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Working with Big Data The amount of data is increasing at exponential rates. Today's systems are generating and recording information on customer behavior, distributed systems, network analysis, sensors and many, many more sources. While the current big trend of mobile data is pushing the current growth, the next big thing—the Internet of Things (IoT)—is going to further increase the rate of growth. What this means for data mining is a new way of thinking. The complex algorithms with high run times need to be improved or discarded, while simpler algorithms that can deal with more samples are becoming more popular to use. As an example, while support vector machines are great classifiers, some variants are difficult to use on very large datasets. In contrast, simpler algorithms such as logistic regression can manage more easily in these scenarios. In this chapter, we will investigate the following: • Big data challenges and applications • The MapReduce paradigm • Hadoop MapReduce • mrjob, a python library to run MapReduce programs on Amazon's infrastructure [ 271 ]
Working with Big Data Big data What makes big data different? Most big-data proponents talk about the four Vs of big data: 1. Volume: The amount of data that we generate and store is growing at an increasing rate, and predictions of the future generally only suggest further increases. Today's multi-gigabyte sized hard drives will turn into exabyte hard drives in a few years, and network throughput traffic will be increasing as well. The signal to noise ratio can be quite difficult, with important data being lost in the mountain of non-important data. 2. Velocity: While related to volume, the velocity of data is increasing too. Modern cars have hundreds of sensors that stream data into their computers, and the information from these sensors needs to be analyzed at a subsecond level to operate the car. It isn't just a case of finding answers in the volume of data; those answers often need to come quickly. 3. Variety: Nice datasets with clearly defined columns are only a small part of the dataset that we have these days. Consider a social media post, which may have text, photos, user mentions, likes, comments, videos, geographic information, and other fields. Simply ignoring parts of this data that don't fit your model will lead to a loss of information, but integrating that information itself can be very difficult. 4. Veracity: With the increase in the amount of data, it can be hard to determine whether the data is being correctly collected—whether it is outdated, noisy, contains outliers, or generally whether it is useful at all. Being able to trust the data is hard when a human can't reliably verify the data itself. External datasets are being increasingly merged into internal ones too, giving rise to more troubles relating to the veracity of the data. These main four Vs (others have proposed additional Vs) outline why big data is different to just lots-of-data. At these scales, the engineering problem of working with the data is often more difficult—let alone the analysis. While there are lots of snake oil salesmen that overstate the ability to use big data, it is hard to deny the engineering challenges and the potential of big-data analytics. The algorithms we have used are to date load the dataset into memory and then to work on the in-memory version. This gives a large benefit in terms of speed of computation, as it is much faster to compute on in-memory data than having to load a sample before we use it. In addition, in-memory data allows us to iterate over the data many times, improving our model. [ 272 ]
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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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Page 313 and 314: Working with Big Data Next, we crea
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Page 317 and 318: Working with Big Data Left-click th
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Page 321 and 322: Next Steps… Extending the IPython
Page 323 and 324: Next Steps… Chapter 3: Predicting
Page 325 and 326: Next Steps… Vowpal Wabbit http://
Page 327 and 328: Next Steps… Deeper networks These
Page 329 and 330: Next Steps… Real-time clusterings
Page 331 and 332: Next Steps… More resources Kaggle
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Page 335 and 336: feature extraction about 82 common
Page 337 and 338: NetworkX about 145 defining 303 URL
Page 339 and 340: scikit-learn package references 305
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Learning Python Data Visualization
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