Semi supervised
LabelPropagation¶
Module Sklearn.Semi_supervised.LabelPropagation
wraps Python class sklearn.semi_supervised.LabelPropagation
.
type t
create¶
constructor and attributes create
val create :
?kernel:[`Knn | `Callable of Py.Object.t | `Rbf] ->
?gamma:float ->
?n_neighbors:int ->
?max_iter:int ->
?tol:[`F of float | `T1e_3 of Py.Object.t] ->
?n_jobs:int ->
unit ->
t
Label 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)
>>> labels[random_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
fit¶
method fit
val fit :
x:Py.Object.t ->
y:Py.Object.t ->
[> tag] Obj.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 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_params¶
method get_params
val get_params :
?deep:bool ->
[> tag] Obj.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.
predict¶
method predict
val predict :
x:[>`ArrayLike] Np.Obj.t ->
[> tag] Obj.t ->
[>`ArrayLike] Np.Obj.t
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_proba¶
method predict_proba
val predict_proba :
x:[>`ArrayLike] Np.Obj.t ->
[> tag] Obj.t ->
[>`ArrayLike] Np.Obj.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) The data matrix.
Returns
- probabilities : ndarray of shape (n_samples, n_classes) Normalized probability distributions across class labels.
score¶
method score
val score :
?sample_weight:[>`ArrayLike] Np.Obj.t ->
x:[>`ArrayLike] Np.Obj.t ->
y:[>`ArrayLike] Np.Obj.t ->
[> tag] Obj.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.
set_params¶
method set_params
val set_params :
?params:(string * Py.Object.t) list ->
[> tag] Obj.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.
x_¶
attribute x_
val x_ : t -> [>`ArrayLike] Np.Obj.t
val x_opt : t -> ([>`ArrayLike] Np.Obj.t) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
classes_¶
attribute classes_
val classes_ : t -> [>`ArrayLike] Np.Obj.t
val classes_opt : t -> ([>`ArrayLike] Np.Obj.t) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
label_distributions_¶
attribute label_distributions_
val label_distributions_ : t -> [>`ArrayLike] Np.Obj.t
val label_distributions_opt : t -> ([>`ArrayLike] Np.Obj.t) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
transduction_¶
attribute transduction_
val transduction_ : t -> [>`ArrayLike] Np.Obj.t
val transduction_opt : t -> ([>`ArrayLike] Np.Obj.t) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
n_iter_¶
attribute n_iter_
val n_iter_ : t -> int
val n_iter_opt : t -> (int) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
to_string¶
method to_string
val to_string: t -> string
Print the object to a human-readable representation.
show¶
method show
val show: t -> string
Print the object to a human-readable representation.
pp¶
method pp
val pp: Format.formatter -> t -> unit
Pretty-print the object to a formatter.
LabelSpreading¶
Module Sklearn.Semi_supervised.LabelSpreading
wraps Python class sklearn.semi_supervised.LabelSpreading
.
type t
create¶
constructor and attributes create
val create :
?kernel:[`Knn | `Callable of Py.Object.t | `Rbf] ->
?gamma:float ->
?n_neighbors:int ->
?alpha:float ->
?max_iter:int ->
?tol:float ->
?n_jobs:int ->
unit ->
t
LabelSpreading 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'} 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 is a strictly positive integer.
-
alpha : float, default=0.2 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 : int, default=30 Maximum number of iterations allowed.
-
tol : float, default=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 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)
>>> labels[random_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¶
method fit
val fit :
x:[>`ArrayLike] Np.Obj.t ->
y:[>`ArrayLike] Np.Obj.t ->
[> tag] Obj.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 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_params¶
method get_params
val get_params :
?deep:bool ->
[> tag] Obj.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.
predict¶
method predict
val predict :
x:[>`ArrayLike] Np.Obj.t ->
[> tag] Obj.t ->
[>`ArrayLike] Np.Obj.t
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_proba¶
method predict_proba
val predict_proba :
x:[>`ArrayLike] Np.Obj.t ->
[> tag] Obj.t ->
[>`ArrayLike] Np.Obj.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) The data matrix.
Returns
- probabilities : ndarray of shape (n_samples, n_classes) Normalized probability distributions across class labels.
score¶
method score
val score :
?sample_weight:[>`ArrayLike] Np.Obj.t ->
x:[>`ArrayLike] Np.Obj.t ->
y:[>`ArrayLike] Np.Obj.t ->
[> tag] Obj.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.
set_params¶
method set_params
val set_params :
?params:(string * Py.Object.t) list ->
[> tag] Obj.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.
x_¶
attribute x_
val x_ : t -> [>`ArrayLike] Np.Obj.t
val x_opt : t -> ([>`ArrayLike] Np.Obj.t) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
classes_¶
attribute classes_
val classes_ : t -> [>`ArrayLike] Np.Obj.t
val classes_opt : t -> ([>`ArrayLike] Np.Obj.t) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
label_distributions_¶
attribute label_distributions_
val label_distributions_ : t -> [>`ArrayLike] Np.Obj.t
val label_distributions_opt : t -> ([>`ArrayLike] Np.Obj.t) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
transduction_¶
attribute transduction_
val transduction_ : t -> [>`ArrayLike] Np.Obj.t
val transduction_opt : t -> ([>`ArrayLike] Np.Obj.t) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
n_iter_¶
attribute n_iter_
val n_iter_ : t -> int
val n_iter_opt : t -> (int) option
This attribute is documented in create
above. The first version raises Not_found
if the attribute is None. The _opt version returns an option.
to_string¶
method to_string
val to_string: t -> string
Print the object to a human-readable representation.
show¶
method show
val show: t -> string
Print the object to a human-readable representation.
pp¶
method pp
val pp: Format.formatter -> t -> unit
Pretty-print the object to a formatter.