how to get the ranks of unlabeled case using K-NN

inceptorfull

Hi all, I have unlabeled data and want to get rank of its nearest cases so I can compare it with them, Its credit rating problems so I have unlabeled customers and want to know the nearest neighbor of them by ranking or how close they are to the good or bad customers

JEdward

It sounds like you aren't needing the k-NN operator, but rather the Cross Distances.  (Other similarity operators are also useable). 
Have a look at this sample process using the Golf dataset.

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="7.0.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="7.0.000" expanded="true" height="68" name="Golf" width="90" x="45" y="187">
        <parameter key="repository_entry" value="//Samples/data/Golf"/>
      </operator>
      <operator activated="true" class="generate_id" compatibility="7.0.000" expanded="true" height="82" name="Generate ID" width="90" x="179" y="238">
        <parameter key="create_nominal_ids" value="true"/>
        <description align="center" color="transparent" colored="false" width="126">Using nominal IDs just to demo.</description>
      </operator>
      <operator activated="true" class="retrieve" compatibility="7.0.000" expanded="true" height="68" name="Retrieve Golf-Testset" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Samples/data/Golf-Testset"/>
      </operator>
      <operator activated="true" class="subprocess" compatibility="7.0.000" expanded="true" height="82" name="Get only 1 record." width="90" x="179" y="34">
        <process expanded="true">
          <operator activated="true" class="sample" compatibility="7.0.000" expanded="true" height="82" name="Sample" width="90" x="45" y="34">
            <parameter key="sample_size" value="1"/>
            <list key="sample_size_per_class"/>
            <list key="sample_ratio_per_class"/>
            <list key="sample_probability_per_class"/>
          </operator>
          <operator activated="true" class="select_attributes" compatibility="7.0.000" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="34">
            <parameter key="attribute_filter_type" value="single"/>
            <parameter key="attribute" value="Play"/>
            <parameter key="invert_selection" value="true"/>
            <parameter key="include_special_attributes" value="true"/>
          </operator>
          <operator activated="true" class="generate_id" compatibility="7.0.000" expanded="true" height="82" name="Generate ID (2)" width="90" x="313" y="34"/>
          <connect from_port="in 1" to_op="Sample" to_port="example set input"/>
          <connect from_op="Sample" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
          <connect from_op="Select Attributes" from_port="example set output" to_op="Generate ID (2)" to_port="example set input"/>
          <connect from_op="Generate ID (2)" from_port="example set output" to_port="out 1"/>
          <portSpacing port="source_in 1" spacing="0"/>
          <portSpacing port="source_in 2" spacing="0"/>
          <portSpacing port="sink_out 1" spacing="0"/>
          <portSpacing port="sink_out 2" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="cross_distances" compatibility="7.0.000" expanded="true" height="103" name="Cross Distances" width="90" x="313" y="85">
        <parameter key="only_top_k" value="true"/>
        <parameter key="k" value="3"/>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="7.0.000" expanded="true" height="82" name="Select only label" width="90" x="447" y="238">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attributes" value="id|Play"/>
        <parameter key="include_special_attributes" value="true"/>
      </operator>
      <operator activated="true" class="join" compatibility="7.0.000" expanded="true" height="82" name="Join to Request" width="90" x="447" y="34">
        <parameter key="use_id_attribute_as_key" value="false"/>
        <list key="key_attributes">
          <parameter key="request" value="id"/>
        </list>
        <description align="center" color="transparent" colored="false" width="126">This join is just to get the original data back rather than just the ID.</description>
      </operator>
      <operator activated="true" class="join" compatibility="7.0.000" expanded="true" height="82" name="Join to Reference" width="90" x="581" y="187">
        <parameter key="use_id_attribute_as_key" value="false"/>
        <list key="key_attributes">
          <parameter key="document" value="id"/>
        </list>
        <description align="center" color="transparent" colored="false" width="126">When the result is joined with the original Reference dataset then the label is used.</description>
      </operator>
      <connect from_op="Golf" from_port="output" to_op="Generate ID" to_port="example set input"/>
      <connect from_op="Generate ID" from_port="example set output" to_op="Cross Distances" to_port="reference set"/>
      <connect from_op="Retrieve Golf-Testset" from_port="output" to_op="Get only 1 record." to_port="in 1"/>
      <connect from_op="Get only 1 record." from_port="out 1" to_op="Cross Distances" to_port="request set"/>
      <connect from_op="Cross Distances" from_port="result set" to_op="Join to Request" to_port="left"/>
      <connect from_op="Cross Distances" from_port="request set" to_op="Join to Request" to_port="right"/>
      <connect from_op="Cross Distances" from_port="reference set" to_op="Select only label" to_port="example set input"/>
      <connect from_op="Select only label" from_port="example set output" to_op="Join to Reference" to_port="right"/>
      <connect from_op="Join to Request" from_port="join" to_op="Join to Reference" to_port="left"/>
      <connect from_op="Join to Reference" from_port="join" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

inceptorfull

thanks a lot for quick reply and help, I will try to get more understanding of it and will give you feedback, but want to know is that based on the K-NN? as i can see the number of neighours and distance so on

also if you have more tutorial on that process i will be pleased to tell me, I will give you feedback soon thanks again apperciate it

inceptorfull

it just give me the distance , I donot distance from what? also I have 516 case so I found huge distance

I want to enter the unlabeled cases to be assigned for the most close similar case, using nearest neighbour, I donot know how to do it
it is something like that

https://dato.com/learn/userguide/nearest_neighbors/nearest_neighbors.html

thanks a gain

JEdward

If you are wanting to assign it to the value of the nearest value then k-NN with k = 1 is what you are looking for.  If it is to look at the nearest 3 cases then k-NN with k = 3 is what you would like, this would assign the missing labels to the closest record where you do have a value by weight vote. 
As you are assigning it to the missing labels, maybe try the operator 'Impute Missing Values' with k-NN inside it. 

If what you are looking is what the k closest records to your sample record is then the similarity operators (such as Cross Distance) are what you need. 

What do you want to happen in your process? 

inceptorfull

am really thankfull for your feedback and keeping up with me, actually my last step in my research depends on that step so hope to help me,

first of all, I want to enter training data to make the model train on ( Neural network or K-nn) whatever is okie,

then enter the unlableled data ( same as exampleset but with missing values in the label column)

the result to give me the best 5 closest and similar cases from the labeled data ( Exampleset) , so I donot know the right operator to use, secondly the results appear like that using the cross distance



but i want it to appear in something like that ( i used spss modeler but there isno predication in it )