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/*
* Copyright (c) 2017-2023 AutoDeployAI
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.pmml4s.model
import org.pmml4s.Since
import org.pmml4s.common.MiningFunction.MiningFunction
import org.pmml4s.common._
import org.pmml4s.data.Series
import org.pmml4s.metadata.Algorithm.Algorithm
import org.pmml4s.metadata.RankBasis.RankBasis
import org.pmml4s.metadata.RankOrder.RankOrder
import org.pmml4s.metadata._
import org.pmml4s.transformations.LocalTransformations
import scala.collection.{immutable, mutable}
/**
* The Association Rule model represents rules where some set of items is associated to another set of items. For
* example a rule can express that a certain product or set of products is often bought in combination with a certain
* set of other products, also known as Market Basket Analysis. An Association Rule model typically has two variables:
* one for grouping records together into transactions (usageType="group") and another that uniquely identifies each
* record (usageType="active"). Alternatively, association rule models can be built on regular data, where each category
* of each categorical field is an item. Yet another possible format of data is a table with true/false values, where
* only the fields having true value in a record are considered valid items.
*
* An Association Rule model consists of four major parts:
* - Model attributes
* - Items
* - ItemSets
* - AssociationRules
*/
class AssociationModel(
var parent: Model,
override val attributes: AssociationAttributes,
override val miningSchema: MiningSchema,
val items: Array[Item],
val itemsets: Array[Itemset],
val associationRules: Array[AssociationRule],
override val output: Option[Output] = None,
override val targets: Option[Targets] = None,
override val localTransformations: Option[LocalTransformations] = None,
override val modelStats: Option[ModelStats] = None,
override val modelExplanation: Option[ModelExplanation] = None,
override val modelVerification: Option[ModelVerification] = None,
override val extensions: immutable.Seq[Extension] = immutable.Seq.empty
) extends Model with HasWrappedAssociationAttributes {
val groupField: Field = {
val fs = miningSchema.getByUsageType(UsageType.group)
if (fs.nonEmpty) field(fs.head.name) else null
}
val activeField: Field = if (groupField != null)
field(miningSchema.getByUsageType(UsageType.active).head.name)
else null
require(groupField == null || activeField != null, "An active field is required when a group field is present")
// An active field could be a derived field
val activeNames = items.map(_.getName).filter(_.isDefined).map(_.get).toSet.toArray
val activeFields: Array[Field] = if (activeNames.nonEmpty) activeNames.map(x => field(x)) else inputFields
private val idToItem: Map[String, String] = items.map(x => (x.id, x.toString)).toMap
private val idToItemset: Map[String, Set[String]] =
itemsets.map(x => (x.id, x.itemRefs.map(y => idToItem(y.itemRef)))).toMap
{
var i = 0
while (i < associationRules.length) {
associationRules(i).init(idToItemset, i)
i += 1
}
}
@transient private var lastGroup: Any = null
@transient private val lastItemset: mutable.Set[String] = mutable.Set.empty
/** Model element type. */
override def modelElement: ModelElement = ModelElement.AssociationModel
/** Predicts values for a given data series. */
override def predict(values: Series): Series = {
val (series, returnInvalid) = prepare(values)
if (returnInvalid) {
return nullSeries
}
val itemset: scala.collection.Set[String] = if (groupField != null) {
val group = groupField.get(series)
val item = activeField.get(series)
if (group != lastGroup) {
lastItemset.clear()
lastGroup = group
}
if (item != null) {
lastItemset += item.toString
}
lastItemset
} else {
lastItemset.clear()
for (elem <- activeFields) {
elem.dataType match {
case BooleanType => if (elem.get(series) == true) lastItemset += elem.name
case _ => {
val value = elem.get(series)
if (value != null) {
lastItemset += (elem.name + "=" + value)
}
}
}
}
lastItemset
}
val outputs = createOutputs()
val criteria = topCriteria
import org.pmml4s.metadata.RankBasis._
outputs.rules = criteria.map(x => {
val selects = associationRules.filter(_.fire(itemset, x._1._1))
val sorted = if (selects.nonEmpty) x._1._2 match {
case `confidence` => selects.sortBy(_.confidence)
case `support` => selects.sortBy(_.support)
case `lift` => if (selects.head.lift.isDefined) selects.sortBy(_.lift.get) else selects.sortBy(_.confidence)
case `leverage` => if (selects.head.leverage.isDefined) selects.sortBy(_.leverage.get) else selects.sortBy(_.confidence)
case `affinity` => if (selects.head.affinity.isDefined) selects.sortBy(_.affinity.get) else selects.sortBy(_.confidence)
} else selects
import org.pmml4s.metadata.RankOrder._
(x._1, x._1._3 match {
case `descending` => sorted.take(x._2)
case `ascending` => sorted.takeRight(x._2).reverse
})
})
result(series, outputs)
}
def topCriteria: Map[(Algorithm, RankBasis, RankOrder), Int] = {
val ofs = candidateOutputFields
ofs.groupBy(_.criterion).map(x => (x._1, x._2.maxBy(_.rank).rank)).toMap
}
/** Creates an object of subclass of ModelOutputs that is for writing into an output series. */
override def createOutputs(): AssociationOutputs = new AssociationOutputs
}
/**
* Obviously the id of an Item must be unique. Furthermore the Item values must be unique, or if they are not unique
* then attributes field and category must distiguish them. That is, an AssocationModel must not have different
* instances of Item where the values of the value, field, and category attribute are all the same. The entries in
* mappedValue may be the same, though. Here are some examples of Items:
*
* @param id An identification to uniquely identify an item.
