def reify_python(self, alias=None):
"""REST call command line in python. See ``reify`` method.
"""
resource_type = get_resource_type(self.resource_id)
resource_name = resource_alias(self.resource_id, alias)
resource_method_suffix = RENAMED_RESOURCES.get(resource_type,
resource_type)
origin_names = [
resource_alias(resource_id, alias) for resource_id in self.origins
]
arguments = ", ".join(origin_names)
if self.suffix:
arguments = "%s%s" % (arguments, self.suffix)
if self.input_data:
arguments = "%s, \\\n%s%s" % ( \
arguments, INDENT,
pprint.pformat(self.input_data).replace("\n", "\n%s" % INDENT))
if self.args:
sort_lists(self.args)
arguments = "%s, \\\n%s%s" % (arguments, \
INDENT, \
pprint.pformat(self.args).replace( \
"\n", "\n%s" % INDENT))
out = "%s = api.%s_%s(%s)\napi.ok(%s)\n\n" % (
resource_name, self.action, resource_method_suffix, arguments,
resource_name)
return out
def reify_python(self, alias=None):
"""REST call command line in python. See ``reify`` method.
"""
resource_type = get_resource_type(self.resource_id)
resource_name = resource_alias(self.resource_id, alias)
resource_method_suffix = RENAMED_RESOURCES.get(
resource_type, resource_type)
origin_names = [resource_alias(resource_id, alias) for resource_id
in self.origins]
arguments = ", ".join(origin_names)
if self.suffix:
arguments = "%s%s" % (arguments, self.suffix)
if self.input_data:
arguments = "%s, \\\n%s%s" % ( \
arguments, INDENT,
pprint.pformat(self.input_data).replace("\n", "\n%s" % INDENT))
if self.args:
sort_lists(self.args)
arguments = "%s, \\\n%s%s" % (arguments, \
INDENT, \
pprint.pformat(self.args).replace( \
"\n", "\n%s" % INDENT))
out = "%s = api.%s_%s(%s)\napi.ok(%s)\n\n" % (
resource_name,
self.action,
resource_method_suffix,
arguments,
resource_name)
return out
def clear(self):
"""Clears the stored resources' ids
"""
for resource_type in RESOURCE_TYPES:
setattr(self, plural(resource_type), [])
setattr(self, RENAMED_RESOURCES.get(resource_type, resource_type),
None)
def clear(self):
"""Clears the stored resources' ids
"""
for resource_type in RESOURCE_TYPES:
setattr(self, plural(resource_type), [])
setattr(self, RENAMED_RESOURCES.get(resource_type,
resource_type), None)
def reify_python(self, alias=None):
"""REST call command line in python. See ``reify`` method.
"""
def resource_alias(resource_id):
"""Returns the alias if found
"""
if isinstance(resource_id, basestring):
return alias.get(resource_id, '"%s"' % resource_id)
elif isinstance(resource_id, list):
alias_names = []
for resource_id_id in resource_id:
alias_names.append(
alias.get(resource_id_id, '"%s"' % resource_id_id))
return repr(alias_names)
resource_type = get_resource_type(self.resource_id)
resource_name = resource_alias(self.resource_id)
resource_method_suffix = RENAMED_RESOURCES.get(
resource_type, resource_type)
origin_names = [resource_alias(resource_id) for resource_id
in self.origins]
arguments = ", ".join(origin_names)
if self.suffix:
arguments = "%s%s" % (arguments, self.suffix)
if self.input_data:
arguments = "%s, \\\n%s%s" % ( \
arguments, INDENT,
pprint.pformat(self.input_data).replace("\n", "\n%s" % INDENT))
if self.args:
sort_lists(self.args)
arguments = "%s, \\\n%s%s" % (arguments, \
INDENT, \
pprint.pformat(self.args).replace( \
"\n", "\n%s" % INDENT))
out = "%s = api.%s_%s(%s)\napi.ok(%s)\n\n" % (
resource_name,
self.action,
resource_method_suffix,
arguments,
resource_name)
return out
def reify_python(self, alias=None):
"""REST call command line in python. See ``reify`` method.
