8000 ENH RandomizedPCA collapsed into PCA · scikit-learn/scikit-learn@1195df9 · GitHub
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ENH RandomizedPCA collapsed into PCA
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‎doc/modules/pipeline.rst

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@@ -38,13 +38,15 @@ is an estimator object::
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>>> from sklearn.svm import SVC
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>>> from sklearn.decomposition import PCA
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>>> estimators = [('reduce_dim', PCA()), ('svm', SVC())]
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>>> clf = Pipeline(estimators)
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>>> clf = Pipeline(estimators)
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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, decision_function_shape=None, degree=3, gamma='auto',
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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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Pipeline(steps=[('reduce_dim', PCA(copy=True, iterated_power=4,
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n_components=None, random_state=None, svd_solver='auto', tol=0.0,
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whiten=False)), ('svm', SVC(C=1.0, cache_size=200, class_weight=None,
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coef0=0.0, decision_function_shape=None, degree=3, gamma='auto',
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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;
@@ -63,23 +65,27 @@ filling in the names automatically::
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The estimators of a pipeline are stored as a list in the ``steps`` attribute::
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>>> clf.steps[0]
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('reduce_dim', PCA(copy=True, n_components=None, whiten=False))
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('reduce_dim', PCA(copy=True, iterated_power=4, n_components=None, random_state=None,
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svd_solver='auto', tol=0.0, whiten=False))
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and as a ``dict`` in ``named_steps``::
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>>> clf.named_steps['reduce_dim']
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PCA(copy=True, n_components=None, whiten=False)
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PCA(copy=True, iterated_power=4, n_components=None, random_state=None,
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svd_solver='auto', tol=0.0, whiten=False)
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Parameters of the estimators in the pipeline can be accessed using the
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``<estimator>__<parameter>`` syntax::
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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,
81+
Pipeline(steps=[('reduce_dim', PCA(copy=True, iterated_power=4,
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n_components=None, random_state=None, svd_solver='auto', tol=0.0,
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whiten=False)), ('svm', SVC(C=10, cache_size=200, class_weight=None,
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coef0=0.0, decision_function_shape=None, degree=3, gamma='auto',
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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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>>> from sklearn.model_selection import GridSearchCV
@@ -150,19 +156,22 @@ and ``value`` is an estimator object::
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>>> from sklearn.decomposition import PCA
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>>> from sklearn.decomposition import KernelPCA
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>>> estimators = [('linear_pca', PCA()), ('kernel_pca', KernelPCA())]
153-
>>> combined = FeatureUnion(estimators)
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>>> combined = FeatureUnion(estimators)
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>>> combined # doctest: +NORMALIZE_WHITESPACE
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FeatureUnion(n_jobs=1, transformer_list=[('linear_pca', PCA(copy=True,
156-
n_components=None, whiten=False)), ('kernel_pca', KernelPCA(alpha=1.0,
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coef0=1, degree=3, eigen_solver='auto', fit_inverse_transform=False,
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gamma=None, kernel='linear', kernel_params=None, max_iter=None,
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n_components=None, n_jobs=1, random_state=None, remove_zero_eig=False, tol=0))],
161+
FeatureUnion(n_jobs=1, transformer_list=[('linear_pca', PCA(copy=True,
162+
iterated_power=4, n_components=None, random_state=None,
163+
svd_solver='auto', tol=0.0, whiten=False)), ('kernel_pca',
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KernelPCA(alpha=1.0, coef0=1, degree=3, eigen_solver='auto',
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fit_inverse_transform=False, gamma=None, kernel='linear',
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kernel_params=None, max_iter=None, n_components=None, n_jobs=1,
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random_state=None, remove_zero_eig=False, tol=0))],
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transformer_weights=None)
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Like pipelines, feature unions have a shorthand constructor called
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:func:`make_union` that does not require explicit naming of the components.
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.. topic:: Examples:
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* :ref:`example_feature_stacker.py`

‎doc/tutorial/statistical_inference/unsupervised_learning.rst

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Original file line numberDiff line numberDiff line change
@@ -275,7 +275,8 @@ data by projecting on a principal subspace.
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>>> from sklearn import decomposition
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>>> pca = decomposition.PCA()
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>>> pca.fit(X)
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PCA(copy=True, n_components=None, whiten=False)
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PCA(copy=True, iterated_power=4, n_components=None, random_state=None,
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svd_solver='auto', tol=0.0, whiten=False)
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>>> print(pca.explained_variance_) # doctest: +SKIP
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[ 2.18565811e+00 1.19346747e+00 8.43026679e-32]
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‎doc/whats_new.rst

