Source code for gemseo.mlearning.quality_measures.cluster_measure

# 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
"""Here is the baseclass to measure the quality of machine learning algorithms.

The concept of clustering quality measure is implemented with the
:class:`.MLClusteringMeasure` class and proposes different evaluation methods.
"""

from __future__ import annotations

from abc import abstractmethod
from typing import TYPE_CHECKING

from gemseo.mlearning.quality_measures.quality_measure import MeasureType
from gemseo.mlearning.quality_measures.quality_measure import MLQualityMeasure
from gemseo.mlearning.resampling.bootstrap import Bootstrap
from gemseo.mlearning.resampling.cross_validation import CrossValidation

if TYPE_CHECKING:
    from collections.abc import Sequence

    from numpy import ndarray

    from gemseo.datasets.dataset import Dataset
    from gemseo.mlearning.clustering.clustering import MLClusteringAlgo
    from gemseo.mlearning.clustering.clustering import MLPredictiveClusteringAlgo


[docs] class MLClusteringMeasure(MLQualityMeasure): """An abstract clustering measure for clustering algorithms.""" algo: MLClusteringAlgo def __init__( self, algo: MLClusteringAlgo, fit_transformers: bool = MLQualityMeasure._FIT_TRANSFORMERS, ) -> None: """ Args: algo: A machine learning algorithm for clustering. """ # noqa: D205 D212 super().__init__(algo, fit_transformers=fit_transformers)
[docs] def compute_learning_measure( # noqa: D102 self, samples: Sequence[int] | None = None, multioutput: bool = True, ) -> MeasureType: return self._compute_measure( self._get_data()[self._pre_process(samples)[0]], self.algo.labels, multioutput, )
@abstractmethod def _compute_measure( self, data: ndarray, labels: ndarray, multioutput: bool = True, ) -> MeasureType: """Compute the quality measure. Args: data: The reference data. labels: The predicted labels. multioutput: Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure. Returns: The value of the quality measure. """ def _get_data(self) -> ndarray: """Get data. Returns: The learning data. """ return self.algo.learning_set.get_view( variable_names=self.algo.var_names ).to_numpy() # TODO: API: remove this alias in the next major release. evaluate_learn = compute_learning_measure
[docs] class MLPredictiveClusteringMeasure(MLClusteringMeasure): """An abstract clustering measure for predictive clustering algorithms.""" algo: MLPredictiveClusteringAlgo def __init__( self, algo: MLPredictiveClusteringAlgo, fit_transformers: bool = MLQualityMeasure._FIT_TRANSFORMERS, ) -> None: """ Args: algo: A machine learning algorithm for predictive clustering. """ # noqa: D205 D212 super().__init__(algo, fit_transformers=fit_transformers)
[docs] def compute_test_measure( # noqa: D102 self, test_data: Dataset, samples: Sequence[int] | None = None, multioutput: bool = True, ) -> MeasureType: self._pre_process(samples) data = test_data.get_view(variable_names=self.algo.var_names).to_numpy() return self._compute_measure(data, self.algo.predict(data), multioutput)
[docs] def compute_cross_validation_measure( # noqa: D102 self, n_folds: int = 5, samples: Sequence[int] | None = None, multioutput: bool = True, randomize: bool = MLClusteringMeasure._RANDOMIZE, seed: int | None = None, store_resampling_result: bool = False, ) -> MeasureType: samples, seed = self._pre_process(samples, seed, randomize) cross_validation = CrossValidation(samples, n_folds, randomize, seed) data = self._get_data() _, predictions = cross_validation.execute( self.algo, store_resampling_result, True, True, self._fit_transformers, store_resampling_result, data, (len(data),), ) return self._compute_measure(data, predictions, multioutput)
[docs] def compute_bootstrap_measure( # noqa: D102 self, n_replicates: int = 100, samples: Sequence[int] | None = None, multioutput: bool = True, seed: int | None = None, store_resampling_result: bool = False, ) -> MeasureType: samples, seed = self._pre_process(samples, seed, True) bootstrap = Bootstrap(samples, n_replicates, seed) data = self._get_data() _, predictions = bootstrap.execute( self.algo, store_resampling_result, True, False, self._fit_transformers, store_resampling_result, data, (len(data),), ) measure = 0 for prediction, split in zip(predictions, bootstrap.splits): measure += self._compute_measure(data[split.test], prediction, multioutput) return measure / n_replicates
# TODO: API: remove these aliases in the next major release. evaluate_test = compute_test_measure evaluate_kfolds = compute_cross_validation_measure evaluate_bootstrap = compute_bootstrap_measure