Learning Data Mining with Python

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Local n-grams https://github.com/robertlayton/authorship_tutorials/blob/master/ LNGTutorial.ipynb Appendix Another form of classifier is local n-gram, which involves choosing the best features per-author, not globally for the entire dataset. I wrote a tutorial on using local n-grams for authorship attribution, available at the above link. Chapter 10 – Clustering News Articles Evaluation The evaluation of clustering algorithms is a difficult problem—on the one hand, we can sort of tell what good clusters look like; on the other hand, if we really know that, we should label some instances and use a supervised classifier! Much has been written on this topic. One slideshow on the topic that is a good introduction to the challenges follows: http://www.cs.kent.edu/~jin/DM08/ClusterValidation.pdf In addition, a very comprehensive (although now a little dated) paper on this topic is here: http://web.itu.edu.tr/sgunduz/courses/verimaden/paper/validity_ survey.pdf. The scikit-learn package does implement a number of the metrics described in those links, with an overview here: http://scikit-learn.org/stable/modules/ clustering.html#clustering-performance-evaluation. Using some of these, you can start evaluating which parameters need to be used for better clusterings. Using a Grid Search, we can find parameters that maximize a metric—just like in classification. Temporal analysis The code we developed in this chapter can be rerun over many months. By adding some tags to each cluster, you can track which topics stay active over time, getting a longitudinal viewpoint of what is being discussed in the world news. To compare the clusters, consider a metric such as the adjusted mutual information score, which was linked to the scikit-learn documentation earlier. See how the clusters change after one month, two months, six months, and a year. [ 305 ]
Next Steps… Real-time clusterings The k-means algorithm can be iteratively trained and updated over time, rather than discrete analyses at given time frames. Cluster movement can be tracked in a number of ways—for instance, you can track which words are popular in each cluster and how much the centroids move per day. Keep the API limits in mind—you probably only need to do one check every few hours to keep your algorithm up-to-date. Chapter 11: Classifying Objects in Images Using Deep Learning Keras and Pylearn2 Other deep learning libraries that are worth looking at, if you are going further with deep learning in Python, are Keras and Pylearn2. They are both based on Theano and have different usages and features. Keras can be found here: https://github.com/fchollet/keras/. Pylearn2 can be found here: http://deeplearning.net/software/pylearn2/. Both are not stable platforms at the time of writing, although Pylearn2 is the more stable of the two. That said, they both do what they do very well and are worth investigating for future projects. Another library called Torch is very popular but, at the time of writing, it doesn't have python bindings (see http://torch.ch/). Mahotas Another package for image processing is Mahotas, including better and more complex image processing techniques that can help achieve better accuracy, although they may come at a high computational cost. However, many image processing tasks are good candidates for parallelization. More techniques on image classification can be found in the research literature, with this survey paper as a good start: http://luispedro.org/software/mahotas/. http://ijarcce.com/upload/january/22-A%20Survey%20on%20Image%20 Classification.pdf [ 306 ]
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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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Chapter 7 Next, we are going to rem
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Chapter 7 Creating a graph Now, we
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Chapter 7 As you can see, it is ver
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Chapter 7 Next, we will only add th
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Chapter 7 The difference in this gr
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Chapter 7 We can graph the entire s
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Chapter 7 Optimizing criteria Our a
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Chapter 7 Next, we need to get the
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• method='nelder-mead': This is u
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Beating CAPTCHAs with Neural Networ
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Chapter 8 The red lines indicate th
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Chapter 8 The combination of an app
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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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Chapter 8 Summary In this chapter,
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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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Page 321 and 322: Next Steps… Extending the IPython
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Page 331 and 332: Next Steps… More resources Kaggle
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Learning Data Mining with Python

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