sparktk.graph.ops.label_propagation module

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#
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#       http://www.apache.org/licenses/LICENSE-2.0
#
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#  distributed under the License is distributed on an "AS IS" BASIS,
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#  See the License for the specific language governing permissions and
#  limitations under the License.
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def label_propagation(self, max_iterations):
    """

    Parameters
    ----------

    Assigns label based off of proximity to different vertices. The labels
    are initially 1 unique label per vertex (the vertex id), and as the
    algorithm runs some of these get erased

    Note this algorithm is neither guaranteed to converge, nor guaranteed to
    converge to the correct value.

    This calls graph frames label propagation which can be found at 

    http://graphframes.github.io/api/scala/index.html#org.graphframes.lib.LabelPropagation

    :return: (Frame) Frame containing the vertex id's and the community they are a member of

    Examples
    --------

        >>> vertex_schema = [('id', int)]
        >>> vertex_rows = [ [1], [2], [3], [4], [5] ]

        >>> edge_rows = [ [1, 2], [1, 3], [2, 3], [1, 4], [4, 5] ]
        >>> edge_schema = [('src', int), ('dst', int)]

        >>> vertex_frame = tc.frame.create(vertex_rows, vertex_schema)
        >>> edge_frame = tc.frame.create(edge_rows, edge_schema)

        >>> graph = tc.graph.create(vertex_frame, edge_frame)

        >>> result = graph.label_propagation(10)
        >>> result.inspect()
        [#]  id  label
        ==============
        [0]   1      1
        [1]   2      2
        [2]   3      2
        [3]   4      2
        [4]   5      1

    """
    from sparktk.frame.frame import Frame
    return Frame(self._tc, self._scala.labelPropagation(max_iterations))

Functions

def label_propagation(

self, max_iterations)

Parameters:

Assigns label based off of proximity to different vertices. The labels are initially 1 unique label per vertex (the vertex id), and as the algorithm runs some of these get erased

Note this algorithm is neither guaranteed to converge, nor guaranteed to converge to the correct value.

This calls graph frames label propagation which can be found at

http://graphframes.github.io/api/scala/index.html#org.graphframes.lib.LabelPropagation

Returns

(Frame):

Frame containing the vertex id's and the community they are a member of

Examples:

>>> vertex_schema = [('id', int)]
>>> vertex_rows = [ [1], [2], [3], [4], [5] ]

>>> edge_rows = [ [1, 2], [1, 3], [2, 3], [1, 4], [4, 5] ]
>>> edge_schema = [('src', int), ('dst', int)]

>>> vertex_frame = tc.frame.create(vertex_rows, vertex_schema)
>>> edge_frame = tc.frame.create(edge_rows, edge_schema)

>>> graph = tc.graph.create(vertex_frame, edge_frame)

>>> result = graph.label_propagation(10)
>>> result.inspect()
[#]  id  label
==============
[0]   1      1
[1]   2      2
[2]   3      2
[3]   4      2
[4]   5      1

Show source ≡

def label_propagation(self, max_iterations):
    """

    Parameters
    ----------

    Assigns label based off of proximity to different vertices. The labels
    are initially 1 unique label per vertex (the vertex id), and as the
    algorithm runs some of these get erased

    Note this algorithm is neither guaranteed to converge, nor guaranteed to
    converge to the correct value.

    This calls graph frames label propagation which can be found at 

    http://graphframes.github.io/api/scala/index.html#org.graphframes.lib.LabelPropagation

    :return: (Frame) Frame containing the vertex id's and the community they are a member of

    Examples
    --------

        >>> vertex_schema = [('id', int)]
        >>> vertex_rows = [ [1], [2], [3], [4], [5] ]

        >>> edge_rows = [ [1, 2], [1, 3], [2, 3], [1, 4], [4, 5] ]
        >>> edge_schema = [('src', int), ('dst', int)]

        >>> vertex_frame = tc.frame.create(vertex_rows, vertex_schema)
        >>> edge_frame = tc.frame.create(edge_rows, edge_schema)

        >>> graph = tc.graph.create(vertex_frame, edge_frame)

        >>> result = graph.label_propagation(10)
        >>> result.inspect()
        [#]  id  label
        ==============
        [0]   1      1
        [1]   2      2
        [2]   3      2
        [3]   4      2
        [4]   5      1

    """
    from sparktk.frame.frame import Frame
    return Frame(self._tc, self._scala.labelPropagation(max_iterations))

Index

Functions

Functions