def _get_data(self, ml_repo: MLRepo):
data = []
if self.data is None:
data.extend(ml_repo.get_names(MLObjectType.TEST_DATA))
data.extend(ml_repo.get_names(MLObjectType.TRAINING_DATA))
else:
data = self.data
return data
def _run_test(self, ml_repo: MLRepo, jobid):
logger.debug('Running regression test ' + self.repo_info.name +
' on model ' +
str(NamingConventions.CalibratedModel(self.model)) +
', version ' + self.model_version)
regression_test = ml_repo.get(self.test_definition,
version=LAST_VERSION)
label = ml_repo.get(regression_test.reference, version=LAST_VERSION)
result = {}
measure_types = self._get_measure_types(ml_repo, regression_test)
for measure_type in measure_types:
measure_name = str(
NamingConventions.Measure({
'model': self.model.split('/')[0],
'data': self.data,
'measure_type': measure_type
}))
measure = ml_repo.get(
measure_name,
version=None,
modifier_versions={
str(NamingConventions.CalibratedModel(self.model)):
self.model_version,
self.data: self.data_version
},
throw_error_not_exist=False,
throw_error_not_unique=True)
if measure == []:
continue
measure_name = str(
NamingConventions.Measure({
'model': label.name.split('/')[0],
'data': self.data,
'measure_type': measure_type
}))
reference_value = ml_repo.get(
measure_name,
version=None,
modifier_versions={
str(NamingConventions.CalibratedModel(label.name)):
label.version,
self.data: self.data_version
},
adjust_modification_info=False)
if regression_test.relative:
if measure.value - reference_value.value < regression_test.tol * reference_value.value:
result[measure_type] = {
'reference_value': reference_value.value,
'value': measure.value
}
else:
if measure.value - reference_value.value < regression_test.tol:
result[measure_type] = {
'reference_value': reference_value.value,
'value': measure.value
}
return result
def test_regression_test(self):
"""Test the regression test framework
"""
repo = MLRepo(user='******')
model_1 = TestClass(1.0, 2.0, repo_info={'name': 'model'})
model_1.repo_info.category = MLObjectType.CALIBRATED_MODEL.value
model_version = repo.add(model_1)
measure_1 = repo_objects.Measure(1.0,
repo_info={
'name':
'model/measure/test_data/max',
'modification_info': {'model'}
})
def _get_measure_types(self, ml_repo: MLRepo, reg_test=None):
if reg_test is None:
reg_test = ml_repo.get(self.test_definition, version=LAST_VERSION)
measure_types = reg_test.measures
if measure_types is None:
tmp = ml_repo.get_names(MLObjectType.MEASURE_CONFIGURATION)
if len(tmp) == 0:
raise Exception(
'No regression test possible since no measure defined.')
m_config = ml_repo.get(tmp[0], version=LAST_VERSION)
measure_types = [
MeasureConfiguration.get_name(x)
for k, x in m_config.measures.items()
]
return measure_types
def _get_models(self, ml_repo: MLRepo):
models_test = defaultdict(set)
if self.models is None:
tmp = ml_repo.get_names(MLObjectType.CALIBRATED_MODEL)
for k in tmp:
m = ml_repo.get(k, full_object=False)
models_test[k].add(m.repo_info[RepoInfoKey.VERSION])
else:
for k, v in self.models.items():
models_test[k].add(v)
if self.labels is None:
labels = ml_repo.get_names(MLObjectType.LABEL)
else:
labels = self.labels
for l in labels:
tmp = ml_repo.get(l)
models_test[tmp.name].add(tmp.version)
return models_test
def test_default_constructor(self):
#example with default
ml_repo = MLRepo(user='******')
#end example with default
# If on of these test fail asince the logic has been modified, please update the documentation in basics.rst
self.assertTrue(
isinstance(ml_repo._ml_repo,
memory_handler.RepoObjectMemoryStorage))
self.assertTrue(
isinstance(ml_repo._numpy_repo, memory_handler.NumpyMemoryStorage))
self.assertTrue(isinstance(ml_repo._job_runner, SimpleJobRunner))
