPython import embed
embed()
# Brief Summary
# print('Best lr:', rnn.best_estimator_.get_params()['lr'])
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Balanced Accuracy:', balanced_accuracy_score(y_test, y_pred))
print('Log Loss:', log_loss(y_test, y_pred_proba))
print('AUC ROC:', roc_area)
conf_matrix = confusion_matrix(y_test, y_pred)
true_neg = conf_matrix[0][0]
true_pos = conf_matrix[1][1]
false_neg = conf_matrix[1][0]
false_pos = conf_matrix[0][1]
print('True Positive Rate:', float(true_pos) / (true_pos + false_neg))
print('True Negative Rate:', float(true_neg) / (true_neg + false_pos))
print('Positive Predictive Value:',
float(true_pos) / (true_pos + false_pos))
print('Negative Predictive Value',
float(true_neg) / (true_neg + false_pos))
create_html_report(args.report_dir, args.output_filename_prefix, y_test,
y_pred, y_pred_proba, args.lr)
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)
# Get the tstops_df for each patient-stay-slice
tstops_df = pd.read_csv(os.path.join(args.tstops_dir, 'TSLICE={tslice}',
'tstops_filtered_{tslice}_hours.csv.gz').format(tslice=tslice))
x_train_curr_tslice, y_train_curr_tslice = get_tslice_x_y(x_train, y_train, tstops_df, id_cols, time_col)
x_test_curr_tslice, y_test_curr_tslice = get_tslice_x_y(x_test, y_test, tstops_df, id_cols, time_col)
# limit sequence length
reduced_T = 200
print('Getting train and test sets for all patient stay slices...')
# Pass each of the 3 dataframes through dataset_loader and 3 different tensors
train_vitals = TidySequentialDataCSVLoader(
x_csv_path=x_train_curr_tslice,
y_csv_path=y_train_curr_tslice,
x_col_names=feature_cols,
idx_col_names=id_cols,
y_col_name='clinical_deterioration_outcome',
y_label_type='per_sequence',
batch_size=45000,
max_seq_len=reduced_T
)
test_vitals = TidySequentialDataCSVLoader(
x_csv_path=x_test_curr_tslice,
y_csv_path=y_test_curr_tslice,
x_col_names=feature_cols,
idx_col_names=id_cols,
y_col_name='clinical_deterioration_outcome',
y_label_type='per_sequence',
batch_size=10,
max_seq_len=reduced_T
)
def main():
parser = argparse.ArgumentParser(description='PyTorch RNN with variable-length numeric sequences wrapper')
parser.add_argument('--train_vitals_csv', type=str,
help='Location of vitals data for training')
parser.add_argument('--test_vitals_csv', type=str,
help='Location of vitals data for testing')
parser.add_argument('--metadata_csv', type=str,
help='Location of metadata for testing and training')
parser.add_argument('--data_dict', type=str)
parser.add_argument('--epochs', type=int, default=1000,
help='Number of epochs')
parser.add_argument('--seed', type=int, default=1111,
help='random seed')
parser.add_argument('--report_dir', type=str, default='html',
help='dir in which to save results report')
parser.add_argument('--simulated_data_dir', type=str, default='simulated_data/2-state/',
help='dir in which to simulated data is saved')
parser.add_argument('--is_data_simulated', type=bool, default=False,
help='boolean to check if data is simulated or from mimic')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = 'cpu'
# extract data
if not(args.is_data_simulated):
train_vitals = TidySequentialDataCSVLoader(
per_tstep_csv_path=args.train_vitals_csv,
per_seq_csv_path=args.metadata_csv,
idx_col_names=['subject_id', 'episode_id'],
x_col_names='__all__',
y_col_name='inhospital_mortality',
y_label_type='per_tstep')
test_vitals = TidySequentialDataCSVLoader(
per_tstep_csv_path=args.test_vitals_csv,
per_seq_csv_path=args.metadata_csv,
idx_col_names=['subject_id', 'episode_id'],
x_col_names='__all__',
y_col_name='inhospital_mortality',
y_label_type='per_tstep')
X_train_with_time_appended, y_train = train_vitals.get_batch_data(batch_id=0)
X_test_with_time_appended, y_test = test_vitals.get_batch_data(batch_id=0)
_,T,F = X_train_with_time_appended.shape
if T>1:
