LabelPropagation (original) (raw)
class sklearn.semi_supervised.LabelPropagation(kernel='rbf', *, gamma=20, n_neighbors=7, max_iter=1000, tol=0.001, n_jobs=None)[source]#
Label Propagation classifier.
Read more in the User Guide.
Parameters:
kernel{‘knn’, ‘rbf’} or callable, default=’rbf’
String identifier for kernel function to use or the kernel function itself. Only ‘rbf’ and ‘knn’ strings are valid inputs. The function passed should take two inputs, each of shape (n_samples, n_features), and return a (n_samples, n_samples) shaped weight matrix.
gammafloat, default=20
Parameter for rbf kernel.
n_neighborsint, default=7
Parameter for knn kernel which need to be strictly positive.
max_iterint, default=1000
Change maximum number of iterations allowed.
tolfloat, default=1e-3
Convergence tolerance: threshold to consider the system at steady state.
n_jobsint, default=None
The number of parallel jobs to run.None means 1 unless in a joblib.parallel_backend context.-1 means using all processors. See Glossaryfor more details.
Attributes:
X_{array-like, sparse matrix} of shape (n_samples, n_features)
Input array.
**classes_**ndarray of shape (n_classes,)
The distinct labels used in classifying instances.
**label_distributions_**ndarray of shape (n_samples, n_classes)
Categorical distribution for each item.
**transduction_**ndarray of shape (n_samples)
Label assigned to each item during fit.
**n_features_in_**int
Number of features seen during fit.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
**n_iter_**int
Number of iterations run.
See also
Alternate label propagation strategy more robust to noise.
References
Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMU-CALD-02-107.pdf
Examples
import numpy as np from sklearn import datasets from sklearn.semi_supervised import LabelPropagation label_prop_model = LabelPropagation() iris = datasets.load_iris() rng = np.random.RandomState(42) random_unlabeled_points = rng.rand(len(iris.target)) < 0.3 labels = np.copy(iris.target) labels[random_unlabeled_points] = -1 label_prop_model.fit(iris.data, labels) LabelPropagation(...)
Fit a semi-supervised label propagation model to X.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target class values with unlabeled points marked as -1. All unlabeled samples will be transductively assigned labels internally, which are stored in transduction_.
Returns:
selfobject
Returns the instance itself.
get_metadata_routing()[source]#
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)[source]#
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.
Perform inductive inference across the model.
Parameters:
Xarray-like of shape (n_samples, n_features)
The data matrix.
Returns:
yndarray of shape (n_samples,)
Predictions for input data.
Predict probability for each possible outcome.
Compute the probability estimates for each single sample in X and each possible outcome seen during training (categorical distribution).
Parameters:
Xarray-like of shape (n_samples, n_features)
The data matrix.
Returns:
probabilitiesndarray of shape (n_samples, n_classes)
Normalized probability distributions across class labels.
score(X, y, sample_weight=None)[source]#
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters:
Xarray-like of shape (n_samples, n_features)
Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
Returns:
scorefloat
Mean accuracy of self.predict(X) w.r.t. y.
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 = '$UNCHANGED$') → LabelPropagation[source]#
Request metadata passed to the score method.
Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
True: metadata is requested, and passed toscoreif 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 aPipeline. Otherwise it has no effect.
Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.
Returns:
selfobject
The updated object.