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A tutorial on tidy cross-validation with R Analyzing NetHack data, part 1: What kills the players Analyzing NetHack data, part 2: What players kill the most Building a shiny app to explore historical newspapers: a step-by-step guide Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 1 Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 2 Curly-Curly, the successor of Bang-Bang Dealing with heteroskedasticity; regression with robust standard errors using R Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer Fast food, causality and R packages, part 1 Fast food, causality and R packages, part 2 For posterity: install {xml2} on GNU/Linux distros Forecasting my weight with R From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack Get text from pdfs or images using OCR: a tutorial with {tesseract} and {magick} Getting data from pdfs using the pdftools package Getting the data from the Luxembourguish elections out of Excel Going from a human readable Excel file to a machine-readable csv with {tidyxl} Historical newspaper scraping with {tesseract} and R How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data Imputing missing values in parallel using {furrr} Intermittent demand, Croston and Die Hard Looking into 19th century ads from a Luxembourguish newspaper with R Making sense of the METS and ALTO XML standards Manipulate dates easily with {lubridate} Manipulating strings with the {stringr} package Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data Missing data imputation and instrumental variables regression: the tidy approach Modern R with the tidyverse is available on Leanpub Objects types and some useful R functions for beginners Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()` R or Python? Why not both? Using Anaconda Python within R with {reticulate} Searching for the optimal hyper-parameters of an ARIMA model in parallel: the tidy gridsearch approach Some fun with {gganimate} Split-apply-combine for Maximum Likelihood Estimation of a linear model Statistical matching, or when one single data source is not enough The best way to visit Luxembourguish castles is doing data science + combinatorial optimization The never-ending editor war (?) The year of the GNU+Linux desktop is upon us: using user ratings of Steam Play compatibility to play around with regex and the tidyverse Using Data Science to read 10 years of Luxembourguish newspapers from the 19th century Using a genetic algorithm for the hyperparameter optimization of a SARIMA model Using cosine similarity to find matching documents: a tutorial using Seneca's letters to his friend Lucilius Using linear models with binary dependent variables, a simulation study Using the tidyverse for more than data manipulation: estimating pi with Monte Carlo methods What hyper-parameters are, and what to do with them; an illustration with ridge regression {disk.frame} is epic {pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr} Building formulae Functional peace of mind Get basic summary statistics for all the variables in a data frame Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash Importing 30GB of data into R with sparklyr Introducing brotools It's lists all the way down It's lists all the way down, part 2: We need to go deeper Keep trying that api call with purrr::possibly() Lesser known dplyr 0.7* tricks Lesser known dplyr tricks Lesser known purrr tricks Make ggplot2 purrr Mapping a list of functions to a list of datasets with a list of columns as arguments Predicting job search by training a random forest on an unbalanced dataset Teaching the tidyverse to beginners Why I find tidyeval useful tidyr::spread() and dplyr::rename_at() in action Easy peasy STATA-like marginal effects with R Functional programming and unit testing for data munging with R available on Leanpub How to use jailbreakr My free book has a cover! Work on lists of datasets instead of individual datasets by using functional programming Method of Simulated Moments with R New website! Nonlinear Gmm with R - Example with a logistic regression Simulated Maximum Likelihood with R Bootstrapping standard errors for difference-in-differences estimation with R Careful with tryCatch Data frame columns as arguments to dplyr functions Export R output to a file I've started writing a 'book': Functional programming and unit testing for data munging with R Introduction to programming econometrics with R Merge a list of datasets together Object Oriented Programming with R: An example with a Cournot duopoly R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest? Read a lot of datasets at once with R Unit testing with R Update to Introduction to programming econometrics with R Using R as a Computer Algebra System with Ryacas

Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash

This is going to be the type of blog posts that would perhaps be better as a gist, but it is easier for me to use my blog as my own personal collection of gists. Plus, someone else might find this useful, so here it is! In this blog post I am going to show a little trick to randomly sample rows from a text file using bash, and then train a model using the {h2o} package. I will also use the {rsparkling} package. From {rsparkling}’s documentation: {rsparkling} is a package that provides an R interface to the H2O Sparkling Water machine learning library. and will be needed to transfer the data from Spark to H2O.

In a previous blog post I used the {sparklyr} package to load a 30GB csv file into R. I created the file by combining around 300 csv files, each around 80MB big. Here, I would like to use the machine learning functions included in the {h2o} packages to train a random forest on this data. However, I only want to have a simple prototype that simply runs, and check if all the packages work well together. If everything is ok, I’ll keep iterating to make the model better (in a possible subsequent post).

