org.apache.spark.mllib.classification

Class LogisticRegressionModel

java.lang.Object

org.apache.spark.mllib.regression.GeneralizedLinearModel

org.apache.spark.mllib.classification.LogisticRegressionModel

All Implemented Interfaces:

java.io.Serializable, ClassificationModel, PMMLExportable, Saveable

public class LogisticRegressionModel
extends GeneralizedLinearModel
implements ClassificationModel, scala.Serializable, Saveable, PMMLExportable

Classification model trained using Multinomial/Binary Logistic Regression.

param: weights Weights computed for every feature. param: intercept Intercept computed for this model. (Only used in Binary Logistic Regression. In Multinomial Logistic Regression, the intercepts will not be a single value, so the intercepts will be part of the weights.) param: numFeatures the dimension of the features. param: numClasses the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so numClasses will be set to 2.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LogisticRegressionModel(Vector weights, double intercept) Constructs a LogisticRegressionModel with weights and intercept for binary classification.
LogisticRegressionModel(Vector weights, double intercept, int numFeatures, int numClasses)

Method Summary

Methods

Modifier and Type	Method and Description
LogisticRegressionModel	clearThreshold() :: Experimental :: Clears the threshold so that predict will output raw prediction scores.
protected java.lang.String	formatVersion() Current version of model save/load format.
scala.Option<java.lang.Object>	getThreshold() :: Experimental :: Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
double	intercept()
static LogisticRegressionModel	load(SparkContext sc, java.lang.String path)
int	numClasses()
int	numFeatures()
protected double	predictPoint(Vector dataMatrix, Vector weightMatrix, double intercept) Predict the result given a data point and the weights learned.
void	save(SparkContext sc, java.lang.String path) Save this model to the given path.
LogisticRegressionModel	setThreshold(double threshold) :: Experimental :: Sets the threshold that separates positive predictions from negative predictions in Binary Logistic Regression.
java.lang.String	toString() Print a summary of the model.
Vector	weights()

Methods inherited from class org.apache.spark.mllib.regression.GeneralizedLinearModel

predict, predict

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.apache.spark.mllib.classification.ClassificationModel

predict, predict, predict

Methods inherited from interface org.apache.spark.mllib.pmml.PMMLExportable

toPMML, toPMML, toPMML, toPMML, toPMML

Constructor Detail

LogisticRegressionModel

public LogisticRegressionModel(Vector weights,
                       double intercept,
                       int numFeatures,
                       int numClasses)

LogisticRegressionModel

public LogisticRegressionModel(Vector weights,
                       double intercept)

Constructs a LogisticRegressionModel with weights and intercept for binary classification.

Parameters:

weights - (undocumented)

intercept - (undocumented)

Method Detail

load

public static LogisticRegressionModel load(SparkContext sc,
                           java.lang.String path)

weights

public Vector weights()

Overrides:

weights in class GeneralizedLinearModel

intercept

public double intercept()

Overrides:

intercept in class GeneralizedLinearModel

numFeatures

public int numFeatures()

numClasses

public int numClasses()

setThreshold

public LogisticRegressionModel setThreshold(double threshold)

:: Experimental :: Sets the threshold that separates positive predictions from negative predictions in Binary Logistic Regression. An example with prediction score greater than or equal to this threshold is identified as an positive, and negative otherwise. The default value is 0.5. It is only used for binary classification.

Parameters:

threshold - (undocumented)

Returns:

(undocumented)

getThreshold

public scala.Option<java.lang.Object> getThreshold()

:: Experimental :: Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions. It is only used for binary classification.

Returns:

(undocumented)

clearThreshold

public LogisticRegressionModel clearThreshold()

:: Experimental :: Clears the threshold so that predict will output raw prediction scores. It is only used for binary classification.

Returns:

(undocumented)

predictPoint

protected double predictPoint(Vector dataMatrix,
                  Vector weightMatrix,
                  double intercept)

Description copied from class: GeneralizedLinearModel

Predict the result given a data point and the weights learned.

Specified by:

predictPoint in class GeneralizedLinearModel

Parameters:

dataMatrix - Row vector containing the features for this data point

weightMatrix - Column vector containing the weights of the model

intercept - Intercept of the model.

Returns:

(undocumented)

save

public void save(SparkContext sc,
        java.lang.String path)

Description copied from interface: Saveable

Save this model to the given path.

This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/

The model may be loaded using Loader.load.

Specified by:

save in interface Saveable

Parameters:

sc - Spark context used to save model data.

path - Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

formatVersion

protected java.lang.String formatVersion()

Description copied from interface: Saveable

Current version of model save/load format.

Specified by:

formatVersion in interface Saveable

toString

public java.lang.String toString()

Description copied from class: GeneralizedLinearModel

Print a summary of the model.

Overrides:

toString in class GeneralizedLinearModel

Returns:

(undocumented)