# Path: discomll/dataset.py
# class Data:
#     def __init__(self, data_tag, X_indices, **kwargs):
#     def generate_urls(self, first_url, last_url):
# 
# Path: discomll/ensemble/forest_distributed_decision_trees.py
# def simple_init(interface, params):
# def map_init(interface, params):
# def map_fit(interface, state, label, inp):
# def map_fit_bootstrap(interface, state, label, inp):
# def reduce_fit(interface, state, label, inp):
# def map_predict_voting(interface, state, label, inp):
# def map_predict_dist(interface, state, label, inp):
# def fit(dataset, trees_per_chunk=1, bootstrap=True, max_tree_nodes=50, min_samples_leaf=10, min_samples_split=5,
#         class_majority=1, separate_max=True, measure="info_gain", accuracy=1, random_state=None, save_results=True,
#         show=False):
# def predict(dataset, fitmodel_url, voting=False, save_results=True, show=False):


from discomll import dataset
from discomll.ensemble import forest_distributed_decision_trees

train = dataset.Data(data_tag=["http://ropot.ijs.si/data/segmentation/train/xaaaaa.gz",
                               "http://ropot.ijs.si/data/segmentation/train/xaaabj.gz"],
                     data_type="gzip",
                     generate_urls=True,
                     X_indices=range(2, 21),
                     id_index=0,
                     y_index=1,
                     X_meta=["c" for i in range(2, 21)],
                     delimiter=",")


# Path: discomll/dataset.py
# class Data:
#     def __init__(self, data_tag, X_indices, **kwargs):
#     def generate_urls(self, first_url, last_url):
# 
# Path: discomll/ensemble/forest_distributed_decision_trees.py
# def simple_init(interface, params):
# def map_init(interface, params):
# def map_fit(interface, state, label, inp):
# def map_fit_bootstrap(interface, state, label, inp):
# def reduce_fit(interface, state, label, inp):
# def map_predict_voting(interface, state, label, inp):
# def map_predict_dist(interface, state, label, inp):
# def fit(dataset, trees_per_chunk=1, bootstrap=True, max_tree_nodes=50, min_samples_leaf=10, min_samples_split=5,
#         class_majority=1, separate_max=True, measure="info_gain", accuracy=1, random_state=None, save_results=True,
#         show=False):
# def predict(dataset, fitmodel_url, voting=False, save_results=True, show=False):


# Path: discomll/examples/big_data/fddt_segmentation.py
from discomll import dataset
from discomll.ensemble import forest_distributed_decision_trees


train = dataset.Data(data_tag=["http://ropot.ijs.si/data/segmentation/train/xaaaaa.gz",
                               "http://ropot.ijs.si/data/segmentation/train/xaaabj.gz"],
                     data_type="gzip",
                     generate_urls=True,
                     X_indices=range(2, 21),
                     id_index=0,
                     y_index=1,
                     X_meta=["c" for i in range(2, 21)],
                     delimiter=",")


test = dataset.Data(data_tag=["http://ropot.ijs.si/data/segmentation/test/xaaaaa.gz",