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)
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)
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'iris'}) 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)
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) 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)
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'my_batch_anomaly_score_name'}) api.ok(batchanomalyscore1)
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)
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)
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)
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) 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)