LogisticRegressionModel (Spark 2.2.1 JavaDoc)

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:

java.io.Serializable, Logging, Params, Identifiable, MLWritable
```
public class LogisticRegressionModel
extends ProbabilisticClassificationModel<Vector,LogisticRegressionModel>
implements MLWritable
```
Model produced by LogisticRegression.

See Also:

Serialized Form

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`static IntParam`	`aggregationDepth()`
`void`	`checkThresholdConsistency()` If `threshold` and `thresholds` are both set, ensures they are consistent.
`static Params`	`clear(Param<?> param)`
`Matrix`	`coefficientMatrix()`
`Vector`	`coefficients()` A vector of model coefficients for "binomial" logistic regression.
`LogisticRegressionModel`	`copy(ParamMap extra)` Creates a copy of this instance with the same UID and some extra params.
`static DoubleParam`	`elasticNetParam()`
`LogisticRegressionSummary`	`evaluate(Dataset<?> dataset)` Evaluates the model on a test dataset.
`static String`	`explainParam(Param<?> param)`
`static String`	`explainParams()`
`static ParamMap`	`extractParamMap()`
`static ParamMap`	`extractParamMap(ParamMap extra)`
`static Param<String>`	`family()`
`Param<String>`	`family()` Param for the name of family which is a description of the label distribution to be used in the model.
`static Param<String>`	`featuresCol()`
`Param<String>`	`featuresCol()` Param for features column name.
`static BooleanParam`	`fitIntercept()`
`static <T> scala.Option<T>`	`get(Param<T> param)`
`static int`	`getAggregationDepth()`
`static <T> scala.Option<T>`	`getDefault(Param<T> param)`
`static double`	`getElasticNetParam()`
`static String`	`getFamily()`
`String`	`getFamily()`
`static String`	`getFeaturesCol()`
`String`	`getFeaturesCol()`
`static boolean`	`getFitIntercept()`
`static String`	`getLabelCol()`
`String`	`getLabelCol()`
`static Matrix`	`getLowerBoundsOnCoefficients()`
`Matrix`	`getLowerBoundsOnCoefficients()`
`static Vector`	`getLowerBoundsOnIntercepts()`
`Vector`	`getLowerBoundsOnIntercepts()`
`static int`	`getMaxIter()`
`static <T> T`	`getOrDefault(Param<T> param)`
`static Param<Object>`	`getParam(String paramName)`
`static String`	`getPredictionCol()`
`String`	`getPredictionCol()`
`static String`	`getProbabilityCol()`
`static String`	`getRawPredictionCol()`
`String`	`getRawPredictionCol()`
`static double`	`getRegParam()`
`static boolean`	`getStandardization()`
`double`	`getThreshold()` Get threshold for binary classification.
`double[]`	`getThresholds()` Get thresholds for binary or multiclass classification.
`static double`	`getTol()`
`static Matrix`	`getUpperBoundsOnCoefficients()`
`Matrix`	`getUpperBoundsOnCoefficients()`
`static Vector`	`getUpperBoundsOnIntercepts()`
`Vector`	`getUpperBoundsOnIntercepts()`
`static String`	`getWeightCol()`
`static <T> boolean`	`hasDefault(Param<T> param)`
`static boolean`	`hasParam(String paramName)`
`static boolean`	`hasParent()`
`boolean`	`hasSummary()` Indicates whether a training summary exists for this model instance.
`double`	`intercept()` The model intercept for "binomial" logistic regression.
