"""Nearest Neighbor Classification""" # Authors: Jake Vanderplas # Fabian Pedregosa # Alexandre Gramfort # Sparseness support by Lars Buitinck # Multi-output support by Arnaud Joly # # License: BSD 3 clause (C) INRIA, University of Amsterdam import warnings from numbers import Integral import numpy as np from sklearn.neighbors._base import _check_precomputed from ..base import ClassifierMixin, _fit_context from ..metrics._pairwise_distances_reduction import ArgKminClassMode from ..utils._param_validation import StrOptions from ..utils.extmath import weighted_mode from ..utils.fixes import _mode from ..utils.validation import _is_arraylike, _num_samples, check_is_fitted from ._base import KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin, _get_weights def _adjusted_metric(metric, metric_kwargs, p=None): metric_kwargs = metric_kwargs or {} if metric == "minkowski": metric_kwargs["p"] = p if p == 2: metric = "euclidean" return metric, metric_kwargs class KNeighborsClassifier(KNeighborsMixin, ClassifierMixin, NeighborsBase): """Classifier implementing the k-nearest neighbors vote. Read more in the :ref:`User Guide `. Parameters ---------- n_neighbors : int, default=5 Number of neighbors to use by default for :meth:`kneighbors` queries. weights : {'uniform', 'distance'}, callable or None, default='uniform' Weight function used in prediction. Possible values: - 'uniform' : uniform weights. All points in each neighborhood are weighted equally. - 'distance' : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away. - [callable] : a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' Algorithm used to compute the nearest neighbors: - 'ball_tree' will use :class:`BallTree` - 'kd_tree' will use :class:`KDTree` - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, default=30 Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. p : int, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. metric : str or callable, default='minkowski' Metric to use for distance computation. Default is "minkowski", which results in the standard Euclidean distance when p = 2. See the documentation of `scipy.spatial.distance `_ and the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. If metric is "precomputed", X is assumed to be a distance matrix and must be square during fit. X may be a :term:`sparse graph`, in which case only "nonzero" elements may be considered neighbors. If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. metric_params : dict, default=None Additional keyword arguments for the metric function. n_jobs : int, default=None The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. Doesn't affect :meth:`fit` method. Attributes ---------- classes_ : array of shape (n_classes,) Class labels known to the classifier effective_metric_ : str or callble The distance metric used. It will be same as the `metric` parameter or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to 'minkowski' and `p` parameter set to 2. effective_metric_params_ : dict Additional keyword arguments for the metric function. For most metrics will be same with `metric_params` parameter, but may also contain the `p` parameter value if the `effective_metric_` attribute is set to 'minkowski'. 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 n_samples_fit_ : int Number of samples in the fitted data. outputs_2d_ : bool False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit otherwise True. See Also -------- RadiusNeighborsClassifier: Classifier based on neighbors within a fixed radius. KNeighborsRegressor: Regression based on k-nearest neighbors. RadiusNeighborsRegressor: Regression based on neighbors within a fixed radius. NearestNeighbors: Unsupervised learner for implementing neighbor searches. Notes ----- See :ref:`Nearest Neighbors ` in the online documentation for a discussion of the choice of ``algorithm`` and ``leaf_size``. .. warning:: Regarding the Nearest Neighbors algorithms, if it is found that two neighbors, neighbor `k+1` and `k`, have identical distances but different labels, the results will depend on the ordering of the training data. https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm Examples -------- >>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import KNeighborsClassifier >>> neigh = KNeighborsClassifier(n_neighbors=3) >>> neigh.fit(X, y) KNeighborsClassifier(...) >>> print(neigh.predict([[1.1]])) [0] >>> print(neigh.predict_proba([[0.9]])) [[0.666... 0.333...]] """ _parameter_constraints: dict = {**NeighborsBase._parameter_constraints} _parameter_constraints.pop("radius") _parameter_constraints.update( {"weights": [StrOptions({"uniform", "distance"}), callable, None]} ) def __init__( self, n_neighbors=5, *, weights="uniform", algorithm="auto", leaf_size=30, p=2, metric="minkowski", metric_params=None, n_jobs=None, ): super().