def __init__(self,
dataset,
scoring,
model=None,
fit_params=None,
cv=4,
n_jobs=None):
self.fit_params = fit_params if fit_params else dict()
if model is None:
model, dataset.df, categoricals, dataset.y = infer_model(
dataset.df, dataset.features, dataset.y, n_jobs)
self.fit_params["categorical_feature"] = categoricals
n_jobs = 1
self.model = model
self.dataset = dataset
self.scoring = scoring
self.cv = cv
self.n_jobs = n_jobs
if self.n_jobs is not None and self.n_jobs > 1:
warning_str = (
"Warning: If your model is multithreaded, please initialise the number"
"of jobs of LOFO to be equal to 1, otherwise you may experience performance issues."
)
warnings.warn(warning_str)
def __init__(self,
df,
features,
target,
scoring,
model=None,
cv=4,
n_jobs=None):
df = df.copy()
self.fit_params = {}
if model is None:
model, df, categoricals = infer_model(df, features, target, n_jobs)
self.fit_params["categorical_feature"] = categoricals
n_jobs = 1
self.model = model
self.df = df
self.features = features
self.target = target
self.scoring = scoring
self.cv = cv
self.n_jobs = n_jobs
if self.n_jobs is not None and self.n_jobs > 1:
warning_str = "Warning: If your model is multithreaded, please initialise the number \
of jobs of LOFO to be equal to 1, otherwise you may experience issues."
warnings.warn(warning_str)