package sklearn

Regression based on k-nearest neighbors.

The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.

Read more in the :ref:`User Guide <regression>`.

.. versionadded:: 0.9

Parameters ---------- n_neighbors : int, default=5 Number of neighbors to use by default for :meth:`kneighbors` queries.

weights : 'uniform', 'distance' or callable, 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' the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of :class:`DistanceMetric` for a list of available metrics. 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.

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 <n_jobs>` for more details. Doesn't affect :meth:`fit` method.

Attributes ---------- effective_metric_ : str or callable The distance metric to use. 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'.

Examples -------- >>> X = [0], [1], [2], [3] >>> y = 0, 0, 1, 1 >>> from sklearn.neighbors import KNeighborsRegressor >>> neigh = KNeighborsRegressor(n_neighbors=2) >>> neigh.fit(X, y) KNeighborsRegressor(...) >>> print(neigh.predict([1.5])) 0.5

See also -------- NearestNeighbors RadiusNeighborsRegressor KNeighborsClassifier RadiusNeighborsClassifier

Notes ----- See :ref:`Nearest Neighbors <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

Fit the model using X as training data and y as target values

Parameters ---------- X : array-like, sparse matrix, BallTree, KDTree Training data. If array or matrix, shape n_samples, n_features, or n_samples, n_samples if metric='precomputed'.

y : array-like, sparse matrix Target values, array of float values, shape = n_samples or n_samples, n_outputs

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point.

Parameters ---------- X : array-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == 'precomputed' 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_neighbors : int Number of neighbors to get (default is the value passed to the constructor).

return_distance : boolean, optional. Defaults to True. If False, distances will not be returned

Returns ------- neigh_dist : array, shape (n_queries, n_neighbors) Array representing the lengths to points, only present if return_distance=True

neigh_ind : array, 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 NearestNeighbors >>> neigh = 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.] >>> neigh.kneighbors(X, return_distance=False) array([1], [2]...)

Computes the (weighted) graph of k-Neighbors for points in X

Parameters ---------- X : array-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == 'precomputed' 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_neighbors : int Number of neighbors for each sample. (default is value passed to the constructor).

mode : 'connectivity', 'distance', optional Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, in 'distance' the edges are Euclidean distance between points.

Returns ------- A : sparse graph in CSR format, shape = n_queries, n_samples_fit n_samples_fit is the number of samples in the fitted data Ai, j is assigned the weight of edge that connects i to j.

Examples -------- >>> X = [0], [3], [1] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=2) >>> neigh.fit(X) NearestNeighbors(n_neighbors=2) >>> A = neigh.kneighbors_graph(X) >>> A.toarray() array([1., 0., 1.], [0., 1., 1.], [1., 0., 1.])

See also -------- NearestNeighbors.radius_neighbors_graph

Predict the target for the provided data

Parameters ---------- X : array-like 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), dtype=int Target values.

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float R^2 of self.predict(X) wrt. y.

Notes ----- The R2 score used when calling ``score`` on a regressor uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`).

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Attribute effective_metric_: get value or raise Not_found if None.

Attribute effective_metric_: get value as an option.

Attribute effective_metric_params_: get value or raise Not_found if None.

Attribute effective_metric_params_: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.