‘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.
Refer to the example entitled showing the impact of the algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’ Algorithm used to compute the nearest neighbors:
Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_sizeint, 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. pfloat, 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. This parameter is expected to be positive. metricstr 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 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 , 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_paramsdict, default=None Additional keyword arguments for the metric function. n_jobsint, default=None The number of parallel jobs to run for neighbors search. > samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=1) neigh.fit(samples) NearestNeighbors(n_neighbors=1) print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]])) 0 means 1 unless in a context. > samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=1) neigh.fit(samples) NearestNeighbors(n_neighbors=1) print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]])) 2 means using all processors. See for more details. Doesn’t affect 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 > samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=1) neigh.fit(samples) NearestNeighbors(n_neighbors=1) print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]])) 4 parameter or a synonym of it, e.g. ‘euclidean’ if the > samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=1) neigh.fit(samples) NearestNeighbors(n_neighbors=1) print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]])) 4 parameter set to ‘minkowski’ and > samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=1) neigh.fit(samples) NearestNeighbors(n_neighbors=1) print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]])) 6 parameter set to 2. effective_metric_params_dict Additional keyword arguments for the metric function. For most metrics will be same with > samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=1) neigh.fit(samples) NearestNeighbors(n_neighbors=1) print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]])) 7 parameter, but may also contain the > samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=1) neigh.fit(samples) NearestNeighbors(n_neighbors=1) print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]])) 6 parameter value if the > samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=1) neigh.fit(samples) NearestNeighbors(n_neighbors=1) print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]])) 9 attribute is set to ‘minkowski’. n_features_in_int Number of features seen during . New in version 0.24. feature_names_in_ndarray of shape ( > X = [[0., 1., 0.], [1., 0., 1.]]0,)neigh.kneighbors(X, return_distance=False) Names of features seen during . Defined only when > X = [[0., 1., 0.], [1., 0., 1.]]1 has feature names that are all strings.neigh.kneighbors(X, return_distance=False) New in version 1.0. n_samples_fit_int Number of samples in the fitted data. outputs_2d_bool False when > X = [[0., 1., 0.], [1., 0., 1.]]2’s shape is (n_samples, ) or (n_samples, 1) during fit otherwise True.neigh.kneighbors(X, return_distance=False) Notes See in the online documentation for a discussion of the choice of > X = [[0., 1., 0.], [1., 0., 1.]]3 andneigh.kneighbors(X, return_distance=False) > X = [[0., 1., 0.], [1., 0., 1.]]4.neigh.kneighbors(X, return_distance=False) Warning Regarding the Nearest Neighbors algorithms, if it is found that two neighbors, neighbor > X = [[0., 1., 0.], [1., 0., 1.]]5 andneigh.kneighbors(X, return_distance=False) > X = [[0., 1., 0.], [1., 0., 1.]]6, have identical distances but different labels, the results will depend on the ordering of the training data.neigh.kneighbors(X, return_distance=False) 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...]] Methods (X, y) Fit the k-nearest neighbors classifier from the training dataset. () Get metadata routing of this object. ([deep]) Get parameters for this estimator. ([X, n_neighbors, return_distance]) Find the K-neighbors of a point. ([X, n_neighbors, mode]) Compute the (weighted) graph of k-Neighbors for points in X. (X) Predict the class labels for the provided data. (X) Return probability estimates for the test data X. (X, y[, sample_weight]) Return the mean accuracy on the given test data and labels. (**params) Set the parameters of this estimator. (*[, sample_weight]) Request metadata passed to the > X = [[0], [3], [1]]4 method.from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=2) fit(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:selfKNeighborsClassifier The fitted k-nearest neighbors classifier. get_metadata_routing() Get metadata routing of this object. Please check on how the routing mechanism works. Returns:routingMetadataRequest A encapsulating routing information. get_params(deep\=True) Get parameters for this estimator. Parameters:deepbool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns:paramsdict Parameter names mapped to their values. kneighbors(X\=None, n_neighbors\=None, return_distance\=True) Find the K-neighbors of a point. Returns indices of and distances to the neighbors of each point. Parameters:X{array-like, sparse matrix}, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’, default=None The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. n_neighborsint, default=None Number of neighbors required for each sample. The default is the value passed to the constructor. return_distancebool, default=True Whether or not to return the distances. Returns:neigh_distndarray of shape (n_queries, n_neighbors) Array representing the lengths to points, only present if return_distance=True. neigh_indndarray of shape (n_queries, n_neighbors) Indices of the nearest points in the population matrix. Examples In the following example, we construct a NearestNeighbors class from an array representing our data set and ask who’s the closest point to [1,1,1] > samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=1) neigh.fit(samples) NearestNeighbors(n_neighbors=1) print(neigh.kneighbors([[1., 1., 1.]])) (array([[0.5]]), array([[2]])) As you can see, it returns [[0.5]], and [[2]], which means that the element is at distance 0.5 and is the third element of samples (indexes start at 0). You can also query for multiple points: > X = [[0., 1., 0.], [1., 0., 1.]]kneighbors_graph(X\=None, n_neighbors\=None, mode\='connectivity')neigh.kneighbors(X, return_distance=False) Compute the (weighted) graph of k-Neighbors for points in X. Parameters:X{array-like, sparse matrix} of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’, default=None The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. For > X = [[0], [3], [1]]9 the shape should be (n_queries, n_indexed). Otherwise the shape should be (n_queries, n_features).from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=2) n_neighborsint, default=None Number of neighbors for each sample. The default is the value passed to the constructor. mode{‘connectivity’, ‘distance’}, default=’connectivity’ Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class. Returns:Asparse-matrix of shape (n_queries, n_samples_fit) `kneighbors`0 is the number of samples in the fitted data.`kneighbors`1 gives the weight of the edge connecting `kneighbors`2 to `kneighbors`3. The matrix is of CSR format. Examples > X = [[0], [3], [1]]predict(X)from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=2) 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:yndarray of shape (n_queries,) or (n_queries, n_outputs) Class labels for each data sample. predict_proba(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:pndarray 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. score(X, y, sample_weight\=None) Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters:Xarray-like of shape (n_samples, n_features) Test samples. yarray-like of shape (n_samples,) or (n_samples, n_outputs) True labels for > X = [[0., 1., 0.], [1., 0., 1.]]1.neigh.kneighbors(X, return_distance=False) sample_weightarray-like of shape (n_samples,), default=None Sample weights. Returns:scorefloat Mean accuracy of `kneighbors`5 w.r.t. > X = [[0., 1., 0.], [1., 0., 1.]]2.neigh.kneighbors(X, return_distance=False) set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as ). The latter have parameters of the form `kneighbors`8 so that it’s possible to update each component of a nested object. Parameters:**paramsdict Estimator parameters. Returns:selfestimator instance Estimator instance. set_score_request(*, sample_weight: | | \= '$UNCHANGED$') → Request metadata passed to the > X = [[0], [3], [1]]4 method.from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=2) Note that this method is only relevant if`weights`0 (see ). Please see on how the routing mechanism works. The options for each parameter are:
The default (`weights`8) retains the existing request. This allows you to change the request for some parameters and not others. New in version 1.3. Note This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a . Otherwise it has no effect. |
