Learning Data Mining with Python

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Chapter 9 The use of function words is less defined by the content of the document and more by the decisions made by the author. This makes them good candidates for separating the authorship traits between different users. For instance, while many Americans are particular about the different in usage between that and which in a sentence, people from other countries, such as Australia, are less particular about this. This means that some Australians will lean towards almost exclusively using one word or the other, while others may use which much more. This difference, combined with thousands of other nuanced differences, makes a model of authorship. Counting function words We can count function words using the CountVectorizer class we used in Chapter 6, Social Media Insight Using Naive Bayes. This class can be passed a vocabulary, which is the set of words it will look for. If a vocabulary is not passed (we didn't pass one in the code of Chapter 6), then it will learn this vocabulary from the dataset. All the words are in the training set of documents (depending on the other parameters of course). First, we set up our vocabulary of function words, which is just a list containing each of them. Exactly which words are function words and which are not is up for debate. I've found this list, from published research, to be quite good: function_words = ["a", "able", "aboard", "about", "above", "absent", "according" , "accordingly", "across", "after", "against", "ahead", "albeit", "all", "along", "alongside", "although", "am", "amid", "amidst", "among", "amongst", "amount", "an", "and", "another", "anti", "any", "anybody", "anyone", "anything", "are", "around", "as", "aside", "astraddle", "astride", "at", "away", "bar", "barring", "be", "because", "been", "before", "behind", "being", "below", "beneath", "beside", "besides", "better", "between", "beyond", "bit", "both", "but", "by", "can", "certain", "circa", "close", "concerning", "consequently", "considering", "could", "couple", "dare", "deal", "despite", "down", "due", "during", "each", "eight", "eighth", "either", "enough", "every", "everybody", "everyone", "everything", "except", "excepting", "excluding", "failing", "few", "fewer", "fifth", "first", "five", "following", "for", "four", "fourth", "from", "front", "given", "good", "great", "had", "half", "have", "he", "heaps", "hence", "her", "hers", "herself", "him", "himself", "his", "however", "i", "if", "in", "including", "inside", [ 193 ]
Authorship Attribution "instead", "into", "is", "it", "its", "itself", "keeping", "lack", "less", "like", "little", "loads", "lots", "majority", "many", "masses", "may", "me", "might", "mine", "minority", "minus", "more", "most", "much", "must", "my", "myself", "near", "need", "neither", "nevertheless", "next", "nine", "ninth", "no", "nobody", "none", "nor", "nothing", "notwithstanding", "number", "numbers", "of", "off", "on", "once", "one", "onto", "opposite", "or", "other", "ought", "our", "ours", "ourselves", "out", "outside", "over", "part", "past", "pending", "per", "pertaining", "place", "plenty", "plethora", "plus", "quantities", "quantity", "quarter", "regarding", "remainder", "respecting", "rest", "round", "save", "saving", "second", "seven", "seventh", "several", "shall", "she", "should", "similar", "since", "six", "sixth", "so", "some", "somebody", "someone", "something", "spite", "such", "ten", "tenth", "than", "thanks", "that", "the", "their", "theirs", "them", "themselves", "then", "thence", "therefore", "these", "they", "third", "this", "those", "though", "three", "through", "throughout", "thru", "thus", "till", "time", "to", "tons", "top", "toward", "towards", "two", "under", "underneath", "unless", "unlike", "until", "unto", "up", "upon", "us", "used", "various", "versus", "via", "view", "wanting", "was", "we", "were", "what", "whatever", "when", "whenever", "where", "whereas", "wherever", "whether", "which", "whichever", "while", "whilst", "who", "whoever", "whole", "whom", "whomever", "whose", "will", "with", "within", "without", "would", "yet", "you", "your", "yours", "yourself", "yourselves"] Now, we can set up an extractor to get the counts of these function words. We will fit this using a pipeline later: from sklearn.feature_extraction.text import CountVectorizer extractor = CountVectorizer(vocabulary=function_words) [ 194 ]
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[ 1 ] www.allitebooks.com
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Learning Data Mining with Python Co
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About the Author Robert Layton has
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Christophe Van Gysel is pursuing a
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www.allitebooks.com
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Table of Contents Preprocessing usi
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Table of Contents Chapter 7: Discov
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Table of Contents GPU optimization
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Preface If you have ever wanted to
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What you need for this book It shou
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Preface Reader feedback Feedback fr
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Getting Started with Data Mining We
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Chapter 1 In the preceding dataset,
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After you have the above "Hello, wo
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Chapter 1 Windows users may need to
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Chapter 1 The dataset we are going
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Chapter 1 As an example, we will co
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We get the names of the features fo
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Chapter 1 Two rules are near the to
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Chapter 1 The scikit-learn library
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We then iterate over all the sample
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Chapter 1 Overfitting is the proble
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Chapter 1 Summary In this chapter,
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Classifying with scikit-learn Estim
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Predicting Sports Winners with Deci
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Recommending Movies Using Affinity
