Source code for gemseo.mlearning.core.unsupervised

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry,
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# Lesser General Public License for more details.
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                         documentation
#        :author: Syver Doving Agdestein
"""This module contains the base class for the unsupervised machine learning algorithms.

The :mod:`~gemseo.mlearning.core.unsupervised` module implements
the concept of unsupervised machine learning models,
where the data has no notion of input or output.

This concept is implemented through the :class:`.MLUnsupervisedAlgo` class,
which inherits from the :class:`.MLAlgo` class.
from __future__ import division, unicode_literals

from typing import Iterable, NoReturn, Optional, Sequence

from numpy import hstack, ndarray

from gemseo.core.dataset import Dataset
from gemseo.mlearning.core.ml_algo import MLAlgo, MLAlgoParameterType, TransformerType

[docs]class MLUnsupervisedAlgo(MLAlgo): """Unsupervised machine learning algorithm. Inheriting classes shall overload the :meth:`!MLUnsupervisedAlgo._fit` method. Attributes: input_names (List[str]): The names of the variables. """ ABBR = "MLUnupervisedAlgo" def __init__( self, data, # type: Dataset transformer=None, # type: Optional[TransformerType] var_names=None, # type: Optional[Iterable[str]] **parameters # type: MLAlgoParameterType ): # type: (...) -> None """ Args: var_names: The names of the variables. If None, consider all variables mentioned in the learning dataset. """ super(MLUnsupervisedAlgo, self).__init__( data, transformer=transformer, var_names=var_names, **parameters ) self.var_names = var_names or data.variables def _learn( self, indices, # type: Optional[Sequence[int]] ): # type: (...) -> None if set(self.var_names) == set(self.learning_set.variables): data = [] for group in self.learning_set.groups: sub_data = self.learning_set.get_data_by_group(group) if group in self.transformer: sub_data = self.transformer[group].fit_transform(sub_data) data.append(sub_data) data = hstack(data) else: data = [] for name in self.var_names: sub_data = self.learning_set.get_data_by_names([name], False) if name in self.transformer: sub_data = self.transformer[name].fit_transform(sub_data) data.append(sub_data) data = hstack(data) if indices is not None: data = data[indices] self._fit(data) def _fit( self, data, # type: ndarray ): # type: (...) -> NoReturn """Fit model on data. Args: data: The data with shape (n_samples, n_variables). """ raise NotImplementedError