Beispiel #1
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    def test_construct_from_graph(self):
        """
        Check viability and correctness of LSTs constructed from a similarity
        graph.
        """
        tree = dcl.construct_tree_from_graph(self.knn_graph, self.density, prune_threshold=self.gamma)

        self._check_tree_viability(tree)
        self._check_tree_correctness(tree)
Beispiel #2
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    def test_construct_from_graph(self):
        """
        Check viability and correctness of LSTs constructed from a similarity
        graph.
        """
        tree = dcl.construct_tree_from_graph(self.knn_graph,
                                             self.density,
                                             prune_threshold=self.gamma)

        self._check_tree_viability(tree)
        self._check_tree_correctness(tree)
Beispiel #3
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## Construct a similarity graph and density estimate by hand. For this demo we
#  use the k-nearest neighbors similarity graph and density estimator that
#  DeBaCl uses by default, but this is the place to plug in custom similarity
#  graphs and density estimators. In particular, this functionality means the
#  user is not limited to tabular data or pre-set distance functions. If the
#  user can make a similarity graph and estimate a density (or pseudo-density),
#  then a level set tree can be estimated with DeBaCl.
k = int(0.05 * n)
knn_graph, radii = dcl.utils.knn_graph(X, k, method='kd-tree')
density = dcl.utils.knn_density(radii, n, p, k)

## Build the level set tree.
gamma = int(0.1 * n)
tree = dcl.construct_tree_from_graph(knn_graph,
                                     density,
                                     prune_threshold=gamma,
                                     verbose=True)
print tree

## Save the tree to disk and load it back (just for demo purposes).
tree.save('crater_tree.lst')

tree2 = dcl.load_tree('crater_tree.lst')
print tree2

## Get leaf clusters and leaf node indices.
leaves = tree.get_leaf_nodes()
labels = tree.get_clusters(method='leaf')

## Plot the tree, coloring the nodes used to cluster.
fig = tree.plot(form='mass', color_nodes=leaves, colormap=plt.cm.Spectral)[0]
Beispiel #4
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## Construct a similarity graph and density estimate by hand. For this demo we
#  use the k-nearest neighbors similarity graph and density estimator that
#  DeBaCl uses by default, but this is the place to plug in custom similarity
#  graphs and density estimators. In particular, this functionality means the
#  user is not limited to tabular data or pre-set distance functions. If the
#  user can make a similarity graph and estimate a density (or pseudo-density),
#  then a level set tree can be estimated with DeBaCl.
k = int(0.05 * n)
knn_graph, radii = dcl.utils.knn_graph(X, k, method='kd-tree')
density = dcl.utils.knn_density(radii, n, p, k)


## Build the level set tree.
gamma = int(0.1 * n)
tree = dcl.construct_tree_from_graph(knn_graph, density, prune_threshold=gamma,
                                     verbose=True)
print tree


## Save the tree to disk and load it back (just for demo purposes).
tree.save('crater_tree.lst')

tree2 = dcl.load_tree('crater_tree.lst')
print tree2


## Get leaf clusters and leaf node indices.
leaves = tree.get_leaf_nodes()
labels = tree.get_clusters(method='leaf')