How to use the elephas.utils.rdd_utils.to_labeled_point function in elephas

def test_mllib_model(spark_context):
    # Build RDD from numpy features and labels
    lp_rdd = to_labeled_point(spark_context, x_train,
                              y_train, categorical=True)

    # Initialize SparkModel from Keras model and Spark context
    spark_model = SparkMLlibModel(
        model=model, frequency='epoch', mode='synchronous')

    # Train Spark model
    spark_model.fit(lp_rdd, epochs=5, batch_size=32, verbose=0,
                    validation_split=0.1, categorical=True, nb_classes=nb_classes)

    # Evaluate Spark model by evaluating the underlying model
    score = spark_model.master_network.evaluate(x_test, y_test, verbose=2)
    print('Test accuracy:', score[1])

def test_from_labeled_rdd_categorical(spark_context):
    features = np.ones((2, 10))
    labels = np.asarray([[0, 0, 1.0], [0, 1.0, 0]])
    lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, True)

    x, y = rdd_utils.from_labeled_point(lp_rdd, True, 3)
    assert x.shape == features.shape
    assert y.shape == labels.shape

def test_lp_to_simple_rdd_categorical(spark_context):
    features = np.ones((2, 10))
    labels = np.asarray([[0, 0, 1.0], [0, 1.0, 0]])
    lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, True)

    rdd = rdd_utils.lp_to_simple_rdd(lp_rdd, categorical=True, nb_classes=3)
    assert rdd.first()[0].shape == (10,)
    assert rdd.first()[1].shape == (3,)

def test_to_labeled_rdd_categorical(spark_context):
    features = np.ones((2, 10))
    labels = np.asarray([[0, 0, 1.0], [0, 1.0, 0]])
    lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, True)
    assert lp_rdd.count() == 2
    first = lp_rdd.first()
    assert first.features.shape == (10,)
    assert first.label == 2.0

def test_to_labeled_rdd_not_categorical(spark_context):
    features = np.ones((2, 10))
    labels = np.asarray([[2.0], [1.0]])
    lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, False)
    assert lp_rdd.count() == 2
    first = lp_rdd.first()
    assert first.features.shape == (10,)
    assert first.label == 2.0

def test_lp_to_simple_rdd_not_categorical(spark_context):
    features = np.ones((2, 10))
    labels = np.asarray([[2.0], [1.0]]).reshape((2,))
    lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, False)

    rdd = rdd_utils.lp_to_simple_rdd(lp_rdd, categorical=False, nb_classes=3)
    assert rdd.first()[0].shape == (10,)
    assert rdd.first()[1] == 2.0

def test_from_labeled_rdd(spark_context):
    features = np.ones((2, 10))
    labels = np.asarray([[2.0], [1.0]]).reshape((2,))
    lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, False)

    x, y = rdd_utils.from_labeled_point(lp_rdd, False, None)
    assert x.shape == features.shape
    assert y.shape == labels.shape

model.add(Dropout(0.2))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))

# Compile model
rms = RMSprop()

# Create Spark context
conf = SparkConf().setAppName('Mnist_Spark_MLP').setMaster('local[8]')
sc = SparkContext(conf=conf)

# Build RDD from numpy features and labels
lp_rdd = to_labeled_point(sc, x_train, y_train, categorical=True)

# Initialize SparkModel from Keras model and Spark context
spark_model = SparkMLlibModel(master_network=model, frequency='epoch', mode='synchronous',
                              master_metrics=['acc'])

# Train Spark model
spark_model.fit(lp_rdd, epochs=5, batch_size=32, verbose=0,
                validation_split=0.1, categorical=True, nb_classes=nb_classes)

# Evaluate Spark model by evaluating the underlying model
score = spark_model.master_network.evaluate(x_test, y_test, verbose=2)
print('Test accuracy:', score[1])

def to_data_frame(sc, features, labels, categorical=False):
    """Convert numpy arrays of features and labels into Spark DataFrame
    """
    lp_rdd = to_labeled_point(sc, features, labels, categorical)
    sql_context = SQLContext(sc)
    df = sql_context.createDataFrame(lp_rdd)
    return df