KMeans Clustering

Intro

The simple_kmeans_db() function enables running the KMeans model inside the database. It uses dplyr programming to abstract the steps needed produce a model, so that it can then be translated into SQL statements in the background.

Example setup

In this example, a simple RSQlite database will be use to load the flights data from the nycflights13 library.

library(dplyr)

con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
RSQLite::initExtension(con)

db_flights <- copy_to(con, nycflights13::flights, "flights")

Running Kmeans clustering

The function simple_kmeans_db() can use with local data, or a remote table, such as the db_flights variable that is a pointer to the “flights” table inside the SQLite database. When piping to the function, the only other required arguments are two or more fields separated by comma. Because it uses ‘tidyeval’, the variable name auto-completion will work.

library(modeldb)

km <- db_flights %>%
simple_kmeans_db(dep_time, distance)

The simple_kmeans_db() function uses a progress bar to show you the current cycle, the maximum cycles it’s expected to run, the current difference between the previous cycle and the current cycle, and the running time. The loop will stop once it wither has two matching consecutive cycles, or if it reaches the maximum number of cycles, as determined by the max_repeats argument.

The final centers are are stored in the centers variable of the returned object

km$centers ## # A tibble: 3 x 2 ## dep_time distance ## <dbl> <dbl> ## 1 890. 791. ## 2 1391. 2355. ## 3 1746. 718. The latest results are stored in the tbl variable of the returned object. The type of the returned table will match the type of the original source, so if it is a remote source, such as database table, then tbl will be a class tbl_sql. This will allow us to do two thing: • View the actual results by running the query via R. This will allow us to perform further operations if needed: head(km$tbl, 10)
## # Source:   lazy query [?? x 3]
## # Database: sqlite 3.22.0 []
##    dep_time distance center
##       <int>    <dbl> <chr>
##  1      517     1400 center_1
##  2      533     1416 center_1
##  3      542     1089 center_1
##  4      544     1576 center_1
##  5      554      762 center_1
##  6      554      719 center_1
##  7      555     1065 center_1
##  8      557      229 center_1
##  9      557      944 center_1
## 10      558      733 center_1
## # ... with more rows
• View the SQL statement that was used to find the final centers:
dbplyr::remote_query(km$tbl) ## <SQL> SELECT dep_time, distance, center ## FROM (SELECT dep_time, distance, center_1, center_2, center_3, CASE ## WHEN (center_1 >= center_1 AND center_1 < center_2 AND center_1 < center_3) THEN ('center_1') ## WHEN (center_2 < center_1 AND center_2 >= center_2 AND center_2 < center_3) THEN ('center_2') ## WHEN (center_3 < center_1 AND center_3 < center_2 AND center_3 >= center_3) THEN ('center_3') ## END AS center ## FROM (SELECT dep_time, distance, SQRT(((889.757881651311 - dep_time) * (889.757881651311 - dep_time)) + ((791.286862996562 - distance) * (791.286862996562 - distance))) AS center_1, SQRT(((1391.08534916316 - dep_time) * (1391.08534916316 - dep_time)) + ((2355.04462033144 - distance) * (2355.04462033144 - distance))) AS center_2, SQRT(((1745.74853136521 - dep_time) * (1745.74853136521 - dep_time)) + ((718.043515631104 - distance) * (718.043515631104 - distance))) AS center_3 ## FROM (SELECT * ## FROM (SELECT dep_time, distance ## FROM flights) ## WHERE (NOT(((dep_time) IS NULL)) AND NOT(((distance) IS NULL)))))) ## WHERE (NOT(((center) IS NULL))) Under the hood The simple_kmeans_db() function uses dplyr and ‘tidyeval’ to run the KMeans algorithm. This means that when combined with dbplyr, the routines can be run inside a database. Unlike other packages that use this same methodology, such as dbplot and tidypredict, simple_kmeans_db() does not create a single dplyr code that can be extracted as SQL. The function produces multiple, serial and dependent SQL statements that run individually inside the database. Each statement uses the current centroids, or centers, to estimate new centroids, and then it uses those centroids in a consecutive SQL statement to see if there was any variance. Effectively, this approach uses R not only as translation layer, but also as an orchestration layer. Safeguards for long running jobs Thesimple_kmeans_db() approach of using multiple and consecutive SQL queries to find the optimal centers, additionally, in KMeans clustering, it matters the order in which the each set of centers is passed. This creates an imperative to find a way to cache the current centers used in a long running job, in case the job is canceled or fails. Starting from the centers that were calculated last, will mean that re-starting the job will not being from “0”, but from a more advanced, read closer, set of centers. The safeguard implemented in this function is trough a file, called kmeans.csv. Each cycle will update the file. The file name can be changed by modifying the safeguard_file argument. Setting the argument to NULL will turn off the safeguard. The file will be saved to the temporary directory of the R session.. In this example we will set the max_repats to 10, so as to artificially avoid finding the optimal means km <- db_flights %>% simple_kmeans_db(dep_time, distance, max_repeats = 10) In the next run, the “kmeans.csv” file is passed as the initial_kmeans argument. This will make simple_kmeans_db() use those centers as the starting point: km <- db_flights %>% simple_kmeans_db(dep_time, distance, initial_kmeans = read_csv(file.path(tempdir(), "kmeans.csv"))) ## Parsed with column specification: ## cols( ## dep_time = col_double(), ## distance = col_double() ## ) The second run took 7 cycles to complete, which adds up to the 17 cycles that it initially took in the first example at the top of this article. Visualizations Because visualizing a large amount of data may be both compute intensive and visually challenging. The modeldb package offers a helper function to aid with this task. The plot_kmeans function uses ‘ggplot2’ to display the results of a KMeans routine. Instead of a scatterplot, it uses a square grid that displays the concentration of intersections per square. The number of squares in the grid can be customized for more or less fine grain. For large result-sets in remote sources, downloading every intersection will be a long running, costly operation. The approach of this function is to divide the x and y plane in a grid and have the remote source figure the total number of intersections, returned as a single number. This reduces the granularity of the visualization, but it speeds up the results. The calculation operations will take place inside the database. It requires a database that supports functions like MIN, which SQLite does not support, so for this example, we will collect the data into R first (please do not use this step if working with a enterprise grade database) km$tbl <- collect(km$tbl) # ONLY USE THIS STEP IF WORKING WITH SQLITE library(ggplot2) km$tbl %>%
plot_kmeans(dep_time, distance)

Reduce the resolution for faster results and larger squares:

km\$tbl %>%
plot_kmeans(dep_time, distance, resolution = 30)