LabelPropagation
¶
-
class
ibex.sklearn.semi_supervised.
LabelPropagation
(kernel='rbf', gamma=20, n_neighbors=7, alpha=None, max_iter=1000, tol=0.001, n_jobs=1)¶ Bases:
sklearn.semi_supervised.label_propagation.LabelPropagation
,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
.
Label Propagation classifier
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.
Deprecated since version 0.19: This parameter will be removed in 0.21. ‘alpha’ is fixed to zero in ‘LabelPropagation’.
- max_iter : integer
- Change 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 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(...)
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
LabelSpreading : Alternate label propagation strategy more robust to noise
-
fit
(X, y)[source]¶ 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
.
- 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