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Chapter 4 The classic algorithm for
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Chapter 4 When loading the file, we
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Chapter 4 We will sample our datase
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Chapter 4 Implementation On the fir
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Chapter 4 We want to break out the
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The process starts by creating dict
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movie_name_data.columns = ["MovieID
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To do this, we will compute the tes
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Chapter 4 - Train Confidence: 1.000
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Extracting Features with Transforme
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Chapter 5 Thought should always be
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Chapter 5 Other features describe a
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Chapter 5 Similarly, we can convert
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Chapter 5 [18, 19, 20], [21, 22, 23
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Chapter 5 Next, we create our trans
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Chapter 5 This returns a different
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Also, we want to set the final colu
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Chapter 5 The downside to transform
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Chapter 5 A transformer is akin to
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We can then create an instance of t
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Chapter 5 Putting it all together N
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Social Media Insight Using Naive Ba
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Discovering Accounts to Follow Usin
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Chapter 7 Next, we will need a list
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Chapter 7 Make sure the filename is
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Chapter 7 cursor = results['next_cu
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Page 168 and 169: Chapter 7 Creating a graph Now, we
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Page 176 and 177: Chapter 7 We can graph the entire s
Page 178 and 179: Chapter 7 Optimizing criteria Our a
Page 180 and 181: Chapter 7 Next, we need to get the
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Page 184 and 185: Beating CAPTCHAs with Neural Networ
Page 186 and 187: Chapter 8 The red lines indicate th
Page 188 and 189: Chapter 8 The combination of an app
Page 190 and 191: Chapter 8 Next we set the font of t
Page 192 and 193: Chapter 8 We can then extract the s
Page 194 and 195: Chapter 8 Our targets are integer v
Page 196 and 197: Chapter 8 Then we iterate over our
Page 198 and 199: Chapter 8 From these predictions, w
Page 200 and 201: Chapter 8 This code correctly predi
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Page 204 and 205: Chapter 8 However, it isn't very go
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Chapter 11 This dataset comes from
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You can change the image index to s
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Chapter 11 Each of these issues has
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Chapter 11 Using Theano, we can def
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Chapter 11 Building a neural networ
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Chapter 11 Finally, we create Thean
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Chapter 11 return [image,] return s
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Chapter 11 Next, we define how the
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Chapter 11 Getting your code to run
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Chapter 11 Setting up the environme
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This will unzip only one Coval.otf
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Chapter 11 First we create the laye
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Chapter 11 Finally, we set the verb
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Chapter 11 Summary In this chapter,
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Working with Big Data Big data What
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Working with Big Data Governments a
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Working with Big Data We start by c
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Working with Big Data The final ste
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Working with Big Data Getting the d
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Working with Big Data If we aren't
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Working with Big Data Before we sta
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Working with Big Data The first val
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Working with Big Data This gives us
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Working with Big Data Next, we crea
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Working with Big Data Then, make a
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Working with Big Data Left-click th
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Working with Big Data The result is
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Next Steps… Extending the IPython
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Next Steps… Chapter 3: Predicting
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Next Steps… Vowpal Wabbit http://
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Next Steps… Deeper networks These
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Next Steps… Real-time clusterings
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Next Steps… More resources Kaggle
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authorship, attributing 185-188 AWS
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feature extraction about 82 common
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NetworkX about 145 defining 303 URL
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scikit-learn package references 305
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Thank you for buying Learning Data
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Learning Python Data Visualization
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