org.apache.spark.ml.regression

Class LinearRegressionModel

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.regression.RegressionModel<Vector,LinearRegressionModel>

org.apache.spark.ml.regression.LinearRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable, MLWritable

public class LinearRegressionModel
extends RegressionModel<Vector,LinearRegressionModel>
implements MLWritable

:: Experimental :: Model produced by LinearRegression.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
Vector	coefficients()
LinearRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<java.lang.String>	featuresCol() Param for features column name.
java.lang.String	getFeaturesCol()
java.lang.String	getLabelCol()
java.lang.String	getPredictionCol()
boolean	hasSummary() Indicates whether a training summary exists for this model instance.
double	intercept()
Param<java.lang.String>	labelCol() Param for label column name.
static LinearRegressionModel	load(java.lang.String path)
int	numFeatures() Returns the number of features the model was trained on.
protected double	predict(Vector features) Predict label for the given features.
Param<java.lang.String>	predictionCol() Param for prediction column name.
static MLReader<LinearRegressionModel>	read()
LinearRegressionTrainingSummary	summary() Gets summary (e.g.
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.
Vector	weights()
MLWriter	write() Returns a MLWriter instance for this ML instance.

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

featuresDataType, setFeaturesCol, setPredictionCol, transform, 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.util.MLWritable

save

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

read

public static MLReader<LinearRegressionModel> read()

load

public static LinearRegressionModel load(java.lang.String path)

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)

coefficients

public Vector coefficients()

intercept

public double intercept()

weights

public Vector weights()

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,LinearRegressionModel>

summary

public LinearRegressionTrainingSummary summary()

Gets summary (e.g. residuals, mse, r-squared ) 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)

Description copied from class: PredictionModel

Predict label for the given features. This internal method is used to implement transform() and output predictionCol.

Specified by:

predict in class PredictionModel<Vector,LinearRegressionModel>

Parameters:

features - (undocumented)

Returns:

(undocumented)

copy

public LinearRegressionModel 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<LinearRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

write

public MLWriter write()

Returns a MLWriter instance for this ML instance.

For LinearRegressionModel, 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)

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<java.lang.String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public java.lang.String getLabelCol()

featuresCol

public Param<java.lang.String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public java.lang.String getFeaturesCol()

predictionCol

public Param<java.lang.String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public java.lang.String getPredictionCol()