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From: JeremyNixon <git@git.apache.org>
To: reviews@spark.apache.org
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Subject: [GitHub] spark pull request: [SPARK-12877] [ML] Add
 train-validation-split ...
Content-Type: text/plain
Message-Id: <20160226221305.E29E1E0211@git1-us-west.apache.org>
Date: Fri, 26 Feb 2016 22:13:05 +0000 (UTC)

Github user JeremyNixon commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11335#discussion_r54309484
  
    --- Diff: python/pyspark/ml/tuning.py ---
    @@ -288,6 +289,195 @@ def copy(self, extra=None):
             return CrossValidatorModel(self.bestModel.copy(extra))
     
     
    +class TrainValidationSplit(Estimator, HasSeed):
    +    """
    +    Train-Validation-Split.
    +    >>> from pyspark.ml.classification import LogisticRegression
    +    >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
    +    >>> from pyspark.mllib.linalg import Vectors
    +    >>> dataset = sqlContext.createDataFrame(
    +    ...     [(Vectors.dense([0.0]), 0.0),
    +    ...      (Vectors.dense([0.4]), 1.0),
    +    ...      (Vectors.dense([0.5]), 0.0),
    +    ...      (Vectors.dense([0.6]), 1.0),
    +    ...      (Vectors.dense([1.0]), 1.0)] * 10,
    +    ...     ["features", "label"])
    +    >>> lr = LogisticRegression()
    +    >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
    +    >>> evaluator = BinaryClassificationEvaluator()
    +    >>> tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
    +    >>> tvsModel = tvs.fit(dataset)
    +    >>> evaluator.evaluate(tvsModel.transform(dataset))
    +    0.8333...
    +
    +    .. versionadded:: 2.0.0
    +    """
    +
    +    estimator = Param(Params._dummy(), "estimator", "estimator to be tested")
    +    estimatorParamMaps = Param(Params._dummy(), "estimatorParamMaps", "estimator param maps")
    +    evaluator = Param(
    +        Params._dummy(), "evaluator",
    +        "evaluator used to select hyper-parameters that maximize the validated metric")
    +    trainRatio = Param(Params._dummy(), "trainRatio", "Param for ratio between train and\
    +     validation data. Must be between 0 and 1.")
    +
    +    @keyword_only
    +    def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
    +                 seed=None):
    +        """
    +        __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
    +                 seed=None)
    +        """
    +        super(TrainValidationSplit, self).__init__()
    +        self._setDefault(trainRatio=0.75)
    +        kwargs = self.__init__._input_kwargs
    +        self._set(**kwargs)
    +
    +    @since("2.0.0")
    +    @keyword_only
    +    def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
    +                  seed=None):
    +        """
    +        setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio= 0.75,\
    --- End diff --
    
    Updated.


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