Data Backends to let mlr3 work transparently with (remote) data bases
LGPL-3.0 License
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
lgr::get_logger("mlr3")$set_threshold("warn")
Package website: release | dev
Extends the mlr3 package with a DataBackend to transparently work with databases. Two additional backends are currently implemented:
DataBackendDplyr
: Relies internally on the abstraction of dplyr and dbplyr.DataBackendDuckDB
: Connector to duckdb.To construct the backends, you have to establish a connection to the DBMS yourself with the DBI package.
For the serverless SQLite and DuckDB, we provide the converters as_sqlite_backend()
and as_duckdb_backend()
.
You can install the released version of mlr3db from CRAN with:
install.packages("mlr3db")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("mlr-org/mlr3db")
library("mlr3db")
# Create a classification task:
task = tsk("spam")
# Convert the task backend from a in-memory backend (DataBackendDataTable)
# to an out-of-memory SQLite backend via DataBackendDplyr.
# A temporary directory is used here to store the database files.
task$backend = as_sqlite_backend(task$backend, path = tempfile())
# Resample a classification tree using a 3-fold CV.
# The requested data will be queried and fetched from the database in the background.
resample(task, lrn("classif.rpart"), rsmp("cv", folds = 3))
library("mlr3db")
# Get an example parquet file from the package install directory:
# spam dataset (tsk("spam")) stored as parquet file
file = system.file(file.path("extdata", "spam.parquet"), package = "mlr3db")
# Create a backend on the file
backend = as_duckdb_backend(file)
# Construct classification task on the constructed backend
task = as_task_classif(backend, target = "type")
# Resample a classification tree using a 3-fold CV.
# The requested data will be queried and fetched from the database in the background.
resample(task, lrn("classif.rpart"), rsmp("cv", folds = 3))