TomLous

import spark.sqlContext.implicits._

case class GroupedKvKRecord(groupId: Long, kvkRecords: Seq[KvKRecord])

// need to broadcast for rdd in rdd mapping
val bcVertices = spark.sparkContext.broadcast(vertices.collectAsMap)

// connectedComponents is the magic here
val groupedKvKRecords = linkedGraph
    .connectedComponents

TomLous

// edges are given the probabilty of the correctness of the edge
val edges: RDD[Edge[Double]] = predictedLinksDataset.rdd.map(
  l => Edge(l.dossierNummer_left.toLong, l.dossierNummer_right.toLong, l.probability))

// vertices are just tuples of the dossiernummer.toLong (VertexId is typed as Long)
val vertices: RDD[(VertexId, KvKRecord)] = predictedLinksDataset.rdd.map(
  k => (k.dossierNummer.toLong, k))

// the graph 
val linkedGraph = Graph(vertices, edges)

TomLous

// probability will be a vector with 2 values resp. for label 0.0 and 1.0. We just need the 1.0 one
val getP1 = udf((v: Vector) => v(1))

val predictionsRaw = lrModel.transform(comparableDataset)

val predictedLinksDataset = predictionsRaw
      .select(
        $"left.dossierNummer".as("dossierNummer_left"),
        $"right.dossierNummer".as("dossierNummer_right"),
        $"features",

TomLous

// load the labeled data
val labeledSet = spark.read.parquet(path).as[LabeledVector]

// split train/test (80/20)
val Array(trainingData, testData) = labeledSet.randomSplit(Array(Config.trainSplit, 1 - Config.trainSplit))

// Basic model
val lr = new LogisticRegression().setMaxIter(200).setRegParam(0.01).setElasticNetParam(0.8)

// Train

TomLous

// Tabulator: checkout http://stackoverflow.com/questions/7539831/scala-draw-table-to-console

def propertyList(kvKRecord: KvKRecord): List[Any] = KvKRecord.unapply(kvKRecord).map(_.productIterator.toList).getOrElse(Nil)

val labeledList: ArrayBuffer[LabeledVector] = ArrayBuffer()

breakable {
  comparableDataset
    .sample(withReplacement = false, Config.sampleFactor)
    .collect()

TomLous

import java.lang.Math._
import info.debatty.java.stringsimilarity.JaroWinkler

case class KvKRecord(dossierNummer: String, vgNummer: String, naamShort: String, naamShortP1: String, naamShortP2: String, naamLong: String, adresV: String, postcodePlaatsV: String, adresC: String, postcodePlaatsC: String, wptf: Int, sbi: Int) {

  def vectorValues: List[Any] = List(dossierNummer, vgNummer, naamShort, naamShortP1, naamShortP2, naamLong, adresV, postcodePlaatsV, adresC, postcodePlaatsC, wptf, sbi)

  def distance(other: KvKRecord): List[Double] = {
    val jw = new JaroWinkler

TomLous

val comparableDataset = kvKDataset.as("l")
      .joinWith(
        kvKDataset.as("r"),
        $"l.adresV" === $"r.adresV" && $"l.postcodePlaatsV" === $"r.postcodePlaatsV" && $"l.dossierNummer" =!= $"r.dossierNummer"
      ).map {
        case (left, right) => (left, right, Vectors.dense(left.distance(right).toArray))
      }
      .toDF("left", "right", "features")
      .as[(KvKRecord, KvKRecord, Vector)]

TomLous

import spark.implicits._

val kvKDataset = spark.read.option("header", true).option("sep", ";").option("ignoreLeadingWhiteSpace", true).option("ignoreTrailingWhiteSpace", true).option("quote", """"""").option("nullValue", "").option("mode", "FAILFAST").csv(path)

kvKDataset.select(
        'DOSSIERNR.as("dossierNummer"),
        'VG_NR.as("vgNummer"),
        'HANDELSN30.as("naamShort"),
        'HANDN1_30.as("naamShortP1"),
        'HANDN2_30.as("naamShortP2"),

jexp

Useful Links

• Slides: Lesser known features in Cypher - Alex Price

• Cypher Query Tuning - Michael

• Cypher Query Tuning - Mark & Petra

ajdapretnar

from Orange.data import Table
from Orange.widgets import gui
from Orange.widgets.widget import OWWidget
from AnyQt.QtWidgets import QApplication, QLabel
from orangecontrib.text import Corpus


class OWWordFinder(OWWidget):
    name = "Word Finder"
    description = "Display whether a word is in a text or not."