def test_config_disk_handler(self):
#diskhandlerconfig
config = {
'user': '******',
'workspace': 'tmp',
'repo_store': {
'type': 'disk_handler',
'config': {
'folder': 'tmp/objects',
'file_format': 'json'
}
},
'numpy_store': {
'type': 'hdf_handler',
'config': {
'folder': 'tmp/repo_data',
'version_files': True
}
},
'job_runner': {
'type': 'simple',
'config': {}
}
}
# end diskhandlerconfig
# instantiate diskhandler
ml_repo = MLRepo(config=config)
# end instantiate diskhandler
# instantiate diskhandler save config
ml_repo = MLRepo(config=config, save_config=True)
# end instantiate diskhandler save config
# instantiate with workspace
ml_repo = MLRepo(workspace='tmp')
def _check(self, ml_repo: MLRepo):
# check if test is based on latest test definition
regression_test = ml_repo.get(self.test_definition,
version=LAST_VERSION)
if regression_test.repo_info.version != self.repo_info.modification_info[
self.test_definition]:
return 'Test is not based on latest test definition, latest version: ' + regression_test.repo_info.version + ', version used for test: ' + self.modification_info[
self.test_definition]
# check if measure config did not change
if regression_test.measures is None:
tmp = ml_repo.get_names(MLObjectType.MEASURE_CONFIGURATION)
if len(tmp) == 0:
raise Exception('No check possible since no measure defined.')
m_config = ml_repo.get(tmp[0], version=LAST_VERSION)
if m_config.repo_info.version != self.repo_info.modification_info[
m_config.repo_info.name]:
return 'Test is not based on latest measure configuration, latest version: ' + m_config.repo_info.version + ', version used for test: ' + self.modification_info[
m_config.repo_info.name]
# check if ref model did not change
label = ml_repo.get(regression_test.reference, version=LAST_VERSION)
if not label.repo_info.name in self.repo_info.modification_info.keys():
return 'Test on different reference model.'
if not label.repo_info.version == self.repo_info.modification_info[
label.repo_info.name]:
return 'Test on old reference model.'
# check if test was on latest data version
if not self.data in self.repo_info.modification_info.keys():
return 'Data of test has changed since last test.'
version = self.data_version
if version == LAST_VERSION:
version = ml_repo._ml_repo.get_latest_version(self.data)
elif version == FIRST_VERSION:
version = ml_repo._ml_repo.get_first_version(self.data)
if not version == self.repo_info.modification_info[self.data]:
return 'Data of test has changed since last test.'
return None
def test_repo_RawData(self):
"""Test RawData within repo
"""
repository = MLRepo(user='******')
job_runner = SimpleJobRunner(repository)
repository._job_runner = job_runner
raw_data = repo_objects.RawData(
np.zeros([10, 1]),
['test_coord'],
repo_info={ # pylint: disable=E0602
repo_objects.RepoInfoKey.NAME.value: 'RawData_Test'
})
repository.add(raw_data, 'test commit', MLObjectType.RAW_DATA)
raw_data_2 = repository.get('RawData_Test')
self.assertEqual(len(raw_data.x_coord_names),
len(raw_data_2.x_coord_names))
self.assertEqual(raw_data.x_coord_names[0],
raw_data_2.x_coord_names[0])
commits = repository.get_commits()
self.assertEqual(len(commits), 1)
self.assertEqual(len(commits[0].objects), 1)
'bigobj_handler',
type=str,
help='definition of store for the big object parts of the repo objects'
)
parser.add_argument(
'-u',
'--user',
type=str,
help=
'username used internally, if not specified, the operating system user is used'
)
args = parser.parse_args()
default_config = _get_default_config()
default_config['workspace'] = args.workspace
if args.user:
default_config['user'] = args.user
else:
default_config['user'] = getpass.getuser()
