DOC update docstrings to reflect libsvm random_state · DataScience2016/scikit-learn@2d97949 · GitHub

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DOC update docstrings to reflect libsvm random_state

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+25-23Lines changed: 25 additions & 23 deletions

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  • modules
    • pipeline.rst
    • svm.rst
  • tutorial
    • basic
      • tutorial.rst
    • statistical_inference
      • supervised_learning.rst
• sklearn
  • grid_search.py
  • svm
    • classes.py

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‎doc/modules/pipeline.rst‎

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+4-2Lines changed: 4 additions & 2 deletions

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Original file line number	Diff line number	Diff line change
@@ -38,7 +38,8 @@ is an estimator object::
38	38	Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None,
39	39	whiten=False)), ('svm', SVC(C=1.0, cache_size=200, class_weight=None,
40	40	coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1,
41		- probability=False, shrinking=True, tol=0.001, verbose=False))])
	41	+ probability=False, random_state=None, shrinking=True, tol=0.001,
	42	+ verbose=False))])
42	43
43	44	The estimators of the pipeline are stored as a list in the ``steps`` attribute::
44	45
@@ -57,7 +58,8 @@ Parameters of the estimators in the pipeline can be accessed using the
57	58	Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None,
58	59	whiten=False)), ('svm', SVC(C=10, cache_size=200, class_weight=None,
59	60	coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1,
60		- probability=False, shrinking=True, tol=0.001, verbose=False))])
	61	+ probability=False, random_state=None, shrinking=True, tol=0.001,
	62	+ verbose=False))])
61	63
62	64	This is particularly important for doing grid searches::
63	65

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‎doc/modules/svm.rst‎

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+6-6Lines changed: 6 additions & 6 deletions

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Original file line number	Diff line number	Diff line change
@@ -77,8 +77,8 @@ size ``[n_samples]``, holding the class labels for the training samples::
77	77	>>> clf = svm.SVC()
78	78	>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
79	79	SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
80		- gamma=0.0, kernel='rbf', max_iter=-1, probability=False, shrinking=True,
81		- tol=0.001, verbose=False)
	80	+ gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None,
	81	+ shrinking=True, tol=0.001, verbose=False)
82	82
83	83	After being fitted, the model can then be used to predict new values::
84	84
@@ -116,8 +116,8 @@ classifiers are constructed and each one trains data from two classes::
116	116	>>> clf = svm.SVC()
117	117	>>> clf.fit(X, Y) # doctest: +NORMALIZE_WHITESPACE
118	118	SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
119		- gamma=0.0, kernel='rbf', max_iter=-1, probability=False, shrinking=True,
120		- tol=0.001, verbose=False)
	119	+ gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None,
	120	+ shrinking=True, tol=0.001, verbose=False)
121	121	>>> dec = clf.decision_function([[1]])
122	122	>>> dec.shape[1] # 4 classes: 4*3/2 = 6
123	123	6
@@ -301,7 +301,7 @@ floating point values instead of integer values::
301	301	>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
302	302	SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,
303	303	epsilon=0.1, gamma=0.0, kernel='rbf', max_iter=-1, probability=False,
304		- shrinking=True, tol=0.001, verbose=False)
	304	+ random_state=None, shrinking=True, tol=0.001, verbose=False)
305	305	>>> clf.predict([[1, 1]])
306	306	array([ 1.5])
307	307
@@ -499,7 +499,7 @@ test vectors must be provided.
499	499	>>> clf.fit(gram, y) # doctest: +NORMALIZE_WHITESPACE
500	500	SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
501	501	gamma=0.0, kernel='precomputed', max_iter=-1, probability=False,
502		- shrinking=True, tol=0.001, verbose=False)
	502	+ random_state=None, shrinking=True, tol=0.001, verbose=False)
503	503	>>> # predict on training examples
504	504	>>> clf.predict(gram)
505	505	array([0, 1])

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‎doc/tutorial/basic/tutorial.rst‎

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+4-4Lines changed: 4 additions & 4 deletions

