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remove DynamicTfVectorizer

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Co-authored-by: map <mpfau@users.noreply.github.com>
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abp 2025-10-20 16:03:20 +02:00
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112
src/mail-app/workerUtils/spamClassification/DynamicTfVectorizer.ts
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@ -1,112 +0,0 @@
const DEFAULT_TOKEN_VOCABULARY_LIMIT = 5000
const DEFAULT_USE_TOKEN_STEMMING = true
export type Stats = {
/// This is the lowest frequency of word found in current vocabulary,
/// which means all included vocabulary item have frequency higher than or equal to this frequency.
lowestIncludedFrequency: number
/// Out of total corpus of token vocabulary, these many items were excluded from vocabulary,
excludedTokenVocabularyCount: number
}
export class DynamicTfVectorizer {
readonly dimension: number
private stats: Stats | null = null
public constructor(
tokenVocabulary: Set<string>,
readonly useTokenStemming: boolean = DEFAULT_USE_TOKEN_STEMMING,
readonly tokenVocabularyLimit: number = DEFAULT_TOKEN_VOCABULARY_LIMIT,
) {
this.tokenVocabulary = tokenVocabulary
// we account for 50% more vocabulary than initially occupied
this.dimension = tokenVocabularyLimit + tokenVocabularyLimit * 0.5
}
private tokenVocabulary: Set<string>
public buildInitialTokenVocabulary(initialTokenizedMails: ReadonlyArray<ReadonlyArray<string>>) {
console.log(initialTokenizedMails)
const allTokenFrequencies = initialTokenizedMails.reduce((_, tokenizedMail) => this.getTokenFrequency(tokenizedMail), new Map<string, number>())
console.log(allTokenFrequencies)
const mostCommonTokens = Array.from(allTokenFrequencies.entries())
.sort((a, b) => b[1] - a[1])
.slice(0, this.tokenVocabularyLimit)
const lowestIncludedFrequency = mostCommonTokens[mostCommonTokens.length - 1][1]
const excludedTokenVocabularyCount = allTokenFrequencies.size - mostCommonTokens.length
console.log(mostCommonTokens)
this.tokenVocabulary = new Set(mostCommonTokens.map(([token, _frequency]) => token))
console.log(this.tokenVocabulary)
this.stats = { lowestIncludedFrequency, excludedTokenVocabularyCount }
}
private getTokenFrequency(tokenCollection: ReadonlyArray<string>, expandTokenVocabulary = false) {
const resultTokenFrequencyMap = new Map<string, number>()
for (let token of tokenCollection) {
if (this.useTokenStemming) {
//token = stemmer(token)
}
if (expandTokenVocabulary && !this.tokenVocabulary.has(token)) {
this.expandTokenVocabulary(token)
}
resultTokenFrequencyMap.set(token, (resultTokenFrequencyMap.get(token) || 0) + 1)
}
return resultTokenFrequencyMap
}
/**
* Expand (add to) the token vocabulary with the new token.
*/
private expandTokenVocabulary(token: string) {
this.tokenVocabulary.add(token)
}
public transform(tokenizedMails: Array<ReadonlyArray<string>>): number[][] {
return this._transform(tokenizedMails, false)
}
/**
* transform method to be used when refitting
* @returns: null in case a full retraining of the model is required
*/
public refitTransform(tokenizedMails: Array<ReadonlyArray<string>>): number[][] | null {
const transformResult = this._transform(tokenizedMails, true)
const availableSpace = this.dimension - this.tokenVocabulary.size
if (availableSpace <= 0) {
return null
} else {
return transformResult
}
}
private _transform(tokenizedMails: Array<ReadonlyArray<string>>, expandTokenVocabulary = false): number[][] {
return tokenizedMails.map((tokenizedMail) => this.vectorize(tokenizedMail, expandTokenVocabulary))
}
// visibleForTesting
public vectorize(tokenizedMail: ReadonlyArray<string>, expandTokenVocabulary = false): number[] {
const tokenFrequencyMap = this.getTokenFrequency(tokenizedMail, expandTokenVocabulary)
let index = 0
let vector = new Array<number>(this.dimension).fill(0)
for (const [token, _] of this.tokenVocabulary.entries()) {
vector[index] = tokenFrequencyMap.get(token) ?? 0
index += 1
}
return vector
}
public getStats(): Stats | null {
return Object.seal(this.stats)
}
}

