Source code for

.. module:: CClassifierNearestCentroid
   :synopsis: Nearest Centroid Classifier

.. moduleauthor:: Biggio Battista <>
.. moduleauthor:: Ambra Demontis <>
.. moduleauthor:: Marco Melis <>

from sklearn.neighbors import NearestCentroid

from secml.array import CArray
from import CClassifierSkLearn

from sklearn.metrics.pairwise import pairwise_distances

[docs]class CClassifierNearestCentroid(CClassifierSkLearn): """CClassifierNearestCentroid. Parameters ---------- metric : str or callable, optional The metric to use when calculating distance between instances in a feature array. Default 'euclidean'. shrink_threshold : float, optional Threshold for shrinking centroids to remove features. preprocess : CPreProcess or str or None, optional Features preprocess to be applied to input data. Can be a CPreProcess subclass or a string with the type of the desired preprocessor. If None, input data is used as is. Attributes ---------- class_type : 'nrst-centroid' """ __class_type = 'nrst-centroid' def __init__(self, metric='euclidean', shrink_threshold=None, preprocess=None): nc = NearestCentroid(metric=metric, shrink_threshold=shrink_threshold) super(CClassifierNearestCentroid, self).__init__( sklearn_model=nc, preprocess=preprocess) @property def metric(self): return CArray(self._sklearn_model.metric) @property def centroids(self): return CArray(self._sklearn_model.centroids_) def _forward(self, x): """ This sklearn classifier only supports predict. So we also implement a simple decision function based on pairwise distances. Parameters ---------- x : CArray Input sample(s) after preprocessing Returns ------- CArray Negative distance values to centroids (i.e., similarity w/ centroid). """ dist = CArray(pairwise_distances( x.get_data(), self._sklearn_model.centroids_, metric=self._sklearn_model.metric)).atleast_2d() return -dist