FeatureValue (feathr-impl 0.10.4-rc5 API)

java.lang.Object
- com.linkedin.feathr.common.FeatureValue

All Implemented Interfaces:

com.esotericsoftware.kryo.KryoSerializable, java.io.Serializable
```
public class FeatureValue
extends java.lang.Object
implements java.io.Serializable, com.esotericsoftware.kryo.KryoSerializable
```
Describes the basic representation of a feature value in Feathr. This is the OLD FeatureValue API. Feathr is migrating to a new FeatureValue API defined at com.linkedin.feathr.common.value.FeatureValue. This implementation of the old API is a wrapper around the new API, with lots of glue to maintain compatibility. SERIALIZATION: For use with Spark RDD API, this class is serializable both by Java Object Serialization and by Kryo. Note that because the serialized representation is based on NTV (name-term-value), serialization will NOT work for tensor features having rank greater than 1. MUTABILITY: For legacy compatibility, instances of this class are mutable under the following conditions: 1. When constructed with zero-argument constructor 2. When constructed with Map constructor 3. When constructed specifically as a term-vector using any createStringTermVector factory methods 4. When deserialized with Kryo or Java-Object Serialization When mutable, instances may be mutated by: 1. Using modifier methods the Map objects returned by .getValue or .getAsTermVector 2. Using modifier methods on ._value public member field. 3. Using the .put method. All mutability support in FeatureValue is DEPRECATED. NOTE: This class will be deprecated soon in favor of FeatureValue

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

static float DEFAULT_VALUE

static java.lang.String EMPTY_TERM

Fields
Modifier and Type	Field and Description
`static float`	`DEFAULT_VALUE`
`static java.lang.String`	`EMPTY_TERM`

Constructor Summary

Constructors
Constructor and Description
`FeatureValue()` Deprecated. Use the static factory methods such as `createNumeric(Number)` to construct features instead
`FeatureValue(FeatureValue featureValueInternal)`
`FeatureValue(java.util.Map<java.lang.String,java.lang.Float> value)` Deprecated. Use the static factory methods such as `createStringTermVector(Map)` to construct features instead
`FeatureValue(java.lang.Object value)` Deprecated. Use the static factory methods such as `createNumeric(Number)` to construct features instead N.B. Although this takes `Object`, there are runtime type checks to ensure the passed type can be converted to a feature value.
`FeatureValue(java.lang.Object value, FeatureTypes type)` Deprecated. Use the static factory methods such as `createNumeric(Number)` to construct features instead
`FeatureValue(TypedTensor tensor)` Constructor to create FeatureValue with a tensor object

Method Summary

All Methods Static Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and Description
`static FeatureValue`	`createBoolean(java.lang.Boolean b)` Creates a boolean feature.
`static FeatureValue`	`createCategorical(java.lang.Character term)` Creates a categorical feature from a character term
`static FeatureValue`	`createCategorical(java.lang.CharSequence term)` Creates a categorical feature from a string term
`static FeatureValue`	`createCategorical(java.lang.Number term)` Deprecated.
`static FeatureValue`	`createCharacterCategoricalSet(java.util.Collection<java.lang.Character> characters)` Creates a categorical set feature from character terms
`static <T extends java.lang.Number> FeatureValue`	`createDenseVector(java.util.List<T> vec)` Creates a dense vector feature from a `List` of numbers
`static <T extends java.lang.Number> FeatureValue`	`createDenseVector(T[] vec)` Creates a dense vector feature from an array of numbers
`static FeatureValue`	`createNumeric(java.lang.Number num)` Creates a numeric feature.
`static <T extends java.lang.Number> FeatureValue`	`createNumericCategoricalSet(java.util.Collection<T> terms)` Creates a categorical set feature from a collection of Numbers that are whole numbers
`static <K extends java.lang.Number,V extends java.lang.Number> FeatureValue`	`createNumericTermVector(java.util.Collection<java.util.Map<K,V>> termValues)` Creates a term vector feature (e.g.
`static <K extends java.lang.Number,V extends java.lang.Number> FeatureValue`	`createNumericTermVector(java.util.Map<K,V> inputMap)` Creates a term vector feature (e.g.
`static <T extends java.lang.CharSequence> FeatureValue`	`createStringCategoricalSet(java.util.Collection<T> terms)` Creates a categorical set feature from string terms
`static <K extends java.lang.CharSequence,V extends java.lang.Number> FeatureValue`	`createStringTermVector(java.util.Collection<java.util.Map<K,V>> termValues)` Creates a term vector feature (AKA sparse vector) from multiple term vectors with string terms
`static <K extends java.lang.CharSequence,V extends java.lang.Number> FeatureValue`	`createStringTermVector(java.util.Map<K,V> inputMap)` Creates a term vector feature (aka sparse vector) using strings for the terms
`static FeatureValue`	`createTensor(java.lang.Object value, TensorType type)` Creates a (tensor) FeatureValue based on the provided TensorType.
`static FeatureValue`	`createTensor(TensorData tensorData)` Create a FeatureValue from a TensorData (adding a TensorType inferred from the primitives of the TensorData).
`boolean`	`equals(java.lang.Object o)`
`java.lang.Boolean`	`getAsBoolean()` Get feature value as boolean
`java.lang.String`	`getAsCategorical()` Returns the single categorical value associated with the feature Note: this method will return `String` even if the feature was created using `createCategorical(Number)` because categorical are meant to contain only discrete values which are represented by String
`java.lang.Float`	`getAsNumeric()` get feature value as number
`java.lang.String`	`getAsString()` Deprecated. use `getAsCategorical()` instead
`TensorData`	`getAsTensorData()`
`java.util.Map<java.lang.String,java.lang.Float>`	`getAsTermVector()` Gets the feature in an NTV term-vector representation.
`TypedTensor`	`getAsTypedTensor()`
`FeatureType`	`getFeatureType()`
`int`	`getNumTerms()` Deprecated.
`java.util.Map<java.lang.String,java.lang.Float>`	`getValue()` Deprecated. Prefer to use `getAsTermVector()` which has a more descriptive name
`int`	`hashCode()`
`boolean`	`isEmpty()` Check if the FeatureValue is empty or not.
`void`	`put(java.lang.String key, java.lang.Float value)` Deprecated. mutability in FeatureValue is deprecated; see class-level Javadoc
`void`	`read(com.esotericsoftware.kryo.Kryo kryo, com.esotericsoftware.kryo.io.Input input)`
`int`	`size()` Deprecated. Prefer to use `getNumTerms()` which has a more descriptive name
`java.lang.String`	`toString()`
`void`	`write(com.esotericsoftware.kryo.Kryo kryo, com.esotericsoftware.kryo.io.Output output)`

Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait

- Field Detail
  - DEFAULT_VALUE
```
public static final float DEFAULT_VALUE
```
    See Also:
    
    Constant Field Values
  - EMPTY_TERM
```
public static final java.lang.String EMPTY_TERM
```
    See Also:
    
    Constant Field Values
- Constructor Detail
  - FeatureValue
```
public FeatureValue(FeatureValue featureValueInternal)
```
  - FeatureValue
```
@Deprecated
public FeatureValue(java.lang.Object value)
```
    Deprecated. Use the static factory methods such as createNumeric(Number) to construct features instead N.B. Although this takes Object, there are runtime type checks to ensure the passed type can be converted to a feature value.
  - FeatureValue
```
@Deprecated
public FeatureValue()
```
    Deprecated. Use the static factory methods such as createNumeric(Number) to construct features instead
  - FeatureValue
```
@Deprecated
public FeatureValue(java.lang.Object value,
                                FeatureTypes type)
```
    Deprecated. Use the static factory methods such as createNumeric(Number) to construct features instead
  - FeatureValue
```
public FeatureValue(TypedTensor tensor)
```
    Constructor to create FeatureValue with a tensor object
    
    Parameters:
    
    tensor - TypedTensor
    
    Throws:
    
    java.lang.NullPointerException - if tensor parameter is null
  - FeatureValue
```
@Deprecated
public FeatureValue(java.util.Map<java.lang.String,java.lang.Float> value)
```
    Deprecated. Use the static factory methods such as createStringTermVector(Map) to construct features instead
- Method Detail
  - put
```
@Deprecated
public void put(java.lang.String key,
                            java.lang.Float value)
```
    Deprecated. mutability in FeatureValue is deprecated; see class-level Javadoc
    
    Modify a term-vector FeatureValue by adding a term-value mapping. Mutability is only allowed under certain conditions, described in the class-level Javadoc.
    
    Throws:
    
    java.lang.RuntimeException - if not mutable
  - isEmpty
```
public boolean isEmpty()
```
    Check if the FeatureValue is empty or not. When it's empty, it return true; otherwise false. Emptiness of FeatureValue is defined in the following way: 1. For tensor based features, empty means an tensor. 2. For legacy term vector based features, empty means an empty vector. Note: Since Boolean NTV feature false is equivalent to empty map, so it's false is considered as empty as well.
  - size
```
@Deprecated
public int size()
```
    Deprecated. Prefer to use getNumTerms() which has a more descriptive name
    
    Throws:
    
    java.lang.RuntimeException - if this instance stores a tensor of rank greater than 1
  - getValue
```
@Deprecated
public java.util.Map<java.lang.String,java.lang.Float> getValue()
```
    Deprecated. Prefer to use getAsTermVector() which has a more descriptive name
  - getNumTerms
```
@Deprecated
public int getNumTerms()
```
    Deprecated.
    
