def save_mercs(dataset, classifier, keyword="default"): suffix = ["mercs", keyword] fn_mod = filename_model(dataset, suffix=suffix, extension='lz4') dump(classifier, fn_mod, compress='lz4') return
def load_pxs(dataset, keyword="default"): suffix = ["pxs", keyword] fn_mod = filename_model(dataset, suffix=suffix) with open(fn_mod, "rb") as f: clf = pkl.load(f) return clf
def load_mercs(dataset, keyword="default"): suffix = ["mercs", keyword] fn_mod = filename_model(dataset, suffix=suffix, extension="lz4") clf = load(fn_mod) return clf
def save_pxs(dataset, classifier, model_keyword="default"): suffix = ["pxs", model_keyword] fn_mod = filename_model(dataset, suffix=suffix) with open(fn_mod, "wb") as f: pkl.dump(classifier, f) return
def fit_pxs(dataset, cwd=None, model_keyword="default", **fit_config): # Model fname suffix = ["pxs", model_keyword] fn_mod = filename_model(dataset, suffix=suffix, extension="xdsl") # Data fname suffix = ["train", "pxs"] fn_train = filename_dataset(dataset, step=2, suffix=suffix, extension="csv") clf = PxS() clf.fit(fn_train, cwd=cwd, model_fname=fn_mod, **fit_config) return clf