def test_create_project(self):
# Count the current # of projects in the environment
projects = mr.list_projects()
assert isinstance(projects, list)
project_count = len(projects)
# Create a new project
repo = mr.default_repository().get('id')
project = mr.create_project(self.project_name, repo)
assert isinstance(project, RestObj)
# Total number of projects should have increased
projects = mr.list_projects()
assert len(projects) == project_count + 1
def register_model(model,
name,
project,
repository=None,
input=None,
version='latest',
files=None,
force=False):
"""Register a model in the model repository.
Parameters
----------
model : swat.CASTable or sklearn.BaseEstimator
The model to register. If an instance of ``swat.CASTable`` the table is assumed to hold an ASTORE, which will
be downloaded and used to construct the model to register. If a scikit-learn estimator, the model will be
pickled and uploaded to the registry and score code will be generated for publishing the model to MAS.
name : str
Designated name for the model in the repository.
project : str or dict
The name or id of the project, or a dictionary representation of the project.
repository : str or dict, optional
The name or id of the repository, or a dictionary representation of the repository. If omitted, the default
repository will be used.
input
version : {'new', 'latest', int}, optional
Version number of the project in which the model should be created.
files :
force : bool, optional
Create dependencies such as projects and repositories if they do not already exist.
Returns
-------
model : RestObj
The newly registered model as an instance of ``RestObj``
Notes
-----
If the specified model is a CAS table the model data and metadata will be written to a temporary zip file and then
imported using model_repository.import_model_from_zip.
If the specified model is from the Scikit-Learn package, the model will be created using
model_repository.create_model and any additional files will be uploaded as content.
Examples
--------
"""
# TODO: Create new version if model already exists
# TODO: Allow file info to be specified
# TODO: Performance stats
files = files or []
# Find the project if it already exists
p = mr.get_project(project) if project is not None else None
# Do we need to create the project first?
create_project = True if p is None and force else False
if p is None and not create_project:
raise ValueError("Project '{}' not found".format(project))
repository = mr.default_repository(
) if repository is None else mr.get_repository(repository)
# Unable to find or create the repo.
if repository is None:
raise ValueError("Unable to find repository '{}'".format(repository))
# If model is a CASTable then assume it holds an ASTORE model.
# Import these via a ZIP file.
if 'swat.cas.table.CASTable' in str(type(model)):
zipfile = utils.create_package_from_astore(model)
if create_project:
project = mr.create_project(project, repository)
model = mr.import_model_from_zip(name,
project,
zipfile,
version=version)
return model
# If the model is an scikit-learn model, generate the model dictionary from it and pickle the model for storage
elif all(
hasattr(model, attr)
for attr in ['_estimator_type', 'get_params']):
# Pickle the model so we can store it
model_pkl = pickle.dumps(model)
files.append({
'name': 'model.pkl',
'file': model_pkl,
'role': 'Python Pickle'
})
# Extract model properties
model = _sklearn_to_dict(model)
model['name'] = name
# Generate PyMAS wrapper
try:
mas_module = from_pickle(model_pkl,
'predict',
input_types=input,
array_input=True)
assert isinstance(mas_module, PyMAS)
# Include score code files from ESP and MAS
files.append({
'name': 'dmcas_packagescorecode.sas',
'file': mas_module.score_code(),
'role': 'Score Code'
})
files.append({
'name': 'dmcas_espscorecode.sas',
'file': mas_module.score_code(dest='ESP'),
'role': 'Score Code'
})
model['inputVariables'] = [
var.as_model_metadata() for var in mas_module.variables
if not var.out
]
model['outputVariables'] = [
var.as_model_metadata() for var in mas_module.variables
if var.out
]
except ValueError:
# PyMAS creation failed, most likely because input data wasn't provided
warnings.warn(
'Unable to determine input/output variables. Model variables will not be specified.'
)
else:
# Otherwise, the model better be a dictionary of metadata
assert isinstance(model, dict)
if create_project:
vars = model.get('inputVariables', []) + model.get(
'outputVariables', [])
target_level = 'Interval' if model.get(
'function') == 'Regression' else None
project = mr.create_project(project,
repository,
variables=vars,
targetLevel=target_level)
model = mr.create_model(model, project)
assert isinstance(model, RestObj)
# Upload any additional files
for file in files:
if isinstance(file, dict):
mr.add_model_content(model, **file)
else:
mr.add_model_content(model, file)
return model