__init__( n_neighbors=n_neighbors, algorithm=algorithm, leaf_size=leaf_size, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs, ) self.weights = weights @_fit_context( # KNeighborsClassifier.metric is not validated yet prefer_skip_nested_validation=False ) def fit(self, X, y): """Fit the k-nearest neighbors classifier from the training dataset. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) or \ (n_samples, n_samples) if metric='precomputed' Training data. y : {array-like, sparse matrix} of shape (n_samples,) or \ (n_samples, n_outputs) Target values. Returns ------- self : KNeighborsClassifier The fitted k-nearest neighbors classifier. """ return self._fit(X, y) def predict(self, X): """Predict the class labels for the provided data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_queries, n_features), \ or (n_queries, n_indexed) if metric == 'precomputed' Test samples. Returns ------- y : ndarray of shape (n_queries,) or (n_queries, n_outputs) Class labels for each data sample. """ check_is_fitted(self, "_fit_method") if self.weights == "uniform": if self._fit_method == "brute" and ArgKminClassMode.is_usable_for( X, self._fit_X, self.metric ): probabilities = self.predict_proba(X) if self.outputs_2d_: return np.stack( [ self.classes_[idx][np.argmax(probas, axis=1)] for idx, probas in enumerate(probabilities) ], axis=1, ) return self.classes_[np.argmax(probabilities, axis=1)] # In that case, we do not need the distances to perform # the weighting so we do not compute them. neigh_ind = self.kneighbors(X, return_distance=False) neigh_dist = None else: neigh_dist, neigh_ind = self.kneighbors(X) classes_ = self.classes_ _y = self._y if not self.outputs_2d_: _y = self._y.reshape((-1, 1)) classes_ = [self.classes_] n_outputs = len(classes_) n_queries = _num_samples(X) weights = _get_weights(neigh_dist, self.weights) y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype) for k, classes_k in enumerate(classes_): if weights is None: mode, _ = _mode(_y[neigh_ind, k], axis=1) else: mode, _ = weighted_mode(_y[neigh_ind, k], weights, axis=1) mode = np.asarray(mode.ravel(), dtype=np.intp) y_pred[:, k] = classes_k.take(mode) if not self.outputs_2d_: y_pred = y_pred.ravel() return y_pred def predict_proba(self, X): """Return probability estimates for the test data X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_queries, n_features), \ or (n_queries, n_indexed) if metric == 'precomputed' Test samples. Returns ------- p : ndarray of shape (n_queries, n_classes), or a list of n_outputs \ of such arrays if n_outputs > 1. The class probabilities of the input samples. Classes are ordered by lexicographic order. """ check_is_fitted(self, "_fit_method") if self.weights == "uniform": # TODO: systematize this mapping of metric for # PairwiseDistancesReductions. metric, metric_kwargs = _adjusted_metric( metric=self.metric, metric_kwargs=self.metric_params, p=self.p ) if ( self._fit_method == "brute" and ArgKminClassMode.is_usable_for(X, self._fit_X, metric) # TODO: Implement efficient multi-output solution and not self.outputs_2d_ ): if self.metric == "precomputed": X = _check_precomputed(X) else: X = self._validate_data( X, accept_sparse="csr", reset=False, order="C" ) probabilities = ArgKminClassMode.compute( X, self._fit_X, k=self.n_neighbors, weights=self.weights, labels=self._y, unique_labels=self.classes_, metric=metric, metric_kwargs=metric_kwargs, # `strategy="parallel_on_X"` has in practice be shown # to be more efficient than `strategy="parallel_on_Y`` # on many combination of datasets. # Hence, we choose to enforce it here. # For more information, see: # https://github.com/scikit-learn/scikit-learn/pull/24076#issuecomment-1445258342 # noqa # TODO: adapt the heuristic for `strategy="auto"` for # `ArgKminClassMode` and use `strategy="auto"`. strategy="parallel_on_X", ) return probabilities # In that case, we do not need the distances to perform # the weighting so we do not compute them. neigh_ind = self.kneighbors(X, return_distance=False) neigh_dist = None else: neigh_dist, neigh_ind = self.kneighbors(X) classes_ = self.classes_ _y = self._y if not self.outputs_2d_: _y = self._y.reshape((-1, 1)) classes_ = [self.classes_] n_queries = _num_samples(X) weights = _get_weights(neigh_dist, self.weights) if weights is None: weights = np.ones_like(neigh_ind) all_rows = np.arange(n_queries) probabilities = [] for k, classes_k in enumerate(classes_): pred_labels = _y[:, k][neigh_ind] proba_k = np.zeros((n_queries, classes_k.size)) # a simple ':' index doesn't work right for i, idx in enumerate(pred_labels.T): # loop is O(n_neighbors) proba_k[all_rows, idx] += weights[:, i] # normalize 'votes' into real [0,1] probabilities normalizer = proba_k.sum(axis=1)[:, np.newaxis] normalizer[normalizer == 0.0] = 1.0 proba_k /= normalizer probabilities.append(proba_k) if not self.outputs_2d_: probabilities = probabilities[0] return probabilities def _more_tags(self): return {"multilabel": True} class RadiusNeighborsClassifier(RadiusNeighborsMixin, ClassifierMixin, NeighborsBase): """Classifier implementing a vote among neighbors within a given radius. Read more in the :ref:`User Guide `. Parameters ---------- radius : float, default=1.0 Range of parameter space to use by default for :meth:`radius_neighbors` queries. weights : {'uniform', 'distance'}, callable or None, default='uniform' Weight function used in prediction. Possible values: - 'uniform' : uniform weights. All points in each neighborhood are weighted equally. - 'distance' : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away. - [callable] : a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights. Uniform weights are used by default. