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Chapter 2 Loading the dataset The dataset we are going to use is called Ionosphere, which is the recording of many high-frequency antennas. The aim of the antennas is to determine whether there is a structure in the ionosphere and a region in the upper atmosphere. Those that have a structure are deemed good, while those that do not are deemed bad. The aim of this application is to build a data mining classifier that can determine whether an image is good or bad. (Image Credit: https://www.flickr.com/photos/geckzilla/16149273389/) This can be downloaded from the UCL Machine Learning data repository, which contains a large number of datasets for different data mining applications. Go to http://archive.ics.uci.edu/ml/datasets/Ionosphere and click on Data Folder. Download the ionosphere.data and ionosphere.names files to a folder on your computer. For this example, I'll assume that you have put the dataset in a directory called Data in your home folder. The location of your home folder depends on your operating system. For Windows, it is usually at C:\Documents and Settings\ username. For Mac or Linux machines, it is usually at /home/ username. You can get your home folder by running this python code: import os print(os.path.expanduser("~")) [ 29 ]
Classifying with scikit-learn Estimators For each row in the dataset, there are 35 values. The first 34 are measurements taken from the 17 antennas (two values for each antenna). The last is either 'g' or 'b'; that stands for good and bad, respectively. Start the IPython Notebook server and create a new notebook called Ionosphere Nearest Neighbors for this chapter. First, we load up the NumPy and csv libraries that we will need for our code: import numpy as np import csv To load the dataset, we first get the filename of the dataset. First, get the folder the dataset is stored in from your data folder: data_filename = os.path.join(data_folder, "Ionosphere", "ionosphere.data") We then create the X and y NumPy arrays to store the dataset in. The sizes of these arrays are known from the dataset. Don't worry if you don't know the size of future datasets—we will use other methods to load the dataset in future chapters and you won't need to know this size beforehand: X = np.zeros((351, 34), dtype='float') y = np.zeros((351,), dtype='bool') The dataset is in a Comma-Separated Values (CSV) format, which is a commonly used format for datasets. We are going to use the csv module to load this file. Import it and set up a csv reader object: with open(data_filename, 'r') as input_file: reader = csv.reader(input_file) Next, we loop over the lines in the file. Each line represents a new set of measurements, which is a sample in this dataset. We use the enumerate function to get the line's index as well, so we can update the appropriate sample in the dataset (X): for i, row in enumerate(reader): We take the first 34 values from this sample, turn each into a float, and save that to our dataset: data = [float(datum) for datum in row[:-1]] X[i] = data [ 30 ]
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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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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 A transformer is akin to
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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 cursor = results['next_cu
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Chapter 7 Creating a graph Now, we
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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 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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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 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 return [image,] return s
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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 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 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 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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