def dt_fit(input_dict):
from discomll.ensemble import forest_distributed_decision_trees
random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
fitmodel_url = forest_distributed_decision_trees.fit(input_dict["dataset"],
trees_per_chunk=input_dict["trees_per_subset"],
max_tree_nodes=input_dict["tree_nodes"],
min_samples_leaf=input_dict["min_samples_leaf"],
min_samples_split=input_dict["min_samples_split"],
class_majority=input_dict["majority"],
bootstrap=input_dict["bootstrap"] == "true",
measure=input_dict["measure"],
accuracy=input_dict["accuracy"],
separate_max=input_dict["separate_max"] == "true",
random_state=random_state,
save_results=True)
return {"fitmodel_url": fitmodel_url}
def dt_fit(input_dict):
from discomll.ensemble import forest_distributed_decision_trees
random_state = None if input_dict["seed"] == "None" else int(
input_dict["seed"])
fitmodel_url = forest_distributed_decision_trees.fit(
input_dict["dataset"],
trees_per_chunk=input_dict["trees_per_subset"],
max_tree_nodes=input_dict["tree_nodes"],
min_samples_leaf=input_dict["min_samples_leaf"],
min_samples_split=input_dict["min_samples_split"],
class_majority=input_dict["majority"],
bootstrap=input_dict["bootstrap"] == "true",
measure=input_dict["measure"],
accuracy=input_dict["accuracy"],
separate_max=input_dict["separate_max"] == "true",
random_state=random_state,
save_results=True)
return {"fitmodel_url": fitmodel_url}
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",
"http://ropot.ijs.si/data/segmentation/test/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=",")
fit_model = forest_distributed_decision_trees.fit(train, 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)
predict_url = forest_distributed_decision_trees.predict(test, fit_model)
print predict_url
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",
"http://ropot.ijs.si/data/segmentation/test/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=",")
fit_model = forest_distributed_decision_trees.fit(train,
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)
predict_url = forest_distributed_decision_trees.predict(test, fit_model)
print predict_url