By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this useful booklet, 4 Cloudera information scientists current a collection of self-contained styles for appearing large-scale facts research with Spark. The authors carry Spark, statistical tools, and real-world info units jointly to coach you ways to technique analytics difficulties by means of example.
You’ll commence with an advent to Spark and its surroundings, after which dive into styles that observe universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields comparable to genomics, safeguard, and finance. in case you have an entry-level figuring out of laptop studying and data, and also you application in Java, Python, or Scala, you’ll locate those styles helpful for engaged on your personal info applications.
• Recommending song and the Audioscrobbler facts set
• Predicting woodland hide with selection trees
• Anomaly detection in community site visitors with K-means clustering
• figuring out Wikipedia with Latent Semantic Analysis
• examining co-occurrence networks with GraphX
• Geospatial and temporal info research at the long island urban Taxi journeys data
• Estimating monetary hazard via Monte Carlo simulation
• studying genomics information and the BDG project
• interpreting neuroimaging information with PySpark and Thunder
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Extra info for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
Res: Int = 9 Sometimes, this abbreviated syntax makes the code easier to read because it avoids duplicating obvious identifiers. Sometimes, this shortcut just makes the code cryptic. The code listings use one or the other according to our best judgment. Shipping Code from the Client to the Cluster We just saw a wide variety of ways to write and apply functions to data in Scala. All of the code that we executed was done against the data inside the head array, which was contained on our client machine.
CountByValue() ... countByValue() ... Map(false -> 596414, true -> 20931) Even though the number of false positives is higher than we would like, this more generous filter still removes 90% of the nonmatching records from our consideration while including every positive match. Even though this is pretty good, it’s possible to do even better; see if you can find a way to use some of the other values from the scores array (both missing and not) to come up with a scoring function that success‐ fully identifies every true match at the cost of less than 100 false positives.
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. This is actually a good thing. A is just a tiny sample of all interactions that could happen. In a way, we believe A is a terribly spotty, and therefore hard-to-explain, view of a simpler underlying reality that is well explained by just some small number of factors, k of them.
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