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/*
* Copyright (c) 2017-2019 AutoDeploy AI
*
* 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.transformations
import java.util.regex.{Matcher, Pattern}
import org.apache.commons.text.similarity.LevenshteinDistance
import org.pmml4s.common.{PmmlElement, Table}
import org.pmml4s.data.Series
import org.pmml4s.metadata.Field
import org.pmml4s.transformations.CountHits.CountHits
import org.pmml4s.transformations.LocalTermWeights.LocalTermWeights
import org.pmml4s.util.Utils
import scala.util.control.Breaks._
/**
* The TextIndex element fully configures how the text in textField should be processed and translated into a frequency
* metric for a particular term of interest. The actual frequency metric to be returned is defined through the
* localTermWeights attribute.
*/
class TextIndex(
val field: Field,
val expression: Expression,
val textIndexNormalizations: Array[TextIndexNormalization] = Array.empty,
val localTermWeights: LocalTermWeights = LocalTermWeights.termFrequency,
val isCaseSensitive: Boolean = false,
val maxLevenshteinDistance: Int = 0,
val countHits: CountHits = CountHits.allHits,
val wordSeparatorCharacterRE: String = "\\s+",
val tokenize: Boolean = true
) extends NumericFieldExpression {
override def eval(series: Series): Double = {
val text = field.get(series)
val term = expression.eval(series)
if (Utils.isMissing(text) || Utils.isMissing(term)) {
Double.NaN
} else {
var textValue = text.toString
textIndexNormalizations.foreach(x => {
textValue = x.normalize(textValue, isCaseSensitive, maxLevenshteinDistance, wordSeparatorCharacterRE, tokenize)
})
val termValue = term.toString
val (textTokens, termTokens) = if (tokenize)
(textValue.split(wordSeparatorCharacterRE), termValue.split(wordSeparatorCharacterRE)) else (
Array(textValue), Array(termValue)
)
if (textTokens.isEmpty || termTokens.isEmpty) {
0.0
} else {
// leading and trailing punctuations should then be removed
val textTokensPunctuationFree = textTokens.map(x => TextIndex.PUNCTUATION_PATTERN.matcher(x).replaceAll(""))
val termTokensPunctuationFree = termTokens.map(x => TextIndex.PUNCTUATION_PATTERN.matcher(x).replaceAll(""))
val textTokensTest = if (isCaseSensitive) textTokensPunctuationFree else textTokensPunctuationFree.map(_.toLowerCase)
val termTokensTest = if (isCaseSensitive) termTokensPunctuationFree else termTokensPunctuationFree.map(_.toLowerCase)
var frequency = 0
var minDistance = Integer.MAX_VALUE
var i = 0
while (i <= textTokensTest.length - termTokensTest.length) {
var sumDistance = 0
var hit = true
breakable {
var j = 0
while (j < termTokensTest.length) {
val levenshteinDistance = new LevenshteinDistance(maxLevenshteinDistance - sumDistance)
val distance = levenshteinDistance.apply(textTokensTest(i + j), termTokensTest(j))
if (distance == -1) {
hit = false
break
} else {
sumDistance += distance
}
j += 1
}
}
if (hit) {
countHits match {
case CountHits.allHits => frequency += 1
case CountHits.bestHits => if (sumDistance < minDistance) {
minDistance = sumDistance
frequency = 1
} else if (sumDistance == minDistance) {
frequency += 1
}
}
}
i += 1
}
localTermWeights match {
case LocalTermWeights.termFrequency => frequency
case LocalTermWeights.binary => if (frequency > 0) 1 else 0
case LocalTermWeights.logarithmic => Math.log(1 + frequency)
case LocalTermWeights.augmentedNormalizedTermFrequency => ???
}
}
}
}
}
/**
* - termFrequency: use the number of times the term occurs in the document (x = freqi).
* - binary: use 1 if the term occurs in the document or 0 if it doesn't (x = χ(freqi)).
* - logarithmic: take the logarithm (base 10) of 1 + the number of times the term occurs in the document.
* (x = log(1 + freqi))
* - augmentedNormalizedTermFrequency: this formula adds to the binary frequency a "normalized" component expressing the
* frequency of a term relative to the highest frequency of terms observed in that document
* (x = 0.5 * (χ(freqi) + (freqi / maxk(freqk))) )
*/
object LocalTermWeights extends Enumeration {
type LocalTermWeights = Value
val termFrequency, binary, logarithmic, augmentedNormalizedTermFrequency = Value
}
/**
* - allHits: count all hits
* - bestHits: count all hits with the lowest Levenshtein distance
*/
object CountHits extends Enumeration {
type CountHits = Value
val allHits, bestHits = Value
}
/**
* A TextIndexNormalization element offers more advanced ways of normalizing text input into a more controlled
* vocabulary that corresponds to the terms being used in invocations of this indexing function. The normalization
* operation is defined through a translation table, specified through a TableLocator or InlineTable element.
*/
class TextIndexNormalization(
val table: Table,
val isCaseSensitive: Option[Boolean],
val maxLevenshteinDistance: Option[Int],
val wordSeparatorCharacterRE: Option[String],
val tokenize: Option[Boolean],
val inField: String = "string",
val outField: String = "stem",
val regexField: String = "regex",
val recursive: Boolean = false
) extends PmmlElement {
def normalize(
text: String,
isCaseSensitive: Boolean,
maxLevenshteinDistance: Int,
wordSeparatorCharacterRE: String,
tokenize: Boolean): String = {
val (rowNum, _) = table.dim
var result = text
val isCaseSensitiveUsed = this.isCaseSensitive.getOrElse(isCaseSensitive)
if (recursive) {
var stop = false
breakable {
while (!stop) {
var i = 0
while (i < rowNum) {
val previous = result
val row = table(i)
val in = row(inField).toString
val out = row(outField).toString
result = row.get(regexField) match {
case Some("true") => {
previous.replaceAll(in, out)
}
case _ => {
Pattern.compile(in, if (isCaseSensitiveUsed) Pattern.LITERAL else Pattern.LITERAL | Pattern.CASE_INSENSITIVE).
matcher(previous).replaceAll(Matcher.quoteReplacement(out))
}
}
if (result != previous) {
stop = true
break
}
i += 1
}
}
}
} else {
var i = 0
while (i < rowNum) {
val row = table(i)
val in = row(inField).toString
val out = row(outField).toString
result = row.get(regexField) match {
case Some("true") => {
result.replaceAll(in, out)
}
case _ => {
Pattern.compile(in, if (isCaseSensitiveUsed) Pattern.LITERAL else Pattern.LITERAL | Pattern.CASE_INSENSITIVE).
matcher(result).replaceAll(Matcher.quoteReplacement(out))
}
}
i += 1
}
}
result
}
}
object TextIndex {
import java.util.regex.Pattern
val PUNCTUATION_PATTERN = Pattern.compile("\\p{Punct}")
}
