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We can also use the model for scoring data on another platform. Now let’s see how it does on the test data by making some predictions and looking at ROC and AUC. We use it to model whether an arrival delay of greater than 15 minutes is influenced by the weather at the departure and arrival airports: To see the influence of weather data on delay in the arrival time, we use ScaleR’s logistic regression routine. We use rxDataStep to split out the 2012 data for testing and keep the rest for training: Train and test a logistic regression model But we import it to XDF first, since it is more efficient when running multiple operations on the dataset: Splitting data for training and test We could use the CSV file of joined airline and weather data as-is for modeling via a ScaleR text data source. We explicitly tell SparkR to save the resultant CSV in 80 separate partitions to enable sufficient parallelism in ScaleR processing: Import to XDF for use by ScaleR We save the data from the final Spark DataFrame 'joinedDF5' to a CSV for input to ScaleR and then close out the SparkR session. That completes the joins we need to do with SparkR. In a similar fashion, we join the weather and airline data based on arrival AirportID and datetime: Save results to CSV for exchange with ScaleR

Following the join, we remove some redundant columns, and rename the kept columns to remove the incoming DataFrame prefix introduced by the join. The outer join allows us to retain all the airline data records even if there is no matching weather data. We now use the SparkR join() function to do a left outer join of the airline and weather data by departure AirportID and datetime. Now we perform similar operations on the weather data: Joining the weather and airline data We only keep the variables needed, and round scheduled departure times down to the nearest hour to enable merging with the latest weather data at departure: Next we do some cleanup on the airline data we’ve imported to rename columns.

This function, like many other Spark methods, are executed lazily, meaning that they are queued for execution but not executed until required. Now we use the SparkR read.df() function to import the weather and airline data to Spark DataFrames.

#Cara menyelesaikan rubik code#

The following code reads each of the hourly raw weather data files, subsets to the columns we need, merges the weather station mapping file, adjusts the date times of measurements to UTC, and then writes out a new version of the file: Importing the airline and weather data to Spark DataFrames Then add an airport code associated with the weather station and convert the measurements from local time to UTC.īegin by creating a file to map the weather station (WBAN) info to an airport code. To prepare the weather data, subset it to the columns needed for modeling: Adding it to the search path allows you to use SparkR, and initialize a SparkR session: Preparing the weather data Next, add Spark_Home to the search path for R packages. Use the following code to set up the Spark environment:

Use the hourly data files and YYYYMMMstation.txt file within each of the zips.

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For this example, download the data for May 2007 – December 2012.

#Cara menyelesaikan rubik zip#

The weather data can be downloaded as zip files in raw form, by month, from the National Oceanic and Atmospheric Administration repository. It is also available as a zip from AirOnTimeCSV.zip. The flight data is available from the U.S. You are introduced to SparkR while walking through this scenario. The steps in this document assume that you have an intermediate level of knowledge of R and R the ScaleR library of ML Server. But the concept of mixing the use of SparkR and ScaleR in one script is also valid in the context of on-premises environments. The code was originally written for ML Server running on Spark in an HDInsight cluster on Azure. You can find this talk at Building a Scalable Data Science Platform with R. This example was initially shared in a talk at Strata 2016 by Mario Inchiosa and Roni Burd. The instructions here show that these requirements are straightforward to achieve. Until this issue is addressed in an upcoming version of ML Server, the workaround is to maintain non-overlapping Spark sessions, and to exchange data through intermediate files. The example uses flight delay and weather data, joined using SparkR.Īlthough both packages run on Apache Hadoop’s Spark execution engine, they are blocked from in-memory data sharing as they each require their own respective Spark sessions.

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This document shows how to predict flight arrival delays using a ScaleR logistic regression model. 6,468 1 1 gold badge 21 21 silver badges 31 31 bronze badges Not the answer you're looking for? Browse other questions tagged rapache-sparksparkr or ask your own question.