# 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
