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# `sklearn.neighbors`.KNeighborsTransformer

class sklearn.neighbors.KNeighborsTransformer(*, mode='distance', n_neighbors=5, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None)

  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 User Guide.

  New in version 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 `BallTree`

      - ‘kd_tree’ will use `KDTree`

      - ‘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, 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. 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 `distance_metrics` for valid metric values.

      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.

      Distance matrices are not supported.

    p float, 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=None

      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_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

    n_samples_fit_int

      Number of samples in the fitted data.

  See also

  `kneighbors_graph`

    Compute the weighted graph of k-neighbors for points in X.

  `RadiusNeighborsTransformer`

    Transform X into a weighted graph of neighbors nearer than a radius.

  Examples

      >>> from sklearn.datasets import load_wine
      >>> from sklearn.neighbors import KNeighborsTransformer
      >>> X, _ = load_wine(return_X_y=True)
      >>> X.shape
      (178, 13)
      >>> transformer = KNeighborsTransformer(n_neighbors=5, mode='distance')
      >>> X_dist_graph = transformer.fit_transform(X)
      >>> X_dist_graph.shape
      (178, 178)

  Methods

  `fit`(X[, y]) | Fit the k-nearest neighbors transformer from the training dataset.
  `fit_transform`(X[, y]) | Fit to data, then transform it.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `kneighbors`([X, n_neighbors, return_distance]) | Find the K-neighbors of a point.
  `kneighbors_graph`([X, n_neighbors, mode]) | Compute the (weighted) graph of k-Neighbors for points in X.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Compute the (weighted) graph of Neighbors for points in X.

  fit(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.

      y Ignored

        Not used, present for API consistency by convention.

    Returns:
      self KNeighborsTransformer

        The fitted k-nearest neighbors transformer.

  fit_transform(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

        Not used, present for API consistency by convention.

    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.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: `["class_name0", "class_name1", "class_name2"]`.

    Parameters:
      input_features array-like of str or None, default=None

        Only used to validate feature names with the names seen in `fit`.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    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 dict

        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_neighbors int, default=None

        Number of neighbors required for each sample. The default is the value passed to the constructor.

      return_distance bool, default=True

        Whether or not to return the distances.

    Returns:
      neigh_dist ndarray of shape (n_queries, n_neighbors)

        Array representing the lengths to points, only present if return_distance=True.

      neigh_ind ndarray 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 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]]...)

  kneighbors_graph(X=None, n_neighbors=None, mode='connectivity')

    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 `metric='precomputed'` the shape should be (n_queries, n_indexed). Otherwise the shape should be (n_queries, n_features).

      n_neighbors int, 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:
      A sparse-matrix of shape (n_queries, n_samples_fit)

        `n_samples_fit` is the number of samples in the fitted data. `A[i, j]` gives the weight of the edge connecting `i` to `j`. The matrix is of CSR format.

    See also

    `NearestNeighbors.radius_neighbors_graph`

      Compute the (weighted) graph of Neighbors for points in X.

    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.]])

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). 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 estimator instance

        Estimator instance.

  transform(X)

    Compute 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.

## Examples using `sklearn.neighbors.KNeighborsTransformer`

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22

Approximate nearest neighbors in TSNE

Approximate nearest neighbors in TSNE

Caching nearest neighbors

Caching nearest neighbors