org.apache.spark.ml.feature
Class VectorIndexer

Object
  org.apache.spark.ml.PipelineStage
      org.apache.spark.ml.Estimator<VectorIndexerModel>
          org.apache.spark.ml.feature.VectorIndexer

All Implemented Interfaces:

java.io.Serializable, Logging, Params

public class VectorIndexer
extends Estimator<VectorIndexerModel>

:: Experimental :: Class for indexing categorical feature columns in a dataset of Vector.

This has 2 usage modes: - Automatically identify categorical features (default behavior) - This helps process a dataset of unknown vectors into a dataset with some continuous features and some categorical features. The choice between continuous and categorical is based upon a maxCategories parameter. - Set maxCategories to the maximum number of categorical any categorical feature should have. - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. If maxCategories = 2, then feature 0 will be declared categorical and use indices {0, 1}, and feature 1 will be declared continuous. - Index all features, if all features are categorical - If maxCategories is set to be very large, then this will build an index of unique values for all features. - Warning: This can cause problems if features are continuous since this will collect ALL unique values to the driver. - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. If maxCategories >= 3, then both features will be declared categorical.

This returns a model which can transform categorical features to use 0-based indices.

Index stability: - This is not guaranteed to choose the same category index across multiple runs. - If a categorical feature includes value 0, then this is guaranteed to map value 0 to index 0. This maintains vector sparsity. - More stability may be added in the future.

TODO: Future extensions: The following functionality is planned for the future: - Preserve metadata in transform; if a feature's metadata is already present, do not recompute. - Specify certain features to not index, either via a parameter or via existing metadata. - Add warning if a categorical feature has only 1 category. - Add option for allowing unknown categories.

See Also:

Serialized Form

Nested Class Summary

static class

VectorIndexer.CategoryStats Helper class for tracking unique values for each feature.

Constructor Summary

VectorIndexer()

VectorIndexer(String uid)

Method Summary

VectorIndexer	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
VectorIndexerModel	fit(DataFrame dataset) Fits a model to the input data.
int	getMaxCategories()
IntParam	maxCategories() Threshold for the number of values a categorical feature can take.
VectorIndexer	setInputCol(String value)
VectorIndexer	setMaxCategories(int value)
VectorIndexer	setOutputCol(String value)
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid()

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, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.Logging

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

Constructor Detail

VectorIndexer

public VectorIndexer(String uid)

VectorIndexer

public VectorIndexer()

Method Detail

uid

public String uid()

setMaxCategories

public VectorIndexer setMaxCategories(int value)

setInputCol

public VectorIndexer setInputCol(String value)

setOutputCol

public VectorIndexer setOutputCol(String value)

fit

public VectorIndexerModel fit(DataFrame dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<VectorIndexerModel>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

Derives the output schema from the input schema.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public VectorIndexer 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 Estimator<VectorIndexerModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

maxCategories

public IntParam maxCategories()

Threshold for the number of values a categorical feature can take. If a feature is found to have > maxCategories values, then it is declared continuous. Must be >= 2.

(default = 20)

Returns:

(undocumented)

getMaxCategories

public int getMaxCategories()