# -*- 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 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 division, unicode_literals
from typing import Dict, List, NoReturn, Optional, Union
from numpy import arange, array_split
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.cluster.cluster import (
MLClusteringAlgo,
MLPredictiveClusteringAlgo,
)
from gemseo.mlearning.qual_measure.quality_measure import MLQualityMeasure
[docs]class MLClusteringMeasure(MLQualityMeasure):
"""An abstract clustering measure for clustering algorithms."""
def __init__(
self,
algo, # type: MLClusteringAlgo
): # type: (...) -> None
"""
Args:
algo: A machine learning algorithm for clustering.
"""
super(MLClusteringMeasure, self).__init__(algo)
[docs] def evaluate_learn(
self,
samples=None, # type: Optional[List[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
samples = self._assure_samples(samples)
if not self.algo.is_trained:
self.algo.learn(samples)
data = self._get_data()[samples]
labels = self.algo.labels
measure = self._compute_measure(data, labels, multioutput)
return measure
def _compute_measure(
self,
data, # type: ndarray
labels, # type: ndarray
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
"""Compute the quality measure.
Args:
data: The reference data.
labels: The predicted labels.
multioutput: If True, return the quality measure for each
output component. Otherwise, average these measures.
Returns:
The value of the quality measure.
"""
raise NotImplementedError
def _get_data(self): # type: (...) -> Dict[str,ndarray]
"""Get data.
Returns:
The learning data indexed by the names of the variables.
"""
names = self.algo.var_names
data = self.algo.learning_set.get_data_by_names(names, False)
return data
[docs]class MLPredictiveClusteringMeasure(MLClusteringMeasure):
"""An abstract clustering measure for predictive clustering algorithms."""
def __init__(
self,
algo, # type: MLPredictiveClusteringAlgo
): # type: (...) -> None
"""
Args:
algo: A machine learning algorithm for predictive clustering.
"""
super(MLPredictiveClusteringMeasure, self).__init__(algo)
[docs] def evaluate_test(
self,
test_data, # type:Dataset
samples=None, # type: Optional[List[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
samples = self._assure_samples(samples)
if not self.algo.is_trained:
self.algo.learn(samples)
names = self.algo.var_names
data = test_data.get_data_by_names(names, False)
predictions = self.algo.predict(data)
measure = self._compute_measure(data, predictions, multioutput)
return measure
[docs] def evaluate_kfolds(
self,
n_folds=5, # type: int
samples=None, # type: Optional[List[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
indices = self._assure_samples(samples)
folds = array_split(indices, n_folds)
data = self._get_data()
qualities = []
for n_fold in range(n_folds):
test_indices = folds[n_fold]
train_indices = npdelete(indices, test_indices)
self.algo.learn(samples=train_indices)
test_data = data[test_indices]
predictions = self.algo.predict(test_data)
quality = self._compute_measure(test_data, predictions, 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[List[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
samples = self._assure_samples(samples)
if isinstance(samples, list):
n_samples = len(samples)
else:
n_samples = samples.size
indices = arange(n_samples)
data = self._get_data()
qualities = []
for _ in range(n_replicates):
train_indices = unique(choice(n_samples, n_samples))
test_indices = npdelete(indices, train_indices)
self.algo.learn(samples=[samples[index] for index in train_indices])
test_data = data[[samples[index] for index in test_indices]]
predictions = self.algo.predict(test_data)
quality = self._compute_measure(test_data, predictions, multioutput)
qualities.append(quality)
quality = sum(qualities) / len(qualities)
return quality
def _compute_measure(
self,
data, # type: ndarray
labels, # type: ndarray
multioutput=True, # type: bool
): # type: (...) -> NoReturn
"""Compute the quality measure.
Args:
data: The reference data.
labels: The predicted labels.
multioutput: If True, return the quality measure for each
output component. Otherwise, average these measures.
Returns:
The value of the quality measure.
"""
raise NotImplementedError
def _get_data(self): # type: (...) -> Dict[str,ndarray]
"""Get data.
Returns:
The learning data indexed by the names of the variables.
"""
names = self.algo.var_names
data = self.algo.learning_set.get_data_by_names(names, False)
return data
