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Specify categorical variables in metadata #120
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12ce677
Adding Attributes when converting to Metadata
mweilsalesforce 1ac45eb
Treated Text types as special uses cases
mweilsalesforce 9c7d9ff
FirstTest
mweilsalesforce f7546db
Tested on DateVectorizers
mweilsalesforce 96a1d8c
Up to GeoLocationTests
mweilsalesforce 0531c68
Up to NumericVectorizerTest
mweilsalesforce 8c64851
Up to OpMapVectorizer
mweilsalesforce 96427de
Up to SmartTextVectorizer
mweilsalesforce d52131a
Up to URLVectorizerTests
mweilsalesforce e9e692c
fix scalastyle
mweilsalesforce 3c1ceeb
Merge branch 'master' into mw/categorical-metadata
mweilsalesforce 95d43cc
with AttributeAsserts
mweilsalesforce 186d81f
import com.salesforce.op.features.types._
mweilsalesforce 78edeca
FreatureType.typeName
mweilsalesforce 34f516f
Avoiding `.get`
mweilsalesforce 23e805d
Addressing remaining PR comments
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FreatureType.typeName
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Original file line number | Diff line number | Diff line change |
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@@ -31,7 +31,7 @@ | |
package com.salesforce.op.utils.spark | ||
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import com.salesforce.op.FeatureHistory | ||
import com.salesforce.op.features.types._ | ||
import com.salesforce.op.features.types.{FeatureType, _} | ||
import org.apache.spark.ml.attribute.{AttributeGroup, BinaryAttribute, NumericAttribute} | ||
import org.apache.spark.ml.linalg.SQLDataTypes._ | ||
import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField} | ||
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@@ -75,8 +75,10 @@ class OpVectorMetadata private | |
newColumns: Array[OpVectorColumnMetadata] | ||
): OpVectorMetadata = OpVectorMetadata(name, newColumns, history) | ||
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val textTypes = Seq(MultiPickList, MultiPickListMap, Text, TextArea, TextAreaMap, TextMap, Binary, BinaryMap, | ||
TextList).map(_.getClass.getName.dropRight(1)) | ||
val categoricalTypes = Seq(FeatureType.typeName[MultiPickList], FeatureType.typeName[MultiPickListMap], | ||
FeatureType.typeName[Text], FeatureType.typeName[TextArea], FeatureType.typeName[TextAreaMap], | ||
FeatureType.typeName[TextMap], FeatureType.typeName[Binary], FeatureType.typeName[BinaryMap], | ||
FeatureType.typeName[TextList]) | ||
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/** | ||
* Serialize to spark metadata | ||
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@@ -96,7 +98,7 @@ class OpVectorMetadata private | |
.putMetadata(OpVectorMetadata.HistoryKey, FeatureHistory.toMetadata(history)) | ||
.build() | ||
val attributes = columns.map { c => | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. val attributes = columns.map {
case c if c.indicatorValue.isDefined || categoricalTypes.exists(c.parentFeatureType.contains) =>
BinaryAttribute.defaultAttr.withName(c.makeColName()).withIndex(c.index)
case c =>
NumericAttribute.defaultAttr.withName(c.makeColName()).withIndex(c.index)
} |
||
if (c.indicatorValue.isDefined || textTypes.exists(c.parentFeatureType.contains)) { | ||
if (c.indicatorValue.isDefined || categoricalTypes.exists(c.parentFeatureType.contains)) { | ||
BinaryAttribute.defaultAttr.withName(c.makeColName()).withIndex(c.index) | ||
} else { | ||
NumericAttribute.defaultAttr.withName(c.makeColName()).withIndex(c.index) | ||
|
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picklist? Combo box? country, state, city, id
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Oh yeah If it is only hashing + count, let's remove all these Text types. Do we only do hashing to Combo box, country, state, city, id?
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we do pivot - so they should be picked up automatically. I think we also do pivot on multiPickList. So you may want to remove the categorical types check completely and only rely on the indicatorValue