# LabelSpreading¶

class ibex.sklearn.semi_supervised.LabelSpreading(kernel='rbf', gamma=20, n_neighbors=7, alpha=0.2, max_iter=30, tol=0.001, n_jobs=1)

Bases: sklearn.semi_supervised.label_propagation.LabelSpreading, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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 User Guide.

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, optional (default = 1)
The number of parallel jobs to run. If -1, then the number of jobs is set to the number of CPU cores.
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.
>>> from sklearn import datasets
>>> 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)
...


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

LabelPropagation : Unregularized graph based semi-supervised learning

fit(X, y)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

X : array-like, 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

self : returns an instance of self.

predict(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Performs inductive inference across the model.

X : array_like, shape = [n_samples, n_features]

y : array_like, shape = [n_samples]
Predictions for input data
predict_proba(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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).

X : array_like, shape = [n_samples, n_features]

probabilities : array, shape = [n_samples, n_classes]
Normalized probability distributions across class labels
score(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Returns 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.

X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
score : float
Mean accuracy of self.predict(X) wrt. y.