def add(self, name, data, input_variables = None, target_variables = None):
"""Add raw data to the repository
Arguments:
data_name {name of data} -- the name of the data added
data {pandas datatable} -- the data as pandas datatable
Keyword Arguments:
input_variables {list of strings} -- list of column names defining the input variables for the machine learning (default: {None}). If None, all variables are used as input
target_variables {list of strings} -- list of column names defining the target variables for the machine learning (default: {None}). If None, no target data is added from the table.
"""
path = 'raw_data/' + name
if input_variables is None:
input_variables = list(data)
if not target_variables is None:
[input_variables.remove(x) for x in target_variables]
else:
# check whether the input_variables are included in the data
if not [item for item in input_variables if item in list(data)] == input_variables:
raise Exception('RawData does not include at least one column included in input_variables')
if target_variables is not None:
# check if target variables are in list
if not [item for item in target_variables if item in list(data)] == target_variables:
raise Exception('RawData does not include at least one column included in target_variables')
raw_data = repo_objects.RawData(data.loc[:, input_variables].values, input_variables, data.loc[:, target_variables].values,
target_variables, repo_info = {RepoInfoKey.NAME: path})
else:
raw_data = repo_objects.RawData(data.loc[:, input_variables].values, input_variables, repo_info = {RepoInfoKey.NAME: path})
v = self._repo.add(raw_data, 'data ' + path + ' added to repository' , category = MLObjectType.RAW_DATA)
obj = self._repo.get(path, version=v, full_object = False)
setattr(self, name, _RawDataItem(path, self._repo, obj))
def test_constructor(self):
# simple construction
x_data = np.zeros([100, 4])
x_names = ['x1', 'x2', 'x3', 'x4']
test_data = repo_objects.RawData(x_data, x_names)
self.assertEqual(test_data.x_data.shape[0], x_data.shape[0])
self.assertEqual(test_data.n_data, 100)
# construction from array
x_data = np.zeros([100])
test_data = repo_objects.RawData(x_data, ['x1'])
self.assertEqual(len(test_data.x_data.shape), 2)
self.assertEqual(test_data.x_data.shape[1], 1)
# construction from list
test_data = repo_objects.RawData([100, 200, 300], ['x1'])
self.assertEqual(test_data.x_data.shape[0], 3)
self.assertEqual(test_data.x_data.shape[1], 1)
def add_from_numpy_file(self, name, filename_X, x_names, filename_Y=None, y_names = None):
path = name
X = load(filename_X)
Y = None
if filename_Y is not None:
Y = load(filename_Y)
raw_data = repo_objects.RawData(X, x_names, Y, y_names, repo_info = {RepoInfoKey.NAME: path})
v = self._repo.add(raw_data, 'data ' + path + ' added to repository' , category = MLObjectType.RAW_DATA)
obj = self._repo.get(path, version=v, full_object = False)
setattr(self, name, _RawDataItem(path, self._repo, obj))
def test_validation(self):
# test if validation works
x_data = np.zeros([100, 2])
# exception because number of coord_names does not equal number of x_coords
with self.assertRaises(Exception):
test_data = repo_objects.RawData( # pylint: disable=W0612
x_data, x_coord_names=[]) # pylint: disable=W0612
# exception because number of y-coords does not equal number of x-coords
y_data = np.zeros([99, 2])
with self.assertRaises(Exception):
test_data = repo_objects.RawData( # pylint: disable=W0612
x_data,
x_coord_names=['x1', 'x2'],
y_data=y_data,
y_coord_names=['y1', 'y2'])
# exception because number of y-coordnamess does not equal number of y-coords
y_data = np.zeros([100, 2])
with self.assertRaises(Exception):
test_data = repo_objects.RawData( # pylint: disable=W0612
x_data,
x_coord_names=['x1', 'x2'],
y_data=y_data,
y_coord_names=['y1'])
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 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()
