# -*- coding: utf-8 -*- """ Nearest Centroid Classification """ # Author: Robert Layton # Olivier Grisel # # License: BSD 3 clause import warnings import numpy as np from scipy import sparse as sp from ..base import BaseEstimator, ClassifierMixin from ..metrics.pairwise import pairwise_distances from ..preprocessing import LabelEncoder from ..utils.validation import check_array, check_X_y, check_is_fitted from ..utils.sparsefuncs import csc_median_axis_0 from ..utils.multiclass import check_classification_targets class NearestCentroid(BaseEstimator, ClassifierMixin): """Nearest centroid classifier. Each class is represented by its centroid, with test samples classified to the class with the nearest centroid. Read more in the :ref:`User Guide `. Parameters ---------- metric: string, or callable The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by metrics.pairwise.pairwise_distances for its metric parameter. The centroids for the samples corresponding to each class is the point from which the sum of the distances (according to the metric) of all samples that belong to that particular class are minimized. If the "manhattan" metric is provided, this centroid is the median and for all other metrics, the centroid is now set to be the mean. shrink_threshold : float, optional (default = None) Threshold for shrinking centroids to remove features. Attributes ---------- centroids_ : array-like, shape = [n_classes, n_features] Centroid of each class Examples -------- >>> from sklearn.neighbors.nearest_centroid import NearestCentroid >>> import numpy as np >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> clf = NearestCentroid() >>> clf.fit(X, y) NearestCentroid(metric='euclidean', shrink_threshold=None) >>> print(clf.predict([[-0.8, -1]])) [1] See also -------- sklearn.neighbors.KNeighborsClassifier: nearest neighbors classifier Notes ----- When used for text classification with tf-idf vectors, this classifier is also known as the Rocchio classifier. References ---------- Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America, 99(10), 6567-6572. The National Academy of Sciences. """ def __init__(self, metric='euclidean', shrink_threshold=None): self.metric = metric self.shrink_threshold = shrink_threshold def fit(self, X, y): """ Fit the NearestCentroid model according to the given training data. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vector, where n_samples in the number of samples and n_features is the number of features. Note that centroid shrinking cannot be used with sparse matrices. y : array, shape = [n_samples] Target values (integers) """ # If X is sparse and the metric is "manhattan", store it in a csc # format is easier to calculate the median. if self.metric == 'manhattan': X, y = check_X_y(X, y, ['csc']) else: X, y = check_X_y(X, y, ['csr', 'csc']) is_X_sparse = sp.issparse(X) if is_X_sparse and self.shrink_threshold: raise ValueError("threshold shrinking not supported" " for sparse input") check_classification_targets(y) n_samples, n_features = X.shape le = LabelEncoder() y_ind = le.fit_transform(y) self.classes_ = classes = le.classes_ n_classes = classes.size if n_classes < 2: raise ValueError('y has less than 2 classes') # Mask mapping each class to its members. self.centroids_ = np.empty((n_classes, n_features), dtype=np.float64) # Number of clusters in each class. nk = np.zeros(n_classes) for cur_class in range(n_classes): center_mask = y_ind == cur_class nk[cur_class] = np.sum(center_mask) if is_X_sparse: center_mask = np.where(center_mask)[0] # XXX: Update other averaging methods according to the metrics. if self.metric == "manhattan": # NumPy does not calculate median of sparse matrices. if not is_X_sparse: self.centroids_[cur_class] = np.median(X[center_mask], axis=0) else: self.centroids_[cur_class] = csc_median_axis_0(X[center_mask]) else: if self.metric != 'euclidean': warnings.warn("Averaging for metrics other than " "euclidean and manhattan not supported. " "The average is set to be the mean." ) self.centroids_[cur_class] = X[center_mask].mean(axis=0) if self.shrink_threshold: dataset_centroid_ = np.mean(X, axis=0) # m parameter for determining deviation m = np.sqrt((1. / nk) + (1. / n_samples)) # Calculate deviation using the standard deviation of centroids. variance = (X - self.centroids_[y_ind]) ** 2 variance = variance.sum(axis=0) s = np.sqrt(variance / (n_samples - n_classes)) s += np.median(s) # To deter outliers from affecting the results. mm = m.reshape(len(m), 1) # Reshape to allow broadcasting. ms = mm * s deviation = ((self.centroids_ - dataset_centroid_) / ms) # Soft thresholding: if the deviation crosses 0 during shrinking, # it becomes zero. signs = np.sign(deviation) deviation = (np.abs(deviation) - self.shrink_threshold) deviation[deviation < 0] = 0 deviation *= signs # Now adjust the centroids using the deviation msd = ms * deviation self.centroids_ = dataset_centroid_[np.newaxis, :] + msd return self def predict(self, X): """Perform classification on an array of test vectors X. The predicted class C for each sample in X is returned. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- C : array, shape = [n_samples] Notes ----- If the metric constructor parameter is "precomputed", X is assumed to be the distance matrix between the data to be predicted and ``self.centroids_``. """ check_is_fitted(self, 'centroids_') X = check_array(X, accept_sparse='csr') return self.classes_[pairwise_distances( X, self.centroids_, metric=self.metric).argmin(axis=1)]