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