1
Ich habe ein Spark-Dataframe, das drei Spalten hat. ['id', 'title', 'desc']. Hier sind 'title' und 'desc' beide Texte. Id ist die ID des Dokuments. Beispiel paar Zeilen aussehen wie unten:TF_IDF-Vektor aus einem Spark-Datenframe mit Text-Spalte erstellen
[Row(id=-33753621, title=u'Royal Bank of Scotland is testing a robot that could solve your banking problems (RBS)', desc=u"If you hate dealing with bank tellers or customer service representatives, then the Royal Bank of Scotland might have a solution for you.If this program is successful, it could be a big step forward on the road to automated customer service through the use of AI, notes Laurie Beaver, research associate for BI Intelligence, Business Insider's premium research service.It's noteworthy that Luvo does not operate via a third-party app such as Facebook Messenger, WeChat, or Kik, all of which are currently trying to create bots that would assist in customer service within their respective platforms.Luvo would be available through the web and through smartphones. It would also use machine learning to learn from its mistakes, which should ultimately help with its response accuracy.Down the road, Luvo would become a supplement to the human staff. It can currently answer 20 set questions but as that number grows, it would allow the human employees to more complicated issues. If a problem is beyond Luvo's comprehension, then it would refer the customer to a bank employee; however,\xa0a user could choose to speak with a human instead of Luvo anyway.AI such as Luvo, if successful, could help businesses become more efficient and increase their productivity, while simultaneously improving customer service capacity, which would consequently\xa0save money that would otherwise go toward manpower.And this trend is already starting. Google, Microsoft, and IBM are investing significantly into AI research. Furthermore, the global AI market is estimated to grow from approximately $420 million in 2014 to $5.05 billion in 2020, according to a forecast by Research and Markets.\xa0The move toward AI would be just one more way in which the digital age is disrupting retail banking. Customers, particularly millennials, are increasingly moving toward digital banking, and as a result, they're walking into their banks' traditional brick-and-mortar branches less often than ever before."),
Row(id=-761323061, title=u'Teen sexting is prompting an overhaul in child pornography laws', desc=u"Rampant teen sexting has left politicians and law enforcement authorities around the country struggling to find some kind of legal middle ground between prosecuting students for child porn and letting them off the hook.Most states consider sexually explicit images of minors to be child pornography, meaning even teenagers who share nude selfies among themselves can, in theory at least, be hit with felony charges that can carry heavy prison sentences and require lifetime registration as a sex offender.Many authorities consider that overkill, however, and at least 20 states have adopted sexting laws with less-serious penalties, mostly within the past five years. Eleven states have made sexting between teens a misdemeanor; in some of those places, prosecutors can require youngsters to take courses on the dangers of social media instead of charging them with a crime.Hawaii passed a 2012 law saying youths can escape conviction if they take steps to delete explicit photos. Arkansas adopted a 2013 law sentencing first-time youth sexters to eight hours of community service. New Mexico last month removed criminal penalties altogether in such cases.At least 12 other states are considering sexting laws this year, many to create new a category of crime that would apply to young people.But one such proposal in Colorado has revealed deep divisions about how to treat the phenomenon. Though prosecutors and researchers agree that felony sex crimes shouldn't apply to a pair of 16-year-olds sending each other selfies, they disagree about whether sexting should be a crime at all.Colorado's bill was prompted by a scandal last year at a Canon City high school where more than 100 students were found with explicit images of other teens. The news sent shockwaves through the city of 16,000. Dozens of students were suspended, and the football team forfeited the final game of the season.Fremont County prosecutors ultimately decided against filing any criminal charges, saying Colorado law doesn't properly distinguish between adult sexual predators and misbehaving teenagers.In a similar case last year out Fayetteville, North Carolina, two dating teens who exchanged nude selfies at age 16 were charged as adults with a felony \u2014 sexual exploitation of a minor. After an uproar, the cha"),
ich diese ‚desc‘ Spalte (tatsächlicher Text des Dokuments) umwandeln möge in einen TF-IDF Vektor in Spark.