default_config['numpy_store']['config']['folder'] = args.workspace + '/hdf'
default_config['repo_store']['config'][
'folder'] = args.workspace + '/objects'
_set_numpy_store_config(args.bigobj_handler, default_config)
tmp = args.obj_handler.split('@')
default_config['repo_store']['type'] = tmp[0]
if tmp[0] == 'git_handler':
if len(tmp) > 1:
default_config['repo_store']['config']['remote'] = tmp[1]
#print('Creating repo with config: ' + str(default_config))
ml_repo = MLRepo(config=default_config, save_config=True)
def test_integration(self):
ml_repo = MLRepo(user='******')
x = np.zeros([10, 1])
y = np.zeros([10])
# define eval and train
ml_repo.add_eval_function(train, repo_name='my_eval_func')
ml_repo.add_training_function(eval, repo_name='my_eval_func')
# end define eval and train
# define add training parameter
training_param = SuperMLTrainingParam()
training_param.median = True
ml_repo.add(
training_param,
message=
'my first training parameter for my own super ml algorithm')
# end define add training parameter
# add own model
ml_repo.add_model('my_model')
# end add own model
ml_repo.run_training()
def test_repo_training_test_data(self):
# init repository with sample in memory handler
repository = MLRepo(user='******')
job_runner = SimpleJobRunner(repository)
repository._job_runner = job_runner
training_data = RawData(
np.zeros([10, 1]), ['x_values'],
np.zeros([10, 1]), ['y_values'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'training_data'})
repository.add(training_data, category=MLObjectType.TRAINING_DATA)
training_data_2 = repository.get_training_data()
self.assertEqual(
training_data_2.repo_info[repo_objects.RepoInfoKey.NAME],
training_data.repo_info[repo_objects.RepoInfoKey.NAME])
test_data = RawData(
np.zeros([10, 1]), ['x_values'],
np.zeros([10, 1]), ['y_values'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'test_data'})
repository.add(test_data, category=MLObjectType.TEST_DATA)
test_data_ref = repository.get('test_data')
self.assertEqual(
test_data_ref.repo_info[repo_objects.RepoInfoKey.NAME],
test_data.repo_info[repo_objects.RepoInfoKey.NAME])
self.assertEqual(
test_data_ref.repo_info[repo_objects.RepoInfoKey.VERSION],
test_data.repo_info[repo_objects.RepoInfoKey.VERSION])
test_data_2 = RawData(
np.zeros([10, 1]), ['x_values'],
np.zeros([10, 1]), ['y_values'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'test_data_2'})
repository.add(test_data_2, category=MLObjectType.TEST_DATA)
test_data_2_ref = repository.get('test_data_2')
self.assertEqual(
test_data_2.repo_info[repo_objects.RepoInfoKey.NAME],
test_data_2_ref.repo_info[repo_objects.RepoInfoKey.NAME])
commits = repository.get_commits()
self.assertEqual(len(commits), 3)
self.assertEqual(commits[1].objects['test_data'],
test_data.repo_info.version)
#self.assertEqual(commits[1].objects['repo_mapping'], 1)
self.assertEqual(commits[2].objects['test_data_2'],
test_data_2.repo_info.version)
def setUp(self):
'''Setup a complete ML repo with two different test data objetcs, training data, model definition etc.
'''
self.repository = MLRepo(user='******')
job_runner = SimpleJobRunner(self.repository)
self.repository._job_runner = job_runner
#### Setup dummy RawData
raw_data = repo_objects.RawData(
np.zeros([10, 1]), ['x0'],
np.zeros([10, 1]), ['y0'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'raw_1'})
self.repository.add(raw_data, category=MLObjectType.RAW_DATA)
raw_data = repo_objects.RawData(
np.zeros([10, 1]), ['x0'],
np.zeros([10, 1]), ['y0'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'raw_2'})
self.repository.add(raw_data, category=MLObjectType.RAW_DATA)
raw_data = repo_objects.RawData(
np.zeros([10, 1]), ['x0'],
