args = parser.parse_args() if args.nrows == 0: nrows = None else: nrows = args.nrows savedir = pjoin("/data/ml2/vishakh/sap/baselines", args.savedir) ft = FeatureTester(outDir=savedir) if args.raw: ft.load_from_hdf5_raw(dname=args.dname, cohort=args.cohort, nrows=nrows) else: ft.load_from_hdf5_latent(dname=args.dname, cohort=args.cohort, nrows=nrows) e = Experiment() e.begin_experiment(fname=args.fname, baseDir=savedir) ft.logistic_regression(save_lc=True) e.log(ft.get_metadata(), ft.get_result()) ft.random_forest() e.log(ft.get_metadata(), ft.get_result()) ft.nearest_neighbors() e.log(ft.get_metadata(), ft.get_result()) e.end_experiment()
from experiment import Experiment from feature_tester import FeatureTester e = Experiment() ft = FeatureTester() e.begin_experiment(fName="benchmarks-hp", note="More hyperparameters in random_forest") ft.load_from_csv() ft.prepare_for_testing() ft.logistic_regression() e.log(ft.get_metadata(), ft.get_result()) ft.random_forest() e.log(ft.get_metadata(), ft.get_result()) ft.nearest_neighbors() e.log(ft.get_metadata(), ft.get_result()) e.view_experiment() e.end_experiment()