from bigml.api import BigML api = BigML() 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, {"name": u"my_batch_centroid_name"}) api.ok(batchcentroid1)
from bigml.api import BigML api = BigML() 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'}})
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) cluster1 = api.create_cluster(dataset1, \ {'name': u"iris dataset's cluster"}) api.ok(cluster1) batchcentroid1 = api.create_batch_centroid(cluster1, dataset1, \ {'name': u"Batch Centroid of iris dataset's cluster with iris dataset", 'output_dataset': True}) api.ok(batchcentroid1) dataset2 = api.get_dataset(batchcentroid1['object']['output_dataset_resource']) api.ok(dataset2) dataset2 = api.update_dataset(dataset2, \ {'fields': {u'000000': {'name': u'cluster'}}, 'name': u'iris dataset - batchcentroid'}) api.ok(dataset2) dataset3 = api.create_dataset(dataset2, \ {'name': u'my_dataset_from_dataset_from_batch_centroid_name', 'new_fields': [{'field': u'( integer ( replace ( field "cluster" ) "Cluster " "" ) )', u'name': u'Cluster'}]})
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) cluster1 = api.create_cluster(dataset1, \ {'name': u"iris dataset's cluster"}) api.ok(cluster1) batchcentroid1 = api.create_batch_centroid(cluster1, dataset1, \ {'name': u"Batch Centroid of iris dataset's cluster with iris dataset", 'output_dataset': True}) api.ok(batchcentroid1) dataset2 = api.get_dataset(batchcentroid1['object']['output_dataset_resource']) api.ok(dataset2) dataset2 = api.update_dataset(dataset2, \ {'name': u'my_dataset_from_batch_centroid_name'}) api.ok(dataset2)
dataset1 = api.create_dataset(source2, args) api.ok(dataset1) 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 " "" ) )',
'000004': {'name': 'species', 'optype': 'categorical', 'term_analysis': {'enabled': True}}}} source2 = api.create_source(source1_file, args) api.ok(source2) args = \ {'objective_field': {'id': '000004'}} dataset1 = api.create_dataset(source2, args) api.ok(dataset1) args = \ {'cluster_seed': 'bigml', 'critical_value': 5} cluster1 = api.create_cluster(dataset1, args) api.ok(cluster1) args = \ {'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 = \ {'fields': {'100000': {'name': 'cluster', 'preferred': True}}, 'objective_field': {'id': '100000'}} dataset3 = api.update_dataset(dataset2, args) api.ok(dataset3) args = \ {'all_fields': True, 'new_fields': [{'field': '( integer ( replace ( field "cluster" ) "Cluster " ' '"" ) )', 'names': ['Cluster']}], 'objective_field': {'id': '100000'}} dataset4 = api.create_dataset(dataset3, args)
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) cluster1 = api.create_cluster(dataset1, \ {'name': u"iris dataset's cluster"}) api.ok(cluster1) batchcentroid1 = api.create_batch_centroid(cluster1, dataset1, \ {'name': u'my_batch_centroid_name'}) api.ok(batchcentroid1)
from bigml.api import BigML api = BigML() 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.get_dataset(batchcentroid1['object']['output_dataset_resource']) api.ok(dataset2) dataset2 = api.update_dataset(dataset2, \ {'fields': {u'000000': {'name': u'cluster'}}, 'name': u'my_dataset_from_batch_centroid_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) 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'}]})
source = api.get_source(source) api.ok(source) dataset = api.get_dataset(dataset) api.ok(dataset) model = api.get_model(model) api.ok(model) #cluster = api.create_cluster(dataset,{"name": "my cluster","k":8}) cluster = api.create_cluster(dataset,{"name": "my cluster","critical_value":1}) #default value is 5 for g-means api.ok(cluster) batch_centroid = api.create_batch_centroid(cluster, dataset, {"name": "my batch centroid", "all_fields": True, "header": True}) api.ok(batch_centroid) api.download_batch_centroid(batch_centroid, filename='my_clusters.csv') #api.download_batch_centroid(batch_centroid, filename='https://raw.githubusercontent.com/gsingle/GXsaiL/master/my_clusters.csv') from git import Repo repo_dir = 'GXsaiL' repo = Repo(repo_dir) file_list = [ 'gxsail.py', 'my_clusters.csv' ] commit_message = 'Add simple regression analysis'