Custom Evaluator in PySpark

Question

I want to optimize the hyper parameters of a PySpark Pipeline using a ranking metric (MAP@k). I have seen in the documentation how to use the metrics defined in the Evaluation (Scala), but I need to define a custom evaluator class because MAP@k is not implemented yet. So I need to do something like:

model = Pipeline(stages=[indexer, assembler, scaler, lg])
paramGrid_lg = ParamGridBuilder() \
    .addGrid(lg.regParam, [0.001, 0.1]) \
    .addGrid(lg.elasticNetParam, [0, 1]) \
    .build()

crossval_lg = CrossValidator(estimator=model,
                      estimatorParamMaps=paramGrid_lg,
                      evaluator=MAPkEvaluator(), 
                      numFolds=2)

where MAPkEvaluator() is my custom evaluator. I've seen a similar question but not the answer.

Is there any example or documentation available for this? Does anyone know if it Is possible to implement it in PySpark? What methods should I implement?

Answer 1

@jarandaf answered the question in the first comment, but for clarity reasons I write how to implement a basic example with a random metric:

import random
from pyspark.ml.evaluation import Evaluator

class RandomEvaluator(Evaluator):

    def __init__(self, predictionCol="prediction", labelCol="label"):
        self.predictionCol = predictionCol
        self.labelCol = labelCol

    def _evaluate(self, dataset):
        """
        Returns a random number. 
        Implement here the true metric
        """
        return random.randint(0,1)

    def isLargerBetter(self):
        return True

Now the following code should work:

from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

paramGrid_lg = ParamGridBuilder() \
    .addGrid(lg.regParam, [0.01, 0.1]) \
    .addGrid(lg.elasticNetParam, [0, 1]) \
    .build()

crossval_lg = CrossValidator(estimator=model,
                      estimatorParamMaps=paramGrid_lg,
                      evaluator= RandomEvaluator(), 
                      numFolds=2)

cvModel = crossval_lg.fit(train_val_data_)

Answer 2

@Amanda answered very well the question, but let me show you something to avoid too. If you check the help of Evaluator() class doing:

help(Evaluator())

you'll see a method defined there:

isLargerBetter(self)
 |      Indicates whether the metric returned by :py:meth:`evaluate` should be maximized
 |      (True, default) or minimized (False).
 |      A given evaluator may support multiple metrics which may be maximized or minimized.
 |      
 |      .. versionadded:: 1.5.0

Now if you your metric needs to be minimized, you need to set this method as:

def isLargerBetter(self):
        return False

The default value for the current method is True.