np.zeros([10, 1]), ['y0'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'raw_3'})
self.repository.add(raw_data, category=MLObjectType.RAW_DATA)
## Setup dummy Test and Training DataSets on RawData
training_data = DataSet('raw_1',
0,
None,
repo_info={
repo_objects.RepoInfoKey.NAME.value:
'training_data_1',
repo_objects.RepoInfoKey.CATEGORY:
MLObjectType.TRAINING_DATA
})
test_data_1 = DataSet('raw_2',
0,
None,
repo_info={
repo_objects.RepoInfoKey.NAME.value:
'test_data_1',
repo_objects.RepoInfoKey.CATEGORY:
MLObjectType.TEST_DATA
})
test_data_2 = DataSet('raw_3',
0,
2,
repo_info={
repo_objects.RepoInfoKey.NAME.value:
'test_data_2',
repo_objects.RepoInfoKey.CATEGORY:
MLObjectType.TEST_DATA
})
self.repository.add([training_data, test_data_1, test_data_2])
## setup dummy preprocessor
self.repository.add_preprocessing_transforming_function(
preprocessor_transforming_function_test,
repo_name='transform_func')
self.repository.add_preprocessing_fitting_function(
preprocessor_fitting_function_test, repo_name='fit_func')
self.repository.add_preprocessor('test_preprocessor_with_fitting',
'transform_func',
'fit_func',
preprocessor_param=None)
self.repository.add_eval_function(eval_func_test, 'eval_func')
self.repository.add_training_function(train_func_test, 'train_func')
self.repository.add(
TestClass(
1,
2,
repo_info={
repo_objects.RepoInfoKey.NAME.value: 'training_param', # pylint: disable=E1123
repo_objects.RepoInfoKey.CATEGORY:
MLObjectType.TRAINING_PARAM
}))
## setup dummy model definition
self.repository.add_model(
'model',
'eval_func',
'train_func',
preprocessors=['test_preprocessor_with_fitting'])
# setup measure configuration
self._setup_measure_config()
# add dummy calibrated model
self._add_calibrated_model()
class RepoTest(unittest.TestCase):
def _setup_measure_config(self):
"""Add a measure configuration with two measures (both MAX) where one measure just uses the coordinate x0
"""
measure_config = repo_objects.MeasureConfiguration(
[(repo_objects.MeasureConfiguration.MAX, ['y0']),
repo_objects.MeasureConfiguration.MAX],
repo_info={RepoInfoKey.NAME.value: 'measure_config'})
self.repository.add(measure_config,
category=MLObjectType.MEASURE_CONFIGURATION,
message='adding measure configuration')
def _add_calibrated_model(self):
self.repository.run_training()
self.repository.set_label('prod')
def setUp(self):
'''Setup a complete ML repo with two different test data objetcs, training data, model definition etc.
'''
self.repository = MLRepo(user='******')
job_runner = SimpleJobRunner(self.repository)
self.repository._job_runner = job_runner
#### Setup dummy RawData
raw_data = repo_objects.RawData(
np.zeros([10, 1]), ['x0'],
np.zeros([10, 1]), ['y0'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'raw_1'})
self.repository.add(raw_data, category=MLObjectType.RAW_DATA)
raw_data = repo_objects.RawData(
np.zeros([10, 1]), ['x0'],
np.zeros([10, 1]), ['y0'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'raw_2'})
self.repository.add(raw_data, category=MLObjectType.RAW_DATA)
raw_data = repo_objects.RawData(
np.zeros([10, 1]), ['x0'],
np.zeros([10, 1]), ['y0'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'raw_3'})
self.repository.add(raw_data, category=MLObjectType.RAW_DATA)
## Setup dummy Test and Training DataSets on RawData
training_data = DataSet('raw_1',
0,
None,
repo_info={
repo_objects.RepoInfoKey.NAME.value:
'training_data_1',
repo_objects.RepoInfoKey.CATEGORY:
MLObjectType.TRAINING_DATA
})
test_data_1 = DataSet('raw_2',
0,
None,
repo_info={
repo_objects.RepoInfoKey.NAME.value:
'test_data_1',
repo_objects.RepoInfoKey.CATEGORY:
MLObjectType.TEST_DATA
})
test_data_2 = DataSet('raw_3',
0,
2,
repo_info={
repo_objects.RepoInfoKey.NAME.value:
