本文整理汇总了Python中bigml.api.BigML.get_dataset方法的典型用法代码示例。如果您正苦于以下问题：Python BigML.get_dataset方法的具体用法？Python BigML.get_dataset怎么用？Python BigML.get_dataset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.get_dataset方法的6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:
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import get_dataset [as 别名]
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)
args = \
{u'fields': {u'100000': {u'name': u'score', u'preferred': True}},
u'objective_field': {u'id': u'100000'}}
dataset3 = api.update_dataset(dataset2, args)
api.ok(dataset3)
示例2: BigML
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import get_dataset [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3: integer
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import get_dataset [as 别名]
source1 = api.create_source("iris.csv")
api.ok(source1)
dataset1 = api.create_dataset(source1, \
{'name': 'iris'})
api.ok(dataset1)
cluster1 = api.create_cluster(dataset1, \
{'name': 'iris'})
api.ok(cluster1)
batchcentroid1 = api.create_batch_centroid(cluster1, dataset1, \
{'name': 'iris dataset with iris', 'output_dataset': True})
api.ok(batchcentroid1)
dataset2 = api.get_dataset(batchcentroid1['object']['output_dataset_resource'])
api.ok(dataset2)
dataset2 = api.update_dataset(dataset2, \
{'name': 'iris dataset with iris'})
api.ok(dataset2)
dataset3 = api.create_dataset(dataset2, \
{'name': 'my_dataset_from_dataset_from_batch_centroid_name',
'new_fields': [{'field': '( integer ( replace ( field "cluster" ) '
'"Cluster " "" ) )',
'name': 'Cluster'}],
'objective_field': {'id': '100000'}})
api.ok(dataset3)
示例4: integer
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import get_dataset [as 别名]
args = \
{u'cluster_seed': u'bigml', u'critical_value': 5}
cluster1 = api.create_cluster(dataset1, args)
api.ok(cluster1)
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}
batchcentroid1 = api.create_batch_centroid(cluster1, dataset1, args)
api.ok(batchcentroid1)
dataset2 = api.get_dataset(batchcentroid1["object"]["output_dataset_resource"])
api.ok(dataset2)
args = \
{u'fields': {u'100000': {u'name': u'cluster', u'preferred': True}},
u'objective_field': {u'id': u'100000'}}
dataset3 = api.update_dataset(dataset2, args)
api.ok(dataset3)
args = \
{u'all_fields': False,
u'new_fields': [{u'field': u'(all)', u'names': [u'cluster']},
{u'field': u'( integer ( replace ( field "cluster" ) "Cluster " "" ) )',
u'names': [u'Cluster']}],
u'objective_field': {u'id': u'100000'}}
dataset4 = api.create_dataset(dataset3, args)示例5:
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import get_dataset [as 别名]
api.ok(source2)
args = \
{u'objective_field': {u'id': u'000004'}}
dataset1 = api.create_dataset(source2, args)
api.ok(dataset1)
args = \
{u'split_candidates': 32}
model1 = api.create_model(dataset1, args)
api.ok(model1)
args = \
{u'fields_map': {u'000001': u'000001',
u'000002': u'000002',
u'000003': u'000003',
u'000004': u'000004'},
u'operating_kind': u'probability',
u'output_dataset': True}
batchprediction1 = api.create_batch_prediction(model1, dataset1, args)
api.ok(batchprediction1)
dataset2 = api.get_dataset(batchprediction1["object"]["output_dataset_resource"])
api.ok(dataset2)
args = \
{u'fields': {u'100000': {u'name': u'species', u'preferred': True}},
u'objective_field': {u'id': u'100000'}}
dataset3 = api.update_dataset(dataset2, args)
api.ok(dataset3)
示例6: integer
# 需要导入模块: from bigml.api import BigML [as 别名]
# 或者: from bigml.api.BigML import get_dataset [as 别名]
source1 = api.create_source("iris.csv")
api.ok(source1)
dataset1 = api.create_dataset(source1)
api.ok(dataset1)
cluster1 = api.create_cluster(dataset1)
api.ok(cluster1)
batchcentroid1 = api.create_batch_centroid(cluster1, dataset1, \
{'output_dataset': True})
api.ok(batchcentroid1)
dataset2 = api.create_dataset(batchcentroid1)
api.ok(dataset2)
dataset2 = api.get_dataset(batchcentroid1)
api.ok(dataset2)
dataset2 = api.update_dataset(dataset2, \
{'fields': {u'000000': {'name': u'cluster'}}})
api.ok(dataset2)
dataset3 = api.create_dataset(dataset2, \
{'input_fields': [u'000000'],
'name': u'my_dataset_from_dataset_from_batch_centroid_name',
'new_fields': [{'field': u'( integer ( replace ( field "cluster" ) "Cluster " "" ) )',
u'name': u'Cluster'}]})
api.ok(dataset3)