Hier ist, was ich dafür gemacht habe.
Zuerst Tokenisierung jedes Textdokuments in 'desc' Spalte mit Tokenizer-Klasse. Dann hat die Stopwords Entfernung mit StopWordsRemover Klasse. Ich konvertiere es dann in einen Beutel mit Wörtern Modell und bekomme den Begriff Häufigkeit mit CountVectorizer Klasse.
Schließlich verwende ich die IDF-Klasse, um die IDF-Gewichtungen auf den Begriff Frequenzvektor anzuwenden.
Das Endergebnis des Datenrahmens zurückgeführt ist, wie unten- nur für die erste Zeile zeigt:
[Row(id=-33753621, title=u'Royal Bank of Scotland is testing a robot that could solve your banking problems (RBS)', desc=u"If you hate dealing with bank tellers or customer service representatives, then the Royal Bank of Scotland might have a solution for you.If this program is successful, it could be a big step forward on the road to automated customer service through the use of AI, notes Laurie Beaver, research associate for BI Intelligence, Business Insider's premium research service.It's noteworthy that Luvo does not operate via a third-party app such as Facebook Messenger, WeChat, or Kik, all of which are currently trying to create bots that would assist in customer service within their respective platforms.Luvo would be available through the web and through smartphones. It would also use machine learning to learn from its mistakes, which should ultimately help with its response accuracy.Down the road, Luvo would become a supplement to the human staff. It can currently answer 20 set questions but as that number grows, it would allow the human employees to more complicated issues. If a problem is beyond Luvo's comprehension, then it would refer the customer to a bank employee; however,\xa0a user could choose to speak with a human instead of Luvo anyway.AI such as Luvo, if successful, could help businesses become more efficient and increase their productivity, while simultaneously improving customer service capacity, which would consequently\xa0save money that would otherwise go toward manpower.And this trend is already starting. Google, Microsoft, and IBM are investing significantly into AI research. Furthermore, the global AI market is estimated to grow from approximately $420 million in 2014 to $5.05 billion in 2020, according to a forecast by Research and Markets.\xa0The move toward AI would be just one more way in which the digital age is disrupting retail banking. Customers, particularly millennials, are increasingly moving toward digital banking, and as a result, they're walking into their banks' traditional brick-and-mortar branches less often than ever before.", tokenised_text=[u'if', u'you', u'hate', u'dealing', u'with', u'bank', u'tellers', u'or', u'customer', u'service', u'representatives,', u'then', u'the', u'royal', u'bank', u'of', u'scotland', u'might', u'have', u'a', u'solution', u'for', u'you.if', u'this', u'program', u'is', u'successful,', u'it', u'could', u'be', u'a', u'big', u'step', u'forward', u'on', u'the', u'road', u'to', u'automated', u'customer', u'service', u'through', u'the', u'use', u'of', u'ai,', u'notes', u'laurie', u'beaver,', u'research', u'associate', u'for', u'bi', u'intelligence,', u'business', u"insider's", u'premium', u'research', u"service.it's", u'noteworthy', u'that', u'luvo', u'does', u'not', u'operate', u'via', u'a', u'third-party', u'app', u'such', u'as', u'facebook', u'messenger,', u'wechat,', u'or', u'kik,', u'all', u'of', u'which', u'are', u'currently', u'trying', u'to', u'create', u'bots', u'that', u'would', u'assist', u'in', u'customer', u'service', u'within', u'their', u'respective', u'platforms.luvo', u'would', u'be', u'available', u'through', u'the', u'web', u'and', u'through', u'smartphones.', u'it', u'would', u'also', u'use', u'machine', u'learning', u'to', u'learn', u'from', u'its', u'mistakes,', u'which', u'should', u'ultimately', u'help', u'with', u'its', u'response', u'accuracy.down', u'the', u'road,', u'luvo', u'would', u'become', u'a', u'supplement', u'to', u'the', u'human', u'staff.', u'it', u'can', u'currently', u'answer', u'20', u'set', u'questions', u'but', u'as', u'that', u'number', u'grows,', u'it', u'would', u'allow', u'the', u'human', u'employees', u'to', u'more', u'complicated', u'issues.', u'if', u'a', u'problem', u'is', u'beyond', u"luvo's", u'comprehension,', u'then', u'it', u'would', u'refer', u'the', u'customer', u'to', u'a', u'bank', u'employee;', u'however,\xa0a', u'user', u'could', u'choose', u'to', u'speak', u'with', u'a', u'human', u'instead', u'of', u'luvo', u'anyway.ai', u'such', u'as', u'luvo,', u'if', u'successful,', u'could', u'help', u'businesses', u'become', u'more', u'efficient', u'and', u'increase', u'their', u'productivity,', u'while', u'simultaneously', u'improving', u'customer', u'service', u'capacity,', u'which', u'would', u'consequently\xa0save', u'money', u'that', u'would', u'otherwise', u'go', u'toward', u'manpower.and', u'this', u'trend', u'is', u'already', u'starting.', u'google,', u'microsoft,', u'and', u'ibm', u'are', u'investing', u'significantly', u'into', u'ai', u'research.', u'furthermore,', u'the', u'global', u'ai', u'market', u'is', u'estimated', u'to', u'grow', u'from', u'approximately', u'$420', u'million', u'in', u'2014', u'to', u'$5.05', u'billion', u'in', u'2020,', u'according', u'to', u'a', u'forecast', u'by', u'research', u'and', u'markets.\xa0the', u'move', u'toward', u'ai', u'would', u'be', u'just', u'one', u'more', u'way', u'in', u'which', u'the', u'digital', u'age', u'is', u'disrupting', u'retail', u'banking.', u'customers,', u'particularly', u'millennials,', u'are', u'increasingly', u'moving', u'toward', u'digital', u'banking,', u'and', u'as', u'a', u'result,', u"they're", u'walking', u'into', u'their', u"banks'", u'traditional', u'brick-and-mortar', u'branches', u'less', u'often', u'than', u'ever', u'before.'], words=[u'hate', u'dealing', u'bank', u'tellers', u'customer', u'service', u'representatives,', u'royal', u'bank', u'scotland', u'solution', u'you.if', u'program', u'successful,', u'big', u'step', u'forward', u'road', u'automated', u'customer', u'service', u'use', u'ai,', u'notes', u'laurie', u'beaver,', u'research', u'associate', u'bi', u'intelligence,', u'business', u"insider's", u'premium', u'research', u"service.it's", u'noteworthy', u'luvo', u'does', u'operate', u'third-party', u'app', u'facebook', u'messenger,', u'wechat,', u'kik,', u'currently', u'trying', u'create', u'bots', u'assist', u'customer', u'service', u'respective', u'platforms.luvo', u'available', u'web', u'smartphones.', u'use', u'machine', u'learning', u'learn', u'mistakes,', u'ultimately', u'help', u'response', u'accuracy.down', u'road,', u'luvo', u'supplement', u'human', u'staff.', u'currently', u'answer', u'20', u'set', u'questions', u'number', u'grows,', u'allow', u'human', u'employees', u'complicated', u'issues.', u'problem', u"luvo's", u'comprehension,', u'refer', u'customer', u'bank', u'employee;', u'however,\xa0a', u'user', u'choose', u'speak', u'human', u'instead', u'luvo', u'anyway.ai', u'luvo,', u'successful,', u'help', u'businesses', u'efficient', u'increase', u'productivity,', u'simultaneously', u'improving', u'customer', u'service', u'capacity,', u'consequently\xa0save', u'money', u'manpower.and', u'trend', u'starting.', u'google,', u'microsoft,', u'ibm', u'investing', u'significantly', u'ai', u'research.', u'furthermore,', u'global', u'ai', u'market', u'estimated', u'grow', u'approximately', u'$420', u'million', u'2014', u'$5.05', u'billion', u'2020,', u'according', u'forecast', u'research', u'markets.\xa0the', u'ai', u'just', u'way', u'digital', u'age', u'disrupting', u'retail', u'banking.', u'customers,', u'particularly', u'millennials,', u'increasingly', u'moving', u'digital', u'banking,', u'result,', u"they're", u'walking', u"banks'", u'traditional', u'brick-and-mortar', u'branches', u'before.'], tf_features=SparseVector(3, {}), tf-idf_features=SparseVector(3, {})),
So drei ersten Spalte sind die original [ ‚id‘, ‚Titel‘, ‚desc‘]. Neue Spalten werden gemäß der verwendeten Transformation hinzugefügt. Wenn Sie Tokeniser und StopWords sehen, funktioniert es gut, da die Ausgabespalten für diese korrekt sind.