'test_data_2',
repo_objects.RepoInfoKey.CATEGORY:
MLObjectType.TEST_DATA
})
self.repository.add([training_data, test_data_1, test_data_2])
## setup dummy preprocessor
self.repository.add_preprocessing_transforming_function(
preprocessor_transforming_function_test,
repo_name='transform_func')
self.repository.add_preprocessing_fitting_function(
preprocessor_fitting_function_test, repo_name='fit_func')
self.repository.add_preprocessor('test_preprocessor_with_fitting',
'transform_func',
'fit_func',
preprocessor_param=None)
self.repository.add_eval_function(eval_func_test, 'eval_func')
self.repository.add_training_function(train_func_test, 'train_func')
self.repository.add(
TestClass(
1,
2,
repo_info={
repo_objects.RepoInfoKey.NAME.value: 'training_param', # pylint: disable=E1123
repo_objects.RepoInfoKey.CATEGORY:
MLObjectType.TRAINING_PARAM
}))
## setup dummy model definition
self.repository.add_model(
'model',
'eval_func',
'train_func',
preprocessors=['test_preprocessor_with_fitting'])
# setup measure configuration
self._setup_measure_config()
# add dummy calibrated model
self._add_calibrated_model()
def test_adding_training_data_exception(self):
'''Tests if adding new training data leads to an exception
'''
with self.assertRaises(Exception):
test_obj = DataSet('raw_data',
repo_info={
repo_objects.RepoInfoKey.CATEGORY:
MLObjectType.TRAINING_DATA.value,
'name':
'test_object'
})
self.repository.add(test_obj)
def test_commit_increase_update(self):
'''Check if updating an object in repository increases commit but does not change mapping
'''
obj = self.repository.get('raw_1')
old_num_commits = len(self.repository.get_commits())
old_version_mapping = self.repository.get('repo_mapping').repo_info[
RepoInfoKey.VERSION]
self.repository.add(obj)
new_num_commits = len(self.repository.get_commits())
new_version_mapping = self.repository.get('repo_mapping').repo_info[
RepoInfoKey.VERSION]
self.assertEqual(old_num_commits + 1, new_num_commits)
self.assertEqual(old_version_mapping, new_version_mapping)
def test_commit_increase_add(self):
'''Check if adding a new object in repository increases commit and does also change the mapping
'''
obj = DataSet('raw_data_1',
0,
None,
repo_info={
RepoInfoKey.NAME.value: 'test...',
RepoInfoKey.CATEGORY: MLObjectType.TEST_DATA
})
old_num_commits = len(self.repository.get_commits())
old_version_mapping = self.repository.get(
'repo_mapping').repo_info.version
self.repository.add(obj)
new_num_commits = len(self.repository.get_commits())
new_version_mapping = self.repository.get(
'repo_mapping').repo_info.version
self.assertEqual(old_num_commits + 1, new_num_commits)
commits = self.repository.get_commits()
def test_DataSet_get(self):
'''Test if getting a DataSet does include all informations from the underlying RawData (excluding numpy data)
'''
obj = self.repository.get('test_data_1')
raw_obj = self.repository.get(obj.raw_data)
for i in range(len(raw_obj.x_coord_names)):
self.assertEqual(raw_obj.x_coord_names[i], obj.x_coord_names[i])
for i in range(len(raw_obj.y_coord_names)):
self.assertEqual(raw_obj.y_coord_names[i], obj.y_coord_names[i])
def test_DataSet_get_full(self):
'''Test if getting a DataSet does include all informations from the underlying RawData (including numpy data)
'''
obj = self.repository.get('test_data_1',
version=repo_store.RepoStore.LAST_VERSION,
full_object=True)
raw_obj = self.repository.get(
obj.raw_data,
version=repo_store.RepoStore.LAST_VERSION,
full_object=True)
for i in range(len(raw_obj.x_coord_names)):
self.assertEqual(raw_obj.x_coord_names[i], obj.x_coord_names[i])
for i in range(len(raw_obj.y_coord_names)):
self.assertEqual(raw_obj.y_coord_names[i], obj.y_coord_names[i])
