LogisticRegression (Spark 2.1.0 JavaDoc)

Object
- org.apache.spark.ml.PipelineStage
- - org.apache.spark.ml.Estimator<M>
  - - org.apache.spark.ml.Predictor<FeaturesType,E,M>
    - - org.apache.spark.ml.classification.Classifier<FeaturesType,E,M>
      - org.apache.spark.ml.classification.ProbabilisticClassifier<Vector,LogisticRegression,LogisticRegressionModel>
        
        org.apache.spark.ml.classification.LogisticRegression

All Implemented Interfaces:

java.io.Serializable, Params, DefaultParamsWritable, Identifiable, MLWritable
```
public class LogisticRegression
extends ProbabilisticClassifier<Vector,LogisticRegression,LogisticRegressionModel>
implements DefaultParamsWritable
```
Logistic regression. Supports multinomial logistic (softmax) regression and binomial logistic regression.

See Also:
Serialized Form

Constructor Summary

Constructors
Constructor and Description

LogisticRegression()

LogisticRegression(String uid)

Constructors
Constructor and Description
`LogisticRegression()`
`LogisticRegression(String uid)`

Method Summary

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)`
`LogisticRegression`	`copy(ParamMap extra)` Creates a copy of this instance with the same UID and some extra params.
`static DoubleParam`	`elasticNetParam()`
`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 M`	`fit(Dataset<?> dataset)`
`static M`	`fit(Dataset<?> dataset, ParamMap paramMap)`
`static scala.collection.Seq<M>`	`fit(Dataset<?> dataset, ParamMap[] paramMaps)`
`static M`	`fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)`
`static M`	`fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)`
`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 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 String`	`getWeightCol()`
`static <T> boolean`	`hasDefault(Param<T> param)`
`static boolean`	`hasParam(String paramName)`
`static boolean`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static Param<String>`	`labelCol()`
`Param<String>`	`labelCol()` Param for label column name.
`static LogisticRegression`	`load(String path)`
`static IntParam`	`maxIter()`
`static Param<?>[]`	`params()`
`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 DoubleParam`	`regParam()`
`static void`	`save(String path)`
`static <T> Params`	`set(Param<T> param, T value)`
`LogisticRegression`	`setAggregationDepth(int value)` Suggested depth for treeAggregate (greater than or equal to 2).
`LogisticRegression`	`setElasticNetParam(double value)` Set the ElasticNet mixing parameter.
`LogisticRegression`	`setFamily(String value)` Sets the value of param `family`.
`static Learner`	`setFeaturesCol(String value)`
`LogisticRegression`	`setFitIntercept(boolean value)` Whether to fit an intercept term.
`static Learner`	`setLabelCol(String value)`
`LogisticRegression`	`setMaxIter(int value)` Set the maximum number of iterations.
`static Learner`	`setPredictionCol(String value)`
`static E`	`setProbabilityCol(String value)`
`static E`	`setRawPredictionCol(String value)`
`LogisticRegression`	`setRegParam(double value)` Set the regularization parameter.
`LogisticRegression`	`setStandardization(boolean value)` Whether to standardize the training features before fitting the model.
`LogisticRegression`	`setThreshold(double value)` Set threshold in binary classification, in range [0, 1].
`LogisticRegression`	`setThresholds(double[] value)` Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class.
`LogisticRegression`	`setTol(double value)` Set the convergence tolerance of iterations.
`LogisticRegression`	`setWeightCol(String value)` Sets the value of param `weightCol`.
`static BooleanParam`	`standardization()`
`static DoubleParam`	`threshold()`
`static DoubleArrayParam`	`thresholds()`
`static DoubleParam`	`tol()`
`static String`	`toString()`
`static StructType`	`transformSchema(StructType schema)`
`String`	`uid()` An immutable unique ID for the object and its derivatives.
`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()`
`static MLWriter`	`write()`

Methods inherited from class org.apache.spark.ml.classification.ProbabilisticClassifier
setProbabilityCol

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

Methods inherited from class org.apache.spark.ml.Predictor
fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Estimator
fit, fit, fit, fit

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.DefaultParamsWritable
write

