"""Nearest Neighbors graph functions""" # Author: Jake Vanderplas # Tom Dupre la Tour # # License: BSD 3 clause (C) INRIA, University of Amsterdam from ._base import KNeighborsMixin, RadiusNeighborsMixin from ._base import NeighborsBase from ._unsupervised import NearestNeighbors from ..base import TransformerMixin from ..utils.validation import check_is_fitted, _deprecate_positional_args def _check_params(X, metric, p, metric_params): """Check the validity of the input parameters""" params = zip(['metric', 'p', 'metric_params'], [metric, p, metric_params]) est_params = X.get_params() for param_name, func_param in params: if func_param != est_params[param_name]: raise ValueError( "Got %s for %s, while the estimator has %s for " "the same parameter." % ( func_param, param_name, est_params[param_name])) def _query_include_self(X, include_self, mode): """Return the query based on include_self param""" if include_self == 'auto': include_self = mode == 'connectivity' # it does not include each sample as its own neighbors if not include_self: X = None return X @_deprecate_positional_args def kneighbors_graph(X, n_neighbors, *, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False, n_jobs=None): """Computes the (weighted) graph of k-Neighbors for points in X Read more in the :ref:`User Guide `. Parameters ---------- X : array-like of shape (n_samples, n_features) or BallTree Sample data, in the form of a numpy array or a precomputed :class:`BallTree`. n_neighbors : int Number of neighbors for each sample. mode : {'connectivity', 'distance'}, default='connectivity' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. metric : str, default='minkowski' The distance metric used to calculate the k-Neighbors for each sample point. The DistanceMetric class gives a list of available metrics. The default distance is 'euclidean' ('minkowski' metric with the p param equal to 2.) 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_params : dict, default=None additional keyword arguments for the metric function. include_self : bool or 'auto', default=False Whether or not to mark each sample as the first nearest neighbor to itself. If 'auto', then True is used for mode='connectivity' and False for mode='distance'. 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. Returns ------- A : sparse matrix of shape (n_samples, n_samples) Graph where A[i, j] is assigned the weight of edge that connects i to j. The matrix is of CSR format. Examples -------- >>> X = [[0], [3], [1]] >>> from sklearn.neighbors import kneighbors_graph >>> A = kneighbors_graph(X, 2, mode='connectivity', include_self=True) >>> A.toarray() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) See Also -------- radius_neighbors_graph """ if not isinstance(X, KNeighborsMixin): X = NearestNeighbors(n_neighbors=n_neighbors, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs).fit(X) else: _check_params(X, metric, p, metric_params) query = _query_include_self(X._fit_X, include_self, mode) return X.kneighbors_graph(X=query, n_neighbors=n_neighbors, mode=mode) @_deprecate_positional_args def radius_neighbors_graph(X, radius, *, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False, n_jobs=None): """Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Read more in the :ref:`User Guide `. Parameters ---------- X : array-like of shape (n_samples, n_features) or BallTree Sample data, in the form of a numpy array or a precomputed :class:`BallTree`. radius : float Radius of neighborhoods. mode : {'connectivity', 'distance'}, default='connectivity' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. metric : str, default='minkowski' The distance metric used to calculate the neighbors within a given radius for each sample point. The DistanceMetric class gives a list of available metrics. The default distance is 'euclidean' ('minkowski' metric with the param equal to 2.) 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_params : dict, default=None additional keyword arguments for the metric function. include_self : bool or 'auto', default=False Whether or not to mark each sample as the first nearest neighbor to itself. If 'auto', then True is used for mode='connectivity' and False for mode='distance'. 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. Returns ------- A : sparse matrix of shape (n_samples, n_samples) Graph where A[i, j] is assigned the weight of edge that connects i to j. The matrix is of CSR format. Examples -------- >>> X = [[0], [3], [1]] >>> from sklearn.neighbors import radius_neighbors_graph >>> A = radius_neighbors_graph(X, 1.5, mode='connectivity', ... include_self=True) >>> A.toarray() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) See Also -------- kneighbors_graph """ if not isinstance(X, RadiusNeighborsMixin): X = NearestNeighbors(radius=radius, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs).fit(X) else: _check_params(X, metric, p, metric_params) query = _query_include_self(X._fit_X, include_self, mode) return X.radius_neighbors_graph(query, radius, mode) class KNeighborsTransformer(KNeighborsMixin, TransformerMixin, NeighborsBase): """Transform X into a (weighted) graph of k nearest neighbors The transformed data is a sparse graph as returned by kneighbors_graph. Read more in the :ref:`User Guide `. .. versionadded:: 0.22 Parameters ---------- mode : {'distance', 'connectivity'}, default='distance' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. n_neighbors : int, default=5 Number of neighbors for each sample in the transformed sparse graph. For compatibility reasons, as each sample is considered as its own neighbor, one extra neighbor will be computed when mode == 'distance'. In this case, the sparse graph contains (n_neighbors + 1) neighbors. 