def pprint(self, resource, out=sys.stdout):
"""Pretty prints a resource or part of it.
"""
if (isinstance(resource, dict)
and 'object' in resource
and 'resource' in resource):
resource_id = resource['resource']
if (SOURCE_RE.match(resource_id) or DATASET_RE.match(resource_id)
or MODEL_RE.match(resource_id)
or EVALUATION_RE.match(resource_id)
or ENSEMBLE_RE.match(resource_id)
or CLUSTER_RE.match(resource_id)
or ANOMALY_RE.match(resource_id)
or TOPIC_MODEL_RE.match(resource_id)
or LOGISTIC_REGRESSION_RE.match(resource_id)
or TIME_SERIES_RE.match(resource_id)
or DEEPNET_RE.match(resource_id)
or FUSION_RE.match(resource_id)
or PCA_RE.match(resource_id)
or LINEAR_REGRESSION_RE.match(resource_id)
or OPTIML_RE.match(resource_id)):
out.write("%s (%s bytes)\n" % (resource['object']['name'],
resource['object']['size']))
elif PREDICTION_RE.match(resource['resource']):
objective_field_name = (
resource['object']['fields'][
resource['object']['objective_fields'][0]]['name'])
input_data = {}
for key, value in resource['object']['input_data'].items():
try:
name = resource['object']['fields'][key]['name']
except KeyError:
name = key
input_data[name] = value
prediction = (
resource['object']['prediction'][
resource['object']['objective_fields'][0]])
out.write("%s for %s is %s\n" % (objective_field_name,
input_data,
prediction))
out.flush()
else:
pprint.pprint(resource, out, indent=4)
def pprint(self, resource, out=sys.stdout):
"""Pretty prints a resource or part of it.
"""
if (isinstance(resource, dict)
and 'object' in resource
and 'resource' in resource):
resource_id = resource['resource']
if (SOURCE_RE.match(resource_id) or DATASET_RE.match(resource_id)
or MODEL_RE.match(resource_id)
or EVALUATION_RE.match(resource_id)
or ENSEMBLE_RE.match(resource_id)
or CLUSTER_RE.match(resource_id)
or ANOMALY_RE.match(resource_id)
or TOPIC_MODEL_RE.match(resource_id)
or LOGISTIC_REGRESSION_RE.match(resource_id)
or TIME_SERIES_RE.match(resource_id)
or DEEPNET_RE.match(resource_id)
or FUSION_RE.match(resource_id)
or PCA_RE.match(resource_id)
or LINEAR_REGRESSION_RE.match(resource_id)
or OPTIML_RE.match(resource_id)):
out.write("%s (%s bytes)\n" % (resource['object']['name'],
resource['object']['size']))
elif PREDICTION_RE.match(resource['resource']):
objective_field_name = (
resource['object']['fields'][
resource['object']['objective_fields'][0]]['name'])
input_data = {}
for key, value in resource['object']['input_data'].items():
try:
name = resource['object']['fields'][key]['name']
except KeyError:
name = key
input_data[name] = value
prediction = (
resource['object']['prediction'][
resource['object']['objective_fields'][0]])
out.write("%s for %s is %s\n" % (objective_field_name,
input_data,
prediction))
out.flush()
else:
pprint.pprint(resource, out, indent=4)