10000 fix doctests · scikit-learn/scikit-learn@e75a6ff · GitHub
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amuelleramueller
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fix doctests
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‎doc/tutorial/basic/tutorial.rst

Lines changed: 24 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -176,9 +176,10 @@ which produces a new array that contains all but
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the last entry of ``digits.data``::
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>>> clf.fit(digits.data[:-1], digits.target[:-1]) # doctest: +NORMALIZE_WHITESPACE
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SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
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gamma=0.001, 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=100.0, cache_size=200, class_weight=None, coef0=0.0,
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compact_decision_function=None, degree=3, gamma=0.001, 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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Now you can predict new values, in particular, we can ask to the
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classifier what is the digit of our last image in the ``digits`` dataset,
@@ -214,9 +215,10 @@ 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,
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tol=0.001, verbose=False)
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>>> import pickle
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>>> s = pickle.dumps(clf)
@@ -288,17 +290,19 @@ maintained::
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>>> iris = datasets.load_iris()
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>>> clf = SVC()
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>>> clf.fit(iris.data, iris.target)
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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,
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tol=0.001, verbose=False)
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>>> list(clf.predict(iris.data[:3]))
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[0, 0, 0]
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>>> clf.fit(iris.data, iris.target_names[iris.target])
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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,
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tol=0.001, verbose=False)
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>>> list(clf.predict(iris.data[:3])) # doctest: +NORMALIZE_WHITESPACE
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['setosa', 'setosa', 'setosa']
@@ -325,16 +329,18 @@ more than once will overwrite what was learned by any previous ``fit()``::
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>>> clf = SVC()
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>>> clf.set_params(kernel='linear').fit(X, y)
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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='linear', 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='linear',
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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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>>> clf.predict(X_test)
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array([1, 0, 1, 1, 0])
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>>> clf.set_params(kernel='rbf').fit(X, y)
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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,
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tol=0.001, verbose=False)
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>>> clf.predict(X_test)
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array([0, 0, 0, 1, 0])
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‎doc/tutorial/statistical_inference/supervised_learning.rst

Lines changed: 4 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -453,9 +453,10 @@ classification --:class:`SVC` (Support Vector Classification).
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>>> from sklearn import svm
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>>> svc = svm.SVC(kernel='linear')
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>>> svc.fit(iris_X_train, iris_y_train) # 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='linear', 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='linear',
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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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.. warning:: **Normalizing data**

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