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
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}
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 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
{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

In part 1 of this series I set up Vowpal Wabbit to classify newspapers content. Now, let’s use the model to make predictions and see how and if we can improve the model. Then, let’s train the model on the whole data.

The first step consists in importing the test data and preparing it. The test data need not be large and thus can be imported and worked on in R.

I need to remove the target column from the test set, or else it will be used to make predictions. If you do not remove this column the accuracy of the model will be very high, but it will be wrong since, of course, you do not have the target column at running time… because it is the column that you want to predict!

```
library("tidyverse")
library("yardstick")
small_test <- read_delim("data_split/small_test.txt", "|",
escape_double = FALSE, col_names = FALSE,
trim_ws = TRUE)
small_test %>%
mutate(X1= " ") %>%
write_delim("data_split/small_test2.txt", col_names = FALSE, delim = "|")
```

I wrote the data in a file called `small_test2.txt`

and can now use my model to make predictions:

`system2("/home/cbrunos/miniconda3/bin/vw", args = "-t -i vw_models/small_oaa.model data_split/small_test2.txt -p data_split/small_oaa.predict")`

The predictions get saved in the file `small_oaa.predict`

, which is a plain text file. Let’s add these
predictions to the original test set:

```
small_predictions <- read_delim("data_split/small_oaa.predict", "|",
escape_double = FALSE, col_names = FALSE,
trim_ws = TRUE)
small_test <- small_test %>%
rename(truth = X1) %>%
mutate(truth = factor(truth, levels = c("1", "2", "3", "4", "5")))
small_predictions <- small_predictions %>%
rename(predictions = X1) %>%
mutate(predictions = factor(predictions, levels = c("1", "2", "3", "4", "5")))
small_test <- small_test %>%
bind_cols(small_predictions)
```

We can use the several metrics included in `{yardstick}`

to evaluate the model’s performance:

```
conf_mat(small_test, truth = truth, estimate = predictions)
accuracy(small_test, truth = truth, estimate = predictions)
```

```
Truth
Prediction 1 2 3 4 5
1 51 15 2 10 1
2 11 6 3 1 0
3 0 0 0 0 0
4 0 0 0 0 0
5 0 0 0 0 0
```

```
# A tibble: 1 x 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 accuracy multiclass 0.570
```

We can see that the model never predicted class `3`

, `4`

or `5`

. Can we improve by adding some
regularization? Let’s find out!

Before trying regularization, let’s try changing the cost function from the logistic function to the hinge function:

```
# Train the model
hinge_oaa_fit <- system2("/home/cbrunos/miniconda3/bin/vw", args = "--oaa 5 -d data_split/small_train.txt --loss_function hinge -f vw_models/hinge_oaa.model", stderr = TRUE)
system2("/home/cbrunos/miniconda3/bin/vw", args = "-i vw_models/hinge_oaa.model -t -d data_split/small_test2.txt -p data_split/hinge_oaa.predict")
predictions <- read_delim("data_split/hinge_oaa.predict", "|",
escape_double = FALSE, col_names = FALSE,
trim_ws = TRUE)
test <- test %>%
select(-predictions)
predictions <- predictions %>%
rename(predictions = X1) %>%
mutate(predictions = factor(predictions, levels = c("1", "2", "3", "4", "5")))
test <- test %>%
bind_cols(predictions)
```

```
conf_mat(test, truth = truth, estimate = predictions)
accuracy(test, truth = truth, estimate = predictions)
```

```
Truth
Prediction 1 2 3 4 5
1 411 120 45 92 1
2 355 189 12 17 0
3 11 2 0 0 0
4 36 4 0 1 0
5 3 0 3 0 0
```

```
# A tibble: 1 x 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 accuracy multiclass 0.462
```

Well, didn’t work out so well, but at least we now know how to change the loss function. Let’s go back to the logistic loss and add some regularization. First, let’s train the model:

`regul_oaa_fit <- system2("/home/cbrunos/miniconda3/bin/vw", args = "--oaa 5 --l1 0.005 --l2 0.005 -d data_split/small_train.txt -f vw_models/small_regul_oaa.model", stderr = TRUE)`

Now we can use it for prediction:

```
system2("/home/cbrunos/miniconda3/bin/vw", args = "-i vw_models/small_regul_oaa.model -t -d data_split/test2.txt -p data_split/small_regul_oaa.predict")
predictions <- read_delim("data_split/small_regul_oaa.predict", "|",
escape_double = FALSE, col_names = FALSE,
trim_ws = TRUE)
test <- test %>%
select(-predictions)
predictions <- predictions %>%
rename(predictions = X1) %>%
mutate(predictions = factor(predictions, levels = c("1", "2", "3", "4", "5")))
test <- test %>%
bind_cols(predictions)
```

We can now use it for predictions:

```
conf_mat(test, truth = truth, estimate = predictions)
accuracy(test, truth = truth, estimate = predictions)
```

```
Truth
Prediction 1 2 3 4 5
1 816 315 60 110 1
2 0 0 0 0 0
3 0 0 0 0 0
4 0 0 0 0 0
5 0 0 0 0 0
```

```
# A tibble: 1 x 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 accuracy multiclass 0.627
```

