FeatureHasher (Spark 2.3.3 JavaDoc)

Object
- org.apache.spark.ml.PipelineStage
- - org.apache.spark.ml.Transformer
  - - org.apache.spark.ml.feature.FeatureHasher

All Implemented Interfaces:

java.io.Serializable, Logging, Params, HasInputCols, HasOutputCol, DefaultParamsWritable, Identifiable, MLWritable
```
public class FeatureHasher
extends Transformer
implements HasInputCols, HasOutputCol, DefaultParamsWritable
```
Feature hashing projects a set of categorical or numerical features into a feature vector of specified dimension (typically substantially smaller than that of the original feature space). This is done using the hashing trick (https://en.wikipedia.org/wiki/Feature_hashing) to map features to indices in the feature vector.
The FeatureHasher transformer operates on multiple columns. Each column may contain either numeric or categorical features. Behavior and handling of column data types is as follows: -Numeric columns: For numeric features, the hash value of the column name is used to map the feature value to its index in the feature vector. By default, numeric features are not treated as categorical (even when they are integers). To treat them as categorical, specify the relevant columns in categoricalCols. -String columns: For categorical features, the hash value of the string "column_name=value" is used to map to the vector index, with an indicator value of 1.0. Thus, categorical features are "one-hot" encoded (similarly to using OneHotEncoder with dropLast=false). -Boolean columns: Boolean values are treated in the same way as string columns. That is, boolean features are represented as "column_name=true" or "column_name=false", with an indicator value of 1.0.
Null (missing) values are ignored (implicitly zero in the resulting feature vector).
The hash function used here is also the MurmurHash 3 used in HashingTF. Since a simple modulo on the hashed value is used to determine the vector index, it is advisable to use a power of two as the numFeatures parameter; otherwise the features will not be mapped evenly to the vector indices.
```
   val df = Seq(
    (2.0, true, "1", "foo"),
    (3.0, false, "2", "bar")
   ).toDF("real", "bool", "stringNum", "string")

   val hasher = new FeatureHasher()
    .setInputCols("real", "bool", "stringNum", "string")
    .setOutputCol("features")

   hasher.transform(df).show(false)

   +----+-----+---------+------+------------------------------------------------------+
   |real|bool |stringNum|string|features                                              |
   +----+-----+---------+------+------------------------------------------------------+
   |2.0 |true |1        |foo   |(262144,[51871,63643,174475,253195],[1.0,1.0,2.0,1.0])|
   |3.0 |false|2        |bar   |(262144,[6031,80619,140467,174475],[1.0,1.0,1.0,3.0]) |
   +----+-----+---------+------+------------------------------------------------------+
 
```
See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

FeatureHasher()

FeatureHasher(String uid)

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

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`StringArrayParam`	`categoricalCols()` Numeric columns to treat as categorical features.
`static Params`	`clear(Param<?> param)`
`FeatureHasher`	`copy(ParamMap extra)` Creates a copy of this instance with the same UID and some extra params.
`static String`	`explainParam(Param<?> param)`
`static String`	`explainParams()`
`static ParamMap`	`extractParamMap()`
`static ParamMap`	`extractParamMap(ParamMap extra)`
`static <T> scala.Option<T>`	`get(Param<T> param)`
`String[]`	`getCategoricalCols()`
`static <T> scala.Option<T>`	`getDefault(Param<T> param)`
`static String[]`	`getInputCols()`
`int`	`getNumFeatures()`
`static <T> T`	`getOrDefault(Param<T> param)`
`static String`	`getOutputCol()`
`static Param<Object>`	`getParam(String paramName)`
`static <T> boolean`	`hasDefault(Param<T> param)`
`static boolean`	`hasParam(String paramName)`
`static StringArrayParam`	`inputCols()`
`static boolean`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static FeatureHasher`	`load(String path)`
`IntParam`	`numFeatures()` Number of features.
`static Param<String>`	`outputCol()`
`static Param<?>[]`	`params()`
`static void`	`save(String path)`
`static <T> Params`	`set(Param<T> param, T value)`
`FeatureHasher`	`setCategoricalCols(String[] value)`
`FeatureHasher`	`setInputCols(scala.collection.Seq<String> values)`
`FeatureHasher`	`setInputCols(String[] value)`
`FeatureHasher`	`setNumFeatures(int value)`
`FeatureHasher`	`setOutputCol(String value)`
`static String`	`toString()`
`Dataset<Row>`	`transform(Dataset<?> dataset)` Transforms the input dataset.
`StructType`	`transformSchema(StructType schema)` :: DeveloperApi ::
`String`	`uid()` An immutable unique ID for the object and its derivatives.
`static MLWriter`	`write()`

