val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?kernel:[ `Knn | `Rbf | `Callable of Py.Object.t ] ->
?gamma:float ->
?n_neighbors:int ->
?alpha:float ->
?max_iter:int ->
?tol:float ->
?n_jobs:[ `Int of int | `None ] ->
unit ->
tLabelSpreading model for semi-supervised learning
This model is similar to the basic Label Propagation algorithm, but uses affinity matrix based on the normalized graph Laplacian and soft clamping across the labels.
Read more in the :ref:`User Guide <label_propagation>`.
Parameters ---------- kernel : 'knn', 'rbf', callable 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 parameter for rbf kernel
n_neighbors : integer > 0 parameter for knn kernel
alpha : float Clamping factor. A value in (0, 1) that specifies the relative amount that an instance should adopt the information from its neighbors as opposed to its initial label. alpha=0 means keeping the initial label information; alpha=1 means replacing all initial information.
max_iter : integer maximum number of iterations allowed
tol : float Convergence tolerance: threshold to consider the system at steady state
n_jobs : int or None, optional (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_ : array, shape = n_samples, n_features Input array.
classes_ : array, shape = n_classes The distinct labels used in classifying instances.
label_distributions_ : array, shape = n_samples, n_classes Categorical distribution for each item.
transduction_ : array, 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 LabelSpreading >>> label_prop_model = LabelSpreading() >>> 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) LabelSpreading(...)
References ---------- Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schoelkopf. Learning with local and global consistency (2004) http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3219
See Also -------- LabelPropagation : Unregularized graph based semi-supervised learning
Fit 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 n_samples by n_samples size matrix will be created from this
y : array_like, shape = n_samples n_labeled_samples (unlabeled points are marked as -1) All unlabeled samples will be transductively assigned labels
Returns ------- self : returns an instance of self.
val get_params : ?deep:bool -> t -> Py.Object.tGet 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)
Returns ------- y : array_like, 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)
Returns ------- probabilities : array, shape = n_samples, n_classes Normalized probability distributions across class labels
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 ---------- 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 -> 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 label_distributions_: see constructor for documentation
val n_iter_ : t -> intAttribute n_iter_: see constructor for documentation
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.