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