dasf.ml.neighbors.neighbors

NearestNeighbors algorithm module.

Classes

NearestNeighbors

Unsupervised learner for implementing neighbor searches.

Module Contents

class dasf.ml.neighbors.neighbors.NearestNeighbors(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None, handle=None, verbose=False, output_type=None, **kwargs)

Bases: dasf.transforms.base.Fit, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

Unsupervised learner for implementing neighbor searches.

Parameters

n_neighborsint, default=5

Number of neighbors to use by default for kneighbors() queries.

radiusfloat, default=1.0

Range of parameter space to use by default for radius_neighbors() queries.

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_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.

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 distance_metrics 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 sparse graph, 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.

pfloat (positive), 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_paramsdict, default=None

Additional keyword arguments for the metric function.

n_jobsint, default=None

The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

Attributes

effective_metric_str

Metric used to compute distances to neighbors.

effective_metric_params_dict

Parameters for the metric used to compute distances to neighbors.

n_features_in_int

Number of features seen during fit.

Added 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.

Added in version 1.0.

n_samples_fit_int

Number of samples in the fitted data.

See Also

KNeighborsClassifierClassifier implementing the k-nearest neighbors

vote.

RadiusNeighborsClassifierClassifier implementing a vote among neighbors

within a given radius.

KNeighborsRegressor : Regression based on k-nearest neighbors. RadiusNeighborsRegressor : Regression based on neighbors within a fixed

radius.

BallTreeSpace partitioning data structure for organizing points in a

multi-dimensional space, used for nearest neighbor search.

Notes

See Nearest Neighbors in the online documentation for a discussion of the choice of algorithm and leaf_size.

https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

Constructor of the class NearestNeighbors.

__nn_cpu

_fit_cpu(X, y=None, **kwargs)

Fit the nearest neighbors estimator from the training dataset using CPU only.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric=’precomputed’

Training data.

yIgnored

Not used, present for API consistency by convention.

Returns

selfNearestNeighbors

The fitted nearest neighbors estimator.

_fit_gpu(X, y=None, **kwargs)

Fit the nearest neighbors estimator from the training dataset using GPU only.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric=’precomputed’

Training data.

yIgnored

Not used, present for API consistency by convention.

Returns

selfNearestNeighbors

The fitted nearest neighbors estimator.

_get_params_cpu(deep=True, **kwargs)

Get parameters for this estimator using CPU only.

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.

_set_params_cpu(**params)

Set the parameters of this estimator using CPU only.

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

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.