    Get the number of terms in feature when interpreted as a term vector.
  - For a numeric feature, this will always be 1
  - For a categorical feature, this will always be 1
  - For a categorical set feature, this will be the number of terms in the set
  - For any vector feature, it will be the number of explicitly present elements in the vector

Returns:: the number of terms in this feature
Throws:: java.lang.RuntimeException - if this instance stores a tensor of rank greater than 1

getFeatureType
```
public FeatureType getFeatureType()
```

getAsTypedTensor
```
public TypedTensor getAsTypedTensor()
```
Returns:

a Quince TypedTensor representation of this feature value

getAsTensorData
```
public TensorData getAsTensorData()
```
Returns:

a Quince TensorData representation of this feature value

getAsTermVector
```
public java.util.Map<java.lang.String,java.lang.Float> getAsTermVector()
```
Gets the feature in an NTV term-vector representation.

Returns:

Map from String term names to Float values

Throws:

java.lang.RuntimeException - if the feature is a tensor of rank greater than 1, then this feature cannot be represented as an NTV term vector

getAsNumeric
```
public java.lang.Float getAsNumeric()
```
get feature value as number

Returns:

number value

Throws:

java.lang.RuntimeException - if this is not a valid numeric feature

getAsString
```
@Deprecated
public java.lang.String getAsString()
```
Deprecated. use getAsCategorical() instead

Gets a CATEGORICAL feature value represented as a string.

Returns:

string value

getAsCategorical
```
public java.lang.String getAsCategorical()
```
Returns the single categorical value associated with the feature Note: this method will return String even if the feature was created using createCategorical(Number) because categorical are meant to contain only discrete values which are represented by String

Returns:

the categorical term as a String

Throws:

java.lang.RuntimeException - if this is not a valid categorical feature

getAsBoolean
```
public java.lang.Boolean getAsBoolean()
```
Get feature value as boolean

Returns:

boolean value

Throws:

java.lang.RuntimeException - if this is not a valid boolean feature

createNumeric
```
public static FeatureValue createNumeric(java.lang.Number num)
```
Creates a numeric feature.

Parameters:

num - Number

Returns:

FeatureValue

createCategorical
```
@Deprecated
public static FeatureValue createCategorical(java.lang.Number term)
```
Deprecated.

Creates a categorical feature using a whole number term which is within some precision of a whole number. The whole number term will be stored as a String. Only whole number terms are accepted because categorical are meant to contain discrete values. The common use case for using number as the term would be to represent some entity ID (e.g. y, x)

Parameters:

term - Number the numeric term which will be stored as a String term

Returns:

a new instance of FeatureValue

Throws:

java.lang.RuntimeException - if num is not within some precision of a whole number

createCategorical
```
public static FeatureValue createCategorical(java.lang.CharSequence term)
```
Creates a categorical feature from a string term

Parameters:

term - CharSequence

Returns:

a new instance of FeatureValue

createCategorical
```
public static FeatureValue createCategorical(java.lang.Character term)
```
Creates a categorical feature from a character term

Parameters:

term - Character

Returns:

a new instance of FeatureValue

createDenseVector
```
public static <T extends java.lang.Number> FeatureValue createDenseVector(java.util.List<T> vec)
```
Creates a dense vector feature from a List of numbers

Type Parameters:

T - the type of Number

Parameters:

vec - List

Returns:

a new instance of FeatureValue

createDenseVector
```
public static <T extends java.lang.Number> FeatureValue createDenseVector(T[] vec)
```
Creates a dense vector feature from an array of numbers

Type Parameters:

T - the type of Number

Parameters:

vec - T[] array of items

Returns:

a new instance of FeatureValue

createStringTermVector
```
public static <K extends java.lang.CharSequence,V extends java.lang.Number> FeatureValue createStringTermVector(java.util.Map<K,V> inputMap)
```
Creates a term vector feature (aka sparse vector) using strings for the terms

Type Parameters:

K - the string term type

V - the numeric value type

Parameters:

inputMap - Map from a string term to numeric values

Returns:

a new instance of FeatureValue

createNumericTermVector
```
public static <K extends java.lang.Number,V extends java.lang.Number> FeatureValue createNumericTermVector(java.util.Map<K,V> inputMap)
```
Creates a term vector feature (e.g. sparse vector) using a numeric to numeric map

Type Parameters:

K - the numeric term type

V - the numeric value type

Parameters:

inputMap - Map from a number term (within some precision of a whole numbers) to numeric value

