8000 [MRG] Fixes issue #6746 "number of power iterations" message by chriskaschner · Pull Request #7010 · scikit-learn/scikit-learn · GitHub
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[MRG] Fixes issue #6746 "number of power iterations" message #7010

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[MRG] Fixes issue #6746 "number of power iterations" message #7010

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fcbaec7
Fixes issue #6746
chriskaschner Jul 16, 2016
26d0fb3
Reference Issue
chriskaschner Jul 28, 2016
0f6a3bc
Fix: fix test case for deprecated pca example
chriskaschner Jul 29, 2016
222232c
Fix: Update Doctests to reflect new value 'iterated_power'
chriskaschner Jul 29, 2016
588d362
Fix: Update Doctests to reflect new value 'iterated_power'
chriskaschner Jul 29, 2016
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10 changes: 5 additions & 5 deletions doc/modules/pipeline.rst
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10000
Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@ is an estimator object::
>>> estimators = [('reduce_dim', PCA()), ('svm', SVC())]
>>> clf = Pipeline(estimators)
>>> clf # doctest: +NORMALIZE_WHITESPACE
Pipeline(steps=[('reduce_dim', PCA(copy=True, iterated_power=4,
Pipeline(steps=[('reduce_dim', PCA(copy=True, iterated_power=None,
n_components=None, random_state=None, svd_solver='auto', tol=0.0,
whiten=False)), ('svm', SVC(C=1.0, cache_size=200, class_weight=None,
coef0=0.0, decision_function_shape=None, degree=3, gamma='auto',
Expand All @@ -65,20 +65,20 @@ filling in the names automatically::
The estimators of a pipeline are stored as a list in the ``steps`` attribute::

>>> clf.steps[0]
('reduce_dim', PCA(copy=True, iterated_power=4, n_components=None, random_state=None,
('reduce_dim', PCA(copy=True, iterated_power=None, n_components=None, random_state=None,
svd_solver='auto', tol=0.0, whiten=False))

and as a ``dict`` in ``named_steps``::

>>> clf.named_steps['reduce_dim']
PCA(copy=True, iterated_power=4, n_components=None, random_state=None,
PCA(copy=True, iterated_power=None, n_components=None, random_state=None,
svd_solver='auto', tol=0.0, whiten=False)

Parameters of the estimators in the pipeline can be accessed using the
``<estimator>__<parameter>`` syntax::

>>> clf.set_params(svm__C=10) # doctest: +NORMALIZE_WHITESPACE
Pipeline(steps=[('reduce_dim', PCA(copy=True, iterated_power=4,
Pipeline(steps=[('reduce_dim', PCA(copy=True, iterated_power=None,
n_components=None, random_state=None, svd_solver='auto', tol=0.0,
whiten=False)), ('svm', SVC(C=10, cache_size=200, class_weight=None,
coef0=0.0, decision_function_shape=None, degree=3, gamma='auto',
Expand Down Expand Up @@ -159,7 +159,7 @@ and ``value`` is an estimator object::
>>> combined = FeatureUnion(estimators)
>>> combined # doctest: +NORMALIZE_WHITESPACE
FeatureUnion(n_jobs=1, transformer_list=[('linear_pca', PCA(copy=True,
iterated_power=4, n_components=None, random_state=None,
iterated_power=None, n_components=None, random_state=None,
svd_solver='auto', tol=0.0, whiten=False)), ('kernel_pca',
KernelPCA(alpha=1.0, coef0=1, copy_X=True, degree=3,
eigen_solver='auto', fit_inverse_transform=False, gamma=None,
Expand Down
4 changes: 2 additions & 2 deletions doc/tutorial/statistical_inference/unsupervised_learning.rst
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Expand Up @@ -105,7 +105,7 @@ algorithms. The simplest clustering algorithm is

Clustering in general and KMeans, in particular, can be seen as a way
of choosing a small number of exemplars to compress the information.
The problem is sometimes known as
The problem is sometimes known as
`vector quantization <https://en.wikipedia.org/wiki/Vector_quantization>`_.
For instance, this can be used to posterize an image::

Expand Down Expand Up @@ -275,7 +275,7 @@ data by projecting on a principal subspace.
>>> from sklearn import decomposition
>>> pca = decomposition.PCA()
>>> pca.fit(X)
PCA(copy=True, iterated_power=4, n_components=None, random_state=None,
PCA(copy=True, iterated_power=None, n_components=None, random_state=None,
svd_solver='auto', tol=0.0, whiten=False)
>>> print(pca.explained_variance_) # doctest: +SKIP
[ 2.18565811e+00 1.19346747e+00 8.43026679e-32]
Expand Down
14 changes: 7 additions & 7 deletions sklearn/decomposition/pca.py
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Expand Up @@ -240,21 +240,21 @@ class PCA(_BasePCA):
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = PCA(n_components=2)
>>> pca.fit(X)
PCA(copy=True, iterated_power=4, n_components=2, random_state=None,
PCA(copy=True, iterated_power=None, n_components=2, random_state=None,
svd_solver='auto', tol=0.0, whiten=False)
>>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.99244... 0.00755...]

>>> pca = PCA(n_components=2, svd_solver='full')
>>> pca.fit(X) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
PCA(copy=True, iterated_power=4, n_components=2, random_state=None,
PCA(copy=True, iterated_power=None, n_components=2, random_state=None,
svd_solver='full', tol=0.0, whiten=False)
>>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.99244... 0.00755...]

>>> pca = PCA(n_components=1, svd_solver='arpack')
>>> pca.fit(X)
PCA(copy=True, iterated_power=4, n_components=1, random_state=None,
PCA(copy=True, iterated_power=None, n_components=1, random_state=None,
svd_solver='arpack', tol=0.0, whiten=False)
>>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.99244...]
Expand All @@ -268,7 +268,7 @@ class PCA(_BasePCA):
"""

def __init__(self, n_components=None, copy=True, whiten=False,
svd_solver='auto', tol=0.0, iterated_power=4,
svd_solver='auto', tol=0.0, iterated_power=None,
random_state=None):
self.n_components = n_components
self.copy = copy
Expand Down Expand Up @@ -541,9 +541,9 @@ class RandomizedPCA(BaseEstimator, TransformerMixin):
.. versionchanged:: 0.18

whiten : bool, optional
When True (False by default) the `components_` vectors are multiplied by
the square root of (n_samples) and divided by the singular values to
ensure uncorrelated outputs with unit component-wise variances.
When True (False by default) the `components_` vectors are multiplied
by the square root of (n_samples) and divided by the singular values
to ensure uncorrelated outputs with unit component-wise variances.

Whitening will remove some information from the transformed signal
(the relative variance scales of the components) but can sometime
Expand Down
2 changes: 1 addition & 1 deletion sklearn/pipeline.py
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Expand Up @@ -561,7 +561,7 @@ def make_union(*transformers):
>>> make_union(PCA(), TruncatedSVD()) # doctest: +NORMALIZE_WHITESPACE
FeatureUnion(n_jobs=1,
transformer_list=[('pca',
PCA(copy=True, iterated_power=4,
PCA(copy=True, iterated_power=None,
n_components=None, random_state=None,
svd_solver='auto', tol=0.0, whiten=False)),
('truncatedsvd',
Expand Down
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