8000 Make BaseSVC abstract again. · scikit-learn/scikit-learn@6776eb6 · GitHub
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Make BaseSVC abstract again.
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‎doc/modules/model_persistence.rst

Lines changed: 4 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -22,9 +22,10 @@ persistence model, namely `pickle <http://docs.python.org/library/pickle.html>`_
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>>> iris = datasets.load_iris()
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>>> X, y = iris.data, iris.target
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>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
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kernel='rbf', max_iter=-1, probability=False, random_state=None,
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shrinking=True, tol=0.001, verbose=False)
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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compact_decision_function=None, degree=3, gamma=0.0, kernel='rbf',
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max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001,
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verbose=False)
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>>> import pickle
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>>> s = pickle.dumps(clf)
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‎doc/modules/pipeline.rst

Lines changed: 6 additions & 6 deletions
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@@ -42,9 +42,9 @@ is an estimator object::
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>>> clf # doctest: +NORMALIZE_WHITESPACE
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Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None,
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whiten=False)), ('svm', SVC(C=1.0, cache_size=200, class_weight=None,
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coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1,
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probability=False, random_state=None, shrinking=True, tol=0.001,
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verbose=False))])
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coef0=0.0, compact_decision_function=None, degree=3, gamma=0.0,
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kernel='rbf', max_iter=-1, probability=False, random_state=None,
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shrinking=True, tol=0.001, verbose=False))])
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The utility function :func:`make_pipeline` is a shorthand
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for constructing pipelines;
@@ -76,9 +76,9 @@ Parameters of the estimators in the pipeline can be accessed using the
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>>> clf.set_params(svm__C=10) # doctest: +NORMALIZE_WHITESPACE
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Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None,
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whiten=False)), ('svm', SVC(C=10, cache_size=200, class_weight=None,
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coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1,
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probability=False, random_state=None, shrinking=True, tol=0.001,
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verbose=False))])
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coef0=0.0, compact_decision_function=None, degree=3, gamma=0.0,
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kernel='rbf', max_iter=-1, probability=False, random_state=None,
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shrinking=True, tol=0.001, verbose=False))])
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This is particularly important for doing grid searches::
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‎doc/modules/svm.rst

Lines changed: 12 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -76,9 +76,10 @@ n_features]`` holding the training samples, and an array y of class labels
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>>> y = [0, 1]
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>>> clf = svm.SVC()
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>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
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gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None,
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shrinking=True, tol=0.001, verbose=False)
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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compact_decision_function=None, degree=3, gamma=0.0, kernel='rbf',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False)
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After being fitted, the model can then be used to predict new values::
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@@ -115,9 +116,10 @@ classifiers are constructed and each one trains data from two classes::
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>>> Y = [0, 1, 2, 3]
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>>> clf = svm.SVC()
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>>> clf.fit(X, Y) # doctest: +NORMALIZE_WHITESPACE
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
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gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None,
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shrinking=True, tol=0.001, verbose=False)
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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compact_decision_function=None, degree=3, gamma=0.0, kernel='rbf',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False)
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>>> dec = clf.decision_function([[1]])
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>>> dec.shape[1] # 4 classes: 4*3/2 = 6
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6
@@ -503,9 +505,10 @@ test vectors must be provided.
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>>> # linear kernel computation
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>>> gram = np.dot(X, X.T)
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>>> clf.fit(gram, y) # doctest: +NORMALIZE_WHITESPACE
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
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gamma=0.0, kernel='precomputed', max_iter=-1, probability=False,
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random_state=None, shrinking=True, tol=0.001, verbose=False)
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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compact_decision_function=None, degree=3, gamma=0.0, kernel='precomputed',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False)
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>>> # predict on training examples
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>>> clf.predict(gram)
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array([0, 1])

‎sklearn/svm/base.py

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -482,8 +482,9 @@ def _get_coef(self):
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return safe_sparse_dot(self._dual_coef_, self.support_vectors_)
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484484

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class BaseSVC(BaseLibSVM, ClassifierMixin):
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class BaseSVC(six.with_metaclass(ABCMeta, BaseLibSVM, ClassifierMixin)):
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"""ABC for LibSVM-based classifiers."""
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@abstractmethod
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def __init__(self, impl, kernel, degree, gamma, coef0, tol, C, nu,
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shrinking, probability, cache_size, class_weight, verbose,
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max_iter, compact_decision_function, random_state):

‎sklearn/svm/classes.py

Lines changed: 6 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -487,9 +487,10 @@ class frequencies.
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>>> from sklearn.svm import SVC
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>>> clf = SVC()
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>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
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gamma=0.0, kernel='rbf', max_iter=-1, probability=False,
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random_state=None, shrinking=True, tol=0.001, verbose=False)
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SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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compact_decision_function=None, degree=3, gamma=0.0, kernel='rbf',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False)
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>>> print(clf.predict([[-0.8, -1]]))
494495
[1]
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@@ -626,7 +627,8 @@ class frequencies.
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>>> from sklearn.svm import NuSVC
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>>> clf = NuSVC()
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>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
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NuSVC(cache_size=200, coef0=0.0, degree=3, gamma=0.0, kernel='rbf',
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NuSVC(cache_size=200, class_weight=None, coef0=0.0,
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compact_decision_function=None, degree=3, gamma=0.0, kernel='rbf',
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max_iter=-1, nu=0.5, probability=False, random_state=None,
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shrinking=True, tol=0.001, verbose=False)
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>>> print(clf.predict([[-0.8, -1]]))

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