714 lines
26 KiB
Python
714 lines
26 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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from numbers import Integral
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import numpy as np
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from ..utils.fixes import _mode
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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 _get_weights
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from ._base import NeighborsBase, KNeighborsMixin, RadiusNeighborsMixin
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from ..base import ClassifierMixin
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from ..utils._param_validation import StrOptions
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class KNeighborsClassifier(KNeighborsMixin, ClassifierMixin, NeighborsBase):
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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'}, callable or None, 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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Metric to use for distance computation. Default is "minkowski", which
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results in the standard Euclidean distance when p = 2. See the
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documentation of `scipy.spatial.distance
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<https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
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the metrics listed in
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:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
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values.
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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`, in which
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case only "nonzero" elements may be considered neighbors.
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If metric is a callable function, it takes two arrays representing 1D
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vectors as inputs and must return one value indicating the distance
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between those vectors. This works for Scipy's metrics, but is less
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efficient than passing the metric name as a string.
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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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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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n_samples_fit_ : int
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Number of samples in the fitted data.
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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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See Also
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--------
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RadiusNeighborsClassifier: Classifier based on neighbors within a fixed radius.
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KNeighborsRegressor: Regression based on k-nearest neighbors.
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RadiusNeighborsRegressor: Regression based on neighbors within a fixed radius.
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NearestNeighbors: Unsupervised learner for implementing neighbor searches.
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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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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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[[0.666... 0.333...]]
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"""
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_parameter_constraints: dict = {**NeighborsBase._parameter_constraints}
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_parameter_constraints.pop("radius")
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_parameter_constraints.update(
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{"weights": [StrOptions({"uniform", "distance"}), callable, None]}
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)
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def __init__(
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self,
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n_neighbors=5,
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*,
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weights="uniform",
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algorithm="auto",
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leaf_size=30,
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p=2,
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metric="minkowski",
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metric_params=None,
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n_jobs=None,
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):
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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,
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metric=metric,
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p=p,
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metric_params=metric_params,
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n_jobs=n_jobs,
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)
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self.weights = weights
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def fit(self, X, y):
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"""Fit the k-nearest neighbors classifier from the training dataset.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
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(n_samples, n_samples) if metric='precomputed'
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Training data.
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y : {array-like, sparse matrix} of shape (n_samples,) or \
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(n_samples, n_outputs)
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Target values.
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Returns
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-------
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self : KNeighborsClassifier
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The fitted k-nearest neighbors classifier.
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"""
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self._validate_params()
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return self._fit(X, y)
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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, sparse matrix} 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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if self.weights == "uniform":
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# In that case, we do not need the distances to perform
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# the weighting so we do not compute them.
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neigh_ind = self.kneighbors(X, return_distance=False)
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neigh_dist = None
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else:
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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, _ = _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, sparse matrix} 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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if self.weights == "uniform":
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# In that case, we do not need the distances to perform
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# the weighting so we do not compute them.
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neigh_ind = self.kneighbors(X, return_distance=False)
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neigh_dist = None
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else:
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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(n_queries)
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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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def _more_tags(self):
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return {"multilabel": True}
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class RadiusNeighborsClassifier(RadiusNeighborsMixin, ClassifierMixin, NeighborsBase):
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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'}, callable or None, 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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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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Metric to use for distance computation. Default is "minkowski", which
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results in the standard Euclidean distance when p = 2. See the
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documentation of `scipy.spatial.distance
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<https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
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the metrics listed in
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:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
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values.
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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`, in which
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case only "nonzero" elements may be considered neighbors.
|
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If metric is a callable function, it takes two arrays representing 1D
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vectors as inputs and must return one value indicating the distance
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between those vectors. This works for Scipy's metrics, but is less
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efficient than passing the metric name as a string.
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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 callable
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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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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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n_samples_fit_ : int
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Number of samples in the fitted data.
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outlier_label_ : int or array-like of shape (n_class,)
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Label which is given for outlier samples (samples with no neighbors
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on given radius).
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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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See Also
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--------
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KNeighborsClassifier : Classifier implementing the k-nearest neighbors
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vote.
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RadiusNeighborsRegressor : Regression based on neighbors within a
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fixed radius.
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KNeighborsRegressor : Regression based on k-nearest neighbors.
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NearestNeighbors : Unsupervised learner for implementing neighbor
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searches.
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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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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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"""
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_parameter_constraints: dict = {
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**NeighborsBase._parameter_constraints,
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"weights": [StrOptions({"uniform", "distance"}), callable, None],
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"outlier_label": [Integral, str, "array-like", None],
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}
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_parameter_constraints.pop("n_neighbors")
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def __init__(
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self,
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radius=1.0,
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*,
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weights="uniform",
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algorithm="auto",
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leaf_size=30,
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p=2,
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metric="minkowski",
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outlier_label=None,
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metric_params=None,
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n_jobs=None,
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):
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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,
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p=p,
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metric_params=metric_params,
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n_jobs=n_jobs,
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)
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self.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 radius neighbors classifier from the training dataset.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
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(n_samples, n_samples) if metric='precomputed'
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Training data.
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y : {array-like, sparse matrix} of shape (n_samples,) or \
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(n_samples, n_outputs)
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Target values.
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Returns
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-------
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self : RadiusNeighborsClassifier
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The fitted radius neighbors classifier.
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"""
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self._validate_params()
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self._fit(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 frequent 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 not isinstance(
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self.outlier_label, str
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):
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if len(self.outlier_label) != len(classes_):
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raise ValueError(
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"The length of outlier_label: {} is "
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"inconsistent with the output "
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"length: {}".format(self.outlier_label, len(classes_))
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)
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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 not isinstance(label, str):
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# ensure the outlier label for each output is a scalar.
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raise TypeError(
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"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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)
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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(
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"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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)
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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, sparse matrix} 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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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, sparse matrix} 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 \
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n_outputs 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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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=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(
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"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." % outliers
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)
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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, 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(
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idx, weights[i], minlength=classes_k.size
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)
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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(
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"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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)
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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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def _more_tags(self):
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return {"multilabel": True}
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