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Original file line numberDiff line numberDiff line change
@@ -22,6 +22,15 @@ New features
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:class:`feature_selection.SelectPercentile` as score functions.
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By `Andrea Bravi`_ and `Nikolay Mayorov`_.
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- Class :class:`decomposition.RandomizedPCA` is now factored into :class:`decomposition.PCA`
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and it is available calling with parameter ``svd_solver='randomized'``.
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The default number of ``n_iter`` for ``'randomized'`` has changed to 4. The old
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behavior of PCA is recovered by ``svd_solver='full'``. An additional solver
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calls `arpack` and performs truncated (non-randomized) SVD. By default,
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the best solver is selected depending on the size of the input and the
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number of components requested.
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(`#5299 <https://github.com/scikit-learn/scikit-learn/pull/5299>`_) by `Giorgio Patrini`_.
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- The Gaussian Process module has been reimplemented and now offers classification
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and regression estimators through :class:`gaussian_process.GaussianProcessClassifier`
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and :class:`gaussian_process.GaussianProcessRegressor`. Among other things, the new
@@ -114,17 +123,26 @@ Bug fixes
114123
- :class:`StratifiedKFold` now raises error if all n_labels for individual classes is less than n_folds.
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(`#6182 <https://github.com/scikit-learn/scikit-learn/pull/6182>`_) by `Devashish Deshpande`_.
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- :class:`RandomizedPCA` default number of `iterated_power` is 2 instead of 3.
118-
This is a speed up with a minor precision decrease. (`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
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- :class:`RandomizedPCA` default number of `iterated_power` is 4 instead of 3.
127+
(`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
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120-
- :func:`randomized_svd` performs 2 power iterations by default, instead or 0.
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In practice this is often enough for obtaining a good approximation of the
122-
true eigenvalues/vectors in the presence of noise. (`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
129+
- :func:`utils.extmath.randomized_svd` performs 4 power iterations by default, instead or 0.
130+
In practice this is enough for obtaining a good approximation of the
131+
true eigenvalues/vectors in the presence of noise. When `n_components` is
132+
small (< .1 * min(X.shape)) `n_iter` is set to 7, unless the user specifies
133+
a higher number. This improves precision with few components.
134+
(`#5299 <https://github.com/scikit-learn/scikit-learn/pull/5299>`_) by `Giorgio Patrini`_.
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124-
- :func:`randomized_range_finder` is more numerically stable when many
136+
- :func:`utils.extmath.randomized_range_finder` is more numerically stable when many
125137
power iterations are requested, since it applies LU normalization by default.
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If `n_iter<2` numerical issues are unlikely, thus no normalization is applied.
127-
Other normalization options are available: 'none', 'LU' and 'QR'. (`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
139+
Other normalization options are available: 'none', 'LU' and 'QR'.
140+
(`#5141 <https://github.com/scikit-learn/scikit-learn/pull/5141>`_) by `Giorgio Patrini`_.
141+
142+
- Whiten/non-whiten inconsistency between components of :class:`decomposition.PCA`
143+
and :class:`decomposition.RandomizedPCA` (now factored into PCA, see the
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New features) is fixed. `components_` are stored with no whitening.
145+
(`#5299 <https://github.com/scikit-learn/scikit-learn/pull/5299>`_) by `Giorgio Patrini`_.
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- Fixed bug in :func:`manifold.spectral_embedding` where diagonal of unnormalized
130148
Laplacian matrix was incorrectly set to 1. (`#4995 <https://github.com/scikit-learn/scikit-learn/pull/4995>`_) By `Peter Fischer`_.
@@ -213,7 +231,8 @@ Changelog
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New features
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............
216-
- All the Scaler classes but :class:`RobustScaler` can be fitted online by
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235+
- All the Scaler classes but :class:`preprocessing.RobustScaler` can be fitted online by
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calling `partial_fit`. By `Giorgio Patrini`_.
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- The new class :class:`ensemble.VotingClassifier` implements a
@@ -445,6 +464,7 @@ Enhancements
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Bug fixes
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.........
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- Fixed non-determinism in :class:`dummy.DummyClassifier` with sparse
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multi-label output. By `Andreas Müller`_.
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‎examples/decomposition/plot_pca_vs_fa_model_selection.py

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Original file line numberDiff line numberDiff line change
@@ -23,7 +23,6 @@
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by Thomas P. Minka is also compared.
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"""
26-
print(__doc__)
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# Authors: Alexandre Gramfort
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# Denis A. Engemann
@@ -38,6 +37,8 @@
3837
from sklearn.model_selection import cross_val_score
3938
from sklearn.model_selection import GridSearchCV
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40+
print(__doc__)
41+
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###############################################################################
4243
# Create the data
4344

@@ -61,7 +62,7 @@
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6364
def compute_scores(X):
64-
pca = PCA()
65+
pca = PCA(svd_solver='full')
6566
fa = FactorAnalysis()
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6768
pca_scores, fa_scores = [], []
@@ -90,7 +91,7 @@ def lw_score(X):
9091
n_components_pca = n_components[np.argmax(pca_scores)]
9192
n_components_fa = n_components[np.argmax(fa_scores)]
9293

93-
pca = PCA(n_components='mle')
94+
pca = PCA(svd_solver='full', n_components='mle')
9495
pca.fit(X)
9596
n_components_pca_mle = pca.n_components_
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@@ -105,7 +106,8 @@ def lw_score(X):
105106
plt.axvline(n_components_pca, color='b',
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label='PCA CV: %d' % n_components_pca, linestyle='--')
107108
plt.axvline(n_components_fa, color='r',
108-
label='FactorAnalysis CV: %d' % n_components_fa, linestyle='--')
109+
label='FactorAnalysis CV: %d' % n_components_fa,
110+
linestyle='--')
109111
plt.axvline(n_components_pca_mle, color='k',
110112
label='PCA MLE: %d' % n_components_pca_mle, linestyle='--')
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