Package org.apache.spark.ml.classification

Class LogisticRegressionModel

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Transformer
org.apache.spark.ml.Model<M>
org.apache.spark.ml.PredictionModel<FeaturesType,M>
org.apache.spark.ml.classification.ClassificationModel<FeaturesType,M>
org.apache.spark.ml.classification.ProbabilisticClassificationModel<Vector,LogisticRegressionModel>
org.apache.spark.ml.classification.LogisticRegressionModel
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, ClassifierParams, LogisticRegressionParams, ProbabilisticClassifierParams, Params, HasAggregationDepth, HasElasticNetParam, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasMaxBlockSizeInMB, HasMaxIter, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasRegParam, HasStandardization, HasThreshold, HasThresholds, HasTol, HasWeightCol, PredictorParams, HasTrainingSummary<LogisticRegressionTrainingSummary>, Identifiable, MLWritable
public class LogisticRegressionModel extends ProbabilisticClassificationModel<Vector,LogisticRegressionModel> implements MLWritable, LogisticRegressionParams, HasTrainingSummary<LogisticRegressionTrainingSummary>
Model produced by LogisticRegression.
See Also:

  • Method Details

    • read

      public static MLReader<LogisticRegressionModel> read()

    • load

      public static LogisticRegressionModel load(String path)

    • family

      public final Param<String> family()
      Description copied from interface: LogisticRegressionParams
      Param for the name of family which is a description of the label distribution to be used in the model. Supported options: - "auto": Automatically select the family based on the number of classes: If numClasses == 1 || numClasses == 2, set to "binomial". Else, set to "multinomial" - "binomial": Binary logistic regression with pivoting. - "multinomial": Multinomial logistic (softmax) regression without pivoting. Default is "auto".

      Specified by:
      family in interface LogisticRegressionParams
      Returns:
      (undocumented)

    • lowerBoundsOnCoefficients

      public Param<Matrix> lowerBoundsOnCoefficients()
      Description copied from interface: LogisticRegressionParams
      The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

      Specified by:
      lowerBoundsOnCoefficients in interface LogisticRegressionParams
      Returns:
      (undocumented)

    • upperBoundsOnCoefficients

      public Param<Matrix> upperBoundsOnCoefficients()
      Description copied from interface: LogisticRegressionParams
      The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

      Specified by:
      upperBoundsOnCoefficients in interface LogisticRegressionParams
      Returns:
      (undocumented)

    • lowerBoundsOnIntercepts

      public Param<Vector> lowerBoundsOnIntercepts()
      Description copied from interface: LogisticRegressionParams
      The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

      Specified by:
      lowerBoundsOnIntercepts in interface LogisticRegressionParams
      Returns:
      (undocumented)

    • upperBoundsOnIntercepts

      public Param<Vector> upperBoundsOnIntercepts()
      Description copied from interface: LogisticRegressionParams
      The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

      Specified by:
      upperBoundsOnIntercepts in interface LogisticRegressionParams
      Returns:
      (undocumented)

    • maxBlockSizeInMB

      public final DoubleParam maxBlockSizeInMB()
      Description copied from interface: HasMaxBlockSizeInMB
      Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be &gt;= 0..
      Specified by:
      maxBlockSizeInMB in interface HasMaxBlockSizeInMB
      Returns:
      (undocumented)

    • aggregationDepth

      public final IntParam aggregationDepth()
      Description copied from interface: HasAggregationDepth
      Param for suggested depth for treeAggregate (&gt;= 2).
      Specified by:
      aggregationDepth in interface HasAggregationDepth
      Returns:
      (undocumented)

    • threshold

      public DoubleParam threshold()
      Description copied from interface: HasThreshold
      Param for threshold in binary classification prediction, in range [0, 1].
      Specified by:
      threshold in interface HasThreshold
      Returns:
      (undocumented)

    • weightCol

      public final Param<String> weightCol()
      Description copied from interface: HasWeightCol
      Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
      Specified by:
      weightCol in interface HasWeightCol
      Returns:
      (undocumented)

    • standardization

      public final BooleanParam standardization()
      Description copied from interface: HasStandardization
      Param for whether to standardize the training features before fitting the model.
      Specified by:
      standardization in interface HasStandardization
      Returns:
      (undocumented)

    • tol

      public final DoubleParam tol()
      Description copied from interface: HasTol
      Param for the convergence tolerance for iterative algorithms (&gt;= 0).
      Specified by:
      tol in interface HasTol
      Returns:
      (undocumented)

    • fitIntercept

      public final BooleanParam fitIntercept()
      Description copied from interface: HasFitIntercept
      Param for whether to fit an intercept term.
      Specified by:
      fitIntercept in interface HasFitIntercept
      Returns:
      (undocumented)

    • maxIter

      public final IntParam maxIter()
      Description copied from interface: HasMaxIter
      Param for maximum number of iterations (&gt;= 0).
      Specified by:
      maxIter in interface HasMaxIter
      Returns:
      (undocumented)

    • elasticNetParam

      public final DoubleParam elasticNetParam()
      Description copied from interface: HasElasticNetParam
      Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
      Specified by:
      elasticNetParam in interface HasElasticNetParam
      Returns:
      (undocumented)

    • regParam

      public final DoubleParam regParam()
      Description copied from interface: HasRegParam
      Param for regularization parameter (&gt;= 0).
      Specified by:
      regParam in interface HasRegParam
      Returns:
      (undocumented)

    • uid

      public String uid()
      Description copied from interface: Identifiable
      An immutable unique ID for the object and its derivatives.
      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • coefficientMatrix

      public Matrix coefficientMatrix()

    • interceptVector

      public Vector interceptVector()

    • numClasses

      public int numClasses()
      Description copied from class: ClassificationModel
      Number of classes (values which the label can take).
      Specified by:
      numClasses in class ClassificationModel<Vector,LogisticRegressionModel>

    • coefficients

      public Vector coefficients()
      A vector of model coefficients for "binomial" logistic regression. If this model was trained using the "multinomial" family then an exception is thrown.