`Vector`	`interceptVector()`
`static boolean`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static Param<String>`	`labelCol()`
`Param<String>`	`labelCol()` Param for label column name.
`static LogisticRegressionModel`	`load(String path)`
`static Param<Matrix>`	`lowerBoundsOnCoefficients()`
`Param<Matrix>`	`lowerBoundsOnCoefficients()` The lower bounds on coefficients if fitting under bound constrained optimization.
`static Param<Vector>`	`lowerBoundsOnIntercepts()`
`Param<Vector>`	`lowerBoundsOnIntercepts()` The lower bounds on intercepts if fitting under bound constrained optimization.
`static IntParam`	`maxIter()`
`int`	`numClasses()`
`int`	`numFeatures()`
`static Param<?>[]`	`params()`
`static void`	`parent_$eq(Estimator<M> x$1)`
`static Estimator<M>`	`parent()`
`static Param<String>`	`predictionCol()`
`Param<String>`	`predictionCol()` Param for prediction column name.
`static Param<String>`	`probabilityCol()`
`static Param<String>`	`rawPredictionCol()`
`Param<String>`	`rawPredictionCol()` Param for raw prediction (a.k.a.
`static MLReader<LogisticRegressionModel>`	`read()`
`static DoubleParam`	`regParam()`
`static void`	`save(String path)`
`static <T> Params`	`set(Param<T> param, T value)`
`static M`	`setFeaturesCol(String value)`
`static M`	`setParent(Estimator<M> parent)`
`static M`	`setPredictionCol(String value)`
`static M`	`setProbabilityCol(String value)`
`static M`	`setRawPredictionCol(String value)`
`LogisticRegressionModel`	`setThreshold(double value)` Set threshold in binary classification, in range [0, 1].
`LogisticRegressionModel`	`setThresholds(double[] value)` Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class.
`static BooleanParam`	`standardization()`
`LogisticRegressionTrainingSummary`	`summary()` Gets summary of model on training set.
`static DoubleParam`	`threshold()`
`static DoubleArrayParam`	`thresholds()`
`static DoubleParam`	`tol()`
`static String`	`toString()`
`static Dataset<Row>`	`transform(Dataset<?> dataset)`
`static Dataset<Row>`	`transform(Dataset<?> dataset, ParamMap paramMap)`
`static Dataset<Row>`	`transform(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)`
`static Dataset<Row>`	`transform(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)`
`static StructType`	`transformSchema(StructType schema)`
`String`	`uid()` An immutable unique ID for the object and its derivatives.
`static Param<Matrix>`	`upperBoundsOnCoefficients()`
`Param<Matrix>`	`upperBoundsOnCoefficients()` The upper bounds on coefficients if fitting under bound constrained optimization.
`static Param<Vector>`	`upperBoundsOnIntercepts()`
`Param<Vector>`	`upperBoundsOnIntercepts()` The upper bounds on intercepts if fitting under bound constrained optimization.
`boolean`	`usingBoundConstrainedOptimization()`
`StructType`	`validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)`
`StructType`	`validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)` Validates and transforms the input schema with the provided param map.
`static Param<String>`	`weightCol()`
`MLWriter`	`write()` Returns a `MLWriter` instance for this ML instance.

