从带有文本列的Spark Dataframe创建TF_IDF向量

Question

[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"),

def tfIdf(df):
    """ This fucntion takes the text data and converts it into a term frequency-Inverse Document Frequency vector

    parameter: 
    returns: dataframe with tf-idf vectors

    """

    # Importing the feature transformation classes for doing TF-IDF 
    from pyspark.ml.feature import Tokenizer, StopWordsRemover, CountVectorizer, IDF

    # Carrying out the Tokenization of the text documents (splitting into words)

    tokenizer = Tokenizer(inputCol="desc", outputCol="tokenised_text")
    tokensDf = tokenizer.transform(df)

    # Carrying out the StopWords Removal for TF-IDF
    stopwordsremover=StopWordsRemover(inputCol='tokenised_text',outputCol='words')
    swremovedDf= stopwordsremover.transform(tokensDf)

    # Creating Term Frequency Vector for each word
    cv=CountVectorizer(inputCol="words", outputCol="tf_features", vocabSize=3, minDF=2.0)
    cvModel=cv.fit(swremovedDf)
    tfDf=cvModel.transform(swremovedDf)

    # Carrying out Inverse Document Frequency on the TF data
    idf=IDF(inputCol="tf_features", outputCol="tf-idf_features")
    idfModel = idf.fit(tfDf)
    tfidfDf = idfModel.transform(tfDf)

    tfidfDf.cache().count()

    return tfidfDf


tfidfDf=tfIdf(sdf_cleaned)

最后，我使用IDF类将IDF权重应用于术语频率向量。

返回的数据帧的最终结果如下所示--仅显示第一行：

[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, {})),

所以前三列是原来的['id'、'title'、'desc']。根据所使用的转换添加新列。如果您看到Tokeniser和StopWords工作正常，因为它们的输出列是正确的。

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})

Answer 1

在这个示例中，tf_features和tf-idf_features不为空。这些都是有效的sparsevectors，所有特性都等于0.0([0.0,0.0,0.0])。

我认为，CountVectorizer的荒谬配置是罪魁祸首。如果vocabSize等于3，则只考虑三个最频繁的术语（“CountVectorizer将构建一个词汇表，该词汇表只考虑语料库中按术语频率排序的顶级vocabSize术语。”）如果在特定文本中没有这些术语，您将得到观察到的输出。

df = sc.parallelize([
    (["a", ], ), # 'a' occurs only once
    (["b", "c"], ), (["c", "c", "d"], ), (["b", "d"], )
]).toDF(["tokens"])

vectorizer = CountVectorizer(
    inputCol="tokens", outputCol="features", vocabSize=3
).fit(df)

# With vocabulary size 3 A is not in the vocabulary (3 most common words)
vectorizer.vocabulary

['c', 'd', 'b']

vectorizer.transform(df).take(3)

[Row(tokens=['a'], features=SparseVector(3, {})),
 Row(tokens=['b', 'c'], features=SparseVector(3, {0: 1.0, 2: 1.0})),
 Row(tokens=['c', 'c', 'd'], features=SparseVector(3, {0: 2.0, 1: 1.0}))]

如您所见，第一个文档不包含词汇表中的任何标记，因此所有特性都等于0。

SparseVector(3, {}).toArray()

array([ 0.,  0.,  0.])

v = SparseVector(3, {0: 2.0, 1: 1.0})

{vectorizer.vocabulary[i]: cnt for (i, cnt) in zip(v.indices, v.values)}

{'c': 2.0, 'd': 1.0}