      Returns:
      Vector

    • intercept

      public double intercept()
      The model intercept for "binomial" logistic regression. If this model was fit with the "multinomial" family then an exception is thrown.

      Returns:
      Double

    • setThreshold

      public LogisticRegressionModel setThreshold(double value)
      Description copied from interface: LogisticRegressionParams
      Set threshold in binary classification, in range [0, 1].

      If the estimated probability of class label 1 is greater than threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.

      Note: Calling this with threshold p is equivalent to calling setThresholds(Array(1-p, p)). When setThreshold() is called, any user-set value for thresholds will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

      Default is 0.5.

      Specified by:
      setThreshold in interface LogisticRegressionParams
      Parameters:
      value - (undocumented)
      Returns:
      (undocumented)

    • getThreshold

      public double getThreshold()
      Description copied from interface: LogisticRegressionParams
      Get threshold for binary classification.

      If thresholds is set with length 2 (i.e., binary classification), this returns the equivalent threshold: 

      1 / (1 + thresholds(0) / thresholds(1))
      . Otherwise, returns `threshold` if set, or its default value if unset. @group getParam @throws IllegalArgumentException if `thresholds` is set to an array of length other than 2.
      Specified by:
      getThreshold in interface HasThreshold
      Specified by:
      getThreshold in interface LogisticRegressionParams
      Returns:
      (undocumented)

    • setThresholds

      public LogisticRegressionModel setThresholds(double[] value)
      Description copied from interface: LogisticRegressionParams
      Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values greater than 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

      Note: When setThresholds() is called, any user-set value for threshold will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

      Specified by:
      setThresholds in interface LogisticRegressionParams
      Overrides:
      setThresholds in class ProbabilisticClassificationModel<Vector,LogisticRegressionModel>
      Parameters:
      value - (undocumented)
      Returns:
      (undocumented)

    • getThresholds

      public double[] getThresholds()
      Description copied from interface: LogisticRegressionParams
      Get thresholds for binary or multiclass classification.

      If thresholds is set, return its value. Otherwise, if threshold is set, return the equivalent thresholds for binary classification: (1-threshold, threshold). If neither are set, throw an exception.

      Specified by:
      getThresholds in interface HasThresholds
      Specified by:
      getThresholds in interface LogisticRegressionParams
      Returns:
      (undocumented)

    • numFeatures

      public int numFeatures()
      Description copied from class: PredictionModel
      Returns the number of features the model was trained on. If unknown, returns -1
      Overrides:
      numFeatures in class PredictionModel<Vector,LogisticRegressionModel>

    • summary

      public LogisticRegressionTrainingSummary summary()
      Gets summary of model on training set. An exception is thrown if hasSummary is false.
      Specified by:
      summary in interface HasTrainingSummary<LogisticRegressionTrainingSummary>
      Returns:
      (undocumented)

    • binarySummary

      public BinaryLogisticRegressionTrainingSummary binarySummary()
      Gets summary of model on training set. An exception is thrown if hasSummary is false or it is a multiclass model.
      Returns:
      (undocumented)

    • evaluate

      public LogisticRegressionSummary evaluate(Dataset<?> dataset)
      Evaluates the model on a test dataset.

      Parameters:
      dataset - Test dataset to evaluate model on.
      Returns:
      (undocumented)

    • predict

      public double predict(Vector features)
      Predict label for the given feature vector. The behavior of this can be adjusted using thresholds.
      Overrides:
      predict in class ClassificationModel<Vector,LogisticRegressionModel>
      Parameters:
      features - (undocumented)
      Returns:
      (undocumented)

    • predictRaw

      public Vector predictRaw(Vector features)
      Description copied from class: ClassificationModel
      Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output ClassificationModel.rawPredictionCol().

      Specified by:
      predictRaw in class ClassificationModel<Vector,LogisticRegressionModel>
      Parameters:
      features - (undocumented)
      Returns:
      vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

    • copy

      public LogisticRegressionModel copy(ParamMap extra)
      Description copied from interface: Params
      Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().
      Specified by:
      copy in interface Params
      Specified by:
      copy in class Model<LogisticRegressionModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • write

      public MLWriter write()
      Returns a MLWriter instance for this ML instance.

      For LogisticRegressionModel, this does NOT currently save the training summary(). An option to save summary() may be added in the future.

      This also does not save the Model.parent() currently.

      Specified by:
      write in interface MLWritable
      Returns:
      (undocumented)

    • toString

      public String toString()
      Specified by:
      toString in interface Identifiable
      Overrides:
      toString in class Object