# -*- coding: utf-8 -*-
# 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 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 division, unicode_literals
from copy import deepcopy
from typing import NoReturn, Optional, Sequence, Union
from numpy import arange
from numpy import delete as npdelete
from numpy import ndarray, unique
from numpy.random import choice
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, # type: MLSupervisedAlgo
): # type: (...) -> None
"""
Args:
algo: A machine learning algorithm for supervised learning.
"""
super(MLErrorMeasure, self).__init__(algo)
[docs] def evaluate_learn(
self,
samples=None, # type: Optional[Sequence[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
samples = self._assure_samples(samples)
self.algo.learn(samples)
inputs = self.algo.input_data[samples]
outputs = self.algo.output_data[samples]
predictions = self.algo.predict(inputs)
measure = self._compute_measure(outputs, predictions, multioutput)
return measure
[docs] def evaluate_test(
self,
test_data, # type:Dataset
samples=None, # type: Optional[Sequence[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
samples = self._assure_samples(samples)
self.algo.learn(samples)
in_grp = test_data.INPUT_GROUP
out_grp = test_data.OUTPUT_GROUP
inputs = test_data.get_data_by_group(in_grp)
outputs = test_data.get_data_by_group(out_grp)
predictions = self.algo.predict(inputs)
measure = self._compute_measure(outputs, predictions, multioutput)
return measure
[docs] def evaluate_kfolds(
self,
n_folds=5, # type: int
samples=None, # type: Optional[Sequence[int]]
multioutput=True, # type: bool
randomize=False, # type:bool
): # type: (...) -> Union[float,ndarray]
folds, samples = self._compute_folds(samples, n_folds, randomize)
in_grp = self.algo.learning_set.INPUT_GROUP
out_grp = self.algo.learning_set.OUTPUT_GROUP
inputs = self.algo.learning_set.get_data_by_group(in_grp)
outputs = self.algo.learning_set.get_data_by_group(out_grp)
algo = deepcopy(self.algo)
qualities = []
for n_fold in range(n_folds):
fold = folds[n_fold]
train = npdelete(samples, fold)
algo.learn(samples=train)
expected = outputs[fold]
predicted = algo.predict(inputs[fold])
quality = self._compute_measure(expected, predicted, multioutput)
qualities.append(quality)
quality = sum(qualities) / len(qualities)
return quality
[docs] def evaluate_bootstrap(
self,
n_replicates=100, # type: int
samples=None, # type: Optional[Sequence[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
samples = self._assure_samples(samples)
n_samples = samples.size
inds = arange(n_samples)
in_grp = self.algo.learning_set.INPUT_GROUP
out_grp = self.algo.learning_set.OUTPUT_GROUP
inputs = self.algo.learning_set.get_data_by_group(in_grp)
outputs = self.algo.learning_set.get_data_by_group(out_grp)
algo = deepcopy(self.algo)
qualities = []
for _ in range(n_replicates):
train = unique(choice(n_samples, n_samples))
test = npdelete(inds, train)
algo.learn([samples[index] for index in train])
test_samples = [samples[index] for index in test]
expected = outputs[test_samples]
predicted = algo.predict(inputs[test_samples])
quality = self._compute_measure(expected, predicted, multioutput)
qualities.append(quality)
quality = sum(qualities) / len(qualities)
return quality
def _compute_measure(
self,
outputs, # type: ndarray
predictions, # type: ndarray
multioutput=True, # type: bool
): # type: (...) -> 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