"""
def resource_alias(resource_id):
"""Returns the alias if found
"""
if isinstance(resource_id, basestring):
return alias.get(resource_id, '"%s"' % resource_id)
elif isinstance(resource_id, list):
alias_names = []
for resource_id_id in resource_id:
alias_names.append(
alias.get(resource_id_id, '"%s"' % resource_id_id))
return repr(alias_names)
resource_type = get_resource_type(self.resource_id)
resource_name = resource_alias(self.resource_id)
resource_method_suffix = RENAMED_RESOURCES.get(
resource_type, resource_type)
origin_names = [resource_alias(resource_id) for resource_id
in self.origins]
arguments = ", ".join(origin_names)
if self.suffix:
arguments = "%s%s" % (arguments, self.suffix)
if self.input_data:
arguments = "%s, \\\n%s%s" % ( \
arguments, INDENT,
pprint.pformat(self.input_data).replace("\n", "\n%s" % INDENT))
if self.args:
sort_lists(self.args)
arguments = "%s, \\\n%s%s" % (arguments, \
INDENT, \
pprint.pformat(self.args).replace( \
"\n", "\n%s" % INDENT))
out = "%s = api.%s_%s(%s)\napi.ok(%s)\n\n" % (
resource_name,
self.action,
resource_method_suffix,
arguments,
resource_name)
return out
def __init__(self,
username=None,
api_key=None,
debug=False,
set_locale=False,
storage=None,
domain=None,
project=None,
organization=None,
short_debug=False):
"""Initializes the BigML API.
If left unspecified, `username` and `api_key` will default to the
values of the `BIGML_USERNAME` and `BIGML_API_KEY` environment
variables respectively.
`dev_mode` has been deprecated. Now all resources coexisit in the
same production environment.
If storage is set to a directory name, the resources obtained in
CRU operations will be stored in the given directory.
If domain is set, the api will point to the specified domain. Default
will be the one in the environment variable `BIGML_DOMAIN` or
`bigml.io` if missing. The expected domain argument is a string or a
Domain object. See Domain class for details.
When project is set to a project ID,
the user is considered to be working in an
organization project. The scope of the API requests will be limited
to this project and permissions should be previously given by the
organization administrator.
When organization is set to an organization ID,
the user is considered to be working for an
organization. The scope of the API requests will be limited to the
projects of the organization and permissions need to be previously
given by the organization administrator.
"""
BigMLConnection.__init__(self,
username=username,
api_key=api_key,
debug=debug,
set_locale=set_locale,
storage=storage,
domain=domain,
project=project,
organization=organization,
short_debug=short_debug)
ResourceHandlerMixin.__init__(self)
SourceHandlerMixin.__init__(self)
DatasetHandlerMixin.__init__(self)
ModelHandlerMixin.__init__(self)
EnsembleHandlerMixin.__init__(self)
PredictionHandlerMixin.__init__(self)
ClusterHandlerMixin.__init__(self)
CentroidHandlerMixin.__init__(self)
AnomalyHandlerMixin.__init__(self)
AnomalyScoreHandlerMixin.__init__(self)
EvaluationHandlerMixin.__init__(self)
BatchPredictionHandlerMixin.__init__(self)
BatchCentroidHandlerMixin.__init__(self)
BatchAnomalyScoreHandlerMixin.__init__(self)
ProjectHandlerMixin.__init__(self)
SampleHandlerMixin.__init__(self)
CorrelationHandlerMixin.__init__(self)
StatisticalTestHandlerMixin.__init__(self)
LogisticRegressionHandlerMixin.__init__(self)
AssociationHandlerMixin.__init__(self)
AssociationSetHandlerMixin.__init__(self)
ScriptHandlerMixin.__init__(self)
ExecutionHandlerMixin.__init__(self)
LibraryHandlerMixin.__init__(self)
TopicModelHandlerMixin.__init__(self)
TopicDistributionHandlerMixin.__init__(self)
BatchTopicDistributionHandlerMixin.__init__(self)
TimeSeriesHandlerMixin.__init__(self)
ForecastHandlerMixin.__init__(self)
DeepnetHandlerMixin.__init__(self)
OptimlHandlerMixin.__init__(self)
FusionHandlerMixin.__init__(self)
ConfigurationHandlerMixin.__init__(self)
PCAHandlerMixin.__init__(self)
ProjectionHandlerMixin.__init__(self)
BatchProjectionHandlerMixin.__init__(self)
LinearRegressionHandlerMixin.__init__(self)
ExternalConnectorHandlerMixin.__init__(self)
self.status_url = "%s%s" % (self.url, STATUS_PATH)
self.getters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.getters[resource_type] = getattr(self, "get_%s" % method_name)
self.creators = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.creators[resource_type] = getattr(self,
"create_%s" % method_name)
self.creaters = self.creators # to be deprecated
self.updaters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.updaters[resource_type] = getattr(self,
"update_%s" % method_name)
self.deleters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.deleters[resource_type] = getattr(self,
"delete_%s" % method_name)
self.listers = {}
for resource_type in RESOURCE_RE:
method_name = IRREGULAR_PLURALS.get( \
resource_type, "%ss" % RENAMED_RESOURCES.get( \
resource_type, resource_type))
self.listers[resource_type] = getattr(self,
"list_%s" % method_name)
def __init__(self,
username=None,
api_key=None,
dev_mode=False,
debug=False,
set_locale=False,
storage=None,
domain=None):
"""Initializes the BigML API.