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' Algorithm used to compute the nearest neighbors: - 'ball_tree' will use :class:`BallTree` - 'kd_tree' will use :class:`KDTree` - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, default=30 Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. p : int, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. metric : str or callable, default='minkowski' Metric to use for distance computation. Default is "minkowski", which results in the standard Euclidean distance when p = 2. See the documentation of `scipy.spatial.distance `_ and the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. If metric is "precomputed", X is assumed to be a distance matrix and must be square during fit. X may be a :term:`sparse graph`, in which case only "nonzero" elements may be considered neighbors. If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. outlier_label : {manual label, 'most_frequent'}, default=None Label for outlier samples (samples with no neighbors in given radius). - manual label: str or int label (should be the same type as y) or list of manual labels if multi-output is used. - 'most_frequent' : assign the most frequent label of y to outliers. - None : when any outlier is detected, ValueError will be raised. metric_params : dict, default=None Additional keyword arguments for the metric function. n_jobs : int, default=None The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. Attributes ---------- classes_ : ndarray of shape (n_classes,) Class labels known to the classifier. effective_metric_ : str or callable The distance metric used. It will be same as the `metric` parameter or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to 'minkowski' and `p` parameter set to 2. effective_metric_params_ : dict Additional keyword arguments for the metric function. For most metrics will be same with `metric_params` parameter, but may also contain the `p` parameter value if the `effective_metric_` attribute is set to 'minkowski'. 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 n_samples_fit_ : int Number of samples in the fitted data. outlier_label_ : int or array-like of shape (n_class,) Label which is given for outlier samples (samples with no neighbors on given radius). outputs_2d_ : bool False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit otherwise True. See Also -------- KNeighborsClassifier : Classifier implementing the k-nearest neighbors vote. RadiusNeighborsRegressor : Regression based on neighbors within a fixed radius. KNeighborsRegressor : Regression based on k-nearest neighbors. NearestNeighbors : Unsupervised learner for implementing neighbor searches. Notes ----- See :ref:`Nearest Neighbors ` in the online documentation for a discussion of the choice of ``algorithm`` and ``leaf_size``. https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm Examples -------- >>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import RadiusNeighborsClassifier >>> neigh = RadiusNeighborsClassifier(radius=1.0) >>> neigh.fit(X, y) RadiusNeighborsClassifier(...) >>> print(neigh.predict([[1.5]])) [0] >>> print(neigh.predict_proba([[1.0]])) [[0.66666667 0.33333333]] """ _parameter_constraints: dict = { **NeighborsBase._parameter_constraints, "weights": [StrOptions({"uniform", "distance"}), callable, None], "outlier_label": [Integral, str, "array-like", None], } _parameter_constraints.pop("n_neighbors") def __init__( self, radius=1.0, *, weights="uniform", algorithm="auto", leaf_size=30, p=2, metric="minkowski", outlier_label=None, metric_params=None, n_jobs=None, ): super().__init__( radius=radius, algorithm=algorithm, leaf_size=leaf_size, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs, ) self.weights = weights self.outlier_label = outlier_label @_fit_context( # RadiusNeighborsClassifier.metric is not validated yet prefer_skip_nested_validation=False ) def fit(self, X, y): """Fit the radius neighbors classifier from the training dataset. