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https://issues.apache.org/jira/browse/SPARK-24217?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16471128#comment-16471128
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shahid edited comment on SPARK-24217 at 5/10/18 9:26 PM:
---------------------------------------------------------
Thanks for the clarification Joseph K. Bradley
Is it really required to append the result with the input dataframe? Because
with the existing implementation, i can able to get the desired output with my
fix.
For eg:
id neighbor similarity
1 [ 2, 3, 4, 5] [ 1.0, 1.0, 1.0, 1.0]
6 [ 7, 8 , 9, 10] [1.0 1.0 1.0 1.0]
Output in *spark.ml* (With my fix)
id prediction
1 0
2 0
3 0
4 0
5 0
6 1
7 1
8 1
9 1
10 1
was (Author: shahid):
Thanks for the clarification Joseph K. Bradley
Is it really required to append the result with the input dataframe? Because
with the existing implementation, i can able to get the desired output with my
fix.
For eg:
id neighbor similarity
1 [ 2, 3, 4, 5] [ 1.0, 1.0, 1.0, 1.0]
6 [ 7, 8 , 9, 10] [1.0 1.0 1.0 1.0]
Output in *spark.ml* (With my fix)
id prediction
1 0
2 0
3 0
4 0
5 0
6 1
7 1
8 1
9 1
10 1
> Power Iteration Clustering is not displaying cluster indices corresponding to
> some vertices.
> --------------------------------------------------------------------------------------------
>
> Key: SPARK-24217
> URL: https://issues.apache.org/jira/browse/SPARK-24217
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.4.0
> Reporter: shahid
> Priority: Major
> Fix For: 2.4.0
>
>
> We should display prediction and id corresponding to all the nodes.
> Currently PIC is not returning the cluster indices of neighbour IDs which are
> not there in the ID column.
> As per the definition of PIC clustering, given in the code,
> PIC takes an affinity matrix between items (or vertices) as input. An
> affinity matrix
> is a symmetric matrix whose entries are non-negative similarities between
> items.
> PIC takes this matrix (or graph) as an adjacency matrix. Specifically, each
> input row includes:
> * {{idCol}}: vertex ID
> * {{neighborsCol}}: neighbors of vertex in {{idCol}}
> * {{similaritiesCol}}: non-negative weights (similarities) of edges between
> the vertex
> in {{idCol}} and each neighbor in {{neighborsCol}}
> * *"PIC returns a cluster assignment for each input vertex."* It appends a
> new column {{predictionCol}}
> containing the cluster assignment in {{[0,k)}} for each row (vertex).
>
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