mirror of
https://github.com/gsi-upm/senpy
synced 2024-11-22 08:12:27 +00:00
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
#
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# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import os
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from pkg_resources import parse_version, get_distribution, DistributionNotFound
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logger = logging.getLogger(__name__)
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MSG = 'GSITK is not (properly) installed.'
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IMPORTMSG = '{} Some functions will be unavailable.'.format(MSG)
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RUNMSG = '{} Install it to use this function.'.format(MSG)
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def raise_exception(*args, **kwargs):
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raise Exception(RUNMSG)
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try:
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gsitk_distro = get_distribution("gsitk")
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GSITK_VERSION = parse_version(gsitk_distro.version)
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if not os.environ.get('DATA_PATH'):
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os.environ['DATA_PATH'] = os.environ.get('SENPY_DATA', 'senpy_data')
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from gsitk.datasets.datasets import DatasetManager
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from gsitk.evaluation.evaluation import Evaluation as Eval # noqa: F401
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from gsitk.evaluation.evaluation import EvalPipeline # noqa: F401
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from sklearn.pipeline import Pipeline
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modules = locals()
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GSITK_AVAILABLE = True
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datasets = {}
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manager = DatasetManager()
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for item in manager.get_datasets():
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for key in item:
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if key in datasets:
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continue
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properties = item[key]
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properties['@id'] = key
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datasets[key] = properties
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def prepare(ds, *args, **kwargs):
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return manager.prepare_datasets(ds, *args, **kwargs)
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except (DistributionNotFound, ImportError) as err:
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logger.debug('Error importing GSITK: {}'.format(err))
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logger.warning(IMPORTMSG)
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GSITK_AVAILABLE = False
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GSITK_VERSION = ()
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DatasetManager = Eval = Pipeline = prepare = raise_exception
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datasets = {}
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