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

- Constructor Detail
  - LogisticRegression
```
public LogisticRegression(String uid)
```
  - LogisticRegression
```
public LogisticRegression()
```
- Method Detail
  - load
```
public static LogisticRegression 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()
```
  - fit
```
public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    scala.collection.Seq<ParamPair<?>> otherParamPairs)
```
  - fit
```
public static M fit(Dataset<?> dataset,
    ParamMap paramMap)
```
  - fit
```
public static scala.collection.Seq<M> fit(Dataset<?> dataset,
                          ParamMap[] paramMaps)
```
  - fit
```
public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    ParamPair<?>... otherParamPairs)
```
  - 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()
```
  - setLabelCol
```
public static Learner setLabelCol(String value)
```
  - setFeaturesCol
```
public static Learner setFeaturesCol(String value)
```
  - setPredictionCol
```
public static Learner setPredictionCol(String value)
```
  - fit
```
public static M fit(Dataset<?> dataset)
```
  - transformSchema
```
public static StructType transformSchema(StructType schema)
```
  - rawPredictionCol
```
public static final Param<String> rawPredictionCol()
```
  - getRawPredictionCol
```
public static final String getRawPredictionCol()
```
  - setRawPredictionCol
```
public static E 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 E setProbabilityCol(String value)
```
  - 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()
```
  - save
```
public static void save(String path)
                 throws java.io.IOException
```
    Throws:
    
    java.io.IOException
  - write
```
public static MLWriter write()
```
  - 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)
  - setRegParam
```
public LogisticRegression setRegParam(double value)
```
    Set the regularization parameter. Default is 0.0.
    
    Parameters:
    value - (undocumented)
    
    Returns:
    (undocumented)
  - setElasticNetParam
```
public LogisticRegression setElasticNetParam(double value)
```
    Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.
    
    Parameters:
    value - (undocumented)
    
    Returns:
    (undocumented)
  - setMaxIter
```
public LogisticRegression setMaxIter(int value)
```
    Set the maximum number of iterations. Default is 100.
    
    Parameters:
    value - (undocumented)
    
    Returns:
    (undocumented)
  - setTol
```
public LogisticRegression setTol(double value)
```
    Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.
    
    Parameters:
    value - (undocumented)
    
    Returns:
    (undocumented)
  - setFitIntercept
```
public LogisticRegression setFitIntercept(boolean value)
```
    Whether to fit an intercept term. Default is true.
    
    Parameters:
    value - (undocumented)
    
    Returns:
    (undocumented)
  - setFamily
```
public LogisticRegression setFamily(String value)
```
    Sets the value of param family. Default is "auto".
    
    Parameters:
    value - (undocumented)
    
    Returns:
    (undocumented)
  - setStandardization
```
public LogisticRegression setStandardization(boolean value)
```
    Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Note that with/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well. Default is true.
    
    Parameters:
    value - (undocumented)
    
    Returns:
    (undocumented)
  - setThreshold
```
public LogisticRegression 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)
  - setWeightCol
```
public LogisticRegression setWeightCol(String value)
```
    Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.
    
    Parameters:
    value - (undocumented)
    
    Returns:
    (undocumented)
  - setThresholds
```
public LogisticRegression 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 ProbabilisticClassifier<Vector,LogisticRegression,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.
    
    Returns:
    (undocumented)
  - setAggregationDepth
```
public LogisticRegression setAggregationDepth(int value)
```
    Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.
    
    Parameters:
    value - (undocumented)
    
    Returns:
    (undocumented)
  - copy
```
public LogisticRegression 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 Predictor<Vector,LogisticRegression,LogisticRegressionModel>
    
    Parameters:
    extra - (undocumented)
    
    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
  - 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 LogisticRegression

Constructor Summary

Method Summary

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

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

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

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

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.DefaultParamsWritable

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

Constructor Detail

LogisticRegression

LogisticRegression

Method Detail

load

toString

params

explainParam

explainParams

isSet

isDefined

hasParam

getParam

set

get

clear

getOrDefault

getDefault

hasDefault

extractParamMap

extractParamMap

fit

fit

fit

fit

labelCol

getLabelCol

featuresCol

getFeaturesCol

predictionCol

getPredictionCol

setLabelCol

setFeaturesCol

setPredictionCol

fit

transformSchema

rawPredictionCol

getRawPredictionCol

setRawPredictionCol

probabilityCol

getProbabilityCol

thresholds

setProbabilityCol

regParam

getRegParam

elasticNetParam

getElasticNetParam

maxIter

getMaxIter

fitIntercept

getFitIntercept

tol

getTol

standardization

getStandardization

weightCol

getWeightCol

threshold

aggregationDepth

getAggregationDepth

family

getFamily

save

write

uid

setRegParam

setElasticNetParam

setMaxIter