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.shared.HasInputCols
getInputCols, inputCols

Methods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol
getOutputCol, outputCol

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

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

- Constructor Detail
  - FeatureHasher
```
public FeatureHasher(String uid)
```
  - FeatureHasher
```
public FeatureHasher()
```
- Method Detail
  - load
```
public static FeatureHasher 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()
```
  - inputCols
```
public static final StringArrayParam inputCols()
```
  - getInputCols
```
public static final String[] getInputCols()
```
  - outputCol
```
public static final Param<String> outputCol()
```
  - getOutputCol
```
public static final String getOutputCol()
```
  - 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)
  - categoricalCols
```
public StringArrayParam categoricalCols()
```
    Numeric columns to treat as categorical features. By default only string and boolean columns are treated as categorical, so this param can be used to explicitly specify the numerical columns to treat as categorical. Note, the relevant columns must also be set in inputCols.
    
    Returns:
    
    (undocumented)
  - numFeatures
```
public IntParam numFeatures()
```
    Number of features. Should be greater than 0. (default = 2^18^)
    
    Returns:
    
    (undocumented)
  - getNumFeatures
```
public int getNumFeatures()
```
  - setNumFeatures
```
public FeatureHasher setNumFeatures(int value)
```
  - setInputCols
```
public FeatureHasher setInputCols(scala.collection.Seq<String> values)
```
  - setInputCols
```
public FeatureHasher setInputCols(String[] value)
```
  - setOutputCol
```
public FeatureHasher setOutputCol(String value)
```
  - getCategoricalCols
```
public String[] getCategoricalCols()
```
  - setCategoricalCols
```
public FeatureHasher setCategoricalCols(String[] value)
```
  - transform
```
public Dataset<Row> transform(Dataset<?> dataset)
```
    Description copied from class: Transformer
    
    Transforms the input dataset.
    
    Specified by:
    
    transform in class Transformer
    
    Parameters:
    
    dataset - (undocumented)
    
    Returns:
    
    (undocumented)
  - copy
```
public FeatureHasher 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 Transformer
    
    Parameters:
    
    extra - (undocumented)
    
    Returns:
    
    (undocumented)
  - transformSchema
```
public StructType transformSchema(StructType schema)
```
    Description copied from class: PipelineStage
    
    :: DeveloperApi ::
    Check transform validity and derive the output schema from the input schema.
    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().
    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
    
    Specified by:
    
    transformSchema in class PipelineStage
    
    Parameters:
    
    schema - (undocumented)
    
    Returns:
    
    (undocumented)

Class FeatureHasher

Constructor Summary

Method Summary

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

Methods inherited from class Object

Methods inherited from interface org.apache.spark.ml.param.shared.HasInputCols

Methods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol

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

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

Constructor Detail

FeatureHasher

FeatureHasher

Method Detail

load

toString

params

explainParam

explainParams

isSet

isDefined

hasParam

getParam

set

get

clear

getOrDefault

getDefault

hasDefault

extractParamMap

extractParamMap

inputCols

getInputCols

outputCol

getOutputCol

save

write

uid

categoricalCols

numFeatures

getNumFeatures

setNumFeatures

setInputCols

setInputCols

setOutputCol

getCategoricalCols

setCategoricalCols

transform

copy

transformSchema