391 lines
14 KiB
Python
391 lines
14 KiB
Python
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"""Nearest Neighbor Classification"""
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# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
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# Fabian Pedregosa <fabian.pedregosa@inria.fr>
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# Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Sparseness support by Lars Buitinck
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# Multi-output support by Arnaud Joly <a.joly@ulg.ac.be>
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#
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# License: BSD 3 clause (C) INRIA, University of Amsterdam
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import numpy as np
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from scipy import stats
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from ..utils.extmath import weighted_mode
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from .base import \
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_check_weights, _get_weights, \
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NeighborsBase, KNeighborsMixin,\
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RadiusNeighborsMixin, SupervisedIntegerMixin
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from ..base import ClassifierMixin
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from ..utils import check_array
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class KNeighborsClassifier(NeighborsBase, KNeighborsMixin,
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SupervisedIntegerMixin, ClassifierMixin):
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"""Classifier implementing the k-nearest neighbors vote.
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Read more in the :ref:`User Guide <classification>`.
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Parameters
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----------
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n_neighbors : int, optional (default = 5)
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Number of neighbors to use by default for :meth:`kneighbors` queries.
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weights : str or callable, optional (default = 'uniform')
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weight function used in prediction. Possible values:
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- 'uniform' : uniform weights. All points in each neighborhood
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are weighted equally.
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- 'distance' : weight points by the inverse of their distance.
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in this case, closer neighbors of a query point will have a
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greater influence than neighbors which are further away.
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- [callable] : a user-defined function which accepts an
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array of distances, and returns an array of the same shape
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containing the weights.
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algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
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Algorithm used to compute the nearest neighbors:
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- 'ball_tree' will use :class:`BallTree`
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- 'kd_tree' will use :class:`KDTree`
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- 'brute' will use a brute-force search.
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- 'auto' will attempt to decide the most appropriate algorithm
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based on the values passed to :meth:`fit` method.
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Note: fitting on sparse input will override the setting of
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this parameter, using brute force.
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leaf_size : int, optional (default = 30)
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Leaf size passed to BallTree or KDTree. This can affect the
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speed of the construction and query, as well as the memory
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required to store the tree. The optimal value depends on the
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nature of the problem.
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p : integer, optional (default = 2)
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Power parameter for the Minkowski metric. When p = 1, this is
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equivalent to using manhattan_distance (l1), and euclidean_distance
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(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
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metric : string or callable, default 'minkowski'
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the distance metric to use for the tree. The default metric is
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minkowski, and with p=2 is equivalent to the standard Euclidean
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metric. See the documentation of the DistanceMetric class for a
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list of available metrics.
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metric_params : dict, optional (default = None)
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Additional keyword arguments for the metric function.
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n_jobs : int, optional (default = 1)
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The number of parallel jobs to run for neighbors search.
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If ``-1``, then the number of jobs is set to the number of CPU cores.
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Doesn't affect :meth:`fit` method.
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Examples
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--------
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>>> X = [[0], [1], [2], [3]]
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>>> y = [0, 0, 1, 1]
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>>> from sklearn.neighbors import KNeighborsClassifier
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>>> neigh = KNeighborsClassifier(n_neighbors=3)
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>>> neigh.fit(X, y) # doctest: +ELLIPSIS
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KNeighborsClassifier(...)
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>>> print(neigh.predict([[1.1]]))
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[0]
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>>> print(neigh.predict_proba([[0.9]]))
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[[ 0.66666667 0.33333333]]
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See also
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--------
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RadiusNeighborsClassifier
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KNeighborsRegressor
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RadiusNeighborsRegressor
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NearestNeighbors
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Notes
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-----
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See :ref:`Nearest Neighbors <neighbors>` in the online documentation
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for a discussion of the choice of ``algorithm`` and ``leaf_size``.
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.. warning::
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Regarding the Nearest Neighbors algorithms, if it is found that two
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neighbors, neighbor `k+1` and `k`, have identical distances
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but different labels, the results will depend on the ordering of the
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training data.
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https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
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"""
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def __init__(self, n_neighbors=5,
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weights='uniform', algorithm='auto', leaf_size=30,
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p=2, metric='minkowski', metric_params=None, n_jobs=1,
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**kwargs):
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self._init_params(n_neighbors=n_neighbors,
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algorithm=algorithm,
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leaf_size=leaf_size, metric=metric, p=p,
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metric_params=metric_params, n_jobs=n_jobs, **kwargs)
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self.weights = _check_weights(weights)
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def predict(self, X):
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"""Predict the class labels for the provided data
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Parameters
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----------
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X : array-like, shape (n_query, n_features), \
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or (n_query, n_indexed) if metric == 'precomputed'
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Test samples.
