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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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 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 >>> 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(...)
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
predict
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Performs inductive inference across the model.
X : array_like, shape = [n_samples, n_features]
- y : array_like, shape = [n_samples]
- Predictions for input data
- A parameter
-
predict_proba
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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
- A parameter
-
score
(X, y, sample_weight=None)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
- A parameter