Reading Resources

When retrieved individually, resources are returned as a dictionary identical to the one you get when you create a new resource. However, the status code will be bigml.api.HTTP_OK if the resource can be retrieved without problems, or one of the HTTP standard error codes otherwise.

To retrieve an existing resource, you just need to use the corresponding get_[resouce type] method. There’s a query string argument that can be used to filter out or limit the attributes obtained:

# gets the source information with no filters
api.get_source("source/5143a51a37203f2cf7000956")
# gets the dataset information with only 10 of the fields
api.get_dataset("dataset/5143a51a37203f2cf7000936",
                query_string="limit=10")
# gets the model information excluding the model predicates tree
api.get_model("model/5143a51a37203f2cf7000956",
              query_string="exclude=root")

Listing Resources

You can list resources with the appropriate api method:

api.list_sources()
api.list_datasets()
api.list_models()
api.list_predictions()
api.list_evaluations()
api.list_ensembles()
api.list_batch_predictions()
api.list_clusters()
api.list_centroids()
api.list_batch_centroids()
api.list_anomalies()
api.list_anomaly_scores()
api.list_batch_anomaly_scores()
api.list_projects()
api.list_samples()
api.list_correlations()
api.list_statistical_tests()
api.list_logistic_regressions()
api.list_linear_regressions()
api.list_associations()
api.list_association_sets()
api.list_topic_models()
api.list_topic_distributions()
api.list_batch_topic_distributions()
api.list_time_series()
api.list_deepnets()
api.list_fusions()
api.list_pcas()
api.list_projections()
api.list_batch_projections()
api.list_forecasts()
api.list_scripts()
api.list_libraries()
api.list_executions()
api.list_external_connectors()

you will receive a dictionary with the following keys:

  • code: If the request is successful you will get a bigml.api.HTTP_OK (200) status code. Otherwise, it will be one of the standard HTTP error codes. See BigML documentation on status codes for more info.
  • meta: A dictionary including the following keys that can help you paginate listings:
    • previous: Path to get the previous page or None if there is no previous page.
    • next: Path to get the next page or None if there is no next page.
    • offset: How far off from the first entry in the resources is the first one listed in the resources key.
    • limit: Maximum number of resources that you will get listed in the resources key.
    • total_count: The total number of resources in BigML.
  • objects: A list of resources as returned by BigML.
  • error: If an error occurs and the resource cannot be created, it will contain an additional code and a description of the error. In this case, meta, and resources will be None.

Filtering Resources

You can filter resources in listings using the syntax and fields labeled as filterable in the BigML documentation for each resource.

A few examples:

Ids of the first 5 sources created before April 1st, 2012

[source['resource'] for source in
  api.list_sources("limit=5;created__lt=2012-04-1")['objects']]

Name of the first 10 datasets bigger than 1MB

[dataset['name'] for dataset in
  api.list_datasets("limit=10;size__gt=1048576")['objects']]

Name of models with more than 5 fields (columns)

[model['name'] for model in api.list_models("columns__gt=5")['objects']]

Ids of predictions whose model has not been deleted

[prediction['resource'] for prediction in
  api.list_predictions("model_status=true")['objects']]

Ordering Resources

You can order resources in listings using the syntax and fields labeled as sortable in the BigML documentation for each resource.

A few examples:

Name of sources ordered by size

[source['name'] for source in api.list_sources("order_by=size")['objects']]

Number of instances in datasets created before April 1st, 2012 ordered by size

[dataset['rows'] for dataset in
  api.list_datasets("created__lt=2012-04-1;order_by=size")['objects']]

Model ids ordered by number of predictions (in descending order).

[model['resource'] for model in
  api.list_models("order_by=-number_of_predictions")['objects']]

Name of predictions ordered by name.

[prediction['name'] for prediction in
  api.list_predictions("order_by=name")['objects']]

Public and shared resources

The previous examples use resources that were created by the same user that asks for their retrieval or modification. If a user wants to share one of her resources, she can make them public or share them. Declaring a resource public means that anyone can see the resource. This can be applied to datasets and models. To turn a dataset public, just update its private property:

api.update_dataset('dataset/5143a51a37203f2cf7000972', {'private': false})

and any user will be able to download it using its id prepended by public:

api.get_dataset('public/dataset/5143a51a37203f2cf7000972')

In the models’ case, you can also choose if you want the model to be fully downloadable or just accesible to make predictions. This is controlled with the white_box property. If you want to publish your model completely, just use:

api.update_model('model/5143a51a37203f2cf7000956', {'private': false,
                 'white_box': true})

Both public models and datasets, will be openly accessible for anyone, registered or not, from the web gallery.

Still, you may want to share your models with other users, but without making them public for everyone. This can be achieved by setting the shared property:

api.update_model('model/5143a51a37203f2cf7000956', {'shared': true})

Shared models can be accessed using their share hash (propery shared_hash in the original model):

api.get_model('shared/model/d53iw39euTdjsgesj7382ufhwnD')

or by using their original id with the creator user as username and a specific sharing api_key you will find as property sharing_api_key in the updated model:

api.get_model('model/5143a51a37203f2cf7000956', shared_username='creator',
              shared_api_key='c972018dc5f2789e65c74ba3170fda31d02e00c3')

Only users with the share link or credentials information will be able to access your shared models.