If left unspecified, `username` and `api_key` will default to the
values of the `BIGML_USERNAME` and `BIGML_API_KEY` environment
variables respectively.
If `dev_mode` is set to `True`, the API will be used in development
mode where the size of your datasets are limited but you are not
charged any credits.
If storage is set to a directory name, the resources obtained in
CRU operations will be stored in the given directory.
If domain is set, the api will point to the specified domain. Default
will be the one in the environment variable `BIGML_DOMAIN` or
`bigml.io` if missing. The expected domain argument is a string or a
Domain object. See Domain class for details.
"""
BigMLConnection.__init__(self,
username=username,
api_key=api_key,
dev_mode=dev_mode,
debug=debug,
set_locale=set_locale,
storage=storage,
domain=domain)
ResourceHandler.__init__(self)
SourceHandler.__init__(self)
DatasetHandler.__init__(self)
ModelHandler.__init__(self)
EnsembleHandler.__init__(self)
PredictionHandler.__init__(self)
ClusterHandler.__init__(self)
CentroidHandler.__init__(self)
AnomalyHandler.__init__(self)
AnomalyScoreHandler.__init__(self)
EvaluationHandler.__init__(self)
BatchPredictionHandler.__init__(self)
BatchCentroidHandler.__init__(self)
BatchAnomalyScoreHandler.__init__(self)
ProjectHandler.__init__(self)
SampleHandler.__init__(self)
CorrelationHandler.__init__(self)
StatisticalTestHandler.__init__(self)
LogisticRegressionHandler.__init__(self)
AssociationHandler.__init__(self)
AssociationSetHandler.__init__(self)
ScriptHandler.__init__(self)
ExecutionHandler.__init__(self)
LibraryHandler.__init__(self)
self.getters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.getters[resource_type] = getattr(self, "get_%s" % method_name)
self.creaters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.creaters[resource_type] = getattr(self,
"create_%s" % method_name)
self.updaters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.updaters[resource_type] = getattr(self,
"update_%s" % method_name)
self.deleters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.deleters[resource_type] = getattr(self,
"delete_%s" % method_name)
def __init__(self, username=None, api_key=None, dev_mode=False,
debug=False, set_locale=False, storage=None, domain=None):
"""Initializes the BigML API.
If left unspecified, `username` and `api_key` will default to the
values of the `BIGML_USERNAME` and `BIGML_API_KEY` environment
variables respectively.
If `dev_mode` is set to `True`, the API will be used in development
mode where the size of your datasets are limited but you are not
charged any credits.
If storage is set to a directory name, the resources obtained in
CRU operations will be stored in the given directory.
If domain is set, the api will point to the specified domain. Default
will be the one in the environment variable `BIGML_DOMAIN` or
`bigml.io` if missing. The expected domain argument is a string or a
Domain object. See Domain class for details.
