RECORD LINKAGE, A REAL USE CASE WITH SPARK ML Alexis Seigneurin

Who I am • Software engineer for 15 years • Consultant at Ippon Tech in Paris, France • Spark trainer • Favorite subjects: Spark, Machine Learning, Cassandra • @aseigneurin

The project • Record Linkage with Machine learning • Use cases: • Find new clients who come from insurance comparison services → Commission • Find duplicates in existing files (acquisitions) • Record Linkage • Entity resolution • Deduplication • Entity disambiguation • …

Overview

• Find duplicates! Purpose +---+-------+------------+----------+------------+---------+------------+ | ID| veh|codptgar_veh|dt_nais_cp|dt_permis_cp|redmaj_cp| formule| +---+-------+------------+----------+------------+---------+------------+ |...|PE28221| 50000|1995-10-12| 2013-10-08| 100.0| TIERS| |...|FO26016| 59270|1967-01-01| 1987-02-01| 100.0|VOL_INCENDIE| |...|FI19107| 77100|1988-09-27| 2009-09-13| 105.0|TOUS_RISQUES| |...|RE07307| 69100|1984-08-15| 2007-04-20| 50.0| TIERS| |...|FO26016| 59270|1967-01-07| 1987-02-01| 105.0|TOUS_RISQUES| +---+-------+------------+----------+------------+---------+------------+

Steps 1. Preprocessing 1. Find potential duplicates 2. Feature engineering 2. Manual labeling of a sample 3. Machine Learning to make predictions on the rest of the records

Prototype • Crafted by a Data Scientist • Not architectured, not versioned, not unit tested… → Not ready for production • Spark, but a lot of Spark SQL (data processing) • Machine Learning in Python (Scikit Learn) → Objective: industrialization of the code

Preprocessing

• Data (CSV) + Schema (JSON) Inputs 000010;Jose;Lester;10/10/1970 000011;José;Lester;10/10/1970 000012;Tyler;Hunt;12/12/1972 000013;Tiler;Hunt;25/12/1972 000014;Patrick;Andrews;1973-12-13 { "tableSchemaBeforeSelection": [ { "name": "ID", "typeField": "StringType", "hardJoin": false }, { "name": "name", "typeField": "StringType", "hardJoin": true, "cleaning": "digitLetter", "listFeature": [ "scarcity" ], "listDistance": [ "equality", "soundLike" ] }, ...

• Spark CSV module → DataFrame Don’t use type inference Data loading +------+-------+-------+----------+ | ID| name|surname| birthDt| +------+-------+-------+----------+ |000010| Jose| Lester|10/10/1970| |000011| José| Lester|10/10/1970| |000012| Tyler| Hunt|12/12/1972| |000013| Tiler| Hunt|25/12/1972| |000014|Patrick|Andrews|1970-10-10| +------+-------+-------+----------+

• Parsing of dates, numbers… • Cleaning of strings Data cleaning +------+-------+-------+----------+ | ID| name|surname| birthDt| +------+-------+-------+----------+ |000010| jose| lester|1970-10-10| |000011| jose| lester|1970-10-10| |000012| tyler| hunt|1972-12-12| |000013| tiler| hunt|1972-12-25| |000014|patrick|andrews| null| +------+-------+-------+----------+

• Convert strings to phonetics (Beider-Morse) • … Feature calculation +------+-------+-------+----------+--------------------+ | ID| name|surname| birthDt| BMencoded_name| +------+-------+-------+----------+--------------------+ |000010| jose| lester|1970-10-10|ios|iosi|ioz|iozi...| |000011| jose| lester|1970-10-10|ios|iosi|ioz|iozi...| |000012| tyler| hunt|1972-12-12| tilir| |000013| tiler| hunt|1972-12-25| tQlir|tili|tilir| |000014|patrick|andrews| null|pYtrQk|pYtrik|pat...| +------+-------+-------+----------+--------------------+

• Auto-join (more on that later…) Find potential duplicates +------+------+---------+...+------+------+---------+... | ID_1|name_1|surname_1|...| ID_2|name_2|surname_2|... +------+------+---------+...+------+------+---------+... |000010| jose| lester|...|000011| jose| lester|... |000012| tyler| hunt|...|000013| tiler| hunt|... +------+------+---------+...+------+------+---------+...

