def dt_predict(input_dict):
from discomll.ensemble import forest_distributed_decision_trees
predictions_url = forest_distributed_decision_trees.predict(input_dict["dataset"],
fitmodel_url=input_dict["fitmodel_url"],
save_results=True)
return {"string": predictions_url}
def dt_predict(input_dict):
from discomll.ensemble import forest_distributed_decision_trees
predictions_url = forest_distributed_decision_trees.predict(
input_dict["dataset"],
fitmodel_url=input_dict["fitmodel_url"],
save_results=True)
return {"string": predictions_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
from disco.core import result_iterator
from discomll import dataset
from discomll.ensemble import forest_distributed_decision_trees
from discomll.utils import model_view
train = dataset.Data(data_tag=[["http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"]],
X_indices=xrange(0, 4),
X_meta="http://ropot.ijs.si/data/datasets_meta/iris_meta.csv",
y_index=4,
delimiter=",")
fit_model = forest_distributed_decision_trees.fit(train, trees_per_chunk=1, bootstrap=False, max_tree_nodes=50,
min_samples_leaf=2, min_samples_split=1, class_majority=1,
separate_max=True, measure="info_gain", accuracy=1, random_state=None,
save_results=True)
print model_view.output_model(fit_model)
# predict training dataset
predictions = forest_distributed_decision_trees.predict(train, fit_model)
# output results
for k, v in result_iterator(predictions):
print k, v[0]
