583 lines
22 KiB
Python
583 lines
22 KiB
Python
"""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 ..utils.validation import _is_arraylike, _num_samples
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import warnings
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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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from ..utils.validation import _deprecate_positional_args
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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, default=5
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Number of neighbors to use by default for :meth:`kneighbors` queries.
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weights : {'uniform', 'distance'} or callable, 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'}, default='auto'
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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, 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 : int, 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 : str 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 :class:`DistanceMetric` for a
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list of available metrics.
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If metric is "precomputed", X is assumed to be a distance matrix and
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must be square during fit. X may be a :term:`sparse graph`,
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in which case only "nonzero" elements may be considered neighbors.
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metric_params : dict, default=None
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Additional keyword arguments for the metric function.
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n_jobs : int, default=None
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The number of parallel jobs to run for neighbors search.
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``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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Doesn't affect :meth:`fit` method.
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Attributes
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----------
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classes_ : array of shape (n_classes,)
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Class labels known to the classifier
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effective_metric_ : str or callble
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The distance metric used. It will be same as the `metric` parameter
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or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
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'minkowski' and `p` parameter set to 2.
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effective_metric_params_ : dict
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Additional keyword arguments for the metric function. For most metrics
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will be same with `metric_params` parameter, but may also contain the
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`p` parameter value if the `effective_metric_` attribute is set to
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'minkowski'.
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outputs_2d_ : bool
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False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
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otherwise True.
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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)
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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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@_deprecate_positional_args
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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=None,
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**kwargs):
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super().__init__(
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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,
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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 of shape (n_queries, n_features), \
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or (n_queries, 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 : ndarray of shape (n_queries,) or (n_queries, 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_queries = _num_samples(X)
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weights = _get_weights(neigh_dist, self.weights)
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y_pred = np.empty((n_queries, 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 of shape (n_queries, n_features), \
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or (n_queries, 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 : ndarray of shape (n_queries, 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_queries = _num_samples(X)
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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_queries, 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, 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 : {'uniform', 'distance'} or callable, 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
|
|
containing the weights.
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Uniform weights are used by default.
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algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
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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, 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 : int, 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 : str 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 :class:`DistanceMetric` for a
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|
list of available metrics.
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If metric is "precomputed", X is assumed to be a distance matrix and
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must be square during fit. X may be a :term:`sparse graph`,
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in which case only "nonzero" elements may be considered neighbors.
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outlier_label : {manual label, 'most_frequent'}, default=None
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label for outlier samples (samples with no neighbors in given radius).
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- manual label: str or int label (should be the same type as y)
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or list of manual labels if multi-output is used.
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- 'most_frequent' : assign the most frequent label of y to outliers.
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- None : when any outlier is detected, ValueError will be raised.
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metric_params : dict, default=None
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Additional keyword arguments for the metric function.
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n_jobs : int, default=None
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The number of parallel jobs to run for neighbors search.
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``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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Attributes
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----------
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classes_ : ndarray of shape (n_classes,)
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Class labels known to the classifier.
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effective_metric_ : str or callble
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The distance metric used. It will be same as the `metric` parameter
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or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
|
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'minkowski' and `p` parameter set to 2.
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effective_metric_params_ : dict
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Additional keyword arguments for the metric function. For most metrics
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will be same with `metric_params` parameter, but may also contain the
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`p` parameter value if the `effective_metric_` attribute is set to
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'minkowski'.
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outputs_2d_ : bool
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False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
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otherwise True.
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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)
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RadiusNeighborsClassifier(...)
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>>> print(neigh.predict([[1.5]]))
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[0]
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>>> print(neigh.predict_proba([[1.0]]))
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[[0.66666667 0.33333333]]
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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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@_deprecate_positional_args
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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, n_jobs=None,
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**kwargs):
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super().__init__(
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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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n_jobs=n_jobs, **kwargs)
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self.weights = _check_weights(weights)
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self.outlier_label = outlier_label
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def fit(self, X, y):
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"""Fit the model using X as training data and y as target values
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Parameters
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----------
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X : BallTree, KDTree or {array-like, sparse matrix} of shape \
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(n_samples, n_features) or (n_samples, n_samples)
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Training data. If array or matrix, the shape is (n_samples,
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n_features), or (n_samples, n_samples) if metric='precomputed'.
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y : {array-like, sparse matrix} of shape (n_samples,) or \
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(n_samples, n_output)
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Target values.
