should make things more reproducible
This pipline can be used to run analyses in a structured way.
the user writes a minimal configuration file which contains only the following information:
- "how do I get the data?", by providing a dataloader
- "what to do with the data?", by providing a scikit pipeline
- "what to do with the result?", by providing output handlers.
Please have a look at the examples for more information.
everything else should be installed by pipenv.
- Create a fresh directory.
- In that directory, call
pipenv install dbispipelineThis call will install a virtual environment as well as all dependencies.
- Write your configuration(s)
pipenv run python -m dbispipeline <yourconfigurationfile.py>
The framework look in multiple directories for its configuration files.
/usr/local/etc/dbispipeline.iniused for system wide default.
$HOME/.config/dbispipeline.iniused for user specific configurations.
./dbispipeline.inifor project sepcifig configurations.
And example configuration file looks like this:
[database] # url to your postgres database host = your.personal.database # your database user name user = user # port of your postgres database, default = 5432 # port = 5432 # password of your database user password = <secure-password> # database to use database = pipelineresults # table to be used result_table = my_super_awesome_results [project] # this will be stored in the database name = dbispipeline-test [mail] # email address to use as sender sender = firstname.lastname@example.org # recipient. This should probably be set on a home-directory-basis. recipient = email@example.com # smtp server address to use smtp_server = smtp.yourserver.com # use smtp authentication, default = no # authenticate = no # username for smtp authentication, required if authenticate = yes # username = foo # password for smtp authentication, required if authenticate = yes # password = bar # port to use for smtp server connection, default = 465 # port = 465
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