X_train = X_train_with_time_appended[:,:,1:]# removing hours column
X_test = X_test_with_time_appended[:,:,1:]# removing hours column
else:# account for collapsed features across time
X_train = X_train_with_time_appended
X_test = X_test_with_time_appended
# 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()
# define a auc scorer function and pass it as callback of skorch to track training and validation AUROC
roc_auc_scorer = make_scorer(roc_auc_score, greater_is_better=True,
needs_threshold=True)
# scale features
X_train = standard_scaler_3d(X_train)
X_test = standard_scaler_3d(X_test)
# Define parameter grid
params = {'lr':[0.0001, 0.0005, 0.001, 0.005, 0.01], 'optimizer__weight_decay':[0.0001, 0.001, 0.01, 0.1, 1, 10]
}
#---------------------------------------------------------------------#
# LSTM with gridsearchcv
#---------------------------------------------------------------------#
print('-------------------------------------------------------------------')
print('Running LSTM converted to logistic regression on collapsed Features')
model_name='logreg_hist'
save_cv_results = SaveCVResults(dirname=args.report_dir, f_history=model_name+'.json')
rnn = RNNBinaryClassifier(
max_epochs=args.epochs,
batch_size=-1,
device=device,
callbacks=[
skorch.callbacks.EpochScoring(roc_auc_scorer, lower_is_better=False, on_train=True, name='aucroc_score_train'),
skorch.callbacks.EpochScoring(roc_auc_scorer, lower_is_better=False, on_train=False, name='aucroc_score_valid'),
skorch.callbacks.EarlyStopping(monitor='aucroc_score_valid', patience=5, threshold=1e-10, threshold_mode='rel',
lower_is_better=False),
save_cv_results,
],
criterion=torch.nn.NLLLoss,
criterion__weight=class_weights,
train_split=skorch.dataset.CVSplit(0.2),
module__rnn_type='LSTM',
module__n_layers=1,
module__n_hiddens=X_train.shape[-1],
module__n_inputs=X_train.shape[-1],
module__convert_to_log_reg=True,
optimizer=torch.optim.Adam)
gs = GridSearchCV(rnn, params, scoring=roc_auc_scorer, refit=True, cv=ShuffleSplit(n_splits=1, test_size=0.2, random_state=14232))
lr_cv = gs.fit(X_train, y_train)
y_pred_proba = lr_cv.best_estimator_.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 logistic regression (Train) : %.3f'%auroc_train_final)
y_pred_proba = lr_cv.best_estimator_.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 logistic regression (Test) : %.3f'%auroc_test_final)
# get the loss plots for logistic regression
plot_training_history(model_name='logreg_hist', model_alias = 'Logistic Regression',report_dir=args.report_dir, params=params, auroc_train_final = auroc_train_final, auroc_test_final=auroc_test_final)
# LSTM
print('-------------------------------------------------------------------')
print('Running LSTM on Collapsed Features')
model_name='lstm_hist'
save_cv_results = SaveCVResults(dirname=args.report_dir, f_history=model_name+'.json')
rnn = RNNBinaryClassifier(
max_epochs=args.epochs,
batch_size=-1,
device=device,
callbacks=[
save_cv_results,
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=5, threshold=0.002, threshold_mode='rel',
lower_is_better=False)
],
criterion=torch.nn.CrossEntropyLoss,
criterion__weight=class_weights,
train_split=skorch.dataset.CVSplit(0.2),
module__rnn_type='LSTM',
module__n_layers=1,
module__n_hiddens=X_train.shape[-1],
module__n_inputs=X_train.shape[-1],
module__convert_to_log_reg=False,
optimizer=torch.optim.Adam)
gs = GridSearchCV(rnn, params, scoring='roc_auc', cv=ShuffleSplit(n_splits=1, test_size=0.2, random_state=14232),
)
rnn_cv = gs.fit(X_train, y_train)
y_pred_proba = rnn_cv.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 = rnn_cv.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)
# get the loss plots for LSTM
plot_training_history(model_name='lstm_hist', model_alias = 'LSTM', report_dir=args.report_dir,
params=params, auroc_train_final = auroc_train_final, auroc_test_final=auroc_test_final)