Methods inherited from class org.apache.spark.ml.classification.ProbabilisticClassificationModel
normalizeToProbabilitiesInPlace, setProbabilityCol, transform

Methods inherited from class org.apache.spark.ml.classification.ClassificationModel
setRawPredictionCol

Methods inherited from class org.apache.spark.ml.PredictionModel
setFeaturesCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Model
hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer
transform, transform, transform

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.Params
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable
toString

Methods inherited from interface org.apache.spark.ml.util.MLWritable
save

Methods inherited from interface org.apache.spark.internal.Logging
initializeLogging, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

- Method Detail
  - read
```
public static MLReader<LogisticRegressionModel> read()
```
  - load
```
public static LogisticRegressionModel load(String path)
```
  - toString
```
public static String toString()
```
  - params
```
public static Param<?>[] params()
```
  - explainParam
```
public static String explainParam(Param<?> param)
```
  - explainParams
```
public static String explainParams()
```
  - isSet
```
public static final boolean isSet(Param<?> param)
```
  - isDefined
```
public static final boolean isDefined(Param<?> param)
```
  - hasParam
```
public static boolean hasParam(String paramName)
```
  - getParam
```
public static Param<Object> getParam(String paramName)
```
  - set
```
public static final <T> Params set(Param<T> param,
                                   T value)
```
  - get
```
public static final <T> scala.Option<T> get(Param<T> param)
```
  - clear
```
public static final Params clear(Param<?> param)
```
  - getOrDefault
```
public static final <T> T getOrDefault(Param<T> param)
```
  - getDefault
```
public static final <T> scala.Option<T> getDefault(Param<T> param)
```
  - hasDefault
```
public static final <T> boolean hasDefault(Param<T> param)
```
  - extractParamMap
```
public static final ParamMap extractParamMap(ParamMap extra)
```
  - extractParamMap
```
public static final ParamMap extractParamMap()
```
  - transform
```
public static Dataset<Row> transform(Dataset<?> dataset,
                                     ParamPair<?> firstParamPair,
                                     scala.collection.Seq<ParamPair<?>> otherParamPairs)
```
  - transform
```
public static Dataset<Row> transform(Dataset<?> dataset,
                                     ParamMap paramMap)
```
  - transform
```
public static Dataset<Row> transform(Dataset<?> dataset,
                                     ParamPair<?> firstParamPair,
                                     ParamPair<?>... otherParamPairs)
```
  - parent
```
public static Estimator<M> parent()
```
  - parent_$eq
```
public static void parent_$eq(Estimator<M> x$1)
```
  - setParent
```
public static M setParent(Estimator<M> parent)
```
  - hasParent
```
public static boolean hasParent()
```
  - labelCol
```
public static final Param<String> labelCol()
```
  - getLabelCol
```
public static final String getLabelCol()
```
  - featuresCol
```
public static final Param<String> featuresCol()
```
  - getFeaturesCol
```
public static final String getFeaturesCol()
```
  - predictionCol
```
public static final Param<String> predictionCol()
```
  - getPredictionCol
```
public static final String getPredictionCol()
```
  - setFeaturesCol
```
public static M setFeaturesCol(String value)
```
  - setPredictionCol
```
public static M setPredictionCol(String value)
```
  - transformSchema
```
public static StructType transformSchema(StructType schema)
```
  - rawPredictionCol
```
public static final Param<String> rawPredictionCol()
```
  - getRawPredictionCol
```
public static final String getRawPredictionCol()
```
  - setRawPredictionCol
```
public static M setRawPredictionCol(String value)
```
  - probabilityCol
```
public static final Param<String> probabilityCol()
```
  - getProbabilityCol
```
public static final String getProbabilityCol()
```
  - thresholds
```
public static final DoubleArrayParam thresholds()
```
  - setProbabilityCol
```
public static M setProbabilityCol(String value)
```
  - transform
```
public static Dataset<Row> transform(Dataset<?> dataset)
```
  - regParam
```
public static final DoubleParam regParam()
```
  - getRegParam
```
public static final double getRegParam()
```
  - elasticNetParam
```
public static final DoubleParam elasticNetParam()
```
  - getElasticNetParam
```
public static final double getElasticNetParam()
```
  - maxIter
```
public static final IntParam maxIter()
```
  - getMaxIter
```
public static final int getMaxIter()
```
  - fitIntercept
```
public static final BooleanParam fitIntercept()
```
  - getFitIntercept
```
public static final boolean getFitIntercept()
```
  - tol
```
public static final DoubleParam tol()
```
  - getTol
```
public static final double getTol()
```
  - standardization
```
public static final BooleanParam standardization()
```
  - getStandardization
```
public static final boolean getStandardization()
```
  - weightCol
```
public static final Param<String> weightCol()
```
  - getWeightCol
```
public static final String getWeightCol()
```
  - threshold
```
public static final DoubleParam threshold()
```
  - aggregationDepth
```
public static final IntParam aggregationDepth()
```
  - getAggregationDepth
```
public static final int getAggregationDepth()
```
  - family
```
public static final Param<String> family()
```
  - getFamily
```
public static String getFamily()
```
  - lowerBoundsOnCoefficients
```
public static Param<Matrix> lowerBoundsOnCoefficients()
```
  - getLowerBoundsOnCoefficients
```
public static Matrix getLowerBoundsOnCoefficients()
```
  - upperBoundsOnCoefficients
```
public static Param<Matrix> upperBoundsOnCoefficients()
```
  - getUpperBoundsOnCoefficients
```
public static Matrix getUpperBoundsOnCoefficients()
```
  - lowerBoundsOnIntercepts
```
public static Param<Vector> lowerBoundsOnIntercepts()
```
  - getLowerBoundsOnIntercepts
```
public static Vector getLowerBoundsOnIntercepts()
```
  - upperBoundsOnIntercepts
```
public static Param<Vector> upperBoundsOnIntercepts()
```
  - getUpperBoundsOnIntercepts
```
public static Vector getUpperBoundsOnIntercepts()
```
  - save
```
public static void save(String path)
                 throws java.io.IOException
```
    Throws:
    