"""
BigMLConnection.__init__(self, username=username, api_key=api_key,
dev_mode=dev_mode, debug=debug,
set_locale=set_locale, storage=storage,
domain=domain)
ResourceHandler.__init__(self)
SourceHandler.__init__(self)
DatasetHandler.__init__(self)
ModelHandler.__init__(self)
EnsembleHandler.__init__(self)
PredictionHandler.__init__(self)
ClusterHandler.__init__(self)
CentroidHandler.__init__(self)
AnomalyHandler.__init__(self)
AnomalyScoreHandler.__init__(self)
EvaluationHandler.__init__(self)
BatchPredictionHandler.__init__(self)
BatchCentroidHandler.__init__(self)
BatchAnomalyScoreHandler.__init__(self)
ProjectHandler.__init__(self)
SampleHandler.__init__(self)
CorrelationHandler.__init__(self)
StatisticalTestHandler.__init__(self)
LogisticRegressionHandler.__init__(self)
AssociationHandler.__init__(self)
AssociationSetHandler.__init__(self)
ScriptHandler.__init__(self)
ExecutionHandler.__init__(self)
LibraryHandler.__init__(self)
TopicModelHandler.__init__(self)
TopicDistributionHandler.__init__(self)
BatchTopicDistributionHandler.__init__(self)
TimeSeriesHandler.__init__(self)
ForecastHandler.__init__(self)
self.getters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.getters[resource_type] = getattr(self, "get_%s" % method_name)
self.creaters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.creaters[resource_type] = getattr(self,
"create_%s" % method_name)
self.updaters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.updaters[resource_type] = getattr(self,
"update_%s" % method_name)
self.deleters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.deleters[resource_type] = getattr(self,
"delete_%s" % method_name)
def __init__(self, username=None, api_key=None, dev_mode=False,
debug=False, set_locale=False, storage=None, domain=None,
project=None, organization=None):
"""Initializes the BigML API.
If left unspecified, `username` and `api_key` will default to the
values of the `BIGML_USERNAME` and `BIGML_API_KEY` environment
variables respectively.
If `dev_mode` is set to `True`, the API will be used in development
mode where the size of your datasets are limited but you are not
charged any credits.
If storage is set to a directory name, the resources obtained in
CRU operations will be stored in the given directory.
If domain is set, the api will point to the specified domain. Default
will be the one in the environment variable `BIGML_DOMAIN` or
`bigml.io` if missing. The expected domain argument is a string or a
Domain object. See Domain class for details.
When project is set to a project ID,
the user is considered to be working in an
organization project. The scope of the API requests will be limited
to this project and permissions should be previously given by the
organization administrator.
When organization is set to an organization ID,
the user is considered to be working for an
organization. The scope of the API requests will be limited to the
projects of the organization and permissions need to be previously
given by the organization administrator.
"""
BigMLConnection.__init__(self, username=username, api_key=api_key,
dev_mode=dev_mode, debug=debug,
set_locale=set_locale, storage=storage,
domain=domain, project=project,
organization=organization)
ResourceHandler.__init__(self)
SourceHandler.__init__(self)
DatasetHandler.__init__(self)
ModelHandler.__init__(self)
EnsembleHandler.__init__(self)
PredictionHandler.__init__(self)
ClusterHandler.__init__(self)
CentroidHandler.__init__(self)
AnomalyHandler.__init__(self)
AnomalyScoreHandler.__init__(self)
EvaluationHandler.__init__(self)
BatchPredictionHandler.__init__(self)
BatchCentroidHandler.__init__(self)
BatchAnomalyScoreHandler.__init__(self)
ProjectHandler.__init__(self)
SampleHandler.__init__(self)
CorrelationHandler.__init__(self)
StatisticalTestHandler.__init__(self)
LogisticRegressionHandler.__init__(self)
AssociationHandler.__init__(self)
AssociationSetHandler.__init__(self)
ScriptHandler.__init__(self)
ExecutionHandler.__init__(self)
LibraryHandler.__init__(self)
TopicModelHandler.__init__(self)
TopicDistributionHandler.__init__(self)
BatchTopicDistributionHandler.__init__(self)
TimeSeriesHandler.__init__(self)
ForecastHandler.__init__(self)
DeepnetHandler.__init__(self)
OptimlHandler.__init__(self)
FusionHandler.__init__(self)
ConfigurationHandler.__init__(self)
PCAHandler.__init__(self)
ProjectionHandler.__init__(self)
BatchProjectionHandler.__init__(self)
LinearRegressionHandler.__init__(self)
self.getters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.getters[resource_type] = getattr(self, "get_%s" % method_name)
self.creaters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.creaters[resource_type] = getattr(self,
"create_%s" % method_name)
self.updaters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.updaters[resource_type] = getattr(self,
"update_%s" % method_name)
self.deleters = {}
for resource_type in RESOURCE_RE:
method_name = RENAMED_RESOURCES.get(resource_type, resource_type)
self.deleters[resource_type] = getattr(self,
"delete_%s" % method_name)
self.listers = {}
for resource_type in RESOURCE_RE:
method_name = IRREGULAR_PLURALS.get( \
resource_type, "%ss" % RENAMED_RESOURCES.get( \
resource_type, resource_type))
self.listers[resource_type] = getattr(self,
"list_%s" % method_name)