self.assertEqual(raw_obj.x_data.shape[0], obj.x_data.shape[0])
obj = self.repository.get('test_data_2',
version=repo_store.RepoStore.LAST_VERSION,
full_object=True)
self.assertEqual(obj.x_data.shape[0], 2)
obj = self.repository.get('training_data_1',
version=repo_store.RepoStore.LAST_VERSION,
full_object=True)
self.assertEqual(obj.x_data.shape[0], 10)
def test_repo_RawData(self):
"""Test RawData within repo
"""
repository = MLRepo(user='******')
job_runner = SimpleJobRunner(repository)
repository._job_runner = job_runner
raw_data = repo_objects.RawData(
np.zeros([10, 1]),
['test_coord'],
repo_info={ # pylint: disable=E0602
repo_objects.RepoInfoKey.NAME.value: 'RawData_Test'
})
repository.add(raw_data, 'test commit', MLObjectType.RAW_DATA)
raw_data_2 = repository.get('RawData_Test')
self.assertEqual(len(raw_data.x_coord_names),
len(raw_data_2.x_coord_names))
self.assertEqual(raw_data.x_coord_names[0],
raw_data_2.x_coord_names[0])
commits = repository.get_commits()
self.assertEqual(len(commits), 1)
self.assertEqual(len(commits[0].objects), 1)
def test_add_model_defaults(self):
"""test add_model using defaults to check whether default logic applies correctly
"""
model_param = TestClass(3,
4,
repo_info={
RepoInfoKey.NAME.value:
'model_param',
RepoInfoKey.CATEGORY:
MLObjectType.MODEL_PARAM.value
}) # pylint: disable=E1123
self.repository.add(model_param)
training_param = TestClass(3,
4,
repo_info={
RepoInfoKey.NAME.value:
'training_param',
RepoInfoKey.CATEGORY:
MLObjectType.TRAINING_PARAM.value
}) # pylint: disable=E1123
self.repository.add(training_param)
self.repository.add_model('model1')
model = self.repository.get('model1')
self.assertEqual(model.eval_function, 'eval_func')
self.assertEqual(model.training_function, 'train_func')
self.assertEqual(model.training_param, 'training_param')
self.assertEqual(model.model_param, 'model_param')
def test_get_history(self):
training_data_history = self.repository.get_history('training_data_1')
self.assertEqual(len(training_data_history), 1)
training_data = self.repository.get('training_data_1')
self.repository.add(training_data)
training_data_history = self.repository.get_history('training_data_1')
self.assertEqual(len(training_data_history), 2)
def test_run_eval_defaults(self):
'''Test running evaluation with default arguments
'''
self.repository.run_evaluation()
def test_run_train_defaults(self):
'''Test running training with default arguments
'''
self.repository.run_training()
def test_run_measure_defaults(self):
self.repository.run_evaluation(
) # run first the evaluation so that there is at least one evaluation
self.repository.run_measures()
def test_repo_training_test_data(self):
# init repository with sample in memory handler
repository = MLRepo(user='******')
job_runner = SimpleJobRunner(repository)
repository._job_runner = job_runner
training_data = RawData(
np.zeros([10, 1]), ['x_values'],
np.zeros([10, 1]), ['y_values'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'training_data'})
repository.add(training_data, category=MLObjectType.TRAINING_DATA)
training_data_2 = repository.get_training_data()
self.assertEqual(
training_data_2.repo_info[repo_objects.RepoInfoKey.NAME],
training_data.repo_info[repo_objects.RepoInfoKey.NAME])
test_data = RawData(
np.zeros([10, 1]), ['x_values'],
np.zeros([10, 1]), ['y_values'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'test_data'})
repository.add(test_data, category=MLObjectType.TEST_DATA)
test_data_ref = repository.get('test_data')
self.assertEqual(
test_data_ref.repo_info[repo_objects.RepoInfoKey.NAME],
test_data.repo_info[repo_objects.RepoInfoKey.NAME])
self.assertEqual(
test_data_ref.repo_info[repo_objects.RepoInfoKey.VERSION],
