pyriemann.classification.KNearestNeighbor¶
- class pyriemann.classification.KNearestNeighbor(n_neighbors=5, metric='riemann', n_jobs=1)¶
Classification by k-nearest neighbors.
Classification by k-nearest neighbors (k-NN). For each matrix of the test set, the pairwise distance to each element of the training set is estimated. The class is affected according to the majority class of the k-nearest neighbors.
- Parameters:
- n_neighborsint, default=5
Number of neighbors.
- metricstring | dict, default=”riemann”
Metric used for means estimation (for the list of supported metrics, see
pyriemann.utils.mean.mean_covariance()
) and for distance estimation (seepyriemann.utils.distance.distance()
). The metric can be a dict with two keys, “mean” and “distance” in order to pass different metrics.- n_jobsint, default=1
Number of jobs to use for the computation. This works by computing each of the distance to the training set in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.
- Attributes:
- classes_ndarray, shape (n_classes,)
Labels for each class.
- covmeans_ndarray, shape (n_matrices, n_channels, n_channels)
Matrices of training set.
- classmeans_ndarray, shape (n_matrices,)
Labels of training set.
See also
Kmeans
MDM
- __init__(n_neighbors=5, metric='riemann', n_jobs=1)¶
Init.
- fit(X, y, sample_weight=None)¶
Fit (store the training data).
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- yndarray, shape (n_matrices,)
Labels for each matrix.
- sample_weightNone
Not used, here for compatibility with sklearn API.
- Returns:
- selfNearestNeighbor instance
The NearestNeighbor instance.
- fit_predict(**kwargs)¶
Warning
DEPRECATED: fit_predict() is deprecated and will be removed in 0.10.0; please use fit().predict().
- fit_transform(X, y, sample_weight=None)¶
Fit and transform in a single function.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- yndarray, shape (n_matrices,)
Labels for each matrix.
- sample_weightNone | ndarray, shape (n_matrices,), default=None
Weights for each matrix. If None, it uses equal weights.
- Returns:
- distndarray, shape (n_matrices, n_classes)
Distance to each centroid according to the metric.
- get_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- predict(X)¶
Get the predictions.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- Returns:
- predndarray of int, shape (n_matrices,)
Predictions for each matrix according to the nearest neighbors.
- predict_proba(X)¶
Predict proba using softmax.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- Returns:
- probndarray, shape (n_matrices, n_classes)
Probabilities for each class.
- score(X, y, sample_weight=None)¶
Return the mean accuracy on the given test data and labels.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Test set of SPD matrices.
- yndarray, shape (n_matrices,)
True labels for each matrix.
- sample_weightNone | ndarray, shape (n_matrices,), default=None
Weights for each matrix.
- Returns:
- scorefloat
Mean accuracy of clf.predict(X) wrt. y.
- set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') KNearestNeighbor ¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns:
- selfobject
The updated object.
- set_output(*, transform=None)¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”, “polars”}, default=None
Configure output of transform and fit_transform.
“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged
Added in version 1.4: “polars” option was added.
- Returns:
- selfestimator instance
Estimator instance.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') KNearestNeighbor ¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.
- transform(X)¶
Get the distance to each centroid.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- Returns:
- distndarray, shape (n_matrices, n_classes)
Distance to each centroid according to the metric.