Altair RISE

A program to recognize and reward our most engaged community members

Nominate Yourself Now!

How to view data

Hi,
Another new fan of this great piece of software...

Anyways, I am currently working on a multiclassification problem.
Of which I have been able to get pretty decent perfromance..
So, I have the performance matrix which tells me the predicted class vs the true class and then example set which contains the list of attributes etc.
But I want to see which example is assigned to which class instead of just the overview which the performance matrix gives.
Because in the end, I am more interested on seeing the "live" example result rather than the statistical measures.
Any clue how can I view that
Thanks

Find more posts tagged with

AI Studio

Accepted answers

All comments

Andrew2

Hello

You have to use the apply model operator with the trained model and the unlabelled data you want to predict.

Here's a fake example that shows this. It also shows the performance of this and highlights overfitting.


<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.008">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.1.008" expanded="true" name="Process">
    <process expanded="true" height="325" width="681">
      <operator activated="true" class="generate_data" compatibility="5.1.008" expanded="true" height="60" name="Generate Data" width="90" x="45" y="30">
        <parameter key="target_function" value="sum classification"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.0.000" expanded="true" height="112" name="Validation" width="90" x="179" y="30">
        <description>A cross-validation evaluating a decision tree model.</description>
        <process expanded="true" height="654" width="466">
          <operator activated="true" class="neural_net" compatibility="5.1.008" expanded="true" height="76" name="Neural Net" width="90" x="188" y="30">
            <list key="hidden_layers"/>
          </operator>
          <connect from_port="training" to_op="Neural Net" to_port="training set"/>
          <connect from_op="Neural Net" from_port="model" to_port="model"/>
          <portSpacing port="source_training" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
        </process>
        <process expanded="true" height="654" width="466">
          <operator activated="true" class="apply_model" compatibility="5.0.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" compatibility="5.0.000" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
          <connect from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
          <connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_averagable 1" spacing="0"/>
          <portSpacing port="sink_averagable 2" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="generate_data" compatibility="5.1.008" expanded="true" height="60" name="Generate Data (2)" width="90" x="179" y="210">
        <parameter key="target_function" value="sum classification"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="5.1.008" expanded="true" height="76" name="Apply Model (2)" width="90" x="313" y="120">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="performance" compatibility="5.1.008" expanded="true" height="76" name="Performance (2)" width="90" x="447" y="120"/>
      <connect from_op="Generate Data" from_port="output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="model" to_op="Apply Model (2)" to_port="model"/>
      <connect from_op="Validation" from_port="averagable 1" to_port="result 1"/>
      <connect from_op="Generate Data (2)" from_port="output" to_op="Apply Model (2)" to_port="unlabelled data"/>
      <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
      <connect from_op="Performance (2)" from_port="performance" to_port="result 2"/>
      <connect from_op="Performance (2)" from_port="example set" to_port="result 3"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
      <portSpacing port="sink_result 4" spacing="0"/>
    </process>
  </operator>
</process>

regards

Andrew

frazman

Hi Andrew,
Thanks for replying. Can you please explain what was that "xml" looking script..
I am mostly working on gui.
I had data saved as csv file
So i imported that csv... did the X-validation..
In X-validation I applied k-nn model.. and thats it.
I never wrote any script..

el_chief

in rapidminer
look for the process tab (middle, near top)
there should be an xml tab beside that
you can copy and paste xml in there and hit the green checkmark and it will use that as your process
it overwrites whatever is in the current process so best to do it on a new, blank process

frazman

Hi Neil,
Thanks!! Also just wanted to convey that I used the info from your blog only to get started (k-NN for document classification).
A quick question.
You usedthe cosine similarity for a measure in k-NN
But I am not able to do so..:(
whenever I try that it says the attributes are not numeric.. Hence i end up using the default measure...
I have like watched that video so many times on hope that maybe i missed something but am not able to spot that out.
I reckon I have to specify when I am importing the data file.
I have data as a csv file??
Thanks

frazman

In case it helps..
Here is my xml file..


