Use to match objects (peer objects) based on categorical and continuous data. First, the objects are matched exactly based on their categorical data, then they are matched within each categorical group based on the Euclidean distance of their continuous data.
The input should be of the following form:
object_id, categorical_data, no_match_groups, cont_point_1, cont_point_2, ..., cont_point_n
with one object per line. Leaving no_match_group as a blank field will cause all objects to be compared within the categorical group. A sample input file, test_data/sample_input_data.csv, is provided for testing.
The output will be:
object_id, peer_object_id_1, peer_object_id_2, ..., peer_object_id_m
The categorical data can be anything. Grouping will be done on the unique values of this field. This can be a group label (group1, group2, etc.), concatenated categorical fields (x:y where x and y are different categorical flags), etc. While any number of data can be used, they must be concatenated into one field.
Any number of continuous dimensions can be used, as long as each object has the same number of dimensions. Objects will be matched based within their categorical groups based on the shortest distance between objects. Euclidean distance in n dimensions is used here, but other distance algorithms could be substituted.
It is possible to specify a separate list of objects from which to peer. The lag file must have the same format as the input file. The objects in the lag file will be used as peers, but will not be peered themselves.
After installing Go, follow these steps:
go get github.com/jefferickson/peer-object-matcher
go install github.com/jefferickson/peer-object-matcher
$GOPATH/bin/peer-object-matcher --input /path/to/input.csv --output /path/to/output.csv
For a listing of all config flags, type:
$GOPATH/bin/peer-object-matcher --help
See peer-object-matching for the Python prototype of this peering algorithm.