type t =
[ `BaseEstimator
| `BaseLabelPropagation
| `ClassifierMixin
| `LabelPropagation
| `Object ]
Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?kernel:[ `Knn | `Callable of Py.Object.t | `Rbf ] ->
?gamma:float ->
?n_neighbors:int ->
?max_iter:int ->
?tol:[ `T1e_3 of Py.Object.t | `F of float ] ->
?n_jobs:int ->
unit ->
tLabel Propagation classifier
Read more in the :ref:`User Guide <label_propagation>`.
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.
gamma : float, default=20 Parameter for rbf kernel.
n_neighbors : int, default=7 Parameter for knn kernel which need to be strictly positive.
max_iter : int, default=1000 Change maximum number of iterations allowed.
tol : float, 1e-3 Convergence tolerance: threshold to consider the system at steady state.
n_jobs : int, default=None The number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
Attributes ---------- X_ : ndarray 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 via the transduction.
n_iter_ : int Number of iterations run.
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) >>> labelsrandom_unlabeled_points = -1 >>> label_prop_model.fit(iris.data, labels) LabelPropagation(...)
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
See Also -------- LabelSpreading : Alternate label propagation strategy more robust to noise
val fit : x:Py.Object.t -> y:Py.Object.t -> [> tag ] Obj.t -> tFit a semi-supervised label propagation model based
All the input data is provided matrix X (labeled and unlabeled) and corresponding label matrix y with a dedicated marker value for unlabeled samples.
Parameters ---------- X : array-like of shape (n_samples, n_features) A matrix of shape (n_samples, n_samples) will be created from this.
y : array-like of shape (n_samples,) `n_labeled_samples` (unlabeled points are marked as -1) All unlabeled samples will be transductively assigned labels.
Returns ------- self : object
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Performs inductive inference across the model.
Parameters ---------- X : array-like of shape (n_samples, n_features) The data matrix.
Returns ------- y : ndarray 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 ---------- X : array-like of shape (n_samples, n_features) The data matrix.
Returns ------- probabilities : ndarray of shape (n_samples, n_classes) Normalized probability distributions across class labels.
val score :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatReturn 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 ---------- X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.
val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
Attribute classes_: get value or raise Not_found if None.
Attribute label_distributions_: get value or raise Not_found if None.
Attribute label_distributions_: get value as an option.
Attribute transduction_: get value or raise Not_found if None.
Attribute transduction_: get value as an option.
val n_iter_ : t -> intAttribute n_iter_: get value or raise Not_found if None.
val n_iter_opt : t -> int optionAttribute n_iter_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.