test_data.repo_info[repo_objects.RepoInfoKey.VERSION])
test_data_2 = RawData(
np.zeros([10, 1]), ['x_values'],
np.zeros([10, 1]), ['y_values'],
repo_info={repo_objects.RepoInfoKey.NAME.value: 'test_data_2'})
repository.add(test_data_2, category=MLObjectType.TEST_DATA)
test_data_2_ref = repository.get('test_data_2')
self.assertEqual(
test_data_2.repo_info[repo_objects.RepoInfoKey.NAME],
test_data_2_ref.repo_info[repo_objects.RepoInfoKey.NAME])
commits = repository.get_commits()
self.assertEqual(len(commits), 3)
self.assertEqual(commits[1].objects['test_data'],
test_data.repo_info.version)
#self.assertEqual(commits[1].objects['repo_mapping'], 1)
self.assertEqual(commits[2].objects['test_data_2'],
test_data_2.repo_info.version)
#self.assertEqual(commits[2].objects['repo_mapping'], 2)
def test_repo_RegressionTest(self):
regression_test_def = ml_tests.RegressionTestDefinition(
repo_info={
RepoInfoKey.NAME: 'regression_test',
RepoInfoKey.CATEGORY: MLObjectType.TEST_DEFINITION.name
})
tests = regression_test_def.create(self.repository)
self.assertEqual(len(tests), 3)
self.repository.add(regression_test_def)
self.repository.run_evaluation()
self.repository.run_measures()
self.repository.run_tests()
def test_add_multiple(self):
"""Test adding multiple objects at once
"""
obj1 = TestClass(5, 4, repo_info={})
obj1.repo_info.name = 'obj1'
v1 = self.repository.add(obj1, category=MLObjectType.CALIBRATED_MODEL)
obj2 = TestClass(2, 3, repo_info={})
obj2.repo_info.name = 'obj2'
self.repository.add([obj1, obj2],
category=MLObjectType.CALIBRATED_MODEL)
new_obj1 = self.repository.get('obj1')
self.assertEqual(new_obj1.repo_info.name, 'obj1')
new_obj2 = self.repository.get('obj2')
self.assertEqual(new_obj2.repo_info.name, 'obj2')
def test_delete(self):
"""Test if deletion works and if it considers if there are dependencies to other objects
"""
obj1 = TestClass(5, 4, repo_info={})
obj1.repo_info.name = 'obj1'
v1 = self.repository.add(obj1, category=MLObjectType.CALIBRATED_MODEL)
obj2 = TestClass(2, 3, repo_info={})
obj2.repo_info.name = 'obj2'
obj2.repo_info.modification_info = {'obj1': v1}
v2 = self.repository.add(obj2, category=MLObjectType.CALIBRATED_MODEL)
# check if an exception is thrown if one tries to delete obj1 although obj2 has
# a dependency on obj1
try:
self.repository.delete('obj1', v1)
self.assertEqual(0, 1)
except:
pass
# now first delete obj2
self.repository.delete('obj2', v2)
# check if obj2 has really been deleted
try:
obj2 = self.repository.get('obj2')
self.assertEqual(0, 1)
except:
pass
#now, deletion of obj 1 should work
try:
self.repository.delete('obj1', v1)
except:
self.assertEqual(0, 1)
try: #check if object really has been deleted
obj1 = self.repository.get('obj1')
self.assertEqual(0, 1)
except:
pass
def test_tutorial(self):
# cleanup disk before running
repo_path = './tmp_tutorial'
try:
shutil.rmtree(repo_path)
# os.path.
except OSError:
pass
# creating in memory storage
ml_repo = MLRepo(user='******')
# end creating in memory storage
# creating new repository
config = {'user': '******',
'workspace': repo_path,
'repo_store':
{
'type': 'disk_handler',
'config': {
'folder': repo_path,
'file_format': 'pickle'
}
},
'numpy_store':
{
'type': 'hdf_handler',
'config': {
'folder': repo_path,
'version_files': True
}
},
'job_runner':
{
'type': 'simple',
'config': {
'throw_job_error': True
}
}
}
ml_repo = MLRepo(user='******', config=config)
# end creating new repository
# specifying job runner
job_runner = SimpleJobRunner(None)
job_runner.set_repo(ml_repo)
ml_repo._job_runner = job_runner
# end specifying job runner
job_runner._throw_job_error = True
from pailab.tools.tree import MLTree
MLTree.add_tree(ml_repo)
