HashOneHotEncoder

Transform a collection of categorical features to binary columns, with at most a single one-value. Similar to OneHotEncoder but uses MurmursHash3 to hash features into buckets to reduce CPU and memory overhead.

Missing values are transformed to zero vectors.

If hashBucketSize is inferred with HLL, the estimate is scaled by sizeScalingFactor to reduce the number of collisions.

Rough table of relationship of scaling factor to % collisions, measured from a corpus of 466544 English words:

sizeScalingFactor     % Collisions
-----------------     ------------
                2     17.9934%
                4     10.5686%
                8      5.7236%
               16      3.0019%
               32      1.5313%
               64      0.7864%
              128      0.3920%
              256      0.1998%
              512      0.0975%
             1024      0.0478%
             2048      0.0236%
             4096      0.0071%

Linear Supertypes

Serializable, Serializable, SettingsBuilder, AnyRef, Any

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def apply(name: String, hashBucketSize: Int = 0, sizeScalingFactor: Double = 8.0): Transformer[String, HLL, Int]

Create a new HashOneHotEncoder instance.
Create a new HashOneHotEncoder instance.
hashBucketSize
number of buckets, or 0 to infer from data with HyperLogLog
sizeScalingFactor
when hashBucketSize is 0, scale HLL estimate by this amount
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fromSettings(setting: Settings): Transformer[String, HLL, Int]

Create a new HashOneHotEncoder from a settings object
Create a new HashOneHotEncoder from a settings object
setting
Settings object

Definition Classes
HashOneHotEncoder → SettingsBuilder
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package transformers

object HashOneHotEncoder extends SettingsBuilder with Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def apply(name: String, hashBucketSize: Int = 0, sizeScalingFactor: Double = 8.0): Transformer[String, HLL, Int]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def fromSettings(setting: Settings): Transformer[String, HLL, Int]

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from SettingsBuilder

Inherited from AnyRef

Inherited from Any

Ungrouped