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Original file line number	Diff line number	Diff line change
@@ -169,8 +169,8 @@ one::
169	169
170	170	>>> clf.fit(digits.data[:-1], digits.target[:-1]) # doctest: +NORMALIZE_WHITESPACE
171	171	SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
172		- gamma=0.001, kernel='rbf', max_iter=-1, probability=False, shrinking=True,
173		- tol=0.001, verbose=False)
	172	+ gamma=0.001, kernel='rbf', max_iter=-1, probability=False,
	173	+ random_state=None, shrinking=True, tol=0.001, verbose=False)
174	174
175	175	Now you can predict new values, in particular, we can ask to the
176	176	classifier what is the digit of our last image in the `digits` dataset,
@@ -207,8 +207,8 @@ persistence model, namely `pickle <http://docs.python.org/library/pickle.html>`_
207	207	>>> X, y = iris.data, iris.target
208	208	>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
209	209	SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
210		- kernel='rbf', max_iter=-1, probability=False, shrinking=True, tol=0.001,
211		- verbose=False)
	210	+ kernel='rbf', max_iter=-1, probability=False, random_state=None,
	211	+ shrinking=True, tol=0.001, verbose=False)
212	212
213	213	>>> import pickle
214	214	>>> s = pickle.dumps(clf)

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‎doc/tutorial/statistical_inference/supervised_learning.rst‎

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+2-2Lines changed: 2 additions & 2 deletions

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Original file line number	Diff line number	Diff line change
@@ -452,8 +452,8 @@ classification --:class:`SVC` (Support Vector Classification).
452	452	>>> svc = svm.SVC(kernel='linear')
453	453	>>> svc.fit(iris_X_train, iris_y_train) # doctest: +NORMALIZE_WHITESPACE
454	454	SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
455		- kernel='linear', max_iter=-1, probability=False, shrinking=True, tol=0.001,
456		- verbose=False)
	455	+ kernel='linear', max_iter=-1, probability=False, random_state=None,
	456	+ shrinking=True, tol=0.001, verbose=False)
457	457
458	458
459	459	.. warning:: **Normalizing data**

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‎sklearn/grid_search.py‎

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+1-1Lines changed: 1 addition & 1 deletion

Original file line number	Diff line number	Diff line change
@@ -644,7 +644,7 @@ class GridSearchCV(BaseSearchCV):
644	644	GridSearchCV(cv=None,
645	645	estimator=SVC(C=1.0, cache_size=..., coef0=..., degree=...,
646	646	gamma=..., kernel='rbf', max_iter=-1, probability=False,
647		- shrinking=True, tol=...),
	647	+ random_state=None, shrinking=True, tol=...),
648	648	fit_params={}, iid=True, loss_func=None, n_jobs=1,
649	649	param_grid=...,
650	650	...)

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‎sklearn/svm/classes.py‎

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+8-8Lines changed: 8 additions & 8 deletions

Original file line number	Diff line number	Diff line change
@@ -247,8 +247,8 @@ class frequencies.
247	247	>>> clf = SVC()
248	248	>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
249	249	SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
250		- gamma=0.0, kernel='rbf', max_iter=-1, probability=False,
251		- shrinking=True, tol=0.001, verbose=False)
	250	+ gamma=0.0, kernel='rbf', max_iter=-1, probability=False,
	251	+ random_state=None, shrinking=True, tol=0.001, verbose=False)
252	252	>>> print(clf.predict([[-0.8, -1]]))
253	253	[1]
254	254
@@ -371,8 +371,8 @@ class NuSVC(BaseSVC):
371	371	>>> clf = NuSVC()
372	372	>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
373	373	NuSVC(cache_size=200, coef0=0.0, degree=3, gamma=0.0, kernel='rbf',
374		- max_iter=-1, nu=0.5, probability=False, shrinking=True, tol=0.001,
375		- verbose=False)
	374	+ max_iter=-1, nu=0.5, probability=False, random_state=None,
	375	+ shrinking=True, tol=0.001, verbose=False)
376	376	>>> print(clf.predict([[-0.8, -1]]))
377	377	[1]
378	378
@@ -490,8 +490,8 @@ class SVR(BaseLibSVM, RegressorMixin):
490	490	>>> clf = SVR(C=1.0, epsilon=0.2)
491	491	>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
492	492	SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma=0.0,
493		- kernel='rbf', max_iter=-1, probability=False, shrinking=True, tol=0.001,
494		- verbose=False)
	493	+ kernel='rbf', max_iter=-1, probability=False, random_state=None,
	494	+ shrinking=True, tol=0.001, verbose=False)
495	495
496	496	See also
497	497	--------
@@ -606,8 +606,8 @@ class NuSVR(BaseLibSVM, RegressorMixin):
606	606	>>> clf = NuSVR(C=1.0, nu=0.1)
607	607	>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
608	608	NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.0, kernel='rbf',
609		- max_iter=-1, nu=0.1, probability=False, shrinking=True, tol=0.001,
610		- verbose=False)
	609	+ max_iter=-1, nu=0.1, probability=False, random_state=None,
	610	+ shrinking=True, tol=0.001, verbose=False)
611	611
612	612	See also
613	613	--------

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