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
Unsupervised machine learning algorithm

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 absolute_import, division, unicode_literals

from future import standard_library
from numpy import hstack

from gemseo.mlearning.core.ml_algo import MLAlgo


[docs]class MLUnsupervisedAlgo(MLAlgo): """Unsupervised machine learning algorithm. Inheriting classes should overload the :meth:`!MLUnsupervisedAlgo._fit` method. """ ABBR = "MLUnupervisedAlgo" def __init__(self, data, transformer=None, var_names=None, **parameters): """Constructor. :param Dataset data: learning dataset :param transformer: transformation strategy for data groups. If None, do not scale data. Default: None. :type transformer: dict(str) :param var_names: names of the variables to consider. :type var_names: list(str) :param parameters: algorithm parameters """ super(MLUnsupervisedAlgo, self).__init__( data, transformer=transformer, var_names=var_names, **parameters ) self.var_names = var_names or data.variables
[docs] def learn(self, samples=None): """Train machine learning algorithm on learning set. :param list(str) names: learning variables. Default: None. :param list(int) samples: training samples (indices). Default: 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 samples is not None: data = data[samples] self._fit(data) self._trained = True
def _fit(self, data): """Fit model on data. :param ndarray data: training data (2D). """ raise NotImplementedError