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) or \ (n_samples, n_samples) if metric='precomputed' Training data. y : {array-like, sparse matrix} of shape (n_samples,) or \ (n_samples, n_outputs) Target values. Returns ------- self : RadiusNeighborsClassifier The fitted radius neighbors classifier. """ self._fit(X, y) classes_ = self.classes_ _y = self._y if not self.outputs_2d_: _y = self._y.reshape((-1, 1)) classes_ = [self.classes_] if self.outlier_label is None: outlier_label_ = None elif self.outlier_label == "most_frequent": outlier_label_ = [] # iterate over multi-output, get the most frequent label for each # output. for k, classes_k in enumerate(classes_): label_count = np.bincount(_y[:, k]) outlier_label_.append(classes_k[label_count.argmax()]) else: if _is_arraylike(self.outlier_label) and not isinstance( self.outlier_label, str ): if len(self.outlier_label) != len(classes_): raise ValueError( "The length of outlier_label: {} is " "inconsistent with the output " "length: {}".format(self.outlier_label, len(classes_)) ) outlier_label_ = self.outlier_label else: outlier_label_ = [self.outlier_label] * len(classes_) for classes, label in zip(classes_, outlier_label_): if _is_arraylike(label) and not isinstance(label, str): # ensure the outlier label for each output is a scalar. raise TypeError( "The outlier_label of classes {} is " "supposed to be a scalar, got " "{}.".format(classes, label) ) if np.append(classes, label).dtype != classes.dtype: # ensure the dtype of outlier label is consistent with y. raise TypeError( "The dtype of outlier_label {} is " "inconsistent with classes {} in " "y.".format(label, classes) ) self.outlier_label_ = outlier_label_ return self def predict(self, X): """Predict the class labels for the provided data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_queries, n_features), \ or (n_queries, n_indexed) if metric == 'precomputed' Test samples. Returns ------- y : ndarray of shape (n_queries,) or (n_queries, n_outputs) Class labels for each data sample. """ probs = self.predict_proba(X) classes_ = self.classes_ if not self.outputs_2d_: probs = [probs] classes_ = [self.classes_] n_outputs = len(classes_) n_queries = probs[0].shape[0] y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype) for k, prob in enumerate(probs): # iterate over multi-output, assign labels based on probabilities # of each output. max_prob_index = prob.argmax(axis=1) y_pred[:, k] = classes_[k].take(max_prob_index) outlier_zero_probs = (prob == 0).all(axis=1) if outlier_zero_probs.any(): zero_prob_index = np.flatnonzero(outlier_zero_probs) y_pred[zero_prob_index, k] = self.outlier_label_[k] if not self.outputs_2d_: y_pred = y_pred.ravel() return y_pred def predict_proba(self, X): """Return probability estimates for the test data X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_queries, n_features), \ or (n_queries, n_indexed) if metric == 'precomputed' Test samples. Returns ------- p : ndarray of shape (n_queries, n_classes), or a list of \ n_outputs of such arrays if n_outputs > 1. The class probabilities of the input samples. Classes are ordered by lexicographic order. """ n_queries = _num_samples(X) neigh_dist, neigh_ind = self.radius_neighbors(X) outlier_mask = np.zeros(n_queries, dtype=bool) outlier_mask[:] = [len(nind) == 0 for nind in neigh_ind] outliers = np.flatnonzero(outlier_mask) inliers = np.flatnonzero(~outlier_mask) classes_ = self.classes_ _y = self._y if not self.outputs_2d_: _y = self._y.reshape((-1, 1)) classes_ = [self.classes_] if self.outlier_label_ is None and outliers.size > 0: raise ValueError( "No neighbors found for test samples %r, " "you can try using larger radius, " "giving a label for outliers, " "or considering removing them from your dataset." % outliers ) weights = _get_weights(neigh_dist, self.weights) if weights is not None: weights = weights[inliers] probabilities = [] # iterate over multi-output, measure probabilities of the k-th output. for k, classes_k in enumerate(classes_): pred_labels = np.zeros(len(neigh_ind), dtype=object) pred_labels[:] = [_y[ind, k] for ind in neigh_ind] proba_k = np.zeros((n_queries, classes_k.size)) proba_inl = np.zeros((len(inliers), classes_k.size)) # samples have different size of neighbors within the same radius if weights is None: for i, idx in enumerate(pred_labels[inliers]): proba_inl[i, :] = np.bincount(idx, minlength=classes_k.size) else: for i, idx in enumerate(pred_labels[inliers]): proba_inl[i, :] = np.bincount( idx, weights[i], minlength=classes_k.size ) proba_k[inliers, :] = proba_inl if outliers.size > 0: _outlier_label = self.outlier_label_[k] label_index = np.flatnonzero(classes_k == _outlier_label) if label_index.size == 1: proba_k[outliers, label_index[0]] = 1.0 else: warnings.warn( "Outlier label {} is not in training " "classes. All class probabilities of " "outliers will be assigned with 0." "".format(self.outlier_label_[k]) ) # normalize 'votes' into real [0,1] probabilities normalizer = proba_k.sum(axis=1)[:, np.newaxis] normalizer[normalizer == 0.0] = 1.0 proba_k /= normalizer probabilities.append(proba_k) if not self.outputs_2d_: probabilities = probabilities[0] return probabilities def _more_tags(self): return {"multilabel": True}