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Chapter 6 Here's an excerpt from The Lord of the Rings, J.R.R. Tolkien: Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in halls of stone, Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne In the Land of Mordor where the Shadows lie. One Ring to rule them all, One Ring to find them, One Ring to bring them all and in the darkness bind them. In the Land of Mordor where the Shadows lie. - J.R.R. Tolkien's epigraph to The Lord of The Rings The word the appears nine times in this quote, while the words in, for, to, and one each appear four times. The word ring appears three times, as does the word of. We can create a dataset from this, choosing a subset of words and counting the frequency: Word the one ring to Frequency 9 4 3 4 We can use the counter class to do a simple count for a given string. When counting words, it is normal to convert all letters to lowercase, which we do when creating the string. The code is as follows: s = """Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in halls of stone, Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne In the Land of Mordor where the Shadows lie. One Ring to rule them all, One Ring to find them, One Ring to bring them all and in the darkness bind them. In the Land of Mordor where the Shadows lie. """.lower() words = s.split() from collections import Counter c = Counter(words) [ 119 ]
Social Media Insight Using Naive Bayes Printing c.most_<strong>com</strong>mon(5) gives the list of the top five most frequently occurring words. Ties are not handled well as only five are given and a very large number of words all share a tie for fifth place. The bag-of-words model has three major types. The first is to use the raw frequencies, as shown in the preceding example. This does have a drawback when documents vary in size from fewer words to many words, as the overall values will be very different. The second model is to use the normalized frequency, where each document's sum equals 1. This is a much better solution as the length of the document doesn't matter as much. The third type is to simply use binary features—a value is 1 if the word occurs at all and 0 if it doesn't. We will use binary representation in this chapter. Another popular (arguably more popular) method for performing normalization is called term frequency - inverse document frequency, or tf-idf. In this weighting scheme, term counts are first normalized to frequencies and then divided by the number of documents in which it appears in the corpus. We will use tf-idf in Chapter 10, Clustering News Articles. There are a number of libraries for working with text data in Python. We will use a major one, called Natural Language ToolKit (NLTK). The scikit-learn library also has the CountVectorizer class that performs a similar action, and it is re<strong>com</strong>mended you take a look at it (we will use it in Chapter 9, Authorship Attribution). However the NLTK version has more options for word tokenization. If you are doing natural language processing in python, NLTK is a great library to use. N-grams A step up from single bag-of-words features is that of n-grams. An n-gram is a subsequence of n consecutive tokens. In this context, a word n-gram is a set of n words that appear in a row. They are counted the same way, with the n-grams forming a word that is put in the bag. The value of a cell in this dataset is the frequency that a particular n-gram appears in the given document. The value of n is a parameter. For English, setting it to between 2 to 5 is a good start, although some applications call for higher values. [ 120 ]
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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
Page 92 and 93: Chapter 4 Implementation On the fir
Page 94 and 95: Chapter 4 We want to break out the
Page 96 and 97: The process starts by creating dict
Page 98 and 99: movie_name_data.columns = ["MovieID
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Page 104 and 105: Extracting Features with Transforme
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Page 108 and 109: Chapter 5 Other features describe a
Page 110 and 111: Chapter 5 Similarly, we can convert
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Page 116 and 117: Chapter 5 This returns a different
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Page 120 and 121: Chapter 5 The downside to transform
Page 122 and 123: Chapter 5 A transformer is akin to
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Page 162 and 163: Chapter 7 Make sure the filename is
Page 164 and 165: 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
Page 182 and 183: • method='nelder-mead': This is u
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
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Chapter 8 We can then extract the s
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Chapter 8 Our targets are integer v
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Chapter 8 Then we iterate over our
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Chapter 8 From these predictions, w
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Chapter 8 This code correctly predi
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The result is shown in the next gra
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Chapter 8 However, it isn't very go
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Authorship Attribution Attributing
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Authorship Attribution If we cannot
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Authorship Attribution After taking
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Authorship Attribution This dataset
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Authorship Attribution "instead", "
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Authorship Attribution Support vect
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Authorship Attribution Kernels When
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Authorship Attribution We can reuse
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Authorship Attribution With our dat
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Authorship Attribution We then reco
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Authorship Attribution If it doesn'
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Authorship Attribution Finally, we
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Clustering News Articles In most of
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Chapter 10 API Endpoints are the ac
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The token object is just a dictiona
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Chapter 10 We then create a list to
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Chapter 10 We are going to use MD5
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Chapter 10 Next, we develop the cod
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Chapter 10 We use clustering techni
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Chapter 10 The k-means algorithm is
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Chapter 10 We only fit the X matrix
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Chapter 10 We then print out the mo
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Chapter 10 Our function definition
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Chapter 10 The result from the prec
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Chapter 10 Implementation Putting a
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Chapter 10 Neural networks can also
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We then call the partial_fit functi
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Classifying Objects in Images Using
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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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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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