org.apache.spark.ml.classification

Class LogisticRegressionModel

java.lang.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

public class LogisticRegressionModel
extends ProbabilisticClassificationModel<Vector,LogisticRegressionModel>

:: Experimental :: Model produced by LogisticRegression.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
void	checkThresholdConsistency() If threshold and thresholds are both set, ensures they are consistent.
LogisticRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
double	getThreshold() Get threshold for binary classification.
double[]	getThresholds() Get thresholds for binary or multiclass classification.
boolean	hasSummary() Indicates whether a training summary exists for this model instance.
double	intercept()
int	numClasses() Number of classes (values which the label can take).
protected double	predict(Vector features) Predict label for the given feature vector.
protected Vector	predictRaw(Vector features) Raw prediction for each possible label.
protected double	probability2prediction(Vector probability) Given a vector of class conditional probabilities, select the predicted label.
protected double	raw2prediction(Vector rawPrediction) Given a vector of raw predictions, select the predicted label.
protected Vector	raw2probabilityInPlace(Vector rawPrediction) Estimate the probability of each class given the raw prediction, doing the computation in-place.
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.
LogisticRegressionTrainingSummary	summary() Gets summary of model on training set.
java.lang.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.
void	validateParams()
Vector	weights()

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

normalizeToProbabilitiesInPlace, predictProbability, raw2probability, setProbabilityCol, transform

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

setRawPredictionCol

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

featuresDataType, setFeaturesCol, setPredictionCol, transformImpl, 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 org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, 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, validateParams

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

toString

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

uid

public java.lang.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)

weights

public Vector weights()

intercept

public double intercept()

setThreshold

public LogisticRegressionModel setThreshold(double value)

Set threshold in binary classification, in range [0, 1].

If the estimated probability of class label 1 is > 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 threshold is set, returns that value. Otherwise, if thresholds is set with length 2 (i.e., binary classification), this returns the equivalent threshold:

1 / (1 + thresholds(0) / thresholds(1))

. Otherwise, returns {@link threshold} default value. @group getParam @throws IllegalArgumentException if {@link 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 >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' 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.

Returns:

(undocumented)

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>

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.

predict

protected 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)

raw2probabilityInPlace

protected Vector raw2probabilityInPlace(Vector rawPrediction)

Description copied from class: ProbabilisticClassificationModel

Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.

This internal method is used to implement transform() and output probabilityCol.

Specified by:

raw2probabilityInPlace in class ProbabilisticClassificationModel<Vector,LogisticRegressionModel>

Parameters:

rawPrediction - (undocumented)

Returns:

Estimated class conditional probabilities (modified input vector)

predictRaw

protected 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 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.

Specified by:

copy in interface Params

Specified by:

copy in class Model<LogisticRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

raw2prediction

protected double raw2prediction(Vector rawPrediction)

Description copied from class: ClassificationModel

Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.

Overrides:

raw2prediction in class ProbabilisticClassificationModel<Vector,LogisticRegressionModel>

Parameters:

rawPrediction - (undocumented)

Returns:

predicted label

probability2prediction

protected double probability2prediction(Vector probability)

Description copied from class: ProbabilisticClassificationModel

Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.

Overrides:

probability2prediction in class ProbabilisticClassificationModel<Vector,LogisticRegressionModel>

Parameters:

probability - (undocumented)

Returns:

predicted label

checkThresholdConsistency

public void checkThresholdConsistency()

If threshold and thresholds are both set, ensures they are consistent.

Throws:

java.lang.IllegalArgumentException - if threshold and thresholds are not equivalent

validateParams

public void validateParams()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

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