Fit models inside the database. modeldb
works with most databases back-ends because it leverages dplyr
and dbplyr
for the final SQL translation of the algorithm. It currently supports:
K-means clustering
Linear regression
Install the CRAN version with:
The development version is available using devtools
as follows:
An easy way to try out the package is by creating a temporary SQLite database, and loading mtcars
to it
con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
RSQLite::initExtension(con)
dplyr::copy_to(con, mtcars)
## # A tibble: 1 x 3
## `(Intercept)` mpg qsec
## <dbl> <dbl> <dbl>
## 1 4.12 -0.156 0.125
The model output can be parsed by tidypredict
to run the predictions in the database. Please see the Linear Regression
article to learn more about how to use linear_regression_db()
To use the simple_kmeans_db()
function, simply pipe the database back end table to the function. This returns a list object that contains two items:
## [1] "k_center" "k_mpg" "k_wt" "mpg" "cyl" "disp"
## [7] "hp" "drat" "wt" "qsec" "vs" "am"
## [13] "gear" "carb"
The SQL statement from tbl
can be extracted using dbplyr
’s remote_query()
## <SQL> SELECT `RHS`.`center` AS `k_center`, `LHS`.`k_mpg` AS `k_mpg`, `LHS`.`k_wt` AS `k_wt`, `RHS`.`mpg` AS `mpg`, `RHS`.`cyl` AS `cyl`, `RHS`.`disp` AS `disp`, `RHS`.`hp` AS `hp`, `RHS`.`drat` AS `drat`, `RHS`.`wt` AS `wt`, `RHS`.`qsec` AS `qsec`, `RHS`.`vs` AS `vs`, `RHS`.`am` AS `am`, `RHS`.`gear` AS `gear`, `RHS`.`carb` AS `carb`
## FROM (SELECT `center` AS `k_center`, `mpg` AS `k_mpg`, `wt` AS `k_wt`
## FROM (SELECT `center`, AVG(`mpg`) AS `mpg`, AVG(`wt`) AS `wt`
## FROM (SELECT `mpg`, `wt`, `center`
## FROM (SELECT *
## FROM (SELECT `mpg`, `cyl`, `disp`, `hp`, `drat`, `wt`, `qsec`, `vs`, `am`, `gear`, `carb`, `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 `mpg`, `cyl`, `disp`, `hp`, `drat`, `wt`, `qsec`, `vs`, `am`, `gear`, `carb`, SQRT(((20.6428571428571 - `mpg`) * (20.6428571428571 - `mpg`)) + ((3.07214285714286 - `wt`) * (3.07214285714286 - `wt`))) AS `center_1`, SQRT(((14.4583333333333 - `mpg`) * (14.4583333333333 - `mpg`)) + ((4.05866666666667 - `wt`) * (4.05866666666667 - `wt`))) AS `center_2`, SQRT(((30.0666666666667 - `mpg`) * (30.0666666666667 - `mpg`)) + ((1.873 - `wt`) * (1.873 - `wt`))) AS `center_3`
## FROM `mtcars`))
## WHERE (NOT(((`center`) IS NULL)))))
## GROUP BY `center`)) AS `LHS`
## RIGHT JOIN (SELECT `mpg`, `cyl`, `disp`, `hp`, `drat`, `wt`, `qsec`, `vs`, `am`, `gear`, `carb`, `center`
## FROM (SELECT `mpg`, `cyl`, `disp`, `hp`, `drat`, `wt`, `qsec`, `vs`, `am`, `gear`, `carb`, `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 `mpg`, `cyl`, `disp`, `hp`, `drat`, `wt`, `qsec`, `vs`, `am`, `gear`, `carb`, SQRT(((20.6428571428571 - `mpg`) * (20.6428571428571 - `mpg`)) + ((3.07214285714286 - `wt`) * (3.07214285714286 - `wt`))) AS `center_1`, SQRT(((14.4583333333333 - `mpg`) * (14.4583333333333 - `mpg`)) + ((4.05866666666667 - `wt`) * (4.05866666666667 - `wt`))) AS `center_2`, SQRT(((30.0666666666667 - `mpg`) * (30.0666666666667 - `mpg`)) + ((1.873 - `wt`) * (1.873 - `wt`))) AS `center_3`
## FROM `mtcars`))
## WHERE (NOT(((`center`) IS NULL)))) AS `RHS`
## ON (`LHS`.`k_center` = `RHS`.`center`)