    java.io.IOException
  - 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
    
    Specified by:
    
    uid in class ProbabilisticClassificationModel<Vector,LogisticRegressionModel>
    
    Returns:
    
    (undocumented)
  - coefficientMatrix
```
public Matrix coefficientMatrix()
```
  - interceptVector
```
public Vector interceptVector()
```
  - numClasses
```
public int numClasses()
```
    Specified by:
    
    numClasses in class ProbabilisticClassificationModel<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)
```
    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.
    
    Parameters:
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
  - getThreshold
```
public double getThreshold()
```
    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.
    Returns:
    
    (undocumented)
  - setThresholds
```
public LogisticRegressionModel setThresholds(double[] value)
```
    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.
    
    Overrides:
    
    setThresholds in class ProbabilisticClassificationModel<Vector,LogisticRegressionModel>
    
    Parameters:
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
  - getThresholds
```
public double[] getThresholds()
```
    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.
    
    Overrides:
    
    getThresholds in class ProbabilisticClassificationModel<Vector,LogisticRegressionModel>
    
    Returns:
    
    (undocumented)
  - numFeatures
```
public int numFeatures()
```
    Overrides:
    
    numFeatures in class ProbabilisticClassificationModel<Vector,LogisticRegressionModel>
  - summary
```
public LogisticRegressionTrainingSummary summary()
```
    Gets summary of model on training set. An exception is thrown if trainingSummary == None.
    
    Returns:
    
    (undocumented)
  - hasSummary
```
public boolean hasSummary()
```
    Indicates whether a training summary exists for this model instance.
  - evaluate
```
public LogisticRegressionSummary evaluate(Dataset<?> dataset)
```
    Evaluates the model on a test dataset.
    
    Parameters:
    
    dataset - Test dataset to evaluate model on.
    
    Returns:
    
    (undocumented)
  - 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 ProbabilisticClassificationModel<Vector,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 parent currently.
    
    Specified by:
    
    write in interface MLWritable
    
    Returns:
    
    (undocumented)
  - family
```
public Param<String> family()
```
    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".
    
    Returns:
    
    (undocumented)
  - getFamily
```
public String getFamily()
```
  - checkThresholdConsistency
```
public void checkThresholdConsistency()
```
    If threshold and thresholds are both set, ensures they are consistent.
    
    Throws:
    
    IllegalArgumentException - if threshold and thresholds are not equivalent
  - lowerBoundsOnCoefficients
```
public Param<Matrix> lowerBoundsOnCoefficients()
```
    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.
    
    Returns:
    
    (undocumented)
  - getLowerBoundsOnCoefficients
```
public Matrix getLowerBoundsOnCoefficients()
```
  - upperBoundsOnCoefficients
```
public Param<Matrix> upperBoundsOnCoefficients()
```
    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.
    
    Returns:
    
    (undocumented)
  - getUpperBoundsOnCoefficients
```
public Matrix getUpperBoundsOnCoefficients()
```
  - lowerBoundsOnIntercepts
```
public Param<Vector> lowerBoundsOnIntercepts()
```
    The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.
    
    Returns:
    
    (undocumented)
  - getLowerBoundsOnIntercepts
```
