def __init__(self,
max_leaf=1000,
test_interval=100,
algorithm="RGF",
loss="Log",
reg_depth=1.0,
l2=0.1,
sl2=None,
normalize=False,
min_samples_leaf=10,
n_iter=None,
n_tree_search=1,
opt_interval=100,
learning_rate=0.5,
calc_prob="sigmoid",
n_jobs=-1,
memory_policy="generous",
verbose=0,
init_model=None):
if not utils.Config().RGF_AVAILABLE:
raise Exception('RGF estimators are unavailable for usage.')
super(RGFClassifier, self).__init__()
self.max_leaf = max_leaf
self.test_interval = test_interval
self.algorithm = algorithm
self.loss = loss
self.reg_depth = reg_depth
self.l2 = l2
self.sl2 = sl2
self.normalize = normalize
self.min_samples_leaf = min_samples_leaf
self.n_iter = n_iter
self.n_tree_search = n_tree_search
self.opt_interval = opt_interval
self.learning_rate = learning_rate
self.calc_prob = calc_prob
self.n_jobs = n_jobs
self.memory_policy = memory_policy
self.verbose = verbose
self.init_model = init_model
def __init__(self,
n_estimators=500,
max_depth=6,
max_leaf=50,
tree_gain_ratio=1.0,
min_samples_leaf=5,
loss="LS",
l1=1.0,
l2=1000.0,
opt_algorithm="rgf",
learning_rate=0.001,
max_bin=None,
min_child_weight=5.0,
data_l2=2.0,
sparse_max_features=80000,
sparse_min_occurences=5,
calc_prob="sigmoid",
n_jobs=-1,
verbose=0):
if not utils.Config().FASTRGF_AVAILABLE:
raise Exception('FastRGF estimators are unavailable for usage.')
super(FastRGFClassifier, self).__init__()
self.n_estimators = n_estimators
self.max_depth = max_depth
self.max_leaf = max_leaf
self.tree_gain_ratio = tree_gain_ratio
self.min_samples_leaf = min_samples_leaf
self.loss = loss
self.l1 = l1
self.l2 = l2
self.opt_algorithm = opt_algorithm
self.learning_rate = learning_rate
self.max_bin = max_bin
self.min_child_weight = min_child_weight
self.data_l2 = data_l2
self.sparse_max_features = sparse_max_features
self.sparse_min_occurences = sparse_min_occurences
self.calc_prob = calc_prob
self.n_jobs = n_jobs
self.verbose = verbose