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Returns
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-------
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y : array of shape [n_samples] or [n_samples, n_outputs]
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Class labels for each data sample.
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"""
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X = check_array(X, accept_sparse='csr')
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neigh_dist, neigh_ind = self.kneighbors(X)
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classes_ = self.classes_
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_y = self._y
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if not self.outputs_2d_:
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_y = self._y.reshape((-1, 1))
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classes_ = [self.classes_]
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n_outputs = len(classes_)
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n_samples = X.shape[0]
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weights = _get_weights(neigh_dist, self.weights)
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y_pred = np.empty((n_samples, n_outputs), dtype=classes_[0].dtype)
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for k, classes_k in enumerate(classes_):
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if weights is None:
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mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
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else:
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mode, _ = weighted_mode(_y[neigh_ind, k], weights, axis=1)
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mode = np.asarray(mode.ravel(), dtype=np.intp)
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y_pred[:, k] = classes_k.take(mode)
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if not self.outputs_2d_:
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y_pred = y_pred.ravel()
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return y_pred
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def predict_proba(self, X):
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"""Return probability estimates for the test data X.
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Parameters
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----------
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X : array-like, shape (n_query, n_features), \
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or (n_query, n_indexed) if metric == 'precomputed'
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Test samples.
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Returns
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-------
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p : array of shape = [n_samples, n_classes], or a list of n_outputs
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of such arrays if n_outputs > 1.
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The class probabilities of the input samples. Classes are ordered
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by lexicographic order.
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"""
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X = check_array(X, accept_sparse='csr')
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neigh_dist, neigh_ind = self.kneighbors(X)
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classes_ = self.classes_
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_y = self._y
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if not self.outputs_2d_:
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_y = self._y.reshape((-1, 1))
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classes_ = [self.classes_]
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n_samples = X.shape[0]
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weights = _get_weights(neigh_dist, self.weights)
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if weights is None:
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weights = np.ones_like(neigh_ind)
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all_rows = np.arange(X.shape[0])
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probabilities = []
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for k, classes_k in enumerate(classes_):
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pred_labels = _y[:, k][neigh_ind]
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proba_k = np.zeros((n_samples, classes_k.size))
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# a simple ':' index doesn't work right
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for i, idx in enumerate(pred_labels.T): # loop is O(n_neighbors)
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proba_k[all_rows, idx] += weights[:, i]
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# normalize 'votes' into real [0,1] probabilities
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normalizer = proba_k.sum(axis=1)[:, np.newaxis]
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normalizer[normalizer == 0.0] = 1.0
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proba_k /= normalizer
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probabilities.append(proba_k)
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if not self.outputs_2d_:
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probabilities = probabilities[0]
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return probabilities
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class RadiusNeighborsClassifier(NeighborsBase, RadiusNeighborsMixin,
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SupervisedIntegerMixin, ClassifierMixin):
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"""Classifier implementing a vote among neighbors within a given radius
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Read more in the :ref:`User Guide <classification>`.
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Parameters
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----------
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radius : float, optional (default = 1.0)
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Range of parameter space to use by default for :meth:`radius_neighbors`
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queries.
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weights : str or callable
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weight function used in prediction. Possible values:
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- 'uniform' : uniform weights. All points in each neighborhood
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are weighted equally.
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- 'distance' : weight points by the inverse of their distance.
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in this case, closer neighbors of a query point will have a
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greater influence than neighbors which are further away.
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- [callable] : a user-defined function which accepts an
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array of distances, and returns an array of the same shape
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containing the weights.
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Uniform weights are used by default.
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algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
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Algorithm used to compute the nearest neighbors:
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- 'ball_tree' will use :class:`BallTree`
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- 'kd_tree' will use :class:`KDTree`
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- 'brute' will use a brute-force search.
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- 'auto' will attempt to decide the most appropriate algorithm
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based on the values passed to :meth:`fit` method.
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Note: fitting on sparse input will override the setting of
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this parameter, using brute force.
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leaf_size : int, optional (default = 30)
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Leaf size passed to BallTree or KDTree. This can affect the
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speed of the construction and query, as well as the memory
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required to store the tree. The optimal value depends on the
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nature of the problem.
