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DOC fix docstring in semi_supervised module following doc guideline (#…
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…16042)
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@shubchat @glemaitre
shubchat authored and glemaitre committed Jan 13, 2020
1 parent dd93e01 commit 594fc85
Showing 1 changed file with 58 additions and 56 deletions.
114 changes: 58 additions & 56 deletions sklearn/semi_supervised/_label_propagation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,29 +76,29 @@ class BaseLabelPropagation(ClassifierMixin, BaseEstimator, metaclass=ABCMeta):
Parameters
----------
kernel : {'knn', 'rbf', callable}
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
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
gamma : float, default=20
Parameter for rbf kernel.
n_neighbors : integer > 0
Parameter for knn kernel
n_neighbors : int, default=7
Parameter for knn kernel. Need to be strictly positive.
alpha : float
Clamping factor
alpha : float, default=1.0
Clamping factor.
max_iter : integer
Change maximum number of iterations allowed
max_iter : int, default=30
Change maximum number of iterations allowed.
tol : float
tol : float, default=1e-3
Convergence tolerance: threshold to consider the system at steady
state
state.
n_jobs : int or None, optional (default=None)
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>`
Expand Down Expand Up @@ -158,11 +158,12 @@ def predict(self, X):
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
Returns
-------
y : array_like, shape = [n_samples]
Predictions for input data
y : ndarray of shape (n_samples,)
Predictions for input data.
"""
probas = self.predict_proba(X)
return self.classes_[np.argmax(probas, axis=1)].ravel()
Expand All @@ -177,12 +178,13 @@ def predict_proba(self, X):
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
Returns
-------
probabilities : array, shape = [n_samples, n_classes]
probabilities : ndarray of shape (n_samples, n_classes)
Normalized probability distributions across
class labels
class labels.
"""
check_is_fitted(self)

Expand Down Expand Up @@ -211,15 +213,15 @@ def fit(self, X, y):
Parameters
----------
X : array-like of shape (n_samples, n_features)
A {n_samples by n_samples} size matrix will be created from this
A matrix of shape (n_samples, n_samples) 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
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 : returns an instance of self.
self : object
"""
X, y = check_X_y(X, y)
self.X_ = X
Expand Down Expand Up @@ -307,43 +309,43 @@ class LabelPropagation(BaseLabelPropagation):
Parameters
----------
kernel : {'knn', 'rbf', callable}
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.
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
gamma : float, default=20
Parameter for rbf kernel.
n_neighbors : integer > 0
Parameter for knn kernel
n_neighbors : int, default=7
Parameter for knn kernel which need to be strictly positive.
max_iter : integer
Change maximum number of iterations allowed
max_iter : int, default=1000
Change maximum number of iterations allowed.
tol : float
tol : float, 1e-3
Convergence tolerance: threshold to consider the system at steady
state
state.
n_jobs : int or None, optional (default=None)
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_ : array, shape = [n_samples, n_features]
X_ : ndarray of shape (n_samples, n_features)
Input array.
classes_ : array, shape = [n_classes]
classes_ : ndarray of shape (n_classes,)
The distinct labels used in classifying instances.
label_distributions_ : array, shape = [n_samples, n_classes]
label_distributions_ : ndarray of shape (n_samples, n_classes)
Categorical distribution for each item.
transduction_ : array, shape = [n_samples]
transduction_ : ndarray of shape (n_samples)
Label assigned to each item via the transduction.
n_iter_ : int
Expand Down Expand Up @@ -413,50 +415,50 @@ class LabelSpreading(BaseLabelPropagation):
Parameters
----------
kernel : {'knn', 'rbf', callable}
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
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
gamma : float, default=20
Parameter for rbf kernel.
n_neighbors : integer > 0
parameter for knn kernel
n_neighbors : int, default=7
Parameter for knn kernel which is a strictly positive integer.
alpha : float
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 : integer
maximum number of iterations allowed
max_iter : int, default=30
Maximum number of iterations allowed.
tol : float
tol : float, default=1e-3
Convergence tolerance: threshold to consider the system at steady
state
state.
n_jobs : int or None, optional (default=None)
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_ : array, shape = [n_samples, n_features]
X_ : ndarray of shape (n_samples, n_features)
Input array.
classes_ : array, shape = [n_classes]
classes_ : ndarray of shape (n_classes,)
The distinct labels used in classifying instances.
label_distributions_ : array, shape = [n_samples, n_classes]
label_distributions_ : ndarray of shape (n_samples, n_classes)
Categorical distribution for each item.
transduction_ : array, shape = [n_samples]
transduction_ : ndarray of shape (n_samples,)
Label assigned to each item via the transduction.
n_iter_ : int
Expand Down

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