<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.008">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.1.008" expanded="true" name="Process">
    <parameter key="parallelize_main_process" value="true"/>
    <process expanded="true" height="521" width="815">
      <operator activated="true" class="read_csv" compatibility="5.1.008" expanded="true" height="60" name="Read CSV" width="90" x="112" y="75">
        <parameter key="csv_file" value="/Users/mohitdeepsingh/Desktop/RMData/data_10k_cleanse.csv"/>
        <parameter key="column_separators" value=","/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations">
          <parameter key="0" value="Comment"/>
        </list>
        <parameter key="encoding" value="MacRoman"/>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="att1.true.attribute_value.attribute"/>
          <parameter key="1" value="att2.true.attribute_value.attribute"/>
          <parameter key="2" value="att3.true.attribute_value.attribute"/>
          <parameter key="3" value="att4.true.attribute_value.attribute"/>
          <parameter key="4" value="att5.true.attribute_value.attribute"/>
          <parameter key="5" value="att6.true.attribute_value.attribute"/>
          <parameter key="6" value="att7.true.attribute_value.attribute"/>
          <parameter key="7" value="att8.true.attribute_value.attribute"/>
          <parameter key="8" value="att9.true.attribute_value.attribute"/>
          <parameter key="9" value="att10.true.attribute_value.attribute"/>
          <parameter key="10" value="att11.true.attribute_value.attribute"/>
          <parameter key="11" value="att12.true.attribute_value.attribute"/>
          <parameter key="12" value="att13.true.attribute_value.label"/>
        </list>
      </operator>
      <operator activated="true" class="text:process_document_from_data" compatibility="5.1.002" expanded="true" height="76" name="Process Documents from Data" width="90" x="112" y="255">
        <list key="specify_weights"/>
        <process expanded="true" height="586" width="922">
          <operator activated="true" class="web:extract_html_text_content" compatibility="5.1.002" expanded="true" height="60" name="Extract Content" width="90" x="112" y="30"/>
          <operator activated="true" class="text:transform_cases" compatibility="5.1.002" expanded="true" height="60" name="Transform Cases" width="90" x="112" y="120"/>
          <operator activated="true" class="text:replace_tokens" compatibility="5.1.002" expanded="true" height="60" name="Replace Tokens" width="90" x="112" y="255">
            <list key="replace_dictionary"/>
          </operator>
          <operator activated="true" class="text:tokenize" compatibility="5.1.002" expanded="true" height="60" name="Tokenize" width="90" x="112" y="390"/>
          <operator activated="true" class="text:filter_stopwords_english" compatibility="5.1.002" expanded="true" height="60" name="Filter Stopwords (English)" width="90" x="380" y="75"/>
          <operator activated="true" class="text:stem_snowball" compatibility="5.1.002" expanded="true" height="60" name="Stem (Snowball)" width="90" x="380" y="210"/>
          <operator activated="true" class="text:filter_by_length" compatibility="5.1.002" expanded="true" height="60" name="Filter Tokens (by Length)" width="90" x="380" y="345">
            <parameter key="min_chars" value="2"/>
            <parameter key="max_chars" value="99"/>
          </operator>
          <connect from_port="document" to_op="Extract Content" to_port="document"/>
          <connect from_op="Extract Content" from_port="document" to_op="Transform Cases" to_port="document"/>
          <connect from_op="Transform Cases" from_port="document" to_op="Replace Tokens" to_port="document"/>
          <connect from_op="Replace Tokens" from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_op="Filter Stopwords (English)" to_port="document"/>
          <connect from_op="Filter Stopwords (English)" from_port="document" to_op="Stem (Snowball)" to_port="document"/>
          <connect from_op="Stem (Snowball)" from_port="document" to_op="Filter Tokens (by Length)" to_port="document"/>
          <connect from_op="Filter Tokens (by Length)" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="5.1.008" expanded="true" height="76" name="Select Attributes" width="90" x="246" y="435">
        <parameter key="attribute_filter_type" value="no_missing_values"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.1.008" expanded="true" height="130" name="Validation" width="90" x="648" y="165">
        <parameter key="number_of_validations" value="100"/>
        <parameter key="use_local_random_seed" value="true"/>
        <parameter key="parallelize_training" value="true"/>
        <parameter key="parallelize_testing" value="true"/>
        <process expanded="true" height="586" width="436">
          <operator activated="true" class="k_nn" compatibility="5.1.008" expanded="true" height="76" name="k-NN" width="90" x="112" y="30">
            <parameter key="k" value="5"/>
            <parameter key="weighted_vote" value="true"/>
            <parameter key="numerical_measure" value="CosineSimilarity"/>
            <parameter key="divergence" value="SquaredEuclideanDistance"/>
          </operator>
          <connect from_port="training" to_op="k-NN" to_port="training set"/>
          <connect from_op="k-NN" from_port="model" to_port="model"/>
          <portSpacing port="source_training" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
        </process>
        <process expanded="true" height="586" width="436">
          <operator activated="true" class="apply_model" compatibility="5.1.008" expanded="true" height="76" name="Apply Model" width="90" x="112" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" compatibility="5.1.008" expanded="true" height="76" name="Performance" width="90" x="87" y="321"/>
          <connect from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
          <connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_averagable 1" spacing="0"/>
          <portSpacing port="sink_averagable 2" spacing="0"/>
          <portSpacing port="sink_averagable 3" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Read CSV" from_port="output" to_op="Process Documents from Data" to_port="example set"/>
      <connect from_op="Process Documents from Data" from_port="example set" to_op="Select Attributes" to_port="example set input"/>
      <connect from_op="Select Attributes" from_port="example set output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="model" to_port="result 1"/>
      <connect from_op="Validation" from_port="training" to_port="result 2"/>
      <connect from_op="Validation" from_port="averagable 1" to_port="result 3"/>
      <connect from_op="Validation" from_port="averagable 2" to_port="result 4"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
      <portSpacing port="sink_result 4" spacing="0"/>
      <portSpacing port="sink_result 5" spacing="0"/>
    </process>
  </operator>
</process>

el_chief

if you use the TF-IDF metric, then your attributes should be numeric, and you can calculate the cosine similarity

otherwise, you could try forcing them to numeric using the nominal to numeric operator

HTH

NM