This vignette describes the three different ways to initialize the R session’s connection to Python using the rMIDAS package as well as reticulate.
rMIDAS relies on Python 3.X to run the MIDAS imputation algorithm
(note: Python 3.9 is as yet untested for full rMIDAS functionality). For
most users, the default settings in rMIDAS will be
sufficient. Both train() and complete() check
if Python has been initialized and, if not, run the required setup using
the best Python 3 version available on your system (as determined by
reticulate). The first time you run
rMIDAS after installation, you may be prompted to
install additional Python dependencies.
If a suitable Python version is not found on your system, you will be asked to manually set the path to a Python binary. You can do this using the next option.
If the automatic setup returns an error or you wish to use a specific
Python binary on your system, you can use the
set_python_env() function in rMIDAS,
providing an exact path to your chosen Python binary:
library(rMIDAS)
set_python_env(x = "~/path/to/bin/python")
# Then proceed as normal...With set_python_env() you can also set a virtualenv or
condaenv environment:
library(rMIDAS)
set_python_env(x = "myenv", type = "virtualenv")
# or
set_python_env(x = "mycondaenv", type = "conda")
# Then proceed as normal...On the GitHub
repository you can also find an environment file
(rmidas-env.yml) which can be used to initialise a new
conda environment that contains Python 3.7 and all required
dependencies.
Once you have downloaded this file, in your console navigate to the download directory and run:
conda env create -f rmidas-env.ymlThen, prior to training a MIDAS model, make sure to load this environment in R:
set_python_env(x = "rmidas-env", type = "conda")Note: reticulate only allows you to set a
Python binary once per R session, so if you wish to switch to a
different Python binary, or have already run train() or
convert(), you will need to restart R prior to using
set_python_env().
If you desire more granular control of the R-Python interface, it is
possible to use reticulate’s in-built Python
configuration tools. Since these commands are outside of
rMIDAS, you must also manually call
midas_setup() after configuring your Python install,
e.g.:
library(rMIDAS)
reticulate::use_condaenv(condaenv = "myenv", conda = "some_conda_executable", required = FALSE)
midas_setup()
# Then proceed as normal...As with option 2, reticulate only allows you to set
a Python binary once per R session. If you wish to switch to a different
Python binary, or have already run train() or
convert(), you will need to restart R prior to changing
Python version and then call midas_setup().
Sometimes the above three options may fail due to system configuration issues. Here we note a few common issues and fixes.
If you are using a Mac, reticulate may be defaulting to Python 2.7 which is not supported by rMIDAS. If this is the case you will have to configure the R session to use a Python 3 binary, as in option 2 above, by running:
set_python_env(x = "/usr/local/bin/python3")
# Then proceed as normal...If this returns an error, it’s likely reticulate
cannot find a Python environment related to the binary. In which case we
recommend restarting the R session and creating a
virtualenv that points to your desired Python 3 binary, as
follows:
reticulate::virtualenv_create(envname = "myenv", python = "/path/to/your/python3/bin")
set_python_env(x = "myenv", type = "virtualenv")
# Then proceed as normal...