Source code for gemseo.mlearning.core.unsupervised
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# 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
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""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 annotations
from abc import abstractmethod
from typing import TYPE_CHECKING
from typing import ClassVar
from typing import NoReturn
from numpy import hstack
from numpy import ndarray
from gemseo.mlearning.core.ml_algo import MLAlgo
from gemseo.mlearning.core.ml_algo import MLAlgoParameterType
from gemseo.mlearning.core.ml_algo import TransformerType
if TYPE_CHECKING:
from collections.abc import Iterable
from collections.abc import Sequence
from gemseo.datasets.dataset import Dataset
[docs]
class MLUnsupervisedAlgo(MLAlgo):
"""Unsupervised machine learning algorithm.
Inheriting classes shall overload the :meth:`!MLUnsupervisedAlgo._fit` method.
"""
input_names: list[str]
"""The names of the variables."""
SHORT_ALGO_NAME: ClassVar[str] = "MLUnsupervisedAlgo"
def __init__(
self,
data: Dataset,
transformer: TransformerType = MLAlgo.IDENTITY,
var_names: Iterable[str] | None = None,
**parameters: MLAlgoParameterType,
) -> None:
"""
Args:
var_names: The names of the variables.
If ``None``, consider all variables mentioned in the learning dataset.
""" # noqa: D205 D212
super().__init__(
data, transformer=transformer, var_names=var_names, **parameters
)
self.var_names = var_names or data.variable_names
def _learn(
self,
indices: Sequence[int] | None,
fit_transformers: bool,
) -> None:
if set(self.var_names) == set(self.learning_set.variable_names):
names = self.learning_set.group_names
arg_name = "group_names"
else:
names = self.var_names
arg_name = "variable_names"
data = []
method_name = "fit_transform" if fit_transformers else "transform"
for name in names:
sub_data = self.learning_set.get_view(**{arg_name: name}).to_numpy()
if name in self.transformer:
sub_data = getattr(self.transformer[name], method_name)(sub_data)
data.append(sub_data)
data = hstack(data)
if indices is not None:
data = data[indices]
self._fit(data)
@abstractmethod
def _fit(
self,
data: ndarray,
) -> NoReturn:
"""Fit model on data.
Args:
data: The data with shape (n_samples, n_variables).
"""