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"""
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SupervisedIntegerMixin.fit(self, X, y)
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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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if self.outlier_label is None:
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outlier_label_ = None
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elif self.outlier_label == 'most_frequent':
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outlier_label_ = []
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# iterate over multi-output, get the most frequest label for each
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# output.
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for k, classes_k in enumerate(classes_):
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label_count = np.bincount(_y[:, k])
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outlier_label_.append(classes_k[label_count.argmax()])
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else:
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if (_is_arraylike(self.outlier_label) and
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not isinstance(self.outlier_label, str)):
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if len(self.outlier_label) != len(classes_):
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raise ValueError("The length of outlier_label: {} is "
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"inconsistent with the output "
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"length: {}".format(self.outlier_label,
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len(classes_)))
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outlier_label_ = self.outlier_label
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else:
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outlier_label_ = [self.outlier_label] * len(classes_)
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for classes, label in zip(classes_, outlier_label_):
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if (_is_arraylike(label) and
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not isinstance(label, str)):
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# ensure the outlier lable for each output is a scalar.
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raise TypeError("The outlier_label of classes {} is "
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"supposed to be a scalar, got "
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"{}.".format(classes, label))
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if np.append(classes, label).dtype != classes.dtype:
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# ensure the dtype of outlier label is consistent with y.
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raise TypeError("The dtype of outlier_label {} is "
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"inconsistent with classes {} in "
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"y.".format(label, classes))
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self.outlier_label_ = outlier_label_
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return self
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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 of shape (n_queries, n_features), \
|
|
or (n_queries, n_indexed) if metric == 'precomputed'
|
|
Test samples.
|
|
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|
Returns
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-------
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y : ndarray of shape (n_queries,) or (n_queries, n_outputs)
|
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Class labels for each data sample.
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|
"""
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probs = self.predict_proba(X)
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classes_ = self.classes_
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if not self.outputs_2d_:
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probs = [probs]
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classes_ = [self.classes_]
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n_outputs = len(classes_)
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n_queries = probs[0].shape[0]
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y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype)
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for k, prob in enumerate(probs):
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# iterate over multi-output, assign labels based on probabilities
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# of each output.
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max_prob_index = prob.argmax(axis=1)
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y_pred[:, k] = classes_[k].take(max_prob_index)
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outlier_zero_probs = (prob == 0).all(axis=1)
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if outlier_zero_probs.any():
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zero_prob_index = np.flatnonzero(outlier_zero_probs)
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y_pred[zero_prob_index, k] = self.outlier_label_[k]
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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 of shape (n_queries, n_features), \
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or (n_queries, 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 : ndarray of shape (n_queries, 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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n_queries = _num_samples(X)
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neigh_dist, neigh_ind = self.radius_neighbors(X)
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outlier_mask = np.zeros(n_queries, dtype=np.bool)
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outlier_mask[:] = [len(nind) == 0 for nind in neigh_ind]
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outliers = np.flatnonzero(outlier_mask)
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inliers = np.flatnonzero(~outlier_mask)
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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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if self.outlier_label_ is None and outliers.size > 0:
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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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'giving a label for outliers, '
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'or considering 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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if weights is not None:
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weights = weights[inliers]
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probabilities = []
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# iterate over multi-output, measure probabilities of the k-th output.
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for k, classes_k in enumerate(classes_):
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pred_labels = np.zeros(len(neigh_ind), dtype=object)
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pred_labels[:] = [_y[ind, k] for ind in neigh_ind]
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proba_k = np.zeros((n_queries, classes_k.size))
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proba_inl = np.zeros((len(inliers), classes_k.size))
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# samples have different size of neighbors within the same radius
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if weights is None:
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for i, idx in enumerate(pred_labels[inliers]):
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proba_inl[i, :] = np.bincount(idx,
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minlength=classes_k.size)
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else:
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for i, idx in enumerate(pred_labels[inliers]):
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proba_inl[i, :] = np.bincount(idx,
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weights[i],
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minlength=classes_k.size)
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proba_k[inliers, :] = proba_inl
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if outliers.size > 0:
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_outlier_label = self.outlier_label_[k]
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label_index = np.flatnonzero(classes_k == _outlier_label)
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if label_index.size == 1:
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proba_k[outliers, label_index[0]] = 1.0
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else:
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warnings.warn('Outlier label {} is not in training '
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'classes. All class probabilities of '
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'outliers will be assigned with 0.'
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''.format(self.outlier_label_[k]))
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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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