8.21.2. sklearn.neighbors.KNeighborsClassifier¶
- class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, warn_on_equidistant=True)¶
Classifier implementing the k-nearest neighbors vote.
Parameters : n_neighbors : int, optional (default = 5)
Number of neighbors to use by default for k_neighbors queries.
weights : str or callable
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’}, optional
Algorithm used to compute the nearest neighbors:
- ‘ball_tree’ will use BallTree
- ‘kd_tree’ will use scipy.spatial.cKDtree
- ‘brute’ will use a brute-force search.
- ‘auto’ will attempt to decide the most appropriate algorithm based on the values passed to fit method.
Note: fitting on sparse input will override the setting of this parameter, using brute force.
leaf_size : int, optional (default = 30)
Leaf size passed to BallTree or cKDTree. 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.
warn_on_equidistant : boolean, optional. Defaults to True.
Generate a warning if equidistant neighbors are discarded. For classification or regression based on k-neighbors, if neighbor k and neighbor k+1 have identical distances but different labels, then the result will be dependent on the ordering of the training data. If the fit method is 'kd_tree', no warnings will be generated.
Notes
See Nearest Neighbors in the online documentation for a discussion of the choice of algorithm and leaf_size.
http://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=2) >>> neigh.fit(X, y) KNeighborsClassifier(...) >>> print neigh.predict([[1.5]]) [0]
Methods
fit(X, y) Fit the model using X as training data and y as target values get_params([deep]) Get parameters for the estimator kneighbors(X[, n_neighbors, return_distance]) Finds the K-neighbors of a point. kneighbors_graph(X[, n_neighbors, mode]) Computes the (weighted) graph of k-Neighbors for points in X predict(X) Predict the class labels for the provided data score(X, y) Returns the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of the estimator. - __init__(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, warn_on_equidistant=True)¶
- fit(X, y)¶
Fit the model using X as training data and y as target values
Parameters : X : {array-like, sparse matrix, BallTree, cKDTree}
Training data. If array or matrix, then the shape is [n_samples, n_features]
y : {array-like, sparse matrix}, shape = [n_samples]
Target values, array of integer values.
- get_params(deep=True)¶
Get parameters for the estimator
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- kneighbors(X, n_neighbors=None, return_distance=True)¶
Finds the K-neighbors of a point.
Returns distance
Parameters : X : array-like, last dimension same as that of fit data
The new point.
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 : dist : array
Array representing the lengths to point, only present if return_distance=True
ind : array
Indices of the nearest points in the population matrix.
Examples
In the following example, we construct a NeighborsClassifier 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(algorithm='auto', leaf_size=30, ...) >>> 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]]...)
- kneighbors_graph(X, n_neighbors=None, mode='connectivity')¶
Computes the (weighted) graph of k-Neighbors for points in X
Parameters : X : array-like, shape = [n_samples, n_features]
Sample data
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 matrix in CSR format, shape = [n_samples, n_samples_fit]
n_samples_fit is the number of samples in the fitted data A[i, 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(algorithm='auto', leaf_size=30, ...) >>> A = neigh.kneighbors_graph(X) >>> A.todense() matrix([[ 1., 0., 1.], [ 0., 1., 1.], [ 1., 0., 1.]])
- predict(X)¶
Predict the class labels for the provided data
Parameters : X: array :
A 2-D array representing the test points.
Returns : labels: array :
List of class labels (one for each data sample).
- score(X, y)¶
Returns the mean accuracy on the given test data and labels.
Parameters : X : array-like, shape = [n_samples, n_features]
Training set.
y : array-like, shape = [n_samples]
Labels for X.
Returns : z : float
- set_params(**params)¶
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :