本文整理汇总了Python中bigml.api.BigML.create_anomaly方法的典型用法代码示例。如果您正苦于以下问题：Python BigML.create_anomaly方法的具体用法？Python BigML.create_anomaly怎么用？Python BigML.create_anomaly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bigml.api.BigML的用法示例。
在下文中一共展示了BigML.create_anomaly方法的5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: BigML
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import create_anomaly [as 别名]
from bigml.api import BigML
api = BigML()
source1 = api.create_source("iris.csv")
api.ok(source1)
dataset1 = api.create_dataset(source1, \
{'name': u'iris dataset'})
api.ok(dataset1)
anomaly1 = api.create_anomaly(dataset1, \
{'name': u"iris dataset's anomaly detector"})
api.ok(anomaly1)
batchanomalyscore1 = api.create_batch_anomaly_score(anomaly1, dataset1, \
{'name': u"Batch Anomaly Score of iris dataset's anomaly detector with iris dataset",
'output_dataset': True})
api.ok(batchanomalyscore1)
dataset2 = api.get_dataset(batchanomalyscore1['object']['output_dataset_resource'])
api.ok(dataset2)
dataset2 = api.update_dataset(dataset2, \
{'fields': {u'000000': {'name': u'score'}},
'name': u'my_dataset_from_batch_anomaly_score_name'})
api.ok(dataset2)
示例2:
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import create_anomaly [as 别名]
u'000002': {u'name': u'petal length', u'optype': u'numeric'},
u'000003': {u'name': u'petal width', u'optype': u'numeric'},
u'000004': {u'name': u'species',
u'optype': u'categorical',
u'term_analysis': {u'enabled': True}}}}
source2 = api.create_source(source1_file, args)
api.ok(source2)
args = \
{u'objective_field': {u'id': u'000004'}}
dataset1 = api.create_dataset(source2, args)
api.ok(dataset1)
args = \
{u'anomaly_seed': u'bigml', u'seed': u'bigml'}
anomaly1 = api.create_anomaly(dataset1, args)
api.ok(anomaly1)
args = \
{u'fields_map': {u'000000': u'000000',
u'000001': u'000001',
u'000002': u'000002',
u'000003': u'000003',
u'000004': u'000004'},
u'output_dataset': True}
batchanomalyscore1 = api.create_batch_anomaly_score(anomaly1, dataset1, args)
api.ok(batchanomalyscore1)
dataset2 = api.get_dataset(batchanomalyscore1["object"]["output_dataset_resource"])
api.ok(dataset2)
示例3: BigML
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import create_anomaly [as 别名]
from bigml.api import BigML
api = BigML()
source1 = api.create_source("iris.csv")
api.ok(source1)
dataset1 = api.create_dataset(source1, \
{'name': u'iris dataset'})
api.ok(dataset1)
anomaly1 = api.create_anomaly(dataset1, \
{'anomaly_seed': u'2c249dda00fbf54ab4cdd850532a584f286af5b6',
'name': u"iris dataset's anomaly detector"})
api.ok(anomaly1)
anomalyscore1 = api.create_anomaly_score(anomaly1, \
{u'petal length': 0.5,
u'petal width': 0.5,
u'sepal length': 1,
u'sepal width': 1,
u'species': u'Iris-setosa'}, \
{'name': u'my_anomaly_score_name'})
api.ok(anomalyscore1)
示例4: BigML
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import create_anomaly [as 别名]
from bigml.api import BigML
api = BigML()
source1 = api.create_source("iris.csv")
api.ok(source1)
dataset1 = api.create_dataset(source1)
api.ok(dataset1)
anomaly1 = api.create_anomaly(dataset1, \
{'name': u'my_anomaly_name'})
api.ok(anomaly1)
示例5: BigML
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import create_anomaly [as 别名]
from bigml.api import BigML
api = BigML()
source1 = api.create_source("iris.csv")
api.ok(source1)
dataset1 = api.create_dataset(source1, \
{'name': u'iris'})
api.ok(dataset1)
anomaly1 = api.create_anomaly(dataset1, \
{'anomaly_seed': u'2c249dda00fbf54ab4cdd850532a584f286af5b6',
'name': u'my_anomaly_name'})
api.ok(anomaly1)