best_val_score = val_score
copy_the_model(model, saved_model)
if val_score < prev_val_score:
early_stop_count -= 1
print("Early stopping count reduced to " + str(early_stop_count))
else:
early_stop_count = MAX_EARLY_STOP_COUNT
prev_val_score = val_score
h_test = []
for i in range(x_test.shape[0] // saved_model.batch_size + 1):
h_test.append(
saved_model.encoder(x_test[i * saved_model.batch_size:(i + 1) *
saved_model.batch_size].transpose(
1, 0,
2).astype(theano.config.floatX)))
h_test = np.concatenate(h_test)
print("Accuracy on test: %0.4f" % clf.get_accuracy(h_test, Y_test))
y_test = clf.predict(h_test)
df = pd.DataFrame(y_test)
df['traj'] = test_traj
groupby = df.groupby('traj').sum()
trip_preds = groupby.idxmax(1)
trip_accuracy = float(
sum([traj_to_driver[k] == v
for (k, v) in trip_preds.iteritems()])) / trip_preds.shape[0]
print("Trip level prediction accuracy = {:.2f}".format(trip_accuracy))
