def test_remove_models(self):
"""can remove a partial set of data"""
# get an initial set of database row counts
num_desc = self.desctab.count_rows()
num_phens = self.phenstab.count_rows()
num_score = self.scoretab.count_rows()
# run a model removal using a small descriptions file
config = IMPCTestConfig()
config.model_descriptions = "prep-IMPC-descriptions-update.tsv"
config.model_phenotypes = None
impc = Phenoscoring(config)
impc.remove()
# the number of rows in tables should decrease
self.assertLess(self.desctab.count_rows(), num_desc,
"number of models should decrease")
self.assertLess(self.phenstab.count_rows(), num_phens,
"number of phenotypes should decrease")
self.assertLess(self.scoretab.count_rows(), num_score,
"number of score entries should decrease")
pipeline = Phenoscoring(config)
if config.action == "build":
# create a new sqlite database with references
pipeline.build()
if config.action == "clearmodels":
# remove all models and model scores from the database
pipeline.clearmodels()
if config.action == "update":
# add new or update existing model defintions/phenotypes
pipeline.update()
if config.action == "remove":
# remove a subset of models from the database
pipeline.remove()
if config.action == "recompute":
# remove all scores and re-generate them from scratch
pipeline.recompute()
if config.action == "export":
# write a database table into a tsv file
pipeline.export()
if config.action == "representations":
# compute complete representations for references and models
pipeline.export_representations()
if config.action == "explain":
# provide step-by-step explanation of scoring
