Package: slingpy
slingpyTheslingpypython package provides starter code for structured, reproducible and maintainable machine learning
projects.slingpyaims to be maximally extensible while maintaining sensible defaults. It is agnostic in terms of modelling backend
(e.g., supportingscikit-learn,torchandtensorflow) and suitable for both production and research-grade machine learning projects.slingpycontains utilities for standard model evaluation workflows, such as nested cross validation, model serialisation, dataset handling, managing high performance computing (HPC) interfaces such as slurm, and by
default writes all experiment artefacts to disk.InstallpipinstallslingpyUseA minimalslingpyproject consists of a base application that defines your basic modelling workflow.importslingpyasspfromtypingimportAnyStr,Dict,Listfromsklearn.linear_modelimportLogisticRegressionclassMyApplication(sp.AbstractBaseApplication):def__init__(self,output_directory:AnyStr="",schedule_on_slurm:bool=False,split_index_outer:int=0,split_index_inner:int=0,num_splits_outer:int=5,num_splits_inner:int=5):super().__init__(output_directory=output_directory,schedule_on_slurm=schedule_on_slurm,split_index_outer=split_index_outer,split_index_inner=split_index_inner,num_splits_outer=num_splits_outer,num_splits_inner=num_splits_inner)defget_metrics(self,set_name:AnyStr)->List[sp.AbstractMetric]:return[sp.metrics.AreaUnderTheCurve()]defload_data(self)->Dict[AnyStr,sp.AbstractDataSource]:data_source_x,data_source_y=sp.datasets.Iris.load_data(self.output_directory)stratifier=sp.StratifiedSplit()rest_indices,training_indices=stratifier.split(data_source_y,test_set_fraction=0.6,split_index=self.split_index_inner)validation_indices,test_indices=stratifier.split(data_source_y.subset(rest_indices),test_set_fraction=0.5,split_index=self.split_index_outer)return{"training_set_x":data_source_x.subset(training_indices),"training_set_y":data_source_y.subset(training_indices),"validation_set_x":data_source_x.subset(validation_indices),"validation_set_y":data_source_y.subset(validation_indices),"test_set_x":data_source_x.subset(test_indices),"test_set_y":data_source_y.subset(test_indices)}deftrain_model(self)->sp.AbstractBaseModel:model=sp.SklearnModel(LogisticRegression())model.fit(self.datasets.training_set_x,self.datasets.training_set_y)returnmodelif__name__=="__main__":app=sp.instantiate_from_command_line(MyApplication)app._run()Your new app can then be executed locally from the command line using:python/project_path/my_application.pyslingpyalso enables execution of your project on a remote HPC cluster, e.g. via slurm, by using:python/project_path/my_application.py--do_schedule_on_slurmApplication parameters are automatically parsed from the command line, e.g.:python/project_path/my_application.py--output_directory=/path/to/output/dirDevelopmentTheslingpycodebase is formatted with Black and Import Sort to ensure consistant code formatting.
These are run throughpre-commit.
If making code changes toslingpy, install the development dependencies and pre-commit git hook with:pipinstall-rrequirements-dev.txt
pre-commitinstallLicenseLicenseAuthorsPatrick Schwab, GlaxoSmithKline plcArash Mehrjou, GlaxoSmithKline plcAndrew Jesson, University of OxfordAshkan Soleymani, MITAcknowledgementsPS and AM are employees and shareholders of GlaxoSmithKline plc.