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Chapter 7 Next, we will only add the edge if it is above a certain threshold. This stops us from adding edges we don't care about—for example, edges with weight 0. By default, our threshold is 0, so we will be including all edges right now. However, we will use this parameter later in the chapter. The code is as follows: if weight >= threshold: If the weight is above the threshold, we add the two users to the graph (they won't be added as a duplicate if they are already in the graph): G.add_node(user1) G.add_node(user2) We then add the edge between them, setting the weight to be the computed similarity: G.add_edge(user1, user2, weight=weight) Once the loops have finished, we have a completed graph and we return it from the function: return G We can now create a graph by calling this function. We start with no threshold, which means all links are created. The code is as follows: G = create_graph(friends) The result is a very strongly connected graph—all nodes have edges, although many of those will have a weight of 0. We will see the weight of the edges by drawing the graph with line widths relative to the weight of the edge—thicker lines indicate higher weights. Due to the number of nodes, it makes sense to make the figure larger to get a clearer sense of the connections: plt.figure(figsize=(10,10)) We are going to draw the edges with a weight, so we need to draw the nodes first. NetworkX uses layouts to determine where to put the nodes and edges, based on certain criteria. Visualizing networks is a very difficult problem, especially as the number of nodes grows. Various techniques exist for visualizing networks, but the degree to which they work depends heavily on your dataset, personal preferences, and the aim of the visualization. I found that the spring_layout worked quite well, but other options such as circular_layout (which is a good default if nothing else works), random_layout, shell_layout, and spectral_layout also exist. [ 149 ]
Discovering Accounts to Follow Using Graph Mining Visit http://networkx.lanl.gov/reference/drawing.html for more details on layouts in NetworkX. Although it adds some complexity, the draw_graphviz option works quite well and is worth investigating for better visualizations. It is well worth considering in real-world uses. Let's use spring_layout for visualization: pos = nx.spring_layout(G) Using our pos layout, we can then position the nodes: nx.draw_networkx_nodes(G, pos) Next, we draw the edges. To get the weights, we iterate over the edges in the graph (in a specific order) and collect the weights: edgewidth = [ d['weight'] for (u,v,d) in G.edges(data=True)] We then draw the edges: nx.draw_networkx_edges(G, pos, width=edgewidth) The result will depend on your data, but it will typically show a graph with a large set of nodes connected quite strongly and a few nodes poorly connected to the rest of the network. [ 150 ]
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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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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
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
Page 206: Chapter 8 Summary In this chapter,
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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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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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