How to use the mlblocks.ml_pipeline.ml_pipeline.MLPipeline function in mlblocks

}

        if optimizer is not None:
            update_params[('lstm_text', 'optimizer')] = optimizer

        if loss is not None:
            update_params[('lstm_text', 'loss')] = loss

        if pad_length is not None:
            update_params[('text_padder', 'pad_length')] = pad_length
            update_params[('lstm_text', 'pad_length')] = pad_length

        self.update_fixed_hyperparams(update_params)


class LstmTextRegressor(MLPipeline):

    BLOCKS = ['tokenizer', 'sequence_padder', 'lstm_text']

    def __init__(self, optimizer=None, loss=None):
        super(LstmTextRegressor, self).__init__()

        update_params = dict()
        if optimizer is not None:
            update_params[('lstm_text', 'optimizer')] = optimizer

        if loss is not None:
            update_params[('lstm_text', 'loss')] = loss

        self.update_fixed_hyperparams(update_params)

from mlblocks.ml_pipeline.ml_pipeline import MLPipeline


class TraditionalImagePipeline(MLPipeline):
    """Traditional image pipeline using HOG features."""

    BLOCKS = ['HOG', 'random_forest_classifier']

from mlblocks.ml_pipeline.ml_pipeline import MLPipeline


class LstmTextClassifier(MLPipeline):
    """LSTM text pipeline via Keras.

    From:
    http://www.developintelligence.com/blog/2017/06/practical-neural-networks-keras-classifying-yelp-reviews/
    """  # noqa

    BLOCKS = ['tokenizer', 'sequence_padder', 'lstm_text', 'convert_class_probs']

    def __init__(self, num_classes, pad_length=None, optimizer=None, loss=None):
        super(LstmTextClassifier, self).__init__()

        update_params = {
            ('lstm_text', 'dense_units'): num_classes,
            ('lstm_text', 'dense_activation'): 'softmax',
            ('lstm_text', 'optimizer'): 'keras.optimizers.Adadelta',
            ('lstm_text', 'loss'): 'keras.losses.categorical_crossentropy'

from mlblocks.ml_pipeline.ml_pipeline import MLPipeline


class RandomForestClassifier(MLPipeline):
    """Random forest classifier pipeline."""

    BLOCKS = ['random_forest_classifier']


class RandomForestRegressor(MLPipeline):
    """Random forest classifier pipeline."""

    BLOCKS = ['random_forest_regressor']

update_params = {
            ('simple_cnn', 'dense2_units'): num_classes,
            ('simple_cnn', 'dense2_activation'): 'softmax',
            ('simple_cnn', 'optimizer'): 'keras.optimizers.Adadelta',
            ('simple_cnn', 'loss'): 'keras.losses.categorical_crossentropy'
        }
        if optimizer is not None:
            update_params[('simple_cnn', 'optimizer')] = optimizer

        if loss is not None:
            update_params[('simple_cnn', 'loss')] = loss

        self.update_fixed_hyperparams(update_params)


class SimpleCnnRegressor(MLPipeline):

    BLOCKS = ['simple_cnn']

    def __init__(self, optimizer=None, loss=None):
        super(SimpleCnnRegressor, self).__init__()

        update_params = {}
        if optimizer is not None:
            update_params[('simple_cnn', 'optimizer')] = optimizer

        if loss is not None:
            update_params[('simple_cnn', 'loss')] = loss

        self.update_fixed_hyperparams(update_params)

from mlblocks.ml_pipeline.ml_pipeline import MLPipeline


class TraditionalTextPipeline(MLPipeline):
    """
    Traditional text pipeline.
    """

    def __new__(cls, *args, **kwargs):
        return MLPipeline.from_ml_json([
            'count_vectorizer', 'to_array', 'tfidf_transformer',
            'multinomial_nb'
        ])

from mlblocks.ml_pipeline.ml_pipeline import MLPipeline


class SimpleCnnClassifier(MLPipeline):
    """CNN image pipeline.

    Based on:
    https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py

    Layers:
        Conv2D
        Conv2D
        MaxPooling2D
        Dropout
        Flatten
        Dense
        Dropout
        Dense
    """
    BLOCKS = ['simple_cnn', 'convert_class_probs']