"automatic feature engineering sample process with demonstrative results"

Telcontar120

I've been playing with the new Automatic Feature Engineering operator, but most of the discussion I have seen so far has centered around the Feature Selection components of its capabilities.  Is there any sample process or further documentation/discussion available for the feature engineering aspects of the operator?  The tutorial process doesn't include feature engineering (and enabling it doesn't produce any new attributes), and the in-program help doesn't discuss the feature engineering options or parameters either.  Even when I have applied this operator to other sample datasets (the usual suspects: Titanic, Sonar, etc.) I haven't been able to get it to generate anything useful.  Any sample processes on available datasets (perhaps those used in development or other suggestions) would be appreciated. Thanks.

lionelderkrikor

Hi Brian,

I had the same feeling as you after the first tests I performed with this new tool.(ie feature generation does not produce new attributes)
But after going further, I adapted a process using the s&p500 dataset and in this case, AFE operator generate some relevant new attributes.
In deed with these new attributes, the final fitness function is lower than with the "original set" : 




The process : 

<?xml version="1.0" encoding="UTF-8"?><process version="9.2.000-SNAPSHOT">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.2.000-SNAPSHOT" expanded="true" name="Process" origin="GENERATED_TUTORIAL">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" breakpoints="after" class="retrieve" compatibility="9.2.000-SNAPSHOT" expanded="true" height="68" name="Retrieve s&amp;p-500-data" width="90" x="45" y="289">
        <parameter key="repository_entry" value="//Samples/Deep Learning/data/s&amp;p-500-data"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.2.000-SNAPSHOT" expanded="true" height="82" name="Set Role" width="90" x="179" y="289">
        <parameter key="attribute_name" value="Adj Close"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="nominal_to_date" compatibility="9.2.000-SNAPSHOT" expanded="true" height="82" name="Nominal to Date" width="90" x="313" y="289">
        <parameter key="attribute_name" value="Date"/>
        <parameter key="date_type" value="date"/>
        <parameter key="date_format" value="yyyy-MM-dd"/>
        <parameter key="time_zone" value="SYSTEM"/>
        <parameter key="locale" value="English (United States)"/>
        <parameter key="keep_old_attribute" value="false"/>
      </operator>
      <operator activated="true" class="split_data" compatibility="9.2.000-SNAPSHOT" expanded="true" height="103" name="Validation Set" origin="GENERATED_TUTORIAL" width="90" x="447" y="289">
        <enumeration key="partitions">
          <parameter key="ratio" value="0.8"/>
          <parameter key="ratio" value="0.2"/>
        </enumeration>
        <parameter key="sampling_type" value="automatic"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <description align="center" color="transparent" colored="false" width="126">Split off 20% validation set.</description>
      </operator>
      <operator activated="true" class="multiply" compatibility="9.2.000-SNAPSHOT" expanded="true" height="103" name="Multiply Training Set" origin="GENERATED_TUTORIAL" width="90" x="514" y="187">
        <description align="center" color="transparent" colored="false" width="126">Copy training set.</description>
      </operator>
      <operator activated="true" class="split_data" compatibility="9.2.000-SNAPSHOT" expanded="true" height="82" name="Split Data for FS" origin="GENERATED_TUTORIAL" width="90" x="581" y="34">
        <enumeration key="partitions">
          <parameter key="ratio" value="0.8"/>
          <parameter key="ratio" value="0.2"/>
        </enumeration>
        <parameter key="sampling_type" value="automatic"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <description align="center" color="transparent" colored="false" width="126">Create data split for feature selection.</description>
      </operator>
      <operator activated="true" class="model_simulator:automatic_feature_engineering" compatibility="9.1.000" expanded="true" height="103" name="Feature Selection" origin="GENERATED_TUTORIAL" width="90" x="715" y="34">
        <parameter key="mode" value="feature selection and generation"/>
        <parameter key="balance for accuracy" value="0.9"/>
        <parameter key="show progress dialog" value="false"/>
        <parameter key="use_local_random_seed" value="true"/>
        <parameter key="local_random_seed" value="1966"/>
        <parameter key="use optimization heuristics" value="true"/>
        <parameter key="maximum generations" value="30"/>
        <parameter key="population size" value="10"/>
        <parameter key="use multi-starts" value="true"/>
        <parameter key="number of multi-starts" value="3"/>
        <parameter key="generations until multi-start" value="10"/>
        <parameter key="use time limit" value="false"/>
        <parameter key="time limit in seconds" value="300"/>
        <parameter key="use subset for generation" value="false"/>
        <parameter key="maximum function complexity" value="6"/>
        <parameter key="use_plus" value="false"/>
        <parameter key="use_diff" value="false"/>
        <parameter key="use_mult" value="true"/>
        <parameter key="use_div" value="true"/>
        <parameter key="reciprocal_value" value="true"/>
        <parameter key="use_square_roots" value="true"/>
        <parameter key="use_exp" value="true"/>
        <parameter key="use_log" value="true"/>
        <parameter key="use_absolute_values" value="true"/>
        <parameter key="use_sgn" value="false"/>
        <parameter key="use_min" value="false"/>
        <parameter key="use_max" value="false"/>
        <process expanded="true">
          <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.2.000-SNAPSHOT" expanded="true" height="103" name="Decision Tree" width="90" x="45" y="34">
            <parameter key="criterion" value="least_square"/>