• Several distance algorithms: • Levenshtein distance, date difference… Distance calculation +------+...+------+...+-------------+--------------+...+----------------+ | ID_1|...| ID_2|...|equality_name|soundLike_name|...|dateDiff_birthDt| +------+...+------+...+-------------+--------------+...+----------------+ |000010|...|000011|...| 0.0| 0.0|...| 0.0| |000012|...|000013|...| 1.0| 0.0|...| 13.0| +------+...+------+...+-------------+--------------+...+----------------+

• Standardization of distances only • Vectorization (2 vectors) Standardization / vectorization +------+------+---------+----------+------+------+---------+----------+------------+--------------+ | ID_1|name_1|surname_1| birthDt_1| ID_2|name_2|surname_2| birthDt_2| distances|other_features| +------+------+---------+----------+------+------+---------+----------+------------+--------------+ |000010| jose| lester|1970-10-10|000011| jose| lester|1970-10-10|[0.0,0.0,...| [2.0,2.0,...| |000012| tyler| hunt|1972-12-12|000013| tiler| hunt|1972-12-25|[1.0,1.0,...| [1.0,2.0,...| +------+------+---------+----------+------+------+---------+----------+------------+--------------+

Spark SQL → DataFrames

From SQL… • Generated SQL requests • Hard to maintain (especially as regards to UDFs) val cleaningRequest = tableSchema.map(x => { x.CleaningFuction match { case (Some(a), _) => a + "(" + x.name + ") as " + x.name case _ => x.name } }).mkString(", ") val cleanedTable = sqlContext.sql("select " + cleaningRequest + " from " + tableName) cleanedTable.registerTempTable(schema.tableName + "_cleaned")

… to DataFrames • DataFrame primitives • More work done by the Scala compiler val cleanedDF = tableSchema.filter(_.cleaning.isDefined).foldLeft(df) { case (df, field) => val udf: UserDefinedFunction = ... // get the cleaning UDF df.withColumn(field.name + "_cleaned", udf.apply(df(field.name))) .drop(field.name) .withColumnRenamed(field.name + "_cleaned", field.name) }

Matching potential duplicates

• Join table A with table B Join strategy #1 Prospects New clients

Join strategy #2 • Union table A with table B • Auto-join • Allows for deduplication, not only matching Prospects New clients Duplicate

Join - Volume of data • Input: 1M records • Cartesian product: 1000 B records → Find an appropriate join condition 0 25 50 75 100

Join condition • Multiples join on 2 fields • Equality of values or custom condition (UDF) • Union between all the intermediate results • E.g. with fields name, surname, birth_date: df1.join(df2, (df1("ID_1") < df2("ID_2"))
 && (df1("name_1") === df2("name_2"))
 && (soundLike(df1("surname_1"), df2("surname_2"))) df1.join(df2, (df1("ID_1") < df2("ID_2"))
 && (df1("name_1") === df2("name_2"))
 && (df1("birth_date_1") === df2("birth_date_2"))) df1.join(df2, (df1("ID_1") < df2("ID_2"))
 && (soundLike(df1("surname_1"), df2("surname_2")))
 && (df1("birth_date_1") === df2("birth_date_2"))) UNION

DataFrames extension

• 3 types of columns DataFrames extension +------+...+------+...+-------------+--------------+...+----------------+ | ID_1|...| ID_2|...|equality_name|soundLike_name|...|dateDiff_birthDt| +------+...+------+...+-------------+--------------+...+----------------+ |000010|...|000011|...| 0.0| 0.0|...| 0.0| |000012|...|000013|...| 1.0| 0.0|...| 13.0| +------+...+------+...+-------------+--------------+...+----------------+ Data Distances Non-distance features

• DataFrame columns have a name and a data type • DataFrameExt = DataFrame + metadata over columns DataFrames extension case class OutputColumn(name: String, columnType: ColumnType)
 
 
 class DataFrameExt(val df: DataFrame, val outputColumns: Seq[OutputColumn]) { def show() = df.show() def drop(colName: String): DataFrameExt = ... def withColumn(colName: String, col: Column, columnType: ColumnType): DataFrameExt = ... ...

Unit testing

Unit testing • Scalatest + Scoverage • Coverage of all the data processing operations • Comparison of Row objects

val resDF = schema.cleanTable(rows)
 "The cleaning process" should "clean text fields" in {
 val res = resDF.select("ID", "name", "surname").collect()
 val expected = Array(
 Row("000010", "jose", "lester"),
 Row("000011", "jose", "lester ea"),
 Row("000012", "jose", "lester")
 )
 res should contain theSameElementsAs expected
 } 
 "The cleaning process" should "parse dates" in { ... Unit testing 000010;Jose;Lester;10/10/1970
 000011;Jose =-+;Lester éà;10/10/1970
 000012;Jose;Lester;invalid date

Labeling

Labeling • Manual operation • Is this a duplicate? → Yes / No • Performed on a sample of the potential duplicates • Between 1000 and 10 000 records

Labeling

Predictions

Predictions • Machine Learning • Random Forests • (Gradient Boosting Trees also give good results) • Training on the potential duplicates labeled by hand • Predictions on the potential duplicates not labeled by hand

Predictions • Sample: 1000 records • Training set: 800 records • Test set: 200 records • Results • True positives: 53 • False positives: 2 • True negatives: 126 • False negatives: 5 → Found 53 duplicates on the 58 expected (53+5) and only 2 errors

Summary & Conclusion

Summary ✓ Single engine for Record Linkage and Deduplication ✓ Machine Learning → Specific rules for each dataset ✓ Higher identification of matches • Previously ~50% → Now 80-90%

Thank you! @aseigneurin