def test_arff_format(self): dataset = os.path.join(self.data_dir, "germancredit") namespace = NameSpace(dataset, 'arff', task='binary.classification', metric='acc_metric', target='class') D = factory.get_data_manager(namespace)
def run(self): if self._parser is None: raise ValueError('You must invoke run() only via start_automl()') self._backend.save_start_time(self._seed) self._stopwatch = StopWatch() datamanager = get_data_manager(namespace=self._parser) self._stopwatch.start_task(datamanager.name) self._logger = self._get_logger(datamanager.name) self._datamanager = datamanager self._dataset_name = datamanager.name self._fit(self._datamanager)
def start_automl(self, parser): self._backend.save_start_time(self._seed) self._stopwatch = StopWatch() datamanager = get_data_manager(namespace=parser) self._stopwatch.start_task(datamanager.name) logger_name = 'AutoML(%d):%s' % (self._seed, datamanager.name) setup_logger(os.path.join(self._tmp_dir, '%s.log' % str(logger_name))) self._logger = get_logger(logger_name) self._datamanager = datamanager self._dataset_name = datamanager.name self.start()
def test_arff_format(self): dataset = os.path.join(self.data_dir, "germancredit") namespace = NameSpace(dataset, "arff", task="binary.classification", metric="acc_metric", target="class") D = factory.get_data_manager(namespace) print D
def test_competition_format(self): dataset = os.path.join(self.data_dir, "31_bac") namespace = NameSpace(dataset, "automl-competition-format") D = factory.get_data_manager(namespace) print D
_metafeatures_encoded_labels = \ autosklearn.metalearning.metafeatures.metafeature.DatasetMetafeatures( D.name, dict()) for metafeature_name in \ autosklearn.metalearning.metafeatures.metafeatures.npy_metafeatures: type_ = "HELPERFUNCTION" if metafeature_name not in \ autosklearn.metalearning.metafeatures.metafeatures.metafeatures.functions \ else "METAFEATURE" _metafeatures_encoded_labels.metafeature_values[metafeature_name] = \ autosklearn.metalearning.metafeatures.metafeature.MetaFeatureValue( metafeature_name, type_, 0, 0, np.NaN, np.NaN, "Memory error during dataset scaling.") mf = _metafeatures_labels mf.metafeature_values.update( _metafeatures_encoded_labels.metafeature_values) mf.dump(mf_filename) return mf if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--output-dir", type=str, required=True) parser.add_argument("--memory-limit", type=int, default=3072) parser = data_manager_factory.populate_argparse_with_data_options(parser) args = parser.parse_args() D = data_manager_factory.get_data_manager(args, encode_labels=False) mf = calculate_metafeatures(D, args.output_dir, args.memory_limit)
def test_competition_format(self): dataset = os.path.join(self.data_dir, "31_bac") namespace = NameSpace(dataset, "automl-competition-format") D = factory.get_data_manager(namespace)