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• method='nelder-mead': This is used to select the Nelder-Mead optimize routing (SciPy supports quite a number of different options) Chapter 7 • args=(friends,): This passes the friends dictionary to the function that is being minimized This function will take quite a while to run. Our graph creation function isn't that fast, nor is the function that computes the Silhouette Coefficient. Decreasing the maxiter value will result in fewer iterations being performed, but we run the risk of finding a suboptimal solution. Running this function, I got a threshold of 0.135 that returns 10 components. The score returned by the minimize function was -0.192. However, we must remember that we negated this value. This means our score was actually 0.192. The value is positive, which indicates that the clusters tend to be more separated than not (a good thing). We could run other models and check whether it results in a better score, which means that the clusters are better separated. We could use this result to recommend users—if a user is in a connected component, then we can recommend other users in that component. This recommendation follows our use of the Jaccard Similarity to find good connections between users, our use of connected components to split them up into clusters, and our use of the optimization technique to find the best model in this setting. However, a large number of users may not be connected at all, so we will use a different algorithm to find clusters for them. Summary In this chapter, we looked at graphs from social networks and how to do cluster analysis on them. We also looked at saving and loading models from scikit-learn by using the classification model we created in Chapter 6, Social Media Insight Using Naive Bayes. We created a graph of friends from a social network, in this case Twitter. We then examined how similar two users were, based on their friends. Users with more friends in common were considered more similar, although we normalize this by considering the overall number of friends they have. This is a commonly used way to infer knowledge (such as age or general topic of discussion) based on similar users. We can use this logic for recommending users to others—if they follow user X and user Y is similar to user X, they will probably like user Y. This is, in many ways, similar to our transaction-led similarity of previous chapters. [ 159 ]
Discovering Accounts to Follow Using Graph Mining The aim of this analysis was to recommend users, and our use of cluster analysis allowed us to find clusters of similar users. To do this, we found connected components on a weighted graph we created based on this similarity metric. We used the NetworkX package for creating graphs, using our graphs, and finding these connected components. We then used the Silhouette Coefficient, which is a metric that evaluates how good a clustering solution is. Higher scores indicate a better clustering, according to the concepts of intra-cluster and inter-cluster distance. SciPy's optimize module was used to find the solution that maximises this value. In this chapter, we compared a few opposites too. Similarity is a measure between two objects, where higher values indicate more similarity between those objects. In contrast, distance is a measure where lower values indicate more similarity. Another contrast we saw was a loss function, where lower scores are considered better (that is, we lost less). Its opposite is the score function, where higher scores are considered better. In the next chapter, we will see how to extract features from another new type of data: images. We will discuss how to use neural networks to identify numbers in images and develop a program to automatically beat CAPTCHA images. [ 160 ]
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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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Page 168 and 169: Chapter 7 Creating a graph Now, we
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Page 178 and 179: Chapter 7 Optimizing criteria Our a
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Page 186 and 187: Chapter 8 The red lines indicate th
Page 188 and 189: Chapter 8 The combination of an app
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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
Page 206: Chapter 8 Summary In this chapter,
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Page 217 and 218: Authorship Attribution "instead", "
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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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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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