By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this functional publication, 4 Cloudera information scientists current a suite of self-contained styles for appearing large-scale facts research with Spark. The authors convey Spark, statistical tools, and real-world facts units jointly to coach you ways to procedure analytics difficulties through example.
You’ll commence with an creation to Spark and its environment, after which dive into styles that observe universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields akin to genomics, safeguard, and finance. when you've got an entry-level realizing of computer studying and facts, and also you application in Java, Python, or Scala, you’ll locate those styles necessary for engaged on your individual information applications.
• Recommending track and the Audioscrobbler facts set
• Predicting woodland conceal with choice trees
• Anomaly detection in community site visitors with K-means clustering
• figuring out Wikipedia with Latent Semantic Analysis
• reading co-occurrence networks with GraphX
• Geospatial and temporal facts research at the big apple urban Taxi journeys data
• Estimating monetary possibility via Monte Carlo simulation
• reading genomics information and the BDG project
• studying neuroimaging information with PySpark and Thunder
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Extra info for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
SPARK-5341 also tracks development on the capability to specify Maven repositories directly when invoking spark-shell and have the JARs from these repositories auto‐ matically show up on Spark’s classpath. Bringing Data from the Cluster to the Client RDDs have a number of methods that allow us to read data from the cluster into the Scala REPL on our client machine. first ... res: String = "id_1","id_2","cmp_fname_c1","cmp_fname_c2",... The first method can be useful for sanity checking a data set, but we’re generally interested in bringing back larger samples of an RDD into the client for analysis.
The factorization can only be approximate because k is small, as shown in Figure 3-1. The Alternating Least Squares Recommender Algorithm | 41 Figure 3-1. Matrix factorization These algorithms are sometimes called matrix completion algorithms, because the original matrix A may be quite sparse, but the product XYT is dense. Very few, if any, entries are 0, and therefore the model is only an approximation to A. It is a model in the sense that it produces (“completes”) a value for even the many entries that are missing (that is, 0) in the original A.
The rows have few values—k. Each value corresponds to a latent feature in the model. So the rows express how much users and artists associate with these latent features, which might correspond to tastes or genres. And it is simply the product of a userfeature and feature-artist matrix that yields a complete estimation of the entire, dense user-artist interaction matrix. The bad news is that A = XYT generally has no solution at all, because X and Y aren’t large enough (technically speaking, too low rank) to perfectly represent A.