Returns:

a new instance of FeatureValue

Throws:

java.lang.RuntimeException - if input numeric key is a not within some precision of a whole numbers (e.g. 1.5f)

java.lang.RuntimeException - if multiple input numeric keys map to the same whole number (e.g. 1.00f and 1.0000d)

createStringTermVector
```
public static <K extends java.lang.CharSequence,V extends java.lang.Number> FeatureValue createStringTermVector(java.util.Collection<java.util.Map<K,V>> termValues)
```
Creates a term vector feature (AKA sparse vector) from multiple term vectors with string terms

Type Parameters:

K - the string term type

V - the numeric value type

Parameters:

termValues - Collection of Map from string term to numeric value

Returns:

a new instance of FeatureValue

Throws:

java.lang.RuntimeException - if input numeric key is a not within some precision of a whole numbers

java.lang.RuntimeException - if multiple input numeric keys map to the same whole number (e.g. 1.00f and 1.0000d)

createNumericTermVector
```
public static <K extends java.lang.Number,V extends java.lang.Number> FeatureValue createNumericTermVector(java.util.Collection<java.util.Map<K,V>> termValues)
```
Creates a term vector feature (e.g. sparse vector) by merging multiple numeric to numeric map

Type Parameters:

K - the numeric term type

V - the numeric value type

Parameters:

termValues - Map from a number term to numeric value

Returns:

a new instance of FeatureValue

Throws:

java.lang.RuntimeException - if input numeric keys are not within some precision of a whole numbers (e.g. 1.5f)

java.lang.RuntimeException - if multiple input numeric keys map to the same whole number (e.g. 1.00f and 1.0000d)

createNumericCategoricalSet
```
public static <T extends java.lang.Number> FeatureValue createNumericCategoricalSet(java.util.Collection<T> terms)
```
Creates a categorical set feature from a collection of Numbers that are whole numbers

Type Parameters:

T - the numeric type

Parameters:

terms - Collection of Number terms

Returns:

a new instance of FeatureValue

Throws:

java.lang.RuntimeException - if input numeric elements are not within some precision of a whole numbers (e.g. 1.5f)

createStringCategoricalSet
```
public static <T extends java.lang.CharSequence> FeatureValue createStringCategoricalSet(java.util.Collection<T> terms)
```
Creates a categorical set feature from string terms

Type Parameters:

T - the string term type

Parameters:

terms - Collection of CharSequence terms

Returns:

a new instance of FeatureValue

createCharacterCategoricalSet
```
public static FeatureValue createCharacterCategoricalSet(java.util.Collection<java.lang.Character> characters)
```
Creates a categorical set feature from character terms

Parameters:

characters - Collection of Character

Returns:

a new instance of FeatureValue

createBoolean
```
public static FeatureValue createBoolean(java.lang.Boolean b)
```
Creates a boolean feature.

Parameters:

b - Boolean

Returns:

a new instance of FeatureValue

createTensor
```
public static FeatureValue createTensor(java.lang.Object value,
                                        TensorType type)
```
Creates a (tensor) FeatureValue based on the provided TensorType.

Parameters:

value - The data used to creates the the tensor.

type - The corresponding type needed to create the tensor.

Throws:

java.lang.IllegalArgumentException - if the data type is not supported or if create a 2-d or higher dimension tensor.

createTensor
```
public static FeatureValue createTensor(TensorData tensorData)
```
Create a FeatureValue from a TensorData (adding a TensorType inferred from the primitives of the TensorData). NOTE that the TensorType will not have any shape, so may be NOT reused for constructing new dense tensors from arrays and lists.

toString
```
public java.lang.String toString()
```
Overrides:

toString in class java.lang.Object

equals
```
public boolean equals(java.lang.Object o)
```
Overrides:

equals in class java.lang.Object

hashCode
```
public int hashCode()
```
Overrides:

hashCode in class java.lang.Object

write

public void write(com.esotericsoftware.kryo.Kryo kryo,
                  com.esotericsoftware.kryo.io.Output output)

Specified by:: write in interface com.esotericsoftware.kryo.KryoSerializable

read

public void read(com.esotericsoftware.kryo.Kryo kryo,
                 com.esotericsoftware.kryo.io.Input input)

Specified by:: read in interface com.esotericsoftware.kryo.KryoSerializable

Class FeatureValue

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_VALUE

EMPTY_TERM

Constructor Detail

FeatureValue

FeatureValue

FeatureValue

FeatureValue

FeatureValue

FeatureValue

Method Detail

put

isEmpty

size

getValue

getNumTerms

getFeatureType

getAsTypedTensor

getAsTensorData

getAsTermVector

getAsNumeric

getAsString

getAsCategorical

getAsBoolean

createNumeric

createCategorical

createCategorical

createCategorical

createDenseVector

createDenseVector

createStringTermVector

createNumericTermVector

createStringTermVector

createNumericTermVector

createNumericCategoricalSet

createStringCategoricalSet

createCharacterCategoricalSet

createBoolean

createTensor

createTensor

toString

equals

hashCode

write

read