code stringlengths 3 6.57k |
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Exception('RawData object does not contain y_data but y_data is given') |
Exception('RawData object has y_data but no y_data is given') |
len(raw_data.y_coord_names ) |
Exception('Number of columns of y_data of RawData object is not equal to number of columns of additional y_data.') |
self._repo.add(raw_data) |
self._repo._numpy_repo.append(self._name, old_version, new_version, numpy_dict) |
self._repo.get_training_data(full_object = False) |
isinstance(training_data, DataSet) |
changed_data_sets.append(training_data) |
self._repo.get_names(MLObjectType.TEST_DATA) |
self._repo.get(d) |
isinstance(data, DataSet) |
changed_data_sets.append(data) |
self._repo.add(changed_data_sets, 'RawData ' + self._name + ' updated, add DataSets depending om the updated RawData.') |
hasattr(self, 'obj') |
self._repo.get(self._name, version=new_version) |
logger.info('Finished appending data to RawData' + self._name) |
_RawDataCollection(_RepoObjectItem) |
__get_name_from_path(path) |
path.split('/') |
__init__(self, repo) |
super(_RawDataCollection, self) |
__init__('raw_data', repo) |
repo.get_names(MLObjectType.RAW_DATA) |
setattr(self, _RawDataCollection.__get_name_from_path(n) |
_RawDataItem(n, repo) |
add(self, name, data, input_variables = None, target_variables = None) |
learning (default: {None}) |
learning (default: {None}) |
list(data) |
input_variables.remove(x) |
isinstance(input_variables, str) |
list(data) |
list(input_variables) |
Exception('RawData does not include at least one column included in input_variables') |
isinstance(target_variables, str) |
list(data) |
list(target_variables) |
Exception('RawData does not include at least one column included in target_variables') |
repo_objects.RawData(data.loc[:, input_variables].values, input_variables, repo_info = {RepoInfoKey.NAME: path}) |
self._repo.add(raw_data, 'data ' + path + ' added to repository' , category = MLObjectType.RAW_DATA) |
self._repo.get(path, version=v, full_object = False) |
setattr(self, name, _RawDataItem(path, self._repo, obj) |
add_from_numpy_file(self, name, filename_X, x_names, filename_Y=None, y_names = None) |
load(filename_X) |
load(filename_Y) |
repo_objects.RawData(X, x_names, Y, y_names, repo_info = {RepoInfoKey.NAME: path}) |
self._repo.add(raw_data, 'data ' + path + ' added to repository' , category = MLObjectType.RAW_DATA) |
self._repo.get(path, version=v, full_object = False) |
setattr(self, name, _RawDataItem(path, self._repo, obj) |
_TrainingDataCollection(_RepoObjectItem) |
__get_name_from_path(path) |
path.split('/') |
__init__(self, repo) |
super(_TrainingDataCollection, self) |
__init__('training_data', None) |
repo.get_names(MLObjectType.TRAINING_DATA) |
setattr(self, _TrainingDataCollection.__get_name_from_path(n) |
_RepoObjectItem(n, repo) |
self.__repo.add(data_set) |
self.__repo.get(name, version=v) |
_RepoObjectItem(name, self.__repo, tmp) |
setattr(self, name, item) |
_TestDataCollection(_RepoObjectItem) |
__get_name_from_path(path) |
path.split('/') |
__init__(self, repo) |
super(_TestDataCollection, self) |
__init__('test_data', None) |
repo.get_names(MLObjectType.TEST_DATA) |
setattr(self, _TestDataCollection.__get_name_from_path(n) |
_RepoObjectItem(n,repo) |
self.__repo.add(data_set) |
self.__repo.get(name, version=v) |
_RepoObjectItem(name, self.__repo, tmp) |
setattr(self, name, item) |
_MeasureItem(_RepoObjectItem) |
__init__(self, name, ml_repo, repo_obj = None) |
super(_MeasureItem, self) |
__init__(name, ml_repo, repo_obj) |
_JobItem(_RepoObjectItem) |
__init__(self, name, ml_repo, repo_obj = None) |
super(_JobItem, self) |
__init__(name, ml_repo, repo_obj) |
_MeasureCollection(_RepoObjectItem) |
__init__(self, name, ml_repo) |
super(_MeasureCollection, self) |
__init__('measures', None) |
ml_repo.get_names(MLObjectType.MEASURE) |
n.split('/') |
len(path) |
range(len(items) |
_RepoObjectItem(path[i], None) |
_MeasureItem(n, ml_repo) |
self._set(path, items) |
_EvalCollection(_RepoObjectItem) |
__init__(self, name, ml_repo) |
super(_EvalCollection, self) |
__init__('eval', None) |
ml_repo.get_names(MLObjectType.EVAL_DATA) |
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