Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 5 Fetching the data Luckily for us, the team behind stackoverflow provides most of the data behind the StackExchange universe to which stackoverflow belongs under a CC Wiki license. While writing this, the latest data dump can be found at http://www.clearbits. net/torrents/2076-aug-2012. Most likely, this page will contain a pointer to an updated dump when you read it. After downloading and extracting it, we have around 37 GB of data in the XML format. This is illustrated in the following table: File Size (MB) Description badges.xml 309 Badges of users comments.xml 3,225 Comments on questions or answers posthistory.xml 18,370 Edit history posts.xml 12,272 Questions and answers—this is what we need users.xml 319 General information about users votes.xml 2,200 Information on votes As the files are more or less self-contained, we can delete all of them except posts. xml; it contains all the questions and answers as individual row tags within the root tag posts. Refer to the following code: [ 91 ]
Classification – Detecting Poor Answers Name Type Description Id Integer This is a unique identifier PostType Integer This describes the category of the post. The following values are of interest to us: • Question • Answer Other values will be ignored ParentId Integer This is a unique identifier of the question to which this answer belongs (missing for questions) CreationDate DateTime This is the date of submission Score Integer This is the score of the post ViewCount Integer or empty This tells us the number of user views for this post Body String This is the complete post as it is encoded in HTML text OwnerUserId Id This is a unique identifier of the poster. If it is 1, it is a wiki question Title String This is the title of the question (missing for answers) AcceptedAnswerId Id This is the ID of the accepted answer (missing for answers) CommentCount Integer This tells us the number of comments for the post Slimming the data down to chewable chunks To speed up our experimentation phase, we should not try to evaluate our classification ideas on a 12 GB file. Instead, we should think of how we can trim it down so that we can still keep a representable snapshot of it while being able to quickly test our ideas. If we filter an XML for row tags that have a CreationDate of 2011 or later, we still end up with over 6 million posts (2,323,184 questions and 4,055,999 answers), which should be enough training data for now. We also do not operate on the XML format as it will slow us down. The simpler the format, the better it is. That's why we parse the remaining XML using Python's cElementTree and write it out to a tab-separated file. [ 92 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
Page 56 and 57: Chapter 2 We can play around with t
Page 58 and 59: Chapter 2 Features and feature engi
Page 60 and 61: Chapter 2 In the preceding screensh
Page 62 and 63: Chapter 2 Binary and multiclass cla
Page 64 and 65: Clustering - Finding Related Posts
Page 66 and 67: Chapter 3 How to do it More robust
Page 68 and 69: Chapter 3 This means that the first
Page 70 and 71: Chapter 3 ... post = posts[i] ... i
Page 72 and 73: Chapter 3 If you have a clear pictu
Page 74 and 75: Chapter 3 Extending the vectorizer
Page 76 and 77: Chapter 3 0.0 >>> print(tfidf("b",
Page 78 and 79: Chapter 3 Flat clustering divides t
Page 80 and 81: Because the cluster centers are mov
Page 82 and 83: Chapter 3 'D:\\data\\379\\raw\\comp
Page 84 and 85: As we have learned previously, we w
Page 86 and 87: Chapter 3 Position Similarity Excer
Page 88: Chapter 3 But before you go there,
Page 91 and 92: Topic Modeling For those who are in
Page 93 and 94: Topic Modeling Sparsity means that
Page 95 and 96: Topic Modeling Although daunting at
Page 97 and 98: Topic Modeling … for tj,v in t:
Page 99 and 100: Topic Modeling Finally, we build th
Page 101 and 102: Topic Modeling Alternatively, we ca
Page 103 and 104: Topic Modeling Topic modeling was f
Page 105: Classification - Detecting Poor Ans
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Page 132 and 133: Classification II - Sentiment Analy
Page 134 and 135: Chapter 6 Getting to know the Bayes
Page 136 and 137: Using Naive Bayes to classify Given
Page 138 and 139: Chapter 6 This denotation "" leads
Page 140 and 141: Chapter 6 Similarly, we do this for
Page 142 and 143: Chapter 6 A quick look at the previ
Page 144 and 145: Chapter 6 To keep our experimentati
Page 146 and 147: Chapter 6 Y = np.zeros(Y.shape[0])
Page 148 and 149: Chapter 6 ° ° Experiment with whe
Page 150 and 151: Chapter 6 We have to be patient whe
Page 152 and 153: Chapter 6 First, we define a range
Page 154 and 155: Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Chapter 7 P greater than N scenario
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Chapter 7 So, we can see that the d
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Chapter 7 Fortunately, scikit-learn
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[ 161 ] Chapter 7 The loading of th
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Chapter 7 Summary In this chapter,
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Regression - Recommendations Improv
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Weights Regression - Recommendation
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Classification III - Music Genre Cl
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Chapter 9 Matplotlib provides the c
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[ 185 ] Chapter 9
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Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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Computer Vision - Pattern Recogniti
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Dimensionality Reduction Sketching
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Dimensionality Reduction However, t
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Dimensionality Reduction To underst
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Dimensionality Reduction In order t
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Dimensionality Reduction Asking the
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Dimensionality Reduction n_ feature
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Dimensionality Reduction Sketching
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Dimensionality Reduction Limitation
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Dimensionality Reduction Now, MDS t
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Dimensionality Reduction Of course,
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Big(ger) Data • Your algorithms c
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Big(ger) Data sleep(4) return 2*x @
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Big(ger) Data Looking under the hoo
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Big(ger) Data def write_result(ofna
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Big(ger) Data Amazon Web Services i
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Big(ger) Data In EC2 parlance, a ru
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Big(ger) Data In this system, pip i
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Big(ger) Data Keys, keys, and more
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Big(ger) Data We can use the same j
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Where to Learn More about Machine L
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• Machined Learnings at http://ww
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Index A AcceptedAnswerId attribute
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F false negative 41 false positive
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inary matrix of recommendations, us
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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