本文整理汇总了Python中disco.core.Job.params[k]方法的典型用法代码示例。如果您正苦于以下问题：Python Job.params[k]方法的具体用法？Python Job.params[k]怎么用？Python Job.params[k]使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类disco.core.Job的用法示例。
在下文中一共展示了Job.params[k]方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: predict
# 需要导入模块: from disco.core import Job [as 别名]
# 或者: from disco.core.Job import params[k] [as 别名]
def predict(dataset, fitmodel_url, voting=False, save_results=True, show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
import discomll
path = "/".join(discomll.__file__.split("/")[:-1] + ["ensemble", "core", ""])
if "drf_fitmodel" not in fitmodel_url:
raise Exception("Incorrect fit model.")
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [("split", Stage("map", input_chain=dataset.params["input_chain"], init=simple_init,
process=map_predict_voting if voting else map_predict_dist))]
job.params = dataset.params
for k, v in result_iterator(fitmodel_url["drf_fitmodel"]):
job.params[k] = v
if len(job.params["forest"]) == 0:
print "Warning: There is no decision trees in forest"
return []
job.run(name="distributed_random_forest_predict", input=dataset.params["data_tag"],
required_files=[path + "decision_tree.py"])
return job.wait(show=show)示例2: predict
# 需要导入模块: from disco.core import Job [as 别名]
# 或者: from disco.core.Job import params[k] [as 别名]
def predict(dataset, fitmodel_url, coeff=0.5, save_results=True, show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
import discomll
path = "/".join(discomll.__file__.split("/")[:-1] + ["ensemble", "core", ""])
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [
("split", Stage("map", input_chain=dataset.params["input_chain"], init=simple_init, process=map_predict))]
if "dwfr_fitmodel" not in fitmodel_url:
raise Exception("Incorrect fit model.")
try:
coeff = float(coeff)
if coeff < 0:
raise Exception("Parameter coeff should be greater than 0.")
except ValueError:
raise Exception("Parameter coeff should be numerical.")
job.params = dataset.params
job.params["coeff"] = coeff
for k, v in result_iterator(fitmodel_url["dwfr_fitmodel"]):
job.params[k] = v
if len(job.params["forest"]) == 0:
print "Warning: There is no decision trees in forest"
return []
job.run(name="distributed_weighted_forest_rand_predict", input=dataset.params["data_tag"],
required_files=[path + "decision_tree.py"])
return job.wait(show=show)