class SparqlFrame extends Transformer
This SparqlFrame Transformer creates a dataframe based on a SPARQL query the resulting columns correspond to projection variables
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- new SparqlFrame()
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$[T](param: Param[T]): T
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==(arg0: Any): Boolean
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- var _collapsByKey: Boolean
- var _featureDescriptions: Map[String, Map[String, Any]]
- var _keyColumnNameString: String
- var _query: String
- var _queryExcecutionEngine: query.spark.SPARQLEngine.Value
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def
asInstanceOf[T0]: T0
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def
clear(param: Param[_]): SparqlFrame.this.type
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clone(): AnyRef
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def
collapsByKey(df: DataFrame): DataFrame
SparqlFrame: The collapsByKey is set to true so we collaps the collumns by id column and als collect feature type information which are available in property .getFeatureTypes
SparqlFrame: The collapsByKey is set to true so we collaps the collumns by id column and als collect feature type information which are available in property .getFeatureTypes
- df
the input noncollapsed dataframe
- returns
the collapsed dataframe
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def
copy(extra: ParamMap): Transformer
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- SparqlFrame → Transformer → PipelineStage → Params
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
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def
defaultCopy[T <: Params](extra: ParamMap): T
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explainParams(): String
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def
extractParamMap(): ParamMap
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def
extractParamMap(extra: ParamMap): ParamMap
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get[T](param: Param[T]): Option[T]
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def
getDefault[T](param: Param[T]): Option[T]
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def
getFeatureDescriptions(): Map[String, Map[String, Any]]
get the description of features after sparql extraction to decide over upcoming preprocessing strategies
get the description of features after sparql extraction to decide over upcoming preprocessing strategies
- returns
map representeing for each column some feature descriptions
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getOrDefault[T](param: Param[T]): T
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def
getParam(paramName: String): Param[Any]
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def
getSemanticTransformerDescription(): RDD[Triple]
gain all inforamtion from this transformer as knowledge graph
gain all inforamtion from this transformer as knowledge graph
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RDD[Trile] describing the meta information
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hasDefault[T](param: Param[T]): Boolean
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hasParam(paramName: String): Boolean
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initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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initializeLogIfNecessary(isInterpreter: Boolean): Unit
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isTraceEnabled(): Boolean
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log: Logger
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logDebug(msg: ⇒ String, throwable: Throwable): Unit
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logName: String
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lazy val
params: Array[Param[_]]
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final
def
set(paramPair: ParamPair[_]): SparqlFrame.this.type
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final
def
set(param: String, value: Any): SparqlFrame.this.type
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final
def
set[T](param: Param[T], value: T): SparqlFrame.this.type
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def
setCollapsByKey(collapsByKey: Boolean): SparqlFrame.this.type
Decide if we want to collaps the dataframe by an idea and collapse the samples so df consists of one row per entity
Decide if we want to collaps the dataframe by an idea and collapse the samples so df consists of one row per entity
- collapsByKey
if yes, it will be collapsed, default is false
- returns
transformer itself
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def
setCollapsColumnName(keyColumnNameString: String): SparqlFrame.this.type
by which column to collapse if it shouldnt be first column
by which column to collapse if it shouldnt be first column
- keyColumnNameString
column name to collapse
- returns
transformer itself
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final
def
setDefault(paramPairs: ParamPair[_]*): SparqlFrame.this.type
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final
def
setDefault[T](param: Param[T], value: T): SparqlFrame.this.type
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- def setExperimentId(experimentURI: Node): SparqlFrame.this.type
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def
setQueryExcecutionEngine(queryExcecutionEngine: query.spark.SPARQLEngine.Value): SparqlFrame.this.type
setter to specify which query execution engine to be used
setter to specify which query execution engine to be used
option one is ontop option two sparqlify
- queryExcecutionEngine
a string either ontop or sparqlify
- returns
the set transformer
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def
setSparqlQuery(queryString: String): SparqlFrame.this.type
setter for query as string
setter for query as string
- queryString
a sparql query defining which features you want to have
- returns
the set transformer
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val
spark: SparkSession
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
transform(dataset: Dataset[_]): DataFrame
creates a native spark Mllib DataFrame with columns corresponding to the projection variables
creates a native spark Mllib DataFrame with columns corresponding to the projection variables
columns are implicitly casted to string or if specified in literals to respective integer ect
- dataset
the knowledge graph as dataset of jena triple
- returns
a dataframe with columns corresponding to projection variables
- Definition Classes
- SparqlFrame → Transformer
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def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
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def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
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def
transformSchema(schema: StructType): StructType
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- SparqlFrame → PipelineStage
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def
transformSchema(schema: StructType, logging: Boolean): StructType
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val
uid: String
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final
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wait(arg0: Long, arg1: Int): Unit
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(Since version ) see corresponding Javadoc for more information.