Source code for gemseo.mlearning.qual_measure.error_measure

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

The concept of error measure is implemented with the :class:`.MLErrorMeasure` class and
proposes different evaluation methods.
from __future__ import annotations

from copy import deepcopy
from typing import NoReturn
from typing import Sequence

from numpy import arange
from numpy import delete as npdelete
from numpy import ndarray
from numpy import unique

from gemseo.core.dataset import Dataset
from gemseo.mlearning.core.supervised import MLSupervisedAlgo
from gemseo.mlearning.qual_measure.quality_measure import MLQualityMeasure

[docs]class MLErrorMeasure(MLQualityMeasure): """An abstract error measure for machine learning.""" def __init__( self, algo: MLSupervisedAlgo, fit_transformers: bool = MLQualityMeasure._FIT_TRANSFORMERS, ) -> None: """ Args: algo: A machine learning algorithm for supervised learning. """ super().__init__(algo, fit_transformers=fit_transformers)
[docs] def evaluate_learn( self, samples: Sequence[int] | None = None, multioutput: bool = True, ) -> float | ndarray: self._train_algo(samples) return self._compute_measure( self.algo.output_data, self.algo.predict(self.algo.input_data), multioutput, )
[docs] def evaluate_test( self, test_data: Dataset, samples: Sequence[int] | None = None, multioutput: bool = True, ) -> float | ndarray: self._train_algo(samples) return self._compute_measure( test_data.get_data_by_names(self.algo.output_names, False), self.algo.predict( test_data.get_data_by_names(self.algo.input_names, False) ), multioutput, )
[docs] def evaluate_kfolds( self, n_folds: int = 5, samples: Sequence[int] | None = None, multioutput: bool = True, randomize: bool = MLQualityMeasure._RANDOMIZE, seed: int | None = None, ) -> float | ndarray: self._train_algo(samples) samples = self._assure_samples(samples) folds, samples = self._compute_folds(samples, n_folds, randomize, seed) input_data = self.algo.input_data output_data = self.algo.output_data algo = deepcopy(self.algo) qualities = [] for fold in folds: algo.learn( samples=npdelete(samples, fold), fit_transformers=self._fit_transformers ) expected = output_data[fold] predicted = algo.predict(input_data[fold]) quality = self._compute_measure(expected, predicted, multioutput) qualities.append(quality) return sum(qualities) / len(qualities)
[docs] def evaluate_bootstrap( self, n_replicates: int = 100, samples: Sequence[int] | None = None, multioutput: bool = True, seed: None | None = None, ) -> float | ndarray: samples = self._assure_samples(samples) self._train_algo(samples) n_samples = samples.size input_data = self.algo.input_data output_data = self.algo.output_data all_indices = arange(n_samples) algo = deepcopy(self.algo) qualities = [] generator = self._get_rng(seed) for _ in range(n_replicates): training_indices = unique(generator.choice(n_samples, n_samples)) test_indices = npdelete(all_indices, training_indices) algo.learn( [samples[index] for index in training_indices], fit_transformers=self._fit_transformers, ) test_samples = [samples[index] for index in test_indices] quality = self._compute_measure( output_data[test_samples], algo.predict(input_data[test_samples]), multioutput, ) qualities.append(quality) return sum(qualities) / len(qualities)
def _compute_measure( self, outputs: ndarray, predictions: ndarray, multioutput: bool = True, ) -> NoReturn: """Compute the quality measure. Args: outputs: The reference data. predictions: The predicted labels. multioutput: Whether to return the quality measure for each output component. If not, average these measures. Returns: The value of the quality measure. """ raise NotImplementedError