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[ENH] PCA on sparse data. #2313

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May 26, 2017
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[ENH] PCA on sparse data. #2313

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2a0bf38
Projector: supports_sparse attribute
acopar May 18, 2017
fe8bf13
TruncatedSVD decomposition
acopar May 18, 2017
5704bd3
owpca: Added TruncatedSVD
acopar May 19, 2017
b032ea1
OWPCA test for sparse data
acopar May 18, 2017
1bc9fdc
PCA, TruncatedSVD remove max_components argument
acopar May 19, 2017
2b6e8fd
OWPCA: Add decomposition and normalization to report
nikicc May 25, 2017
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Projector: supports_sparse attribute 
supports_sparse indicates whether projector can handle sparse data.
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@acopar
acopar committed May 26, 2017
commit 2a0bf38bacd63a968a29ea4a8603dcb832e505ea
2 changes: 2 additions & 0 deletions Orange/projection/base.py
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Expand Up @@ -82,6 +82,8 @@ class SklProjector(Projector, metaclass=WrapperMeta):
__wraps__ = None
_params = {}
name = 'skl projection'
supports_sparse = False
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Should this be moved to Projector?


preprocessors = [Orange.preprocess.Continuize(),
Orange.preprocess.SklImpute()]

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3 changes: 3 additions & 0 deletions Orange/projection/pca.py
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Expand Up @@ -34,6 +34,7 @@ def score(self, data):
class PCA(SklProjector, _FeatureScorerMixin):
__wraps__ = skl_decomposition.PCA
name = 'pca'
supports_sparse = False

def __init__(self, n_components=None, copy=True, whiten=False,
svd_solver='auto', tol=0.0, iterated_power='auto',
Expand Down Expand Up @@ -61,6 +62,7 @@ def fit(self, X, Y=None):
class SparsePCA(SklProjector):
__wraps__ = skl_decomposition.SparsePCA
name = 'sparse pca'
supports_sparse = False

def __init__(self, n_components=None, alpha=1, ridge_alpha=0.01,
max_iter=1000, tol=1e-8, method='lars', n_jobs=1, U_init=None,
Expand Down Expand Up @@ -125,6 +127,7 @@ def pca_variable(i):
class IncrementalPCA(SklProjector):
__wraps__ = skl_decomposition.IncrementalPCA
name = 'incremental pca'
supports_sparse = False

def __init__(self, n_components=None, whiten=False, copy=True,
batch_size=None, preprocessors=None):
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