TfIdfKNNModel

Instance Constructors

new TfIdfKNNModel(uid: String, idfDf: DataFrame, userDf: DataFrame)

uid
The UID
idfDf
The data frame with IDF weightings for all items
userDf
The data frame of all users and items the model was fitted on, and their TF-IDF weightings

Value Members

final def !=(arg0: Any): Boolean

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

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def clear(param: Param[_]): TfIdfKNNModel.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def copy(extra: ParamMap): TfIdfKNNModel

Definition Classes
TfIdfKNNModel → Model → Transformer → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
final val filterUserItems: BooleanParam

Whether the items of a user should be filtered from the recommendations for the user Default: false
Whether the items of a user should be filtered from the recommendations for the user Default: false

Definition Classes
KNNParams
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def get[T](param: Param[T]): Option[T]

Definition Classes
Params
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
def getFilterUserItems: Boolean

Definition Classes
KNNParams
def getItemCol: String

Definition Classes
KNNParams
def getK: Int

Definition Classes
KNNParams
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
def getParam(paramName: String): Param[Any]

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
def getUserCol: String

Definition Classes
KNNParams
final def hasDefault[T](param: Param[T]): Boolean

Definition Classes
Params
def hasParam(paramName: String): Boolean

Definition Classes
Params
def hasParent: Boolean

Definition Classes
Model
def hashCode(): Int

Definition Classes
AnyRef → Any
val idfDf: DataFrame

The data frame with IDF weightings for all items
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
final def isDefined(param: Param[_]): Boolean

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def isSet(param: Param[_]): Boolean

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
final val itemCol: Param[String]

The name of the item id column of integers from 0 to number of items in training dataset Default: "itemid"
The name of the item id column of integers from 0 to number of items in training dataset Default: "itemid"

Definition Classes
KNNParams
final val k: IntParam

The number of nearest neighbours Default: 150
The number of nearest neighbours Default: 150

Definition Classes
KNNParams
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
lazy val params: Array[Param[_]]

Definition Classes
Params
var parent: Estimator[TfIdfKNNModel]

Definition Classes
Model
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
def recommendForUserSubset(queryDataset: Dataset[_], numItems: Int): DataFrame

Returns top numItems items recommended for each user id in the input data set
Returns top numItems items recommended for each user id in the input data set
queryDataset
The dataset containing a column of user ids. The column name must match userCol
numItems
The maximum number of recommendations for each user
returns
A dataframe of (userCol: Int, recommendations), where recommendations are stored as an array of (score: Float, itemCol: Int) rows. Or if exploded a dataframe of (userCol: Int, score: Float, itemCol: Int) rows.
def recommendFromNearestUsers(queryDf: DataFrame, nnDf: DataFrame, numItems: Int): DataFrame

Definition Classes
KNNRecommender
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): TfIdfKNNModel.this.type

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): TfIdfKNNModel.this.type

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): TfIdfKNNModel.this.type

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): TfIdfKNNModel.this.type

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): TfIdfKNNModel.this.type

Attributes
protected
Definition Classes
Params
def setFilterUserItems(value: Boolean): TfIdfKNNModel.this.type

Definition Classes
KNNRecommender
def setK(value: Int): TfIdfKNNModel.this.type

Definition Classes
KNNRecommender
def setParent(parent: Estimator[TfIdfKNNModel]): TfIdfKNNModel

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def toTfIdf(idfDf: DataFrame): DataFrame

Definition Classes
_TfIdfKNN
def transform(dataset: Dataset[_]): DataFrame

Definition Classes
TfIdfKNNModel → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
def transformSchema(schema: StructType): StructType

Definition Classes
TfIdfKNNModel → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String

The UID
The UID

Definition Classes
TfIdfKNNModel → Identifiable
final val userCol: Param[String]

The name of the user id column of integers Default: "userid"
The name of the user id column of integers Default: "userid"

Definition Classes
KNNParams
val userDf: DataFrame

The data frame of all users and items the model was fitted on, and their TF-IDF weightings
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( ... )
def write: MLWriter

Definition Classes
TfIdfKNNModel → MLWritable

Related Docs: object TfIdfKNNModel | package recommendation

class TfIdfKNNModel extends Model[TfIdfKNNModel] with _TfIdfKNN with KNNRecommender with MLWritable

Instance Constructors

new TfIdfKNNModel(uid: String, idfDf: DataFrame, userDf: DataFrame)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final def clear(param: Param[_]): TfIdfKNNModel.this.type

def clone(): AnyRef

def copy(extra: ParamMap): TfIdfKNNModel

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val filterUserItems: BooleanParam

def finalize(): Unit

final def get[T](param: Param[T]): Option[T]

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

def getFilterUserItems: Boolean

def getItemCol: String

def getK: Int

final def getOrDefault[T](param: Param[T]): T

def getParam(paramName: String): Param[Any]

final def getPredictionCol: String

def getUserCol: String

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

val idfDf: DataFrame

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

def isTraceEnabled(): Boolean

final val itemCol: Param[String]

final val k: IntParam

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val params: Array[Param[_]]

var parent: Estimator[TfIdfKNNModel]

final val predictionCol: Param[String]

def recommendForUserSubset(queryDataset: Dataset[_], numItems: Int): DataFrame

def recommendFromNearestUsers(queryDf: DataFrame, nnDf: DataFrame, numItems: Int): DataFrame

def save(path: String): Unit

final def set(paramPair: ParamPair[_]): TfIdfKNNModel.this.type

final def set(param: String, value: Any): TfIdfKNNModel.this.type

final def set[T](param: Param[T], value: T): TfIdfKNNModel.this.type

final def setDefault(paramPairs: ParamPair[_]*): TfIdfKNNModel.this.type

final def setDefault[T](param: Param[T], value: T): TfIdfKNNModel.this.type

def setFilterUserItems(value: Boolean): TfIdfKNNModel.this.type

def setK(value: Int): TfIdfKNNModel.this.type

def setParent(parent: Estimator[TfIdfKNNModel]): TfIdfKNNModel

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

def toString(): String

def toTfIdf(idfDf: DataFrame): DataFrame

def transform(dataset: Dataset[_]): DataFrame