            <parameter key="maximal_depth" value="10"/>
            <parameter key="apply_pruning" value="true"/>
            <parameter key="confidence" value="0.1"/>
            <parameter key="apply_prepruning" value="true"/>
            <parameter key="minimal_gain" value="0.01"/>
            <parameter key="minimal_leaf_size" value="2"/>
            <parameter key="minimal_size_for_split" value="4"/>
            <parameter key="number_of_prepruning_alternatives" value="3"/>
          </operator>
          <operator activated="true" class="apply_model" compatibility="9.2.000-SNAPSHOT" expanded="true" height="82" name="Apply Model FS" origin="GENERATED_TUTORIAL" width="90" x="246" y="136">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <operator activated="true" class="performance" compatibility="9.2.000-SNAPSHOT" expanded="true" height="82" name="Performance" width="90" x="380" y="136">
            <parameter key="use_example_weights" value="true"/>
          </operator>
          <connect from_port="example set source" to_op="Decision Tree" to_port="training set"/>
          <connect from_op="Decision Tree" from_port="model" to_op="Apply Model FS" to_port="model"/>
          <connect from_op="Decision Tree" from_port="exampleSet" to_op="Apply Model FS" to_port="unlabelled data"/>
          <connect from_op="Apply Model FS" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
          <connect from_op="Performance" from_port="performance" to_port="performance sink"/>
          <portSpacing port="source_example set source" spacing="0"/>
          <portSpacing port="sink_performance sink" spacing="0"/>
        </process>
        <description align="center" color="transparent" colored="false" width="126">Feature selection.</description>
      </operator>
      <operator activated="true" class="multiply" compatibility="9.2.000-SNAPSHOT" expanded="true" height="103" name="Multiply Feature Set" origin="GENERATED_TUTORIAL" width="90" x="849" y="238">
        <description align="center" color="transparent" colored="false" width="126">Copy feature set.</description>
      </operator>
      <operator activated="true" class="model_simulator:apply_feature_set" compatibility="9.1.000" expanded="true" height="82" name="Apply Feature Set on Validation" origin="GENERATED_TUTORIAL" width="90" x="983" y="391">
        <parameter key="handle missings" value="true"/>
        <parameter key="keep originals" value="false"/>
        <parameter key="originals special role" value="true"/>
        <description align="center" color="transparent" colored="false" width="126">Apply feature set on validation set.</description>
      </operator>
      <operator activated="true" class="model_simulator:apply_feature_set" compatibility="9.1.000" expanded="true" height="82" name="Apply Feature Set on Training" origin="GENERATED_TUTORIAL" width="90" x="983" y="187">
        <parameter key="handle missings" value="true"/>
        <parameter key="keep originals" value="false"/>
        <parameter key="originals special role" value="true"/>
        <description align="center" color="transparent" colored="false" width="126">Apply feature set on training set.</description>
      </operator>
      <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.2.000-SNAPSHOT" expanded="true" height="103" name="Decision Tree (2)" width="90" x="1117" y="187">
        <parameter key="criterion" value="least_square"/>
        <parameter key="maximal_depth" value="10"/>
        <parameter key="apply_pruning" value="true"/>
        <parameter key="confidence" value="0.1"/>
        <parameter key="apply_prepruning" value="true"/>
        <parameter key="minimal_gain" value="0.01"/>
        <parameter key="minimal_leaf_size" value="2"/>
        <parameter key="minimal_size_for_split" value="4"/>
        <parameter key="number_of_prepruning_alternatives" value="3"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="9.2.000-SNAPSHOT" expanded="true" height="82" name="Apply Model" origin="GENERATED_TUTORIAL" width="90" x="1251" y="289">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
        <description align="center" color="transparent" colored="false" width="126">Apply final prediction model on validation set.</description>
      </operator>
      <connect from_op="Retrieve s&amp;p-500-data" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Nominal to Date" to_port="example set input"/>
      <connect from_op="Nominal to Date" from_port="example set output" to_op="Validation Set" to_port="example set"/>
      <connect from_op="Validation Set" from_port="partition 1" to_op="Multiply Training Set" to_port="input"/>
      <connect from_op="Validation Set" from_port="partition 2" to_op="Apply Feature Set on Validation" to_port="example set"/>
      <connect from_op="Multiply Training Set" from_port="output 1" to_op="Split Data for FS" to_port="example set"/>
      <connect from_op="Multiply Training Set" from_port="output 2" to_op="Apply Feature Set on Training" to_port="example set"/>
      <connect from_op="Split Data for FS" from_port="partition 1" to_op="Feature Selection" to_port="example set in"/>
      <connect from_op="Feature Selection" from_port="feature set" to_op="Multiply Feature Set" to_port="input"/>
      <connect from_op="Feature Selection" from_port="population" to_port="result 2"/>
      <connect from_op="Feature Selection" from_port="optimization log" to_port="result 3"/>
      <connect from_op="Multiply Feature Set" from_port="output 1" to_op="Apply Feature Set on Training" to_port="feature set"/>
      <connect from_op="Multiply Feature Set" from_port="output 2" to_op="Apply Feature Set on Validation" to_port="feature set"/>
      <connect from_op="Apply Feature Set on Validation" from_port="example set" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Apply Feature Set on Training" from_port="example set" to_op="Decision Tree (2)" to_port="training set"/>
      <connect from_op="Decision Tree (2)" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="252"/>
      <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,

Lionel