For fast prototyping, using 30GB of data is not a good idea, so I am going to sample 500000 from this file using the linux command line (works on macOS too and also on Windows if you installed the linux subsystem). Why not use R to sample 500000 rows? Because on my machine, loading the 30GB file takes 25 minutes. Sampling half a million lines from it would take quite long too. So here are some bash lines that do that directly on the file, without needing to load it into R beforehand:

[18-03-03 21:50] brodriguesco in /Documents/AirOnTimeCSV ➤ get_seeded_random()
  openssl enc -aes-256-ctr -pass pass:"$seed" -nosalt \
  </dev/zero 2>/dev/null

[18-03-03 21:50] brodriguesco in /Documents/AirOnTimeCSV ➤ sed "1 d" combined.csv | shuf --random-source=<(get_seeded_random 42) -n 500000 > small_combined_temp.csv

[18-03-03 21:56] brodriguesco in /Documents/AirOnTimeCSV ➤ head -1 combined.csv > colnames.csv

[18-03-03 21:56] brodriguesco in /Documents/AirOnTimeCSV ➤ cat colnames.csv small_combined_temp.csv > small_combined.csv

The first function I took from the gnu coreutils manual which allows me to fix the random seed to reproduce the same sampling of the file. Then I use "sed 1 d" cobmined.csv to remove the first line of combined.csv which is the header of the file. Then, I pipe the result of sed using | to shuf which does the shuffling. The option --random-source=<(get_seeded_random 42) fixes the seed, and -n 500000 only shuffles 500000 and not the whole file. The final bit of the line, > small_combined_temp.csv, saves the result to small_cobmined_temp.csv. Because I need to add back the header, I use head -1 to extract the first line of combined.csv and save it into colnames.csv. Finally, I bind the rows of both files using cat colnames.csv small_combined_temp.csv and save the result into small_combined.cvs. Taken together, all these steps took about 5 minutes (without counting the googling around for finding how to pass a fixed seed to shuf).

Now that I have this small dataset, I can write a small prototype:

First, you need to install {sparklyr}, {rsparkling} and {h2o}. Refer to this to know how to install the packages. I had a mismatch between the version of H2O that was automatically installed when I installed the {h2o} package, and the version of Spark that {sparklyr} installed but thankfully the {h2o} package returns a very helpful error message with the following lines:

detach("package:rsparkling", unload = TRUE)
                       if ("package:h2o" %in% search()) { detach("package:h2o", unload = TRUE) }
                       if (isNamespaceLoaded("h2o")){ unloadNamespace("h2o") }
                       install.packages("h2o", type = "source", repos = "https://h2o-release.s3.amazonaws.com/h2o/rel-weierstrass/2/R")

which tells you which version to install.

So now, let’s load everything:

## ----------------------------------------------------------------------
## Your next step is to start H2O:
##     > h2o.init()
## For H2O package documentation, ask for help:
##     > ??h2o
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit http://docs.h2o.ai
## ----------------------------------------------------------------------
## Attaching package: 'h2o'
## The following objects are masked from 'package:stats':
##     cor, sd, var
## The following objects are masked from 'package:base':
##     &&, %*%, %in%, ||, apply, as.factor, as.numeric, colnames,
##     colnames<-, ifelse, is.character, is.factor, is.numeric, log,
##     log10, log1p, log2, round, signif, trunc
## H2O is not running yet, starting it now...
## Note:  In case of errors look at the following log files:
##     /tmp/Rtmph48vf9/h2o_cbrunos_started_from_r.out
##     /tmp/Rtmph48vf9/h2o_cbrunos_started_from_r.err
## Starting H2O JVM and connecting: .. Connection successful!
## R is connected to the H2O cluster: 
##     H2O cluster uptime:         1 seconds 944 milliseconds 
##     H2O cluster version: 
##     H2O cluster version age:    4 months and 15 days !!! 
##     H2O cluster name:           H2O_started_from_R_cbrunos_bpn152 
##     H2O cluster total nodes:    1 
##     H2O cluster total memory:   6.98 GB 
##     H2O cluster total cores:    12 
##     H2O cluster allowed cores:  12 
##     H2O cluster healthy:        TRUE 
##     H2O Connection ip:          localhost 
##     H2O Connection port:        54321 
##     H2O Connection proxy:       NA 
##     H2O Internal Security:      FALSE 
##     H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4 
##     R Version:                  R version 3.4.4 (2018-03-15)
## Warning in h2o.clusterInfo(): 
## Your H2O cluster version is too old (4 months and 15 days)!
## Please download and install the latest version from http://h2o.ai/download/

I left all the startup messages because they’re quite helpful. Especially that bit telling you to start H2O with h2o.init(). If something’s wrong, h2o.init() will give you helpful information.

