H2OSparkPredictor

Instance Constructors

new H2OSparkPredictor(tp: String, src: String)

load a trained model with a type and source (name/path).
load a trained model with a type and source (name/path).
tp
the model's type: mojo vs pojo
src
the name/path of the pojo/mojo

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 _predictRow(row: Row, cols: Array[String]): (String, Double)

Predict on Row, one Row is one instance, helpinng function, used in predictRow
Predict on Row, one Row is one instance, helpinng function, used in predictRow
row
one instance
cols
Row column names (dataframe column names)
returns
label-score pair (if applicable)
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
lazy val easyModel: EasyPredictModelWrapper

Attributes
protected
Definition Classes
H2OPredictor
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] )
lazy val genModel: GenModel

Attributes
protected
Definition Classes
H2OPredictor
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

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

Definition Classes
Any
val modelCategory: ModelCategory

Definition Classes
H2OPredictor
lazy val names: Array[String]

feature and response column names
feature and response column names

Attributes
protected
Definition Classes
H2OPredictor
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predict(rowData: RowData): (String, Double)

predict on each RowData
predict on each RowData
returns
Tuple2[String, Double]: String: class/cluster/low-dimension-string/regression-value-string Double: confidence score if applicable

Definition Classes
H2OPredictor
def predictDF(df: DataFrame, keys: Array[String] = Array.empty[String], predLabelName: String = "label", predScoreName: String = "score")(implicit sqlContext: SQLContext): DataFrame

prediction on a whole dataframe with key attached
prediction on a whole dataframe with key attached
df
data
keys
keys, default=Array()
predLabelName
the column name used to store the prediction label, default="label"
predScoreName
the column name used to store the prediction score, default="score"
returns
dataframe with key(s), label, score
def predictRdd(rdd: RDD[(String, Any)]): (String, Double)

Predict on RDD
Predict on RDD
rdd
One instance is a RDD[Tuple2[String, Any]], where each element is a feature (name-value pair)
returns
label-score pairs (if applicable)
def predictRow(row: Row, cols: Array[String], keys: Array[String] = Array()): Row

Prediction on Row data with key attached
Prediction on Row data with key attached
row
data
cols
data column names
keys
keys, array of strings, kept in the result, default=Array()
returns
a row of predicted label with confidence score (if applicalbe) and original keys (if provided)
def predictRowCond(row: Row, cols: Array[String], usePrediction: (Row) ⇒ Boolean, labelCol: String, score: Double, keys: Array[String] = Array()): Row

Conditional Prediction on Row data with key attached, along with non-prediction value/score
Conditional Prediction on Row data with key attached, along with non-prediction value/score
row
data
cols
data column names
usePrediction
the logic function to decide whether apply the model on this particular instance
labelCol
origin label column, if not applying the model, this column's value will be directy retrieved
keys
origin keys, default=Array()
returns
the row of predictions with keys (optional)
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 prediction

class H2OSparkPredictor extends H2OPredictor with Serializable

Instance Constructors

new H2OSparkPredictor(tp: String, src: String)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def _predictRow(row: Row, cols: Array[String]): (String, Double)

final def asInstanceOf[T0]: T0

def clone(): AnyRef

lazy val easyModel: EasyPredictModelWrapper

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

lazy val genModel: GenModel

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val modelCategory: ModelCategory

lazy val names: Array[String]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def predict(rowData: RowData): (String, Double)

def predictDF(df: DataFrame, keys: Array[String] = Array.empty[String], predLabelName: String = "label", predScoreName: String = "score")(implicit sqlContext: SQLContext): DataFrame

def predictRdd(rdd: RDD[(String, Any)]): (String, Double)

def predictRow(row: Row, cols: Array[String], keys: Array[String] = Array()): Row

def predictRowCond(row: Row, cols: Array[String], usePrediction: (Row) ⇒ Boolean, labelCol: String, score: Double, keys: Array[String] = Array()): Row

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 H2OPredictor

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped