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Clojure for Data Science

Clojure for Data Science

By : Garner
5 (4)
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Clojure for Data Science

Clojure for Data Science

5 (4)
By: Garner
Buy this Book

Overview of this book

The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Statistics
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1. Statistics
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Downloading the sample code
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Running the examples
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Downloading the data
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Inspecting the data
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Data scrubbing
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Descriptive statistics
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Variance
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Quantiles
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Binning data
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Histograms
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The normal distribution
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Poincaré's baker
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Skewness
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Comparative visualizations
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The importance of visualizations
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Adding columns
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Comparative visualizations of electorate data
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Visualizing the Russian election data
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Comparative visualizations
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Summary
2
2. Inference
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2. Inference
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Introducing AcmeContent
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Download the sample code
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Load and inspect the data
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Visualizing the dwell times
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The exponential distribution
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The central limit theorem
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Standard error
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Samples and populations
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Confidence intervals
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Visualizing different populations
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Hypothesis testing
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Testing a new site design
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The t-statistic
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Performing the t-test
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One-sample t-test
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Resampling
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Testing multiple designs
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Multiple comparisons
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The browser simulation
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jStat
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B1
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Plotting probability densities
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State and Reagent
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Simulating multiple tests
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The Bonferroni correction
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Analysis of variance
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The F-distribution
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The F-statistic
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The F-test
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Effect size
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Summary
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3. Correlation
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3. Correlation
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About the data
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Inspecting the data
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Visualizing the data
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The log-normal distribution
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Covariance
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Pearson's correlation
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Hypothesis testing
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Confidence intervals
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Regression
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Ordinary least squares
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Goodness-of-fit and R-square
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Multiple linear regression
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Matrices
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The normal equation
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Multiple R-squared
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Adjusted R-squared
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Collinearity
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Prediction
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Summary
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4. Classification
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4. Classification
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About the data
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Inspecting the data
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Comparisons with relative risk and odds
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The standard error of a proportion
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The binomial distribution
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Significance testing proportions
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Chi-squared multiple significance testing
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Classification with logistic regression
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Implementing logistic regression with Incanter
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Probability
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Naive Bayes classification
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Decision trees
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Classification with clj-ml
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Bias and variance
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Ensemble learning and random forests
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Saving the classifier to a file
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Summary
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5. Big Data
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5. Big Data
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Downloading the code and data
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The reducers library
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Mathematical folds with Tesser
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Multiple regression with gradient descent
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Scaling gradient descent with Hadoop
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Stochastic gradient descent
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Summary
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6. Clustering
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6. Clustering
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Downloading the data
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Extracting the data
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Inspecting the data
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Clustering text
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Creating term frequency vectors
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Clustering with k-means and Incanter
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Better clustering with TF-IDF
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Large-scale clustering with Mahout
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Running k-means clustering with Mahout
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Cluster evaluation measures
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The drawbacks of k-means
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The curse of dimensionality
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Summary
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7. Recommender Systems
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7. Recommender Systems
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Download the code and data
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Inspect the data
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Parse the data
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Types of recommender systems
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Item-based and user-based recommenders
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Slope One recommenders
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Building a user-based recommender with Mahout
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k-nearest neighbors
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Recommender evaluation with Mahout
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Probabilistic methods for large sets
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Jaccard similarity for large sets with MinHash
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Dimensionality reduction
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Large-scale machine learning with Apache Spark and MLlib
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Machine learning on Spark with MLlib
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Summary
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8. Network Analysis
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8. Network Analysis
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Download the data
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Graph traversal with Loom
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Breadth-first and depth-first search
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Finding the shortest path
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Whole-graph analysis
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Scale-free networks
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Distributed graph computation with GraphX
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Summary
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9. Time Series
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9. Time Series
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About the data
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Fitting curves with a linear model
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Time series decomposition
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Discrete time models
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Maximum likelihood estimation
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Time series forecasting
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Summary
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10. Visualization
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10. Visualization
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Download the code and data
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Exploratory data visualization
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Using Quil for visualization
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Visualization for communication
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Summary
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Index
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Index

Whole-graph analysis

Let's turn our attention away from the smaller graphs we've been working with towards the larger graph of followers provided by the twitter_combined.txt file. This contains over 2.4 million edges and will provide a more interesting sample to work with.

One of the simplest metrics to determine about a whole graph is its density. For directed graphs, this is defined as the number of edges |E|, over the number of vertices |V| multiplied by one less than itself.

Whole-graph analysis

For a connected graph (one where every vertex is connected to every other vertex by an edge), the density would be 1. By contrast, a disconnected graph (one with no edges) would have a density of 0. Loom implements graph density as the alg/density function. Let's calculate the density of the larger Twitter graph:

(defn ex-8-17 []
  (->> (load-edges "twitter_combined.txt")
       (apply loom/digraph)
       (alg/density)
       (double)))

;; 2.675E-4

This seems very sparse, but bear in...

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Clojure for Data Science
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