How to use the mindsdb.libs.controllers.predictor.Predictor function in MindsDB

def test():
    from mindsdb.libs.controllers.predictor import Predictor


    mdb = Predictor(name='home_rentals')

    mdb.learn(
        from_data="https://raw.githubusercontent.com/mindsdb/mindsdb/master/docs/examples/basic/home_rentals.csv",
        # the path to the file where we can learn from, (note: can be url)
        to_predict='rental_price',  # the column we want to learn to predict given all the data in the file
        sample_margin_of_error=0.02
    )

    mdb = Predictor(name='home_rentals')

    a = mdb.predict(when={'number_of_rooms': 10})

    print('-------Preidiction output------------')
    print(a.predicted_values)

def test():
    from mindsdb.libs.controllers.predictor import Predictor
    from mindsdb import CONFIG

    CONFIG.DEBUG_BREAK_POINT = PHASE_DATA_EXTRACTOR

    mdb = Predictor(name='home_rentals')


    mdb.learn(
        from_data="https://s3.eu-west-2.amazonaws.com/mindsdb-example-data/home_rentals.csv",
        # the path to the file where we can learn from, (note: can be url)
        to_predict='rental_price',  # the column we want to learn to predict given all the data in the file
        sample_margin_of_error=0.02
    )

def test():
    from mindsdb.libs.controllers.predictor import Predictor
    from mindsdb import CONFIG

    mdb = Predictor(name='home_rentals')

    mdb.learn(
        from_data="https://s3.eu-west-2.amazonaws.com/mindsdb-example-data/home_rentals.csv",
        # the path to the file where we can learn from, (note: can be url)
        to_predict='rental_price',  # the column we want to learn to predict given all the data in the file
        #sample_margin_of_error=0.02,
        stop_training_in_x_seconds=6
    )

    #use the model to make predictions
    result = mdb.predict(
        when={"number_of_rooms": 2, "sqft": 1384})

    result[0].explain()

    when = {"number_of_rooms": 1,"sqft": 384}

def test():
    from mindsdb.libs.controllers.predictor import Predictor
    from mindsdb import CONFIG

    CONFIG.DEBUG_BREAK_POINT = PHASE_STATS_GENERATOR

    mdb = Predictor(name='home_rentals')

    mdb.learn(
        from_data="https://s3.eu-west-2.amazonaws.com/mindsdb-example-data/home_rentals.csv",
        # the path to the file where we can learn from, (note: can be url)
        to_predict='rental_price',  # the column we want to learn to predict given all the data in the file
        sample_margin_of_error=0.02
    )

def test():
    from mindsdb.libs.controllers.predictor import Predictor
    from mindsdb import CONFIG

    CONFIG.DEBUG_BREAK_POINT = PHASE_MODEL_TRAINER

    mdb = Predictor(name='home_rentals')

    mdb.learn(
        from_data="https://raw.githubusercontent.com/mindsdb/mindsdb/master/docs/examples/basic/home_rentals.csv",
        # the path to the file where we can learn from, (note: can be url)
        to_predict='rental_price',  # the column we want to learn to predict given all the data in the file
        sample_margin_of_error=0.02,
        stop_training_in_x_seconds=10
    )

def test():
    from mindsdb.libs.controllers.predictor import Predictor


    mdb = Predictor(name='home_rentals')

    mdb.learn(
        from_data="https://raw.githubusercontent.com/mindsdb/mindsdb/master/docs/examples/basic/home_rentals.csv",
        # the path to the file where we can learn from, (note: can be url)
        to_predict='rental_price',  # the column we want to learn to predict given all the data in the file
        sample_margin_of_error=0.02
    )

    mdb = Predictor(name='home_rentals')

    a = mdb.predict(when={'number_of_rooms': 10})

    print('-------Preidiction output------------')
    print(a.predicted_values)