6
src/mail-app/workerUtils/spamClassification/SpamClassifier.ts
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@ -1,6 +1,5 @@
import { assertWorkerOrNode } from "../../../common/api/common/Env" import { assertWorkerOrNode } from "../../../common/api/common/Env"
import { assertNotNull, defer, groupByAndMap, isNotNull, Nullable, promiseMap, tokenize } from "@tutao/tutanota-utils" import { assertNotNull, defer, groupByAndMap, isNotNull, Nullable, promiseMap, tokenize } from "@tutao/tutanota-utils"
import { DynamicTfVectorizer } from "./DynamicTfVectorizer"
import { HashingVectorizer } from "./HashingVectorizer" import { HashingVectorizer } from "./HashingVectorizer"
import { import {
ML_BITCOIN_REGEX, ML_BITCOIN_REGEX,
@ -110,7 +109,7 @@ export class SpamClassifier {
private readonly initializer: SpamClassificationInitializer, private readonly initializer: SpamClassificationInitializer,
private readonly deterministic: boolean = false, private readonly deterministic: boolean = false,
private readonly preprocessConfiguration: PreprocessConfiguration = DEFAULT_PREPROCESS_CONFIGURATION, private readonly preprocessConfiguration: PreprocessConfiguration = DEFAULT_PREPROCESS_CONFIGURATION,
private readonly vectorizer: DynamicTfVectorizer | HashingVectorizer = new HashingVectorizer(), private readonly vectorizer: HashingVectorizer = new HashingVectorizer(),
) { ) {
this.classifier = new Map() this.classifier = new Map()
} }
@ -228,9 +227,6 @@ export class SpamClassifier {
const preprocessingTime = performance.now() - preprocessingStart const preprocessingTime = performance.now() - preprocessingStart
const vectorizationStart = performance.now() const vectorizationStart = performance.now()
if (this.vectorizer instanceof DynamicTfVectorizer) {
this.vectorizer.buildInitialTokenVocabulary(tokenizedMails)
}
const vectors = await this.vectorizer.transform(tokenizedMails) const vectors = await this.vectorizer.transform(tokenizedMails)
const labels = mails.map((mail) => (mail.isSpam ? 1 : 0)) const labels = mails.map((mail) => (mail.isSpam ? 1 : 0))

1
test/tests/Suite.ts
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@ -82,7 +82,6 @@ import "./api/worker/search/SuggestionFacadeTest.js"
import "./serviceworker/SwTest.js" import "./serviceworker/SwTest.js"
import "./api/worker/facades/KeyVerificationFacadeTest.js" import "./api/worker/facades/KeyVerificationFacadeTest.js"
import "./api/worker/utils/SleepDetectorTest.js" import "./api/worker/utils/SleepDetectorTest.js"
import "./api/worker/utils/spamClassification/TfIdfVectorizerTest.js"
import "./api/worker/utils/spamClassification/HashingVectorizerTest.js" import "./api/worker/utils/spamClassification/HashingVectorizerTest.js"
import "./api/worker/utils/spamClassification/PreprocessPatternsTest.js" import "./api/worker/utils/spamClassification/PreprocessPatternsTest.js"
import "./calendar/AlarmSchedulerTest.js" import "./calendar/AlarmSchedulerTest.js"

71
test/tests/api/worker/utils/spamClassification/TfIdfVectorizerTest.ts
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@ -1,71 +0,0 @@
import o from "@tutao/otest"
import { DynamicTfVectorizer } from "../../../../../../src/mail-app/workerUtils/spamClassification/DynamicTfVectorizer"
o.spec("DynamicTfVectorizer", () => {
const tokenize = (text: string): string[] =>
text
.toLowerCase()
.split(/\s+/)
.map((t) => t.replace(/[^a-z0-9-]/gi, "")) // remove punctuation
.filter((t) => t.length > 1)
const rawDocuments = [
"Tuta is an encrypted email service that prioritizes privacy and open-source principles.",
"With Tuta, your emails and contacts are stored securely using end-to-end encryption.",
"With Tuta, you can create a completely encrypted zero-knowledge calendar, try now!",
"Unlike traditional email providers, Tuta never collects user data or scans your messages.",
"Millions of people choose Tuta to protect their personal and professional communication.",
]
const tokenizedDocuments = rawDocuments.map(tokenize)
o("constructor throws if docIds and documents mismatch", () => {
// o(() => new DynamicTfVectorizer(["doc1"], [["token1"], ["token2"]])).throws(Error)
})
// o("builds correct vocabulary with filtered tokens", () => {
// const vectorizer = new DynamicTfVectorizer()
// vectorizer.initializeVocabulary(tokenizedDocuments)
// o(vectorizer.vocabulary.includes("tuta")).equals(true)
// o(vectorizer.vocabulary.includes("email")).equals(true)
// o(vectorizer.vocabulary.includes("a")).equals(false)
// })
// o("vectorize returns correct TF vector", () => {
// const vectorizer = new DynamicTfVectorizer()
// vectorizer.initializeVocabulary(tokenizedDocuments)
// const tokens = ["email", "encryption"]
// const vector = vectorizer.vectorize(tokens)
// o(vector.length).equals(vectorizer.featureVectorDimension)
//
// const emailIndex = vectorizer.vocabulary.includes("email")!
// const encryptionIndex = vectorizer.vocabulary.includes("encryption")!
// o(emailIndex).equals(true)
// o(encryptionIndex).equals(true)
// })
// o("transform returns correct tensor shape", () => {
// const vectorizer = new DynamicTfVectorizer()
// vectorizer.initializeVocabulary(tokenizedDocuments)
// const inputTokens = [
// ["privacy", "encryption"],
// ["user", "data"],
// ]
// const vector = vectorizer.transform(inputTokens)
//
// o(vector.length).equals(2)
// o(vector[0].length).equals(vectorizer.featureVectorDimension)
//
// const allZeros = Array.from(vector.flat()).every((v) => v === 0)
// o(allZeros).equals(false)
// })
// o("adds unknown words to vocabulary when still enough space", () => {
// const vectorizer = new DynamicTfVectorizer()
// vectorizer.initializeVocabulary(tokenizedDocuments)
// const tokens = ["hannover", "munich"]
// const vector = vectorizer.vectorize(tokens)
// const nonZero = vector.some((val) => val > 0)
// o(nonZero).equals(true)
// })
})