207 lines
7.8 KiB
Python
207 lines
7.8 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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Nearest Centroid Classification
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"""
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# Author: Robert Layton <robertlayton@gmail.com>
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# Olivier Grisel <olivier.grisel@ensta.org>
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#
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# License: BSD 3 clause
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import warnings
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import numpy as np
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from scipy import sparse as sp
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from ..base import BaseEstimator, ClassifierMixin
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from ..metrics.pairwise import pairwise_distances
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from ..preprocessing import LabelEncoder
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from ..utils.validation import check_array, check_is_fitted
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from ..utils.validation import _deprecate_positional_args
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from ..utils.sparsefuncs import csc_median_axis_0
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from ..utils.multiclass import check_classification_targets
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class NearestCentroid(ClassifierMixin, BaseEstimator):
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"""Nearest centroid classifier.
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Each class is represented by its centroid, with test samples classified to
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the class with the nearest centroid.
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Read more in the :ref:`User Guide <nearest_centroid_classifier>`.
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Parameters
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----------
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metric : str or callable
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The metric to use when calculating distance between instances in a
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feature array. If metric is a string or callable, it must be one of
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the options allowed by metrics.pairwise.pairwise_distances for its
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metric parameter.
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The centroids for the samples corresponding to each class is the point
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from which the sum of the distances (according to the metric) of all
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samples that belong to that particular class are minimized.
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If the "manhattan" metric is provided, this centroid is the median and
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for all other metrics, the centroid is now set to be the mean.
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.. versionchanged:: 0.19
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``metric='precomputed'`` was deprecated and now raises an error
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shrink_threshold : float, default=None
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Threshold for shrinking centroids to remove features.
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Attributes
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----------
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centroids_ : array-like of shape (n_classes, n_features)
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Centroid of each class.
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classes_ : array of shape (n_classes,)
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The unique classes labels.
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Examples
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--------
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>>> from sklearn.neighbors import NearestCentroid
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>>> import numpy as np
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>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
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>>> y = np.array([1, 1, 1, 2, 2, 2])
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>>> clf = NearestCentroid()
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>>> clf.fit(X, y)
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NearestCentroid()
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>>> print(clf.predict([[-0.8, -1]]))
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[1]
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See Also
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--------
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KNeighborsClassifier : Nearest neighbors classifier.
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Notes
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-----
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When used for text classification with tf-idf vectors, this classifier is
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also known as the Rocchio classifier.
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References
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----------
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Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002). Diagnosis of
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multiple cancer types by shrunken centroids of gene expression. Proceedings
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of the National Academy of Sciences of the United States of America,
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99(10), 6567-6572. The National Academy of Sciences.
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"""
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@_deprecate_positional_args
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def __init__(self, metric='euclidean', *, shrink_threshold=None):
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self.metric = metric
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self.shrink_threshold = shrink_threshold
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def fit(self, X, y):
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"""
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Fit the NearestCentroid model according to the given training data.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Training vector, where n_samples is the number of samples and
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n_features is the number of features.
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Note that centroid shrinking cannot be used with sparse matrices.
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y : array-like of shape (n_samples,)
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Target values (integers)
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"""
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if self.metric == 'precomputed':
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raise ValueError("Precomputed is not supported.")
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# If X is sparse and the metric is "manhattan", store it in a csc
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# format is easier to calculate the median.
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if self.metric == 'manhattan':
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X, y = self._validate_data(X, y, accept_sparse=['csc'])
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else:
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X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc'])
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is_X_sparse = sp.issparse(X)
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if is_X_sparse and self.shrink_threshold:
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raise ValueError("threshold shrinking not supported"
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" for sparse input")
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check_classification_targets(y)
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n_samples, n_features = X.shape
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le = LabelEncoder()
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y_ind = le.fit_transform(y)
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self.classes_ = classes = le.classes_
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n_classes = classes.size
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if n_classes < 2:
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raise ValueError('The number of classes has to be greater than'
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' one; got %d class' % (n_classes))
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# Mask mapping each class to its members.
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self.centroids_ = np.empty((n_classes, n_features), dtype=np.float64)
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# Number of clusters in each class.
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nk = np.zeros(n_classes)
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for cur_class in range(n_classes):
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center_mask = y_ind == cur_class
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nk[cur_class] = np.sum(center_mask)
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if is_X_sparse:
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center_mask = np.where(center_mask)[0]
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# XXX: Update other averaging methods according to the metrics.
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if self.metric == "manhattan":
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# NumPy does not calculate median of sparse matrices.
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if not is_X_sparse:
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self.centroids_[cur_class] = np.median(X[center_mask], axis=0)
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else:
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self.centroids_[cur_class] = csc_median_axis_0(X[center_mask])
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else:
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if self.metric != 'euclidean':
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warnings.warn("Averaging for metrics other than "
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"euclidean and manhattan not supported. "
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"The average is set to be the mean."
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)
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self.centroids_[cur_class] = X[center_mask].mean(axis=0)
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if self.shrink_threshold:
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if np.all(np.ptp(X, axis=0) == 0):
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raise ValueError("All features have zero variance. "
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"Division by zero.")
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dataset_centroid_ = np.mean(X, axis=0)
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# m parameter for determining deviation
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m = np.sqrt((1. / nk) - (1. / n_samples))
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# Calculate deviation using the standard deviation of centroids.
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variance = (X - self.centroids_[y_ind]) ** 2
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variance = variance.sum(axis=0)
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s = np.sqrt(variance / (n_samples - n_classes))
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s += np.median(s) # To deter outliers from affecting the results.
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mm = m.reshape(len(m), 1) # Reshape to allow broadcasting.
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ms = mm * s
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deviation = ((self.centroids_ - dataset_centroid_) / ms)
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# Soft thresholding: if the deviation crosses 0 during shrinking,
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# it becomes zero.
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signs = np.sign(deviation)
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deviation = (np.abs(deviation) - self.shrink_threshold)
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np.clip(deviation, 0, None, out=deviation)
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deviation *= signs
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# Now adjust the centroids using the deviation
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msd = ms * deviation
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self.centroids_ = dataset_centroid_[np.newaxis, :] + msd
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return self
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def predict(self, X):
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"""Perform classification on an array of test vectors X.
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The predicted class C for each sample in X is returned.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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Returns
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-------
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C : ndarray of shape (n_samples,)
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Notes
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-----
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If the metric constructor parameter is "precomputed", X is assumed to
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be the distance matrix between the data to be predicted and
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``self.centroids_``.
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"""
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check_is_fitted(self)
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X = check_array(X, accept_sparse='csr')
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return self.classes_[pairwise_distances(
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X, self.centroids_, metric=self.metric).argmin(axis=1)]
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