# A convenient way to add RawData is simply to use the method add on the raw_data collection.
# This method just takes a pandas dataframe and the specification, which columns belong to the input
# and which to the targets.
try:
# read pandas
import pandas as pd
data = pd.read_csv('./examples/boston_housing/housing.csv')
# end read pandas
except:
data = pd.read_csv('../examples/boston_housing/housing.csv')
# extract data
input_variables = ['RM', 'LSTAT', 'PTRATIO']
target_variables = ['MEDV']
x = data.loc[:, input_variables].values
y = data.loc[:, target_variables].values
# end extract data
# add RawData snippet
from pailab import RawData, RepoInfoKey
raw_data = RawData(x, input_variables, y, target_variables, repo_info={
RepoInfoKey.NAME: 'raw_data/boston_housing'})
ml_repo.add(raw_data)
# end adding RawData snippet
# ml_repo.tree.raw_data.add('boston_housing', data, input_variables=[
# 'RM', 'LSTAT', 'PTRATIO'], target_variables=['MEDV'])
# add DataSet
# create DataSet objects for training and test data
training_data = DataSet('raw_data/boston_housing', 0, 300,
repo_info={RepoInfoKey.NAME: 'training_data', RepoInfoKey.CATEGORY: MLObjectType.TRAINING_DATA})
test_data = DataSet('raw_data/boston_housing', 301, None,
repo_info={RepoInfoKey.NAME: 'test_data', RepoInfoKey.CATEGORY: MLObjectType.TEST_DATA})
# add the objects to the repository
version_list = ml_repo.add(
[training_data, test_data], message='add training and test data')
# end adding DataSet
# add model
import pailab.externals.sklearn_interface as sklearn_interface
from sklearn.tree import DecisionTreeRegressor
sklearn_interface.add_model(
ml_repo, DecisionTreeRegressor(), model_param={'max_depth': 5})
# end adding model
# run training
job_id = ml_repo.run_training()
# end running training
# run evaluation
job_id = ml_repo.run_evaluation()
# end running evaluation
# add measures snippet
ml_repo.add_measure(MeasureConfiguration.MAX)
ml_repo.add_measure(MeasureConfiguration.R2)
# end add measure snippet
# run measures snippet
job_ids = ml_repo.run_measures()
# end run measures snippet
print(ml_repo.get_names(MLObjectType.MEASURE))
# get measures
max_measure = ml_repo.get(
'DecisionTreeRegressor/measure/training_data/max')
print(str(max_measure.value))
# end getting measures
# label snippet
from pailab import LAST_VERSION
ml_repo.set_label('prod', 'DecisionTreeRegressor/model',
model_version=LAST_VERSION, message='we found our first production model')
# end label snippet
# test definition snippet
import pailab.tools.tests
reg_test = pailab.tools.tests.RegressionTestDefinition(
reference='prod', models=None, data=None, labels=None,
measures=[MeasureConfiguration.MAX], tol=1000)
reg_test.repo_info.name = 'reg_test'
ml_repo.add(reg_test, message='regression test definition')
# end test definition snippet
# add test snippet
tests = ml_repo.run_tests()
# end add test snippet
print(tests)
# run check snippet
import pailab.tools.checker as checker
inconsistencies = checker.run(ml_repo)
# end run check snippet
print(inconsistencies)
# add inconsistency snippet
param = ml_repo.get('DecisionTreeRegressor/model_param')
param.sklearn_params['max_depth'] = 2
version = ml_repo.add(param)
# end add inconsistency snippet
inconsistencies = checker.run(ml_repo)
print(inconsistencies)
ml_repo.run_training()
inconsistencies = checker.run(ml_repo)
print(inconsistencies)
ml_repo.run_evaluation(run_descendants=True)
print(checker.run(ml_repo))
# add second test data snippet
test_data_2 = DataSet('raw_data/boston_housing', 0, 50,
repo_info={RepoInfoKey.NAME: 'test_data_2',
RepoInfoKey.CATEGORY: MLObjectType.TEST_DATA}
)
ml_repo.add(test_data_2)
ml_repo.run_evaluation(run_descendants=True)
# end add second test data snippet
print(checker.Tests.run(ml_repo))
ml_repo.run_tests()
# check tests
print(checker.Tests.run(ml_repo))
# end check tests
# cleanup after running
# job_runner.close_connection()
ml_repo._ml_repo.close_connection()
try:
shutil.rmtree(repo_path)
# os.path.
except OSError:
pass
