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net.sansa_stack.ml.spark.similarity.experiment

SimilarityPipelineExperiment

object SimilarityPipelineExperiment

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  11. def main(args: Array[String]): Unit

    main method reading in the parameter space, creating the spark, starting all combinations of parameters for experiments and storing the resulting processing times in a dataframe

    main method reading in the parameter space, creating the spark, starting all combinations of parameters for experiments and storing the resulting processing times in a dataframe

    args

    args(0) has to be the conf file specifiying all needed (hyper)parameters

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  15. def runExperiment(sparkMaster: String, pipelineComponents: List[String], run: Int, inputPath: String, similarityEstimationMode: String, parametersFeatureExtractorMode: String, parameterCountVectorizerMinDf: Int, parameterCountVectorizerMaxVocabSize: Int, parameterSimilarityAlpha: Double, parameterSimilarityBeta: Double, parameterNumHashTables: Int, parameterSimilarityAllPairThreshold: Double, parameterSimilarityNearestNeighborsK: Int, parameterOnlyMovieSimilarity: Boolean, showDataFrames: Boolean = false): Row

    we create call one explicit defined pipeline to aquire processing times

    we create call one explicit defined pipeline to aquire processing times

    sparkMaster

    spark session

    pipelineComponents

    which part of the pipeline are called

    run

    number of runs of the same experiment

    inputPath

    path to the file we evaluate on

    similarityEstimationMode

    which similarity estimation we take

    parametersFeatureExtractorMode

    mode how we get gain features for uris throur FeatureExtractorModel

    parameterCountVectorizerMinDf

    minDf value for count Vectorizer from MLlib

    parameterCountVectorizerMaxVocabSize

    maxVocabSize value for count Vectorizer from MLlib

    parameterSimilarityAlpha

    alpha value for tversky and others

    parameterSimilarityBeta

    beta value for tversky and others

    parameterNumHashTables

    num hash tables minhash creates

    parameterSimilarityAllPairThreshold

    threshold for min or max similarity/distance

    parameterSimilarityNearestNeighborsK

    number of proposed nearestneighbors

    parameterOnlyMovieSimilarity

    if in comparison only movies from datasets should be taken into account

    showDataFrames

    whether intermediate resulting dataframes should be proposed

    returns

    provides a Row with all entries of hyperparameters and measured processing times

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    (Since version ) see corresponding Javadoc for more information.

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