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. metric : str or callable, default='minkowski' metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Distance matrices are not supported. Valid values for metric are: - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan'] - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. p : int, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. 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_params : dict, default=None Additional keyword arguments for the metric function. n_jobs : int, default=1 The number of parallel jobs to run for neighbors search. If ``-1``, then the number of jobs is set to the number of CPU cores. Attributes ---------- 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_samples_fit_ : int Number of samples in the fitted data. Examples -------- >>> from sklearn.manifold import Isomap >>> from sklearn.neighbors import KNeighborsTransformer >>> from sklearn.pipeline import make_pipeline >>> estimator = make_pipeline( ... KNeighborsTransformer(n_neighbors=5, mode='distance'), ... Isomap(neighbors_algorithm='precomputed')) """ @_deprecate_positional_args def __init__(self, *, mode='distance', n_neighbors=5, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=1): super(KNeighborsTransformer, self).__init__( n_neighbors=n_neighbors, radius=None, algorithm=algorithm, leaf_size=leaf_size, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs) self.mode = mode def fit(self, X, y=None): """Fit the k-nearest neighbors transformer 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. Returns ------- self : KNeighborsTransformer The fitted k-nearest neighbors transformer. """ return self._fit(X) def transform(self, X): """Computes the (weighted) graph of Neighbors for points in X Parameters ---------- X : array-like of shape (n_samples_transform, n_features) Sample data. Returns ------- Xt : sparse matrix of shape (n_samples_transform, n_samples_fit) Xt[i, j] is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit. The matrix is of CSR format. """ check_is_fitted(self) add_one = self.mode == 'distance' return self.kneighbors_graph(X, mode=self.mode, n_neighbors=self.n_neighbors + add_one) def fit_transform(self, X, y=None): """Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters ---------- X : array-like of shape (n_samples, n_features) Training set. y : ignored Returns ------- Xt : sparse matrix of shape (n_samples, n_samples) Xt[i, j] is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit. The matrix is of CSR format. """ return self.fit(X).transform(X) def _more_tags(self): return { '_xfail_checks': { 'check_methods_sample_order_invariance': 'check is not applicable.' } } class RadiusNeighborsTransformer(RadiusNeighborsMixin, TransformerMixin, NeighborsBase): """Transform X into a (weighted) graph of neighbors nearer than a radius The transformed data is a sparse graph as returned by radius_neighbors_graph. Read more in the :ref:`User Guide `. .. versionadded:: 0.22 Parameters ---------- mode : {'distance', 'connectivity'}, default='distance' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. radius : float, default=1. Radius of neighborhood in the transformed sparse graph. 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. metric : str or callable, default='minkowski' metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Distance matrices are not supported. Valid values for metric are: - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan'] - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. p : int, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. 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_params : dict, default=None Additional keyword arguments for the metric function. n_jobs : int, default=1 The number of parallel jobs to run for neighbors search. If ``-1``, then the number of jobs is set to the number of CPU cores. Attributes ---------- 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_samples_fit_ : int Number of samples in the fitted data. Examples -------- >>> from sklearn.cluster import DBSCAN >>> from sklearn.neighbors import RadiusNeighborsTransformer >>> from sklearn.pipeline import make_pipeline >>> estimator = make_pipeline( ... RadiusNeighborsTransformer(radius=42.0, mode='distance'), ... DBSCAN(min_samples=30, metric='precomputed')) """ @_deprecate_positional_args def __init__(self, *, mode='distance', radius=1., algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=1): super(RadiusNeighborsTransformer, self).__init__( n_neighbors=None, radius=radius, algorithm=algorithm, leaf_size=leaf_size, metric=metric, p=p, metric_params=metric_params, n_jobs=n_jobs) self.mode = mode def fit(self, X, y=None): """Fit the radius neighbors transformer 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. Returns ------- self : RadiusNeighborsTransformer The fitted radius neighbors transformer. """ return self._fit(X) def transform(self, X): """Computes the (weighted) graph of Neighbors for points in X Parameters ---------- X : array-like of shape (n_samples_transform, n_features) Sample data Returns ------- Xt : sparse matrix of shape (n_samples_transform, n_samples_fit) Xt[i, j] is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit. The matrix is of CSR format. """ check_is_fitted(self) return self.radius_neighbors_graph(X, mode=self.mode, sort_results=True) def fit_transform(self, X, y=None): """Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters ---------- X : array-like of shape (n_samples, n_features) Training set. y : ignored Returns ------- Xt : sparse matrix of shape (n_samples, n_samples) Xt[i, j] is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit. The matrix is of CSR format. """ return self.fit(X).transform(X) def _more_tags(self): return { '_xfail_checks': { 'check_methods_sample_order_invariance': 'check is not applicable.' } }