class FeatureHasher extends Transformer with HasInputCols with HasOutputCol with HasNumFeatures with 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]) | +----+-----+---------+------+------------------------------------------------------+
- Annotations
- @Since("2.3.0")
- Source
- FeatureHasher.scala
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- FeatureHasher
- DefaultParamsWritable
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- HasNumFeatures
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- final def $[T](param: Param[T]): T
An alias for
getOrDefault().An alias for
getOrDefault().- Attributes
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- final def ==(arg0: Any): Boolean
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- AnyRef → Any
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- val categoricalCols: StringArrayParam
Numeric columns to treat as categorical features.
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 should also be set in
inputCols, categorical columns not set ininputColswill be listed in a warning.- Annotations
- @Since("2.3.0")
- final def clear(param: Param[_]): FeatureHasher.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
- def clone(): AnyRef
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- protected[lang]
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- AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- def copy(extra: ParamMap): FeatureHasher
Creates a copy of this instance with the same UID and some extra 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().- Definition Classes
- FeatureHasher → Transformer → PipelineStage → Params
- Annotations
- @Since("2.3.0")
- def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to
defaultParamMap, and explicitly set Params are copied from and toparamMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.- to
the target instance, which should work with the same set of default Params as this source instance
- extra
extra params to be copied to the target's
paramMap- returns
the target instance with param values copied
- Attributes
- protected
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- Params
- final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
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- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
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- def explainParam(param: Param[_]): String
Explains a param.
Explains a param.
- param
input param, must belong to this instance.
- returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
- Definition Classes
- Params
- def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam().- Definition Classes
- Params
- final def extractParamMap(): ParamMap
extractParamMapwith no extra values.extractParamMapwith no extra values.- Definition Classes
- Params
- final def extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
- Definition Classes
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- final def get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
- Definition Classes
- Params
- def getCategoricalCols: Array[String]
- Annotations
- @Since("2.3.0")
- final def getClass(): Class[_ <: AnyRef]
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- @IntrinsicCandidate() @native()
- final def getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
- final def getInputCols: Array[String]
- Definition Classes
- HasInputCols
- final def getNumFeatures: Int
- Definition Classes
- HasNumFeatures
- final def getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
- Definition Classes
- Params
- final def getOutputCol: String
- Definition Classes
- HasOutputCol
- def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
- final def hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
- Definition Classes
- Params
- def hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
- Definition Classes
- Params
- def hashCode(): Int
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- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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- final val inputCols: StringArrayParam
Param for input column names.
Param for input column names.
- Definition Classes
- HasInputCols
- final def isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
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- final def isInstanceOf[T0]: Boolean
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- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
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- def logName: String
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- final def notifyAll(): Unit
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- @IntrinsicCandidate() @native()
- final val numFeatures: IntParam
Param for Number of features.
Param for Number of features. Should be greater than 0.
- Definition Classes
- HasNumFeatures
- final val outputCol: Param[String]
Param for output column name.
Param for output column name.
- Definition Classes
- HasOutputCol
- lazy val params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
- Definition Classes
- Params
- Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
- def save(path: String): Unit
Saves this ML instance to the input path, a shortcut of
write.save(path).Saves this ML instance to the input path, a shortcut of
write.save(path).- Definition Classes
- MLWritable
- Annotations
- @Since("1.6.0") @throws("If the input path already exists but overwrite is not enabled.")
- final def set(paramPair: ParamPair[_]): FeatureHasher.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
- final def set(param: String, value: Any): FeatureHasher.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
- final def set[T](param: Param[T], value: T): FeatureHasher.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
- def setCategoricalCols(value: Array[String]): FeatureHasher.this.type
- Annotations
- @Since("2.3.0")
- final def setDefault(paramPairs: ParamPair[_]*): FeatureHasher.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter
setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.- paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
- Attributes
- protected
- Definition Classes
- Params
- final def setDefault[T](param: Param[T], value: T): FeatureHasher.this.type
Sets a default value for a param.
- def setInputCols(value: Array[String]): FeatureHasher.this.type
- Annotations
- @Since("2.3.0")
- def setInputCols(values: String*): FeatureHasher.this.type
- Annotations
- @Since("2.3.0")
- def setNumFeatures(value: Int): FeatureHasher.this.type
- Annotations
- @Since("2.3.0")
- def setOutputCol(value: String): FeatureHasher.this.type
- Annotations
- @Since("2.3.0")
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- FeatureHasher → Identifiable → AnyRef → Any
- Annotations
- @Since("3.0.0")
- def transform(dataset: Dataset[_]): DataFrame
Transforms the input dataset.
Transforms the input dataset.
- Definition Classes
- FeatureHasher → Transformer
- Annotations
- @Since("2.3.0")
- def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
- dataset
input dataset
- paramMap
additional parameters, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since("2.0.0")
- def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
- dataset
input dataset
- firstParamPair
the first param pair, overwrite embedded params
- otherParamPairs
other param pairs, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since("2.0.0") @varargs()
- def transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during
transformSchemaand raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate().Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- FeatureHasher → PipelineStage
- Annotations
- @Since("2.3.0")
- def transformSchema(schema: StructType, logging: Boolean): StructType
:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
- val uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
- Definition Classes
- FeatureHasher → Identifiable
- Annotations
- @Since("2.3.0")
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
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- def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from HasNumFeatures
Inherited from HasOutputCol
Inherited from HasInputCols
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.