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import sklearn.manifold as skl_manifold |
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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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eps=0.001, n_jobs=1, random_state=None, |
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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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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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max_iter=None, path_method='auto', |
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neighbors_algorithm='auto', preprocessors=None): |
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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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eigen_solver='auto', tol=1e-06 , max_iter=100, |
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method='standard', hessian_tol=0.0001, |
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modified_tol=1e-12, neighbors_algorithm='auto', |
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random_state=None, preprocessors=None): |
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super().__init__(preprocessors=preprocessors) |
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self.params = vars() |
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hugobuddel /
orange3
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