# Step 6: Evaluation
predictions = trainer.predict(small_eval_dataset)

# Confusion matrix
cm = confusion_matrix(small_eval_dataset['label'], predictions.predictions.argmax(-1))
sns.heatmap(cm, annot=True, fmt='d')
plt.title('Confusion Matrix')
plt.show()

# ROC Curve
fpr, tpr, _ = roc_curve(small_eval_dataset['label'], predictions.predictions[:, 1])
roc_auc = auc(fpr, tpr)

plt.figure(figsize=(1.618 * 5, 5))
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()