def gpc_classifier(params):
clf = GaussianProcessClassifier()
clf.set_params(**params)
return clf
class GaussianProcess(Classifier):
r"""Implementation of gaussian process classifier.
Date:
2020
Author:
Luka Pečnik
License:
MIT
Reference:
Rasmussen, Carl Edward, and Hannes Nickisch. "Gaussian processes for machine learning (GPML) toolbox." The Journal of Machine Learning Research 11 (2010): 3011-3015.
Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html
See Also:
* :class:`niaaml.classifiers.Classifier`
"""
Name = 'Gaussian Process Classifier'
def __init__(self, **kwargs):
r"""Initialize GaussianProcess instance.
"""
warnings.filterwarnings(action='ignore',
category=ChangedBehaviorWarning)
warnings.filterwarnings(action='ignore', category=ConvergenceWarning)
warnings.filterwarnings(action='ignore',
category=DataConversionWarning)
warnings.filterwarnings(action='ignore',
category=DataDimensionalityWarning)
warnings.filterwarnings(action='ignore', category=EfficiencyWarning)
warnings.filterwarnings(action='ignore', category=FitFailedWarning)
warnings.filterwarnings(action='ignore', category=NonBLASDotWarning)
warnings.filterwarnings(action='ignore',
category=UndefinedMetricWarning)
self._params = dict(
max_iter_predict=ParameterDefinition(MinMax(50, 200), np.uint),
warm_start=ParameterDefinition([True, False]),
multi_class=ParameterDefinition(['one_vs_rest', 'one_vs_one']))
self.__gaussian_process = GPC()
def set_parameters(self, **kwargs):
r"""Set the parameters/arguments of the algorithm.
"""
self.__gaussian_process.set_params(**kwargs)
def fit(self, x, y, **kwargs):
r"""Fit GaussianProcess.
Arguments:
x (pandas.core.frame.DataFrame): n samples to classify.
y (pandas.core.series.Series): n classes of the samples in the x array.
Returns:
None
"""
self.__gaussian_process.fit(x, y)
def predict(self, x, **kwargs):
r"""Predict class for each sample (row) in x.
Arguments:
x (pandas.core.frame.DataFrame): n samples to classify.
Returns:
pandas.core.series.Series: n predicted classes.
"""
return self.__gaussian_process.predict(x)
def to_string(self):
r"""User friendly representation of the object.
Returns:
str: User friendly representation of the object.
"""
return Classifier.to_string(self).format(
name=self.Name,
args=self._parameters_to_string(
self.__gaussian_process.get_params()))