shuffle=True,
batch_size=2056,
verbose=False)
# Predict on the test set
test_preds = model.predict(x=test_data)
# Get metrics at 0.50 decision threshold
auc = roc_auc_score(y_true=test_labels, y_score=test_preds)
acc = get_acc(preds=test_preds[:, 1], labels=test_labels[:, 1])
sens = get_sens(preds=test_preds[:, 1], labels=test_labels[:, 1])
spec = get_spec(preds=test_preds[:, 1], labels=test_labels[:, 1])
# Get metrics at optimal decision threshold
opt_thresh = get_opt_thresh(preds=test_preds[:, 1],
labels=test_labels[:, 1],
n_thresholds=10000)
test_acc_opt = get_acc(preds=test_preds[:, 1],
labels=test_labels[:, 1],
threshold=opt_thresh)
test_sens_opt = get_sens(preds=test_preds[:, 1],
labels=test_labels[:, 1],
threshold=opt_thresh)
test_spec_opt = get_spec(preds=test_preds[:, 1],
labels=test_labels[:, 1],
threshold=opt_thresh)
# Get the class predictions
class_preds = test_preds * np.zeros_like(test_preds)
class_preds[test_preds >= opt_thresh] = 1
dnn.fit(x=g2_d,
y=g2_labels,
epochs=__N_EPOCHS,
shuffle=True,
batch_size=2048,
verbose=False)
# Calculate the predictions
g1_preds = dnn.predict(x=g1_d)
# Get and store ROC AUC
g1_auc = 100 * roc_auc_score(y_true=g1_labels, y_score=g1_preds)
auc_scores[run_idx] = g1_auc
# Get optimal decision threshold
opt_thresh = get_opt_thresh(preds=g1_preds[:, 1],
labels=g1_labels[:, 1])
# Store performance metrics
accs[run_idx] = 100 * get_acc(
preds=g1_preds[:, 1], labels=g1_labels[:, 1], threshold=opt_thresh)
sens[run_idx] = 100 * get_sens(
preds=g1_preds[:, 1], labels=g1_labels[:, 1], threshold=opt_thresh)
spec[run_idx] = 100 * get_spec(
preds=g1_preds[:, 1], labels=g1_labels[:, 1], threshold=opt_thresh)
# Plot ROC curve
plt_roc_curve(preds=g1_preds[:, 1],
labels=g1_labels[:, 1],
save_str='dnn_run_%d_roc' % run_idx,
save=True)
# Report AUC at this run