# 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
"""The base class to assess the quality of a predictive clusterer."""
from __future__ import annotations
from typing import TYPE_CHECKING
from gemseo.mlearning.clustering.quality.base_clusterer_quality import (
BaseClustererQuality,
)
from gemseo.mlearning.core.quality.base_ml_algo_quality import BaseMLAlgoQuality
from gemseo.mlearning.core.quality.base_ml_algo_quality import MeasureType
from gemseo.mlearning.resampling.bootstrap import Bootstrap
from gemseo.mlearning.resampling.cross_validation import CrossValidation
if TYPE_CHECKING:
from collections.abc import Sequence
from gemseo import Dataset
from gemseo.mlearning.clustering.algos.base_predictive_clusterer import (
BasePredictiveClusterer,
)
[docs]
class BasePredictiveClustererQuality(BaseClustererQuality):
"""The base class to assess the quality of a predictive clusterer."""
algo: BasePredictiveClusterer
def __init__(
self,
algo: BasePredictiveClusterer,
fit_transformers: bool = BaseMLAlgoQuality._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] = (),
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] = (),
multioutput: bool = True,
randomize: bool = BaseClustererQuality._RANDOMIZE,
seed: int | None = None,
store_resampling_result: bool = False,
) -> MeasureType:
samples, seed = self._pre_process(samples, seed, randomize)
data = self._get_data()
cross_validation = CrossValidation(samples, n_folds, randomize, seed)
_, predictions = cross_validation.execute(
self.algo,
return_models=store_resampling_result,
input_data=data,
fit_transformers=self._fit_transformers,
store_sampling_result=store_resampling_result,
)
return self._compute_measure(data, predictions, multioutput)
[docs]
def compute_bootstrap_measure( # noqa: D102
self,
n_replicates: int = 100,
samples: Sequence[int] = (),
multioutput: bool = True,
seed: int | None = None,
store_resampling_result: bool = False,
) -> MeasureType:
samples, seed = self._pre_process(samples, seed, True)
data = self._get_data()
bootstrap = Bootstrap(samples, n_replicates, seed)
_, predictions = bootstrap.execute(
self.algo,
return_models=store_resampling_result,
input_data=data,
stack_predictions=False,
fit_transformers=self._fit_transformers,
store_sampling_result=store_resampling_result,
)
measure = 0
for prediction, split in zip(predictions, bootstrap.splits):
measure += self._compute_measure(data[split.test], prediction, multioutput)
return measure / n_replicates