Allerdings bin ich nicht sicher, warum tf_features Spalte von CountVectorizer und tf_idf_features von IDF-Klasse sind Null und kein Vektor von TF-IDF-Werte.
Auch in Spark übergeben wir ein Dokument aus jeder Spalte Zelle. Wie findet Spark das Vokabular für den Vektor? Das Vokabular sind die einzigartigen Wörter, die im gesamten Korpus (alle Dokumente) und nicht nur in einem Dokument vorkommen. Die Anzahl von tf ist die Häufigkeit von Begriffen, die in jedem Dokument vorkommen. Also akkumuliert Spark alle Zellenwerte in "Desc" und findet das einzigartige Vokabular für dasselbe und zählt dann für jedes Dokument in jeder Zelle für die Termhäufigkeit?
Bitte beraten.
Edit1:
änderte ich den vocabsize da offensichtlich 3 macht keinen Sinn. und jetzt bekomme ich die tf_futures. wie unten:
tf_features=SparseVector(2000, {6: 1.0, 8: 1.0, 14: 1.0, 17: 2.0, 18: 1.0, 20: 1.0, 32: 1.0, 35: 2.0, 42: 1.0, 52: 1.0, 53: 3.0, 54: 1.0, 62: 1.0, 65: 1.0, 68: 1.0, 79: 1.0, 93: 4.0, 95: 2.0, 98: 1.0, 118: 1.0, 132: 1.0, 133: 1.0, 149: 1.0, 157: 1.0, 167: 5.0, 202: 3.0, 215: 1.0, 219: 1.0, 224: 1.0, 232: 1.0, 265: 3.0, 302: 1.0, 303: 1.0, 324: 2.0, 330: 1.0, 355: 1.0, 383: 1.0, 395: 1.0, 405: 1.0, 432: 1.0, 456: 1.0, 466: 1.0, 472: 1.0, 501: 1.0, 525: 1.0, 537: 1.0, 548: 1.0, 620: 1.0, 630: 1.0, 639: 1.0, 657: 1.0, 662: 1.0, 674: 1.0, 720: 1.0, 734: 1.0, 975: 1.0, 1003: 1.0, 1057: 1.0, 1148: 1.0, 1187: 1.0, 1255: 1.0, 1273: 1.0, 1294: 1.0, 1386: 1.0, 1400: 1.0, 1463: 1.0, 1477: 1.0, 1491: 1.0, 1724: 1.0, 1898: 1.0, 1937: 3.0, 1954: 1.0})
Ich versuche das zu verstehen. Erster Wert ist die Nr. von Merkmalen (Begriffen). Dies ist auch die Vokabelliste, die ich eingegeben habe. Aber was ist das andere Wörterbuch hier? ist der Schlüssel "Index" des Begriffs (Wort) und Wert ist der Begriff Häufigkeit ?. Wenn ja, wie ordnen wir dann diese Wortindizes den ursprünglichen Wörtern zu? Wenn ich zum Beispiel wissen möchte, welche Wörter die oben genannten Zahlen haben, wie wird der Schlüssel des Diktats der Wortfolge zugeordnet?
Zweitens ist dieser Ausgang kein Vektor (wie ein Wörterbuch). Ist das Verbrauchsmaterial für jeden ML Algo? Ich würde tropisch einen Funktionsvektor und nicht ein Diktat benötigen. Wie funktioniert das?