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Issues (4082)

Orange/projection/manifold.py (26 issues)

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import sklearn.manifold as skl_manifold
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The import sklearn.manifold could not be resolved.

This can be caused by one of the following:

1. Missing Dependencies

This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands.

# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version.

2. Missing __init__.py files

This error could also result from missing __init__.py files in your module folders. Make sure that you place one file in each sub-folder.

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from Orange.distance import SklDistance, SpearmanDistance, PearsonDistance
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from Orange.projection import SklProjector
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__all__ = ["MDS", "Isomap", "LocallyLinearEmbedding"]
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class MDS(SklProjector):
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    __wraps__ = skl_manifold.MDS
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    name = 'mds'
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    def __init__(self, n_components=2, metric=True, n_init=4, max_iter=300,
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The argument n_components seems to be unused.
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The argument metric seems to be unused.
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The argument max_iter seems to be unused.
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The argument n_init seems to be unused.
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                 eps=0.001, n_jobs=1, random_state=None,
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The argument random_state seems to be unused.
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The argument eps seems to be unused.
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The argument n_jobs seems to be unused.
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                 dissimilarity='euclidean',
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                 preprocessors=None):
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        super().__init__(preprocessors=preprocessors)
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        self.params = vars()
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        self._metric = dissimilarity
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    def __call__(self, data):
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        distances = SklDistance, SpearmanDistance, PearsonDistance
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        if isinstance(self._metric, distances):
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            data = self.preprocess(data)
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            X, Y, domain = data.X, data.Y, data.domain
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            dist_matrix = self._metric(X)
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            self.params['dissimilarity'] = 'precomputed'
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            clf = self.fit(dist_matrix, Y=Y)
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        elif self._metric is 'precomputed':
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            dist_matrix, Y, domain = data, None, None
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            clf = self.fit(dist_matrix, Y=Y)
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        else:
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            data = self.preprocess(data)
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            X, Y, domain = data.X, data.Y, data.domain
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            clf = self.fit(X, Y=Y)
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        clf.domain = domain
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        return clf
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    def fit(self, X, init=None, Y=None):
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Arguments number differs from overridden 'fit' method
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        proj = self.__wraps__(**self.params)
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        return proj.fit(X, init=init, y=Y)
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class Isomap(SklProjector):
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    __wraps__ = skl_manifold.Isomap
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    name = 'isomap'
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    def __init__(self, n_neighbors=5, n_components=2, eigen_solver='auto',
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The argument eigen_solver seems to be unused.
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The argument n_components seems to be unused.
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The argument n_neighbors seems to be unused.
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                 max_iter=None, path_method='auto',
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The argument path_method seems to be unused.
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The argument max_iter seems to be unused.
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                 neighbors_algorithm='auto', preprocessors=None):
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The argument neighbors_algorithm seems to be unused.
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        super().__init__(preprocessors=preprocessors)
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        self.params = vars()
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class LocallyLinearEmbedding(SklProjector):
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    __wraps__ = skl_manifold.LocallyLinearEmbedding
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    name = 'lle'
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    def __init__(self, n_neighbors=5, n_components=2, reg=0.001,
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The argument n_components seems to be unused.
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The argument reg seems to be unused.
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The argument n_neighbors seems to be unused.
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                 eigen_solver='auto', tol=1e-06 , max_iter=100,
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The argument tol seems to be unused.
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The argument eigen_solver seems to be unused.
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The argument max_iter seems to be unused.
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                 method='standard', hessian_tol=0.0001,
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The argument method seems to be unused.
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The argument hessian_tol seems to be unused.
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                 modified_tol=1e-12, neighbors_algorithm='auto',
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The argument neighbors_algorithm seems to be unused.
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The argument modified_tol seems to be unused.
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                 random_state=None, preprocessors=None):
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The argument random_state seems to be unused.
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        super().__init__(preprocessors=preprocessors)
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        self.params = vars()
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