def __init__(self, leaf_model=LinearSVC(), split_classifier=LinearSVC(), num_features_per_node=None, max_depth=3, min_leaf_size=50, randomize_split_params={}, randomize_leaf_params={}, verbose=False): # check everyone's types -- I can't give up the OCaml instincts # also, if running this code remotely it's nice to know when something # goes wrong before we send an object over to AWS check_estimator(leaf_model) check_estimator(split_classifier) check_int(max_depth) check_int(min_leaf_size) check_dict(randomize_split_params) check_dict(randomize_leaf_params) check_bool(verbose) self.leaf_model = leaf_model self.split_classifier = split_classifier self.max_depth = max_depth self.min_leaf_size = min_leaf_size self.num_features_per_node = num_features_per_node self.randomize_split_params = randomize_split_params self.randomize_leaf_params = randomize_leaf_params self.verbose = verbose self.root = None self.classes = None
def __init__(self, leaf_model = LinearSVC(), split_classifier = LinearSVC(), num_features_per_node = None, max_depth=3, min_leaf_size=50, randomize_split_params={}, randomize_leaf_params={}, verbose = False): # check everyone's types -- I can't give up the OCaml instincts # also, if running this code remotely it's nice to know when something # goes wrong before we send an object over to AWS check_estimator(leaf_model) check_estimator(split_classifier) check_int(max_depth) check_int(min_leaf_size) check_dict(randomize_split_params) check_dict(randomize_leaf_params) check_bool(verbose) self.leaf_model = leaf_model self.split_classifier = split_classifier self.max_depth = max_depth self.min_leaf_size = min_leaf_size self.num_features_per_node = num_features_per_node self.randomize_split_params = randomize_split_params self.randomize_leaf_params = randomize_leaf_params self.verbose = verbose self.root = None self.classes = None
def __init__(self, k, base_model, verbose=False): check_int(k) check_estimator(base_model) check_bool(verbose) self.k = k self.base_model = base_model self.verbose = verbose self.clusters = MiniBatchKMeans(k) self.models = None