""" Nearest Centroid Classification """ # Author: Robert Layton # Olivier Grisel # # License: BSD 3 clause import warnings from numbers import Real import numpy as np from scipy import sparse as sp from sklearn.metrics.pairwise import _VALID_METRICS from ..base import BaseEstimator, ClassifierMixin, _fit_context from ..metrics.pairwise import pairwise_distances_argmin from ..preprocessing import LabelEncoder from ..utils._param_validation import Interval, StrOptions from ..utils.multiclass import check_classification_targets from ..utils.sparsefuncs import csc_median_axis_0 from ..utils.validation import check_is_fitted class NearestCentroid(ClassifierMixin, BaseEstimator): """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 : str or callable, default="euclidean" Metric to use for distance computation. See the documentation of `scipy.spatial.distance `_ and the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. Note that "wminkowski", "seuclidean" and "mahalanobis" are not supported. 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. .. deprecated:: 1.3 Support for metrics other than `euclidean` and `manhattan` and for callables was deprecated in version 1.3 and will be removed in version 1.5. .. versionchanged:: 0.19 `metric='precomputed'` was deprecated and now raises an error shrink_threshold : float, default=None Threshold for shrinking centroids to remove features. Attributes ---------- centroids_ : array-like of shape (n_classes, n_features) Centroid of each class. classes_ : array of shape (n_classes,) The unique classes labels. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- 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. Examples -------- >>> from sklearn.neighbors 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() >>> print(clf.predict([[-0.8, -1]])) [1] """ _valid_metrics = set(_VALID_METRICS) - {"mahalanobis", "seuclidean", "wminkowski"} _parameter_constraints: dict = { "metric": [ StrOptions( _valid_metrics, deprecated=_valid_metrics - {"manhattan", "euclidean"} ), callable, ], "shrink_threshold": [Interval(Real, 0, None, closed="neither"), None], } def __init__(self, metric="euclidean", *, shrink_threshold=None): self.metric = metric self.shrink_threshold = shrink_threshold @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y): """ Fit the NearestCentroid model according to the given training data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. Note that centroid shrinking cannot be used with sparse matrices. y : array-like of shape (n_samples,) Target values. Returns ------- self : object Fitted estimator. """ if isinstance(self.metric, str) and self.metric not in ( "manhattan", "euclidean", ): warnings.warn( ( "Support for distance metrics other than euclidean and " "manhattan and for callables was deprecated in version " "1.3 and will be removed in version 1.5." ), FutureWarning, ) # 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 = self._validate_data(X, y, accept_sparse=["csc"]) else: X, y = self._validate_data(X, y, accept_sparse=["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( "The number of classes has to be greater than one; got %d class" % (n_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] 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: # TODO(1.5) remove warning when metric is only manhattan or euclidean 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: if np.all(np.ptp(X, axis=0) == 0): raise ValueError("All features have zero variance. Division by zero.") dataset_centroid_ = np.mean(X, axis=0) # m parameter for determining deviation m = np.sqrt((1.0 / nk) - (1.0 / 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 np.clip(deviation, 0, None, out=deviation) deviation *= signs # Now adjust the centroids using the deviation msd = ms * deviation self.centroids_ = dataset_centroid_[np.newaxis, :] + msd return self # TODO(1.5) remove note about precomputed metric 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, sparse matrix} of shape (n_samples, n_features) Test samples. Returns ------- C : ndarray of shape (n_samples,) The predicted classes. 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) X = self._validate_data(X, accept_sparse="csr", reset=False) return self.classes_[ pairwise_distances_argmin(X, self.centroids_, metric=self.metric) ]