Now that all this is loaded, I can start working on the data (the steps below are explained in detail in my previous blog post):

spark_dir = "/my_2_to_disk/spark/"

config = spark_config()

config$`sparklyr.shell.driver-memory` <- "4G"
config$`sparklyr.shell.executor-memory` <- "4G"
config$`spark.yarn.executor.memoryOverhead` <- "512"
config$`sparklyr.shell.driver-java-options` = paste0("-Djava.io.tmpdir=", spark_dir)

sc = spark_connect(master = "local", config = config)

Another useful function that allows you to check if everything is alright is h2o_context():


Sparkling Water Context:
 * H2O name: sparkling-water-cbrunos_local-1520111879840
 * cluster size: 1
 * list of used nodes:
  (executorId, host, port)

  Open H2O Flow in browser: (CMD + click in Mac OSX)

Now, let’s load the data into R with {sparklyr}:

air = spark_read_csv(sc, name = "air", path = "small_combined.csv")

Of course, here, using Spark is overkill, because small_combined.csv is only around 100MB big, so no need for {sparklyr} but as stated in the beginning this is only to have a quick and dirty prototype. Once all the pieces are working together, I can iterate on the real data, for which {sparklyr} will be needed. Now, if I needed to use {dplyr} I could use it on air, but I don’t want to do anything on it, so I convert it to a h2o data frame. h2o data frames are needed as arguments for the machine learning algorithms included in the {h2o} package. as_h2o_frame() is a function included in {rsparkling}:

air_hf = as_h2o_frame(sc, air)

Then, I convert the columns I need to factors (I am only using factors here):

air_hf$ORIGIN = as.factor(air_hf$ORIGIN)
air_hf$UNIQUE_CARRIER = as.factor(air_hf$UNIQUE_CARRIER)
air_hf$DEST = as.factor(air_hf$DEST)

{h2o} functions need the names of the predictors and of the target columns, so let’s define that:

target = "ARR_DELAY"
predictors = c("UNIQUE_CARRIER", "ORIGIN", "DEST")

Now, let’s train a random Forest, without any hyper parameter tweaking:

model = h2o.randomForest(predictors, target, training_frame = air_hf)

Now that this runs, I will in the future split the data into training, validation and test set, and train a model with better hyper parameters. For now, let’s take a look at the summary of model:

Model Details:

H2ORegressionModel: drf
Model Key:  DRF_model_R_1520111880605_1
Model Summary:
  number_of_trees number_of_internal_trees model_size_in_bytes min_depth
1              50                       50            11055998        20
  max_depth mean_depth min_leaves max_leaves mean_leaves
1        20   20.00000       1856       6129  4763.42000

H2ORegressionMetrics: drf
** Reported on training data. **
** Metrics reported on Out-Of-Bag training samples **

MSE:  964.9246
RMSE:  31.06324
MAE:  17.65517
Mean Residual Deviance :  964.9246

Scoring History:
             timestamp   duration number_of_trees training_rmse training_mae
1  2018-03-03 22:52:24  0.035 sec               0
2  2018-03-03 22:52:25  1.275 sec               1      30.93581     17.78216
3  2018-03-03 22:52:25  1.927 sec               2      31.36998     17.78867
4  2018-03-03 22:52:26  2.272 sec               3      31.36880     17.80359
5  2018-03-03 22:52:26  2.564 sec               4      31.29683     17.79467
6  2018-03-03 22:52:26  2.854 sec               5      31.31226     17.79467
7  2018-03-03 22:52:27  3.121 sec               6      31.26214     17.78542
8  2018-03-03 22:52:27  3.395 sec               7      31.20749     17.75703
9  2018-03-03 22:52:27  3.666 sec               8      31.19706     17.74753
10 2018-03-03 22:52:27  3.935 sec               9      31.16108     17.73547
11 2018-03-03 22:52:28  4.198 sec              10      31.13725     17.72493
12 2018-03-03 22:52:32  8.252 sec              27      31.07608     17.66648
13 2018-03-03 22:52:36 12.462 sec              44      31.06325     17.65474
14 2018-03-03 22:52:38 14.035 sec              50      31.06324     17.65517
2          957.02450
3          984.07580
4          984.00150
5          979.49147
6          980.45794
7          977.32166
8          973.90720
9          973.25655
10         971.01272
11         969.52856
12         965.72249
13         964.92530
14         964.92462

Variable Importances: (Extract with `h2o.varimp`)

Variable Importances:
        variable relative_importance scaled_importance percentage
1         ORIGIN    291883392.000000          1.000000   0.432470
2           DEST    266749168.000000          0.913890   0.395230
3 UNIQUE_CARRIER    116289536.000000          0.398411   0.172301

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