* @param value The value of the item as in the input data.
* @param field
* @param category
* @param mappedValue Optional, a value to which the original item value is mapped. For instance, this could be a
* product name if the original value is an EAN code.
* @param weight The weight of the item. For example, the price or value of an item.
*/
class Item(val id: String,
val value: Any,
@Since("4.3")
val field: Option[Field] = None,
@Since("4.3")
val category: Option[String] = None,
val mappedValue: Option[String] = None,
val weight: Option[Double] = None) extends PmmlElement {
override def toString: String = {
val str = value.toString
if (field.isEmpty && category.isEmpty) {
str
} else if (field.isDefined && category.isDefined) {
if (str.contains('=')) {
str
} else if (field.get.name.contains('=')) {
field.get.name
} else if (category.get != "true") {
field.get.name + "=" + category.get
} else {
field.get.name + "=" + str
}
} else if (field.isDefined) {
if (field.get.name.contains('=')) {
field.get.name
} else {
field.get.name + "=" + str
}
} else {
str
}
}
def getName: Option[String] = field.map(_.name) orElse ({
if (value.toString.contains('=')) {
Some(value.toString.split('=')(0))
} else None
})
}
/**
* Item references point to elements of type Item
*
* @param itemRef Contains the identification of an item.
*/
class ItemRef(val itemRef: String) extends PmmlElement
/**
*
* @param itemRefs Item references point to elements of type Item
* @param id An identification to uniquely identify an Itemset.
* @param support The relative support of the Itemset:
* support(set) = (number of transactions containing the set) / (total number of transactions)
* @param numberOfItems The number of Items contained in this Itemset
*/
class Itemset(val itemRefs: Set[ItemRef],
val id: String,
val support: Option[Double] = None,
val numberOfItems: Option[Int] = None) extends PmmlElement {
}
/**
* We consider association rules of the form " => " next:
*
* @param antecedent The id value of the itemset which is the antecedent of the rule. We represent the itemset by the
* letter A.
* @param consequent The id value of the itemset which is the consequent of the rule. We represent the itemset by the
* letter C.
* @param support The support of the rule, that is, the relative frequency of transactions that contain A and C:
* support(A->C) = support(A+C)
* @param confidence The confidence of the rule:
* confidence(A->C) = support(A+C) / support(A)
* @param lift A very popular measure of interestingness of a rule is lift. Lift values greater than 1.0 indicate
* that transactions containing A tend to contain C more often than transactions that do not contain A:
* lift(A->C) = confidence(A->C) / support(C)
* @param leverage Another measure of interestingness is leverage. An association with higher frequency and lower lift
* may be more interesting than an alternative rule with lower frequency and higher lift. The former
* can be more important in practice because it applies to more cases. The value is the difference
* between the observed frequency of A+C and the frequency that would be expected if A and C were
* independent:
* leverage(A->C) = support(A->C) - support(A)*support(C)
* @param affinity Also known as Jaccard Similarity, affinity is a measure of the transactions that contain both the
* antecedent and consequent (intersect) compared to those that contain the antecedent or the
* consequent (union):
* affinity(A->C) = support(A+C) / [ support(A) + support(C) - support(A+C)]
* @param id An identification to uniquely identify an association rule.