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p : integer, optional (default = 2)
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Power parameter for the Minkowski metric. When p = 1, this is
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equivalent to using manhattan_distance (l1), and euclidean_distance
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(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
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metric : string or callable, default 'minkowski'
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the distance metric to use for the tree. The default metric is
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minkowski, and with p=2 is equivalent to the standard Euclidean
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metric. See the documentation of the DistanceMetric class for a
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list of available metrics.
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outlier_label : int, optional (default = None)
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Label, which is given for outlier samples (samples with no
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neighbors on given radius).
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If set to None, ValueError is raised, when outlier is detected.
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metric_params : dict, optional (default = None)
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Additional keyword arguments for the metric function.
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Examples
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--------
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>>> X = [[0], [1], [2], [3]]
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>>> y = [0, 0, 1, 1]
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>>> from sklearn.neighbors import RadiusNeighborsClassifier
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>>> neigh = RadiusNeighborsClassifier(radius=1.0)
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>>> neigh.fit(X, y) # doctest: +ELLIPSIS
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RadiusNeighborsClassifier(...)
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>>> print(neigh.predict([[1.5]]))
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[0]
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See also
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--------
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KNeighborsClassifier
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RadiusNeighborsRegressor
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KNeighborsRegressor
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NearestNeighbors
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Notes
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-----
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See :ref:`Nearest Neighbors <neighbors>` in the online documentation
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for a discussion of the choice of ``algorithm`` and ``leaf_size``.
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https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
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"""
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def __init__(self, radius=1.0, weights='uniform',
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algorithm='auto', leaf_size=30, p=2, metric='minkowski',
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outlier_label=None, metric_params=None, **kwargs):
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self._init_params(radius=radius,
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algorithm=algorithm,
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leaf_size=leaf_size,
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metric=metric, p=p, metric_params=metric_params,
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**kwargs)
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self.weights = _check_weights(weights)
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self.outlier_label = outlier_label
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def predict(self, X):
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"""Predict the class labels for the provided data
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Parameters
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----------
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X : array-like, shape (n_query, n_features), \
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or (n_query, n_indexed) if metric == 'precomputed'
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Test samples.
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Returns
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-------
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y : array of shape [n_samples] or [n_samples, n_outputs]
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Class labels for each data sample.
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"""
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X = check_array(X, accept_sparse='csr')
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n_samples = X.shape[0]
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neigh_dist, neigh_ind = self.radius_neighbors(X)
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inliers = [i for i, nind in enumerate(neigh_ind) if len(nind) != 0]
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outliers = [i for i, nind in enumerate(neigh_ind) if len(nind) == 0]
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classes_ = self.classes_
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_y = self._y
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if not self.outputs_2d_:
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_y = self._y.reshape((-1, 1))
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classes_ = [self.classes_]
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n_outputs = len(classes_)
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if self.outlier_label is not None:
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neigh_dist[outliers] = 1e-6
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elif outliers:
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raise ValueError('No neighbors found for test samples %r, '
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'you can try using larger radius, '
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'give a label for outliers, '
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'or consider removing them from your dataset.'
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% outliers)
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weights = _get_weights(neigh_dist, self.weights)
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y_pred = np.empty((n_samples, n_outputs), dtype=classes_[0].dtype)
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for k, classes_k in enumerate(classes_):
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pred_labels = np.zeros(len(neigh_ind), dtype=object)
|
||
|
|
pred_labels[:] = [_y[ind, k] for ind in neigh_ind]
|
||
|
|
if weights is None:
|
||
|
|
mode = np.array([stats.mode(pl)[0]
|
||
|
|
for pl in pred_labels[inliers]], dtype=np.int)
|
||
|
|
else:
|
||
|
|
mode = np.array([weighted_mode(pl, w)[0]
|
||
|
|
for (pl, w)
|
||
|
|
in zip(pred_labels[inliers], weights[inliers])],
|
||
|
|
dtype=np.int)
|
||
|
|
|
||
|
|
mode = mode.ravel()
|
||
|
|
|
||
|
|
y_pred[inliers, k] = classes_k.take(mode)
|
||
|
|
|
||
|
|
if outliers:
|
||
|
|
y_pred[outliers, :] = self.outlier_label
|
||
|
|
|
||
|
|
if not self.outputs_2d_:
|
||
|
|
y_pred = y_pred.ravel()
|
||
|
|
|
||
|
|
return y_pred
|