public Vector getLowerBoundsOnIntercepts()
```
  - upperBoundsOnIntercepts
```
public Param<Vector> upperBoundsOnIntercepts()
```
    The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.
    
    Returns:
    
    (undocumented)
  - getUpperBoundsOnIntercepts
```
public Vector getUpperBoundsOnIntercepts()
```
  - usingBoundConstrainedOptimization
```
public boolean usingBoundConstrainedOptimization()
```
  - validateAndTransformSchema
```
public StructType validateAndTransformSchema(StructType schema,
                                             boolean fitting,
                                             DataType featuresDataType)
```
  - rawPredictionCol
```
public Param<String> rawPredictionCol()
```
    Param for raw prediction (a.k.a. confidence) column name.
    
    Returns:
    
    (undocumented)
  - getRawPredictionCol
```
public String getRawPredictionCol()
```
  - validateAndTransformSchema
```
public StructType validateAndTransformSchema(StructType schema,
                                             boolean fitting,
                                             DataType featuresDataType)
```
    Validates and transforms the input schema with the provided param map.
    
    Parameters:
    
    schema - input schema
    
    fitting - whether this is in fitting
    
    featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.
    
    Returns:
    
    output schema
  - labelCol
```
public Param<String> labelCol()
```
    Param for label column name.
    
    Returns:
    
    (undocumented)
  - getLabelCol
```
public String getLabelCol()
```
  - featuresCol
```
public Param<String> featuresCol()
```
    Param for features column name.
    
    Returns:
    
    (undocumented)
  - getFeaturesCol
```
public String getFeaturesCol()
```
  - predictionCol
```
public Param<String> predictionCol()
```
    Param for prediction column name.
    
    Returns:
    
    (undocumented)
  - getPredictionCol
```
public String getPredictionCol()
```

Class LogisticRegressionModel

Method Summary

Methods inherited from class org.apache.spark.ml.classification.ProbabilisticClassificationModel

Methods inherited from class org.apache.spark.ml.classification.ClassificationModel

Methods inherited from class org.apache.spark.ml.PredictionModel

Methods inherited from class org.apache.spark.ml.Model

Methods inherited from class org.apache.spark.ml.Transformer

Methods inherited from class Object

Methods inherited from interface org.apache.spark.ml.param.Params

Methods inherited from interface org.apache.spark.ml.util.Identifiable

Methods inherited from interface org.apache.spark.ml.util.MLWritable

Methods inherited from interface org.apache.spark.internal.Logging

Method Detail

read

load

toString

params

explainParam

explainParams

isSet

isDefined

hasParam

getParam

set

get

clear

getOrDefault

getDefault

hasDefault

extractParamMap

extractParamMap

transform

transform

transform

parent

parent_$eq

setParent

hasParent

labelCol

getLabelCol

featuresCol

getFeaturesCol

predictionCol

getPredictionCol

setFeaturesCol

setPredictionCol

transformSchema

rawPredictionCol

getRawPredictionCol

setRawPredictionCol

probabilityCol

getProbabilityCol

thresholds

setProbabilityCol

transform

regParam

getRegParam

elasticNetParam

getElasticNetParam

maxIter

getMaxIter

fitIntercept

getFitIntercept

tol

getTol

standardization

getStandardization

weightCol

getWeightCol

threshold

aggregationDepth

getAggregationDepth

family

getFamily

lowerBoundsOnCoefficients

getLowerBoundsOnCoefficients

upperBoundsOnCoefficients

getUpperBoundsOnCoefficients

lowerBoundsOnIntercepts

getLowerBoundsOnIntercepts