*/
class AssociationRule(val antecedent: String,
val consequent: String,
val support: Double,
val confidence: Double,
val lift: Option[Double] = None,
val leverage: Option[Double] = None,
val affinity: Option[Double] = None,
val id: Option[String] = None) extends HasPredictedValue
with HasEntityId
with HasConfidence
with PmmlElement {
var antecedentItemset: Set[String] = _
var consequentItemset: Set[String] = _
// 1 -based index
var indexId: String = "-1"
lazy val antecedentRule: String =
(if (antecedentItemset.size > 1) antecedentItemset.mkString("{", " , ", "}") else antecedentItemset.head)
lazy val consequentRule: String =
(if (consequentItemset.size > 1) consequentItemset.mkString("{", " , ", "}") else consequentItemset.head)
lazy val rule: String = antecedentRule + " -> " + consequentRule
def init(idToItemset: Map[String, Set[String]], index: Int): Unit = {
antecedentItemset = idToItemset(antecedent)
consequentItemset = idToItemset(consequent)
indexId = (index + 1).toString
}
import org.pmml4s.metadata.Algorithm._
def fire(itemset: scala.collection.Set[String], algorithm: Algorithm): Boolean = algorithm match {
case `recommendation` => antecedentItemset.subsetOf(itemset)
case `exclusiveRecommendation` => antecedentItemset.subsetOf(itemset) && !consequentItemset.subsetOf(itemset)
case `ruleAssociation` => antecedentItemset.subsetOf(itemset) && consequentItemset.subsetOf(itemset)
}
override def predictedValue: Any = consequentRule
override def entityId: String = id.getOrElse(indexId)
}
trait HasAssociationAttributes extends HasModelAttributes {
/**
* The number of transactions contained in the input data.
*/
def numberOfTransactions: Int
/**
* The number of items contained in the largest transaction.
*/
def maxNumberOfItemsPerTA: Option[Int]
/**
* The average number of items contained in a transaction.
*/
def avgNumberOfItemsPerTA: Option[Double]
/**
* The minimum relative support value (#supporting transactions / #total transactions) satisfied by all rules.
*/
def minimumSupport: Double
/**
* The minimum confidence value satisfied by all rules. Confidence is calculated as (support (rule) /
* support(antecedent)).
*/
def minimumConfidence: Double
/**
* The maximum number of items contained in a rule which was used to limit the number of rules.
*/
def lengthLimit: Option[Int]
/**
* The number of different items contained in the input data. This number may be greater than or equal to the number
* of items contained in the model. The value will be greater if any items in the input data are excluded from the
* model, as a consequence of not being referenced by the model.
*/
def numberOfItems: Int
/**
* The number of itemsets contained in the model.
*/
def numberOfItemsets: Int
/**
* The number of rules contained in the model.
*/
def numberOfRules: Int
}
trait HasWrappedAssociationAttributes extends HasWrappedModelAttributes with HasAssociationAttributes {
override def attributes: AssociationAttributes
override def numberOfTransactions: Int = attributes.numberOfTransactions
override def maxNumberOfItemsPerTA: Option[Int] = attributes.maxNumberOfItemsPerTA
override def avgNumberOfItemsPerTA: Option[Double] = attributes.avgNumberOfItemsPerTA
override def minimumSupport: Double = attributes.minimumSupport
override def minimumConfidence: Double = attributes.minimumConfidence
override def lengthLimit: Option[Int] = attributes.lengthLimit
override def numberOfItems: Int = attributes.numberOfItems
override def numberOfItemsets: Int = attributes.numberOfItemsets
override def numberOfRules: Int = attributes.numberOfRules
}
class AssociationAttributes(
val numberOfTransactions: Int,
val minimumSupport: Double,
val minimumConfidence: Double,
val numberOfItems: Int,
val numberOfItemsets: Int,
val numberOfRules: Int,
val maxNumberOfItemsPerTA: Option[Int] = None,
val avgNumberOfItemsPerTA: Option[Double] = None,
val lengthLimit: Option[Int] = None,
override val functionName: MiningFunction = MiningFunction.associationRules,
override val modelName: Option[String] = None,
override val algorithmName: Option[String] = None,
override val isScorable: Boolean = true
) extends ModelAttributes(functionName, modelName, algorithmName, isScorable)
with HasAssociationAttributes {
def this(attributes: ModelAttributes,
numberOfTransactions: Int,
minimumSupport: Double,
minimumConfidence: Double,
numberOfItems: Int,
numberOfItemsets: Int,
numberOfRules: Int,
maxNumberOfItemsPerTA: Option[Int],
avgNumberOfItemsPerTA: Option[Double],
lengthLimit: Option[Int]) = {
this(numberOfTransactions, minimumSupport, minimumConfidence, numberOfItems, numberOfItemsets, numberOfRules,
maxNumberOfItemsPerTA, avgNumberOfItemsPerTA, lengthLimit, attributes.functionName, attributes.modelName,
attributes.algorithmName, attributes.isScorable)
}
}
class AssociationOutputs extends ModelOutputs with HasAssociationRules {
var rules: Map[(Algorithm, RankBasis, RankOrder), Array[AssociationRule]] = _
def setRules(rules: Map[(Algorithm, RankBasis, RankOrder), Array[AssociationRule]]): this.type = {
this.rules = rules
this
}
override def getRules(criterion: (Algorithm, RankBasis, RankOrder)): Array[AssociationRule] = rules(criterion)
}