So accuracy improved, but the model only predicts class 1 now… let’s try with other hyper-parameters values:

`regul_oaa_fit <- system2("/home/cbrunos/miniconda3/bin/vw", args = "--oaa 5 --l1 0.00015 --l2 0.00015 -d data_split/small_train.txt -f vw_models/small_regul_oaa.model", stderr = TRUE)`

```
conf_mat(test, truth = truth, estimate = predictions)
accuracy(test, truth = truth, estimate = predictions)
```

```
Truth
Prediction 1 2 3 4 5
1 784 300 57 108 1
2 32 14 3 2 0
3 0 1 0 0 0
4 0 0 0 0 0
5 0 0 0 0 0
```

```
# A tibble: 1 x 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 accuracy multiclass 0.613
```

So accuracy is lower than previously, but at least more categories get correctly predicted. Depending on your needs, you should consider different metrics. Especially for classification problems, you might not be interested in accuracy, in particular if the data is severely unbalanced.

Anyhow, to finish this blog post, let’s train the model on the whole data and measure the time it takes to run the full model.

Let’s first split the whole data into a training and a testing set:

```
nb_lines <- system2("cat", args = "text_fr.txt | wc -l", stdout = TRUE)
system2("split", args = paste0("-l", floor(as.numeric(nb_lines)*0.995), " text_fr.txt data_split/"))
system2("mv", args = "data_split/aa data_split/train.txt")
system2("mv", args = "data_split/ab data_split/test.txt")
```

The whole data contains 260247 lines, and the training set weighs 667MB, which is quite large. Let’s train the simple multiple classifier on the data and see how long it takes:

```
tic <- Sys.time()
oaa_fit <- system2("/home/cbrunos/miniconda3/bin/vw", args = "--oaa 5 -d data_split/train.txt -f vw_models/oaa.model", stderr = TRUE)
Sys.time() - tic
```

`Time difference of 4.73266 secs`

Yep, you read that right. Training the classifier on 667MB of data took less than 5 seconds!

Let’s take a look at the final object:

`oaa_fit`

```
[1] "final_regressor = vw_models/oaa.model"
[2] "Num weight bits = 18"
[3] "learning rate = 0.5"
[4] "initial_t = 0"
[5] "power_t = 0.5"
[6] "using no cache"
[7] "Reading datafile = data_split/train.txt"
[8] "num sources = 1"
[9] "average since example example current current current"
[10] "loss last counter weight label predict features"
[11] "1.000000 1.000000 1 1.0 2 1 253"
[12] "0.500000 0.000000 2 2.0 2 2 499"
[13] "0.250000 0.000000 4 4.0 2 2 6"
[14] "0.250000 0.250000 8 8.0 1 1 2268"
[15] "0.312500 0.375000 16 16.0 1 1 237"
[16] "0.250000 0.187500 32 32.0 1 1 557"
[17] "0.171875 0.093750 64 64.0 1 1 689"
[18] "0.179688 0.187500 128 128.0 2 2 208"
[19] "0.144531 0.109375 256 256.0 1 1 856"
[20] "0.136719 0.128906 512 512.0 4 4 4"
[21] "0.122070 0.107422 1024 1024.0 1 1 1353"
[22] "0.106934 0.091797 2048 2048.0 1 1 571"
[23] "0.098633 0.090332 4096 4096.0 1 1 43"
[24] "0.080566 0.062500 8192 8192.0 1 1 885"
[25] "0.069336 0.058105 16384 16384.0 1 1 810"
[26] "0.062683 0.056030 32768 32768.0 2 2 467"
[27] "0.058167 0.053650 65536 65536.0 1 1 47"
[28] "0.056061 0.053955 131072 131072.0 1 1 495"
[29] ""
[30] "finished run"
[31] "number of examples = 258945"
[32] "weighted example sum = 258945.000000"
[33] "weighted label sum = 0.000000"
[34] "average loss = 0.054467"
[35] "total feature number = 116335486"
```

Let’s use the test set and see how the model fares:

```
conf_mat(test, truth = truth, estimate = predictions)
accuracy(test, truth = truth, estimate = predictions)
```

```
Truth
Prediction 1 2 3 4 5
1 537 175 52 100 1
2 271 140 8 9 0
3 1 0 0 0 0
4 7 0 0 1 0
5 0 0 0 0 0
```

```
# A tibble: 1 x 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 accuracy multiclass 0.521
```

Better accuracy can certainly be achieved with hyper-parameter tuning… maybe the subject for a
future blog post? In any case I am very impressed with Vowpal Wabbit and am certainly looking forward
to future developments of `{RVowpalWabbit}`

!

Hope you enjoyed! If you found this blog post useful, you might want to follow me on twitter for blog post updates and buy me an espresso or paypal.me.