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We get the names of the features for the premise and conclusion and print out the rule in a readable format: premise_name = features[premise] conclusion_name = features[conclusion] print("Rule: If a person buys {0} they will also buy {1}".format(premise_name, conclusion_name)) Then we print out the Support and Confidence of this rule: print(" - Support: {0}".format(support[(premise, conclusion)])) print(" - Confidence: {0:.3f}".format(confidence[(premise, conclusion)])) Chapter 1 We can test the code by calling it in the following way—feel free to experiment with different premises and conclusions: Ranking to find the best rules Now that we can <strong>com</strong>pute the support and confidence of all rules, we want to be able to find the best rules. To do this, we perform a ranking and print the ones with the highest values. We can do this for both the support and confidence values. To find the rules with the highest support, we first sort the support dictionary. Dictionaries do not support ordering by default; the items() function gives us a list containing the data in the dictionary. We can sort this list using the itemgetter class as our key, which allows for the sorting of nested lists such as this one. Using itemgetter(1) allows us to sort based on the values. Setting reverse=True gives us the highest values first: from operator import itemgetter sorted_support = sorted(support.items(), key=itemgetter(1), re verse=True) [ 13 ]
Getting Started with Data Mining We can then print out the top five rules: for index in range(5): print("Rule #{0}".format(index + 1)) premise, conclusion = sorted_support[index][0] print_rule(premise, conclusion, support, confidence, features) The result will look like the following: Similarly, we can print the top rules based on confidence. First, <strong>com</strong>pute the sorted confidence list: sorted_confidence = sorted(confidence.items(), key=itemgetter(1), reverse=True) Next, print them out using the same method as before. Note the change to sorted_confidence on the third line; for index in range(5): print("Rule #{0}".format(index + 1)) premise, conclusion = sorted_confidence[index][0] print_rule(premise, conclusion, support, confidence, features) [ 14 ]
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Page 3 and 4: Learning Data Mining with Python Co
Page 5 and 6: About the Author Robert Layton has
Page 7 and 8: Christophe Van Gysel is pursuing a
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Page 11 and 12: Table of Contents Preprocessing usi
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Page 18 and 19: Preface If you have ever wanted to
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Page 40 and 41: Chapter 1 The scikit-learn library
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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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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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