Building Machine Learning Systems with Python - Richert, Coelho

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Because the cluster centers are moved, we have to reassign the cluster labels and recalculate the cluster centers. After iteration 2, we get the following clustering: Chapter 3 The arrows show the movements of the cluster centers. After five iterations in this example, the cluster centers don't move noticeably any more (Scikit's tolerance threshold is 0.0001 by default). After the clustering has settled, we just need to note down the cluster centers and their identity. When each new document comes in, we have to vectorize and compare it with all the cluster centers. The cluster center with the smallest distance to our new post vector belongs to the cluster we will assign to the new post. Getting test data to evaluate our ideas on In order to test clustering, let us move away from the toy text examples and find a dataset that resembles the data we are expecting in the future so that we can test our approach. For our purpose, we need documents on technical topics that are already grouped together so that we can check whether our algorithm works as expected when we apply it later to the posts we hope to receive. [ 65 ]
Clustering – Finding Related Posts One standard dataset in machine learning is the 20newsgroup dataset, which contains 18,826 posts from 20 different newsgroups. Among the groups' topics are technical ones such as comp.sys.mac.hardware or sci.crypt as well as more politics- and religion-related ones such as talk.politics.guns or soc.religion. christian. We will restrict ourselves to the technical groups. If we assume each newsgroup is one cluster, we can nicely test whether our approach of finding related posts works. The dataset can be downloaded from http://people.csail.mit.edu/ jrennie/20Newsgroups. Much more simple, however, is to download it from MLComp at http://mlcomp.org/datasets/379 (free registration required). Scikit already contains custom loaders for that dataset and rewards you with very convenient data loading options. The dataset comes in the form of a ZIP file, dataset-379-20news-18828_WJQIG. zip, which we have to unzip to get the folder 379, which contains the datasets. We also have to notify Scikit about the path containing that data directory. It contains a metadata file and three directories test, train, and raw. The test and train directories split the whole dataset into 60 percent of training and 40 percent of testing posts. For convenience, the dataset module also contains the function fetch_20newsgroups, which downloads that data into the desired directory. The website http://mlcomp.org is used for comparing machinelearning programs on diverse datasets. It serves two purposes: finding the right dataset to tune your machine-learning program and exploring how other people use a particular dataset. For instance, you can see how well other people's algorithms performed on particular datasets and compare them. Either you set the environment variable MLCOMP_DATASETS_HOME or you specify the path directly with the mlcomp_root parameter when loading the dataset as follows: >>> import sklearn.datasets >>> MLCOMP_DIR = r"D:\data" >>> data = sklearn.datasets.load_mlcomp("20news-18828", mlcomp_ root=MLCOMP_DIR) >>> print(data.filenames) array(['D:\\data\\379\\raw\\comp.graphics\\1190-38614', 'D:\\data\\379\\raw\\comp.graphics\\1383-38616', 'D:\\data\\379\\raw\\alt.atheism\\487-53344', ..., 'D:\\data\\379\\raw\\rec.sport.hockey\\10215-54303', 'D:\\data\\379\\raw\\sci.crypt\\10799-15660', [ 66 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Page 29 and 30: Getting Started with Python Machine
Page 48 and 49: Learning How to Classify with Real-
Page 50 and 51: Chapter 2 We are using Matplotlib;
Page 52 and 53: Chapter 2 The last few lines select
Page 54 and 55: Chapter 2 error = 0.0 for ei in ran
Page 56 and 57: Chapter 2 We can play around with t
Page 58 and 59: Chapter 2 Features and feature engi
Page 60 and 61: Chapter 2 In the preceding screensh
Page 62 and 63: Chapter 2 Binary and multiclass cla
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Page 66 and 67: Chapter 3 How to do it More robust
Page 68 and 69: Chapter 3 This means that the first
Page 70 and 71: Chapter 3 ... post = posts[i] ... i
Page 72 and 73: Chapter 3 If you have a clear pictu
Page 74 and 75: Chapter 3 Extending the vectorizer
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Page 91 and 92: Topic Modeling For those who are in
Page 93 and 94: Topic Modeling Sparsity means that
Page 95 and 96: Topic Modeling Although daunting at
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Page 99 and 100: Topic Modeling Finally, we build th
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Classification II - Sentiment Analy
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Chapter 6 Getting to know the Bayes
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Using Naive Bayes to classify Given
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Chapter 6 This denotation "" leads
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Chapter 6 Similarly, we do this for
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Chapter 6 A quick look at the previ
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Chapter 6 To keep our experimentati
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Chapter 6 Y = np.zeros(Y.shape[0])
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Chapter 6 ° ° Experiment with whe
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Chapter 6 We have to be patient whe
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Chapter 6 First, we define a range
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Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Chapter 7 P greater than N scenario
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Chapter 7 So, we can see that the d
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Chapter 7 Fortunately, scikit-learn
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[ 161 ] Chapter 7 The loading of th
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Chapter 7 Summary In this chapter,
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Regression - Recommendations Improv
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Weights Regression - Recommendation
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Classification III - Music Genre Cl
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Chapter 9 Matplotlib provides the c
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[ 185 ] Chapter 9
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Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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Computer Vision - Pattern Recogniti
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Dimensionality Reduction Sketching
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Dimensionality Reduction However, t
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Dimensionality Reduction To underst
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Dimensionality Reduction In order t
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Dimensionality Reduction Asking the
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Dimensionality Reduction n_ feature
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Dimensionality Reduction Sketching
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Dimensionality Reduction Limitation
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Dimensionality Reduction Now, MDS t
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Dimensionality Reduction Of course,
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Big(ger) Data • Your algorithms c
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Big(ger) Data sleep(4) return 2*x @
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Big(ger) Data Looking under the hoo
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Big(ger) Data def write_result(ofna
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Big(ger) Data Amazon Web Services i
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Big(ger) Data In EC2 parlance, a ru
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Big(ger) Data In this system, pip i
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Big(ger) Data Keys, keys, and more
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Big(ger) Data We can use the same j
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Where to Learn More about Machine L
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• Machined Learnings at http://ww
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Index A AcceptedAnswerId attribute
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F false negative 41 false positive
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inary matrix of recommendations, us
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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