def train_deepsurv(data_df, r_splits):
epochs = 100
verbose = True
num_nodes = [32]
out_features = 1
batch_norm = True
dropout = 0.6
output_bias = False
c_index_at = []
c_index_30 = []
time_auc_30 = []
time_auc_60 = []
time_auc_365 = []
for i in range(len(r_splits)):
print("\nIteration %s"%(i))
#DATA PREP
df_train, df_val, df_test, df_test_30 = prepare_datasets(data_df, r_splits[i][2], r_splits[i][1], r_splits[i][0])
xcols = list(df_train.columns)
for col_name in ["subject_id", "event", "duration"]:
if col_name in xcols:
xcols.remove(col_name)
cols_standardize = xcols
standardize = [([col], StandardScaler()) for col in cols_standardize]
x_mapper = DataFrameMapper(standardize)
x_train = x_mapper.fit_transform(df_train).astype('float32')
x_val = x_mapper.transform(df_val).astype('float32')
x_test = x_mapper.transform(df_test).astype('float32')
x_test_30 = x_mapper.transform(df_test_30).astype('float32')
labtrans = CoxTime.label_transform()
get_target = lambda df: (df['duration'].values, df['event'].values)
y_train = labtrans.fit_transform(*get_target(df_train))
y_val = labtrans.transform(*get_target(df_val))
durations_test, events_test = get_target(df_test)
durations_test_30, events_test_30 = get_target(df_test_30)
val = tt.tuplefy(x_val, y_val)
(train_x, train_y), (val_x, val_y), (test_x, test_y), _ = df2array(data_df, df_train, df_val, df_test, df_test_30)
#MODEL
in_features = x_train.shape[1]
callbacks = [tt.callbacks.EarlyStopping()]
net = tt.practical.MLPVanilla(in_features, num_nodes, out_features, batch_norm, dropout, output_bias=output_bias)
model = CoxPH(net, tt.optim.Adam)
model.optimizer.set_lr(0.0001)
if x_train.shape[0] % 2:
batch_size = 255
else:
batch_size = 256
log = model.fit(x_train, y_train, batch_size, epochs, callbacks, val_data=val, val_batch_size=batch_size)
model.compute_baseline_hazards()
surv = model.predict_surv_df(x_test)
ev = EvalSurv(surv, durations_test, events_test, censor_surv='km')
c_index_at.append(ev.concordance_td())
surv_30 = model.predict_surv_df(x_test_30)
ev_30 = EvalSurv(surv_30, durations_test_30, events_test_30, censor_surv='km')
c_index_30.append(ev_30.concordance_td())
for time_x in [30, 60, 365]:
va_auc, va_mean_auc = cumulative_dynamic_auc(train_y, test_y, model.predict(x_test).flatten(), time_x)
eval("time_auc_" + str(time_x)).append(va_auc[0])
print("C-index_30:", c_index_30[i])
print("C-index_AT:", c_index_at[i])
print("time_auc_30", time_auc_30[i])
print("time_auc_60", time_auc_60[i])
print("time_auc_365", time_auc_365[i])
return c_index_at, c_index_30, time_auc_30, time_auc_60, time_auc_365
lrfinder = model.lr_finder(x_train, y_train, batch_size, tolerance=10)
best = lrfinder.get_best_lr()
model.optimizer.set_lr(best)
epochs = args.epochs
callbacks = [tt.callbacks.EarlyStopping(patience=patience)]
verbose = True
log = model.fit(x_train, y_train_transformed, batch_size, epochs, callbacks, verbose, val_data = val_transformed, val_batch_size = batch_size)
# Evaluation ===================================================================
val_loss = min(log.monitors['val_'].scores['loss']['score'])
# get Ctd
ctd = concordance_index(event_times = durations_test_transformed,
predicted_scores = model.predict(x_test).reshape(-1),
event_observed = events_test)
# set time grid for numerical integration to get IBS and IBLL
if durations_test.min()>0:
time_grid = np.linspace(durations_test.min(), durations_test.max(), 100)
else:
durations_test_copy = durations_test.copy()
durations_test_copy.sort()
time_grid = np.linspace(durations_test_copy[1], durations_test.max(), 100)
# time_grid = np.linspace(durations_test.min(), durations_test.max(), 100)
# transform time grid into DSAFT scale for fair comparison
# pdb.set_trace()
time_grid = np.exp(scaler_train.transform(np.log(time_grid.reshape(-1, 1)))).reshape(-1)
# grid interval for numerical integration
ds = np.array(time_grid - np.array([0.0] + time_grid[:-1].tolist()))
def train_LSTMCox(data_df, r_splits):
epochs = 100
verbose = True
in_features = 768
out_features = 1
batch_norm = True
dropout = 0.6
output_bias = False
c_index_at = []
c_index_30 = []
time_auc_30 = []
time_auc_60 = []
time_auc_365 = []
for i in range(len(r_splits)):
print("\nIteration %s"%(i))
#DATA PREP
df_train, df_val, df_test, df_test_30 = prepare_datasets(data_df, r_splits[i][2], r_splits[i][1], r_splits[i][0])
x_train = np.array(df_train["x0"].tolist()).astype("float32")
x_val = np.array(df_val["x0"].tolist()).astype("float32")
x_test = np.array(df_test["x0"].tolist()).astype("float32")
x_test_30 = np.array(df_test_30["x0"].tolist()).astype("float32")
labtrans = CoxTime.label_transform()
get_target = lambda df: (df['duration'].values, df['event'].values)
y_train = labtrans.fit_transform(*get_target(df_train))
y_val = labtrans.transform(*get_target(df_val))
durations_test, events_test = get_target(df_test)
durations_test_30, events_test_30 = get_target(df_test_30)
val = tt.tuplefy(x_val, y_val)
(train_x, train_y), (val_x, val_y), (test_x, test_y), _ = df2array(data_df, df_train, df_val, df_test, df_test_30)
#MODEL
callbacks = [tt.callbacks.EarlyStopping()]
net = LSTMCox(768, 32, 1, 1)
model = CoxPH(net, tt.optim.Adam)
model.optimizer.set_lr(0.0001)
if x_train.shape[0] % 2:
batch_size = 255
else:
batch_size = 256
log = model.fit(x_train, y_train, batch_size, epochs, callbacks, val_data=val, val_batch_size=batch_size)
model.compute_baseline_hazards()
surv = model.predict_surv_df(x_test)
ev = EvalSurv(surv, durations_test, events_test, censor_surv='km')
c_index_at.append(ev.concordance_td())
surv_30 = model.predict_surv_df(x_test_30)
ev_30 = EvalSurv(surv_30, durations_test_30, events_test_30, censor_surv='km')
c_index_30.append(ev_30.concordance_td())
for time_x in [30, 60, 365]:
va_auc, va_mean_auc = cumulative_dynamic_auc(train_y, test_y, model.predict(x_test).flatten(), time_x)
eval("time_auc_" + str(time_x)).append(va_auc[0])
print("C-index_30:", c_index_30[i])
print("C-index_AT:", c_index_at[i])
print("time_auc_30", time_auc_30[i])
print("time_auc_60", time_auc_60[i])
print("time_auc_365", time_auc_365[i])
return c_index_at, c_index_30, time_auc_30, time_auc_60, time_auc_365
