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From jkbradley <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-13430][PySpark][ML] Python API for trai...
Date Wed, 30 Mar 2016 21:20:40 GMT
Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11621#discussion_r57965926
  
    --- Diff: python/pyspark/ml/classification.py ---
    @@ -233,6 +234,210 @@ def intercept(self):
             """
             return self._call_java("intercept")
     
    +    @property
    +    @since("2.0.0")
    +    def summary(self):
    +        """
    +        Gets summary (e.g. residuals, mse, r-squared ) of model on
    +        training set. An exception is thrown if
    +        `trainingSummary == None`.
    +        """
    +        java_blrt_summary = self._call_java("summary")
    +        return BinaryLogisticRegressionTrainingSummary(java_blrt_summary)
    +
    +    @property
    +    @since("2.0.0")
    +    def hasSummary(self):
    +        """
    +        Indicates whether a training summary exists for this model
    +        instance.
    +        """
    +        return self._call_java("hasSummary")
    +
    +    @since("2.0.0")
    +    def evaluate(self, dataset):
    +        """
    +        Evaluates the model on a test dataset.
    +
    +        :param dataset:
    +          Test dataset to evaluate model on.
    +        """
    +        java_blr_summary = self._call_java("evaluate", dataset)
    +        return BinaryLogisticRegressionSummary(java_blr_summary)
    +
    +
    +class LogisticRegressionSummary(JavaCallable):
    +    """
    +    Abstraction for Logistic Regression Results for a given model.
    +
    +    .. versionadded:: 2.0.0
    +    """
    +
    +    @property
    +    @since("2.0.0")
    +    def predictions(self):
    +        """
    +        Dataframe outputted by the model's `transform` method.
    +        """
    +        return self._call_java("predictions")
    +
    +    @property
    +    @since("2.0.0")
    +    def probabilityCol(self):
    +        """
    +        Field in "predictions" which gives the calibrated probability
    +        of each instance as a vector.
    +        """
    +        return self._call_java("probabilityCol")
    +
    +    @property
    +    @since("2.0.0")
    +    def labelCol(self):
    +        """
    +        Field in "predictions" which gives the true label of each
    +        instance.
    +        """
    +        return self._call_java("labelCol")
    +
    +    @property
    +    @since("2.0.0")
    +    def featuresCol(self):
    +        """
    +        Field in "predictions" which gives the features of each instance
    +        as a vector.
    +        """
    +        return self._call_java("featuresCol")
    +
    +
    +class LogisticRegressionTrainingSummary(LogisticRegressionSummary):
    --- End diff --
    
    ```@inherit_doc```


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