package sklearn

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sklearn
- Sklearn

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type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?kernel:[ `Knn | `Rbf | `Callable of Py.Object.t ] ->
  ?gamma:float ->
  ?n_neighbors:int ->
  ?max_iter:int ->
  ?tol:float ->
  ?n_jobs:int ->
  unit ->
  t

Label Propagation classifier

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

max_iter : integer Change 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 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 -> t -> t

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 -> Dict.t

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.

val predict : x:Arr.t -> t -> Arr.t

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

val predict_proba : x:Arr.t -> t -> Arr.t

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

val score : ?sample_weight:Arr.t -> x:Arr.t -> y:Arr.t -> t -> float

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 -> t

Set 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.

val x_ : t -> Arr.t

Attribute X_: get value or raise Not_found if None.

val x_opt : t -> Arr.t option

Attribute X_: get value as an option.

val classes_ : t -> Arr.t

Attribute classes_: get value or raise Not_found if None.

val classes_opt : t -> Arr.t option

Attribute classes_: get value as an option.

val label_distributions_ : t -> Arr.t

Attribute label_distributions_: get value or raise Not_found if None.

val label_distributions_opt : t -> Arr.t option

Attribute label_distributions_: get value as an option.

val transduction_ : t -> Arr.t

Attribute transduction_: get value or raise Not_found if None.

val transduction_opt : t -> Arr.t option

Attribute transduction_: get value as an option.

val n_iter_ : t -> int

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> int option

Attribute n_iter_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.