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Window Slider Validation Model Problem

CleoCleo Member Posts: 44 Maven
edited November 2018 in Help
Hello,

I have a process which has a Sliding Window Validation operator (training window width of 30) and within the training area I support vector machine learner connect to the .  The SVM operator  is connect to the  “tra” and “mod” ports.  On the testing I have an Apply Model operator and a write model operator.  Also I have a write model operator (writing to a different location then the one connected to the SVM), connected to the “mod” port on the Sliding Window Validation.

My problem is when I use any combination of the models written on the exact same data used in the Testing area of the Sliding Window Validation operator I get very different results.

Do I have to preprocess the data before it goes it connects to the model, and if so how?

Within the documentation under parameters it is noted that:
create complete model
Indicates if a model of the complete data set should be additionally build after estimation.
Default value: false

I not find a check box anywhere to set its value to true.  However the model writer operator connected to the “mod” port of the sliding window validation operator is createing a file. 

Any suggestions?

Thanks in advance,
Cleo

Answers

  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Hi Cleo,
    the parameter has been removed, since you now select by connecting the port of the complete model if it should be built. Unfortunately we didn't manage to update all documentation yet. There are soooo many operators, which documentation needs to be rewritten...
    I must admit, that I didn't understand your setting and your problem. Could you please post a process and describe in detail what you are going to achieve with it?

    Greetings,
      Sebastian
  • CleoCleo Member Posts: 44 Maven
    Thank you for the offer of looking over my process.

    When you run the process Sliding Window Trainer: you should notice two different performance vectors, but I was expecting them to be equal.  When I hover over the “ave” port on the Validation operator rapidminer indicates that it is: Validation.averagable 1 ← Write Performance.through ← Performance.performance

    However when I read the performance vector I get a different result.  It seems to me that the “ave” port is outputting the performance of the training vector and not the testing vector. 1) How do I get the performance vector from the test portion of the validation?

    In the Sliding window Trainer, I added two model writers, attached to the validation mod port and the second attached to the SVM mod port.  In the sliding window reader, I load the testing data, then multiply the data, and run it through both of the models.  The validation mod results gives me the actual label, while the SVM model gives me the same results as the exa from the performance operator within the trainer.  2)Therefore should I use the SVM model for future data?

    3)If possible could you please add any preprocessing operator to the process?  I have a disabled attempt but cannot seem to get it to work correctly.

    Thanks again,
    Cleo


    Sliding Training
    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="5.0">
      <context>
        <input>
          <location/>
        </input>
        <output>
          <location/>
          <location/>
          <location/>
        </output>
        <macros/>
      </context>
      <operator activated="true" class="process" expanded="true" name="Process">
        <process expanded="true" height="302" width="975">
          <operator activated="true" class="read_excel" expanded="true" height="60" name="Read Excel" width="90" x="45" y="30">
            <parameter key="excel_file" value="C:\Projects\RM5\Test\raw_data.xls"/>
          </operator>
          <operator activated="true" class="set_role" expanded="true" height="76" name="Set ID" width="90" x="180" y="30">
            <parameter key="name" value="id"/>
            <parameter key="target_role" value="TheID"/>
          </operator>
          <operator activated="true" class="set_role" expanded="true" height="76" name="Set Label" width="90" x="315" y="30">
            <parameter key="name" value="Label"/>
            <parameter key="target_role" value="label"/>
          </operator>
          <operator activated="true" class="set_role" expanded="true" height="76" name="Set Date" width="90" x="447" y="30">
            <parameter key="name" value="date"/>
            <parameter key="target_role" value="date"/>
          </operator>
          <operator activated="true" class="series:sliding_window_validation" expanded="true" height="112" name="Validation" width="90" x="581" y="30">
            <parameter key="training_window_width" value="50"/>
            <parameter key="test_window_width" value="50"/>
            <process expanded="true" height="302" width="279">
              <operator activated="false" class="series:process_series" expanded="true" height="76" name="ValueSeriesPreprocessing" width="90" x="45" y="120">
                <parameter key="start_attribute" value="1"/>
                <parameter key="end_attribute" value="9"/>
                <process expanded="true" height="302" width="443">
                  <operator activated="false" class="series:normalize" expanded="true" height="60" name="SeriesNormalization" width="90" x="112" y="30"/>
                  <connect from_port="series" to_op="SeriesNormalization" to_port="series"/>
                  <connect from_op="SeriesNormalization" from_port="series" to_port="series"/>
                  <portSpacing port="source_series" spacing="0"/>
                  <portSpacing port="source_input 1" spacing="0"/>
                  <portSpacing port="sink_series" spacing="0"/>
                </process>
              </operator>
              <operator activated="true" class="support_vector_machine" expanded="true" height="112" name="SVM_Radial" width="90" x="179" y="30">
                <parameter key="kernel_type" value="radial"/>
                <parameter key="C" value="4847.123204531062"/>
              </operator>
              <connect from_port="training" to_op="SVM_Radial" to_port="training set"/>
              <connect from_op="SVM_Radial" 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="460" width="532">
              <operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="write_model" expanded="true" height="60" name="Write Model" width="90" x="246" y="30">
                <parameter key="model_file" value="C:\Projects\RM5\Test\SVM_model.mod"/>
              </operator>
              <operator activated="true" class="performance_binominal_classification" expanded="true" height="76" name="Performance" width="90" x="45" y="210">
                <parameter key="main_criterion" value="true_positive"/>
              </operator>
              <operator activated="true" class="write_performance" expanded="true" height="60" name="Write Performance" width="90" x="179" y="165">
                <parameter key="performance_file" value="C:\Projects\RM5\Test\performance.per"/>
              </operator>
              <operator activated="true" class="write_excel" expanded="true" height="60" name="Write Excel (2)" width="90" x="45" y="390">
                <parameter key="excel_file" value="C:\Projects\RM5\Test\Training_output2.xls"/>
              </operator>
              <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="Apply Model" from_port="model" to_op="Write Model" to_port="input"/>
              <connect from_op="Performance" from_port="performance" to_op="Write Performance" to_port="input"/>
              <connect from_op="Performance" from_port="example set" to_op="Write Excel (2)" to_port="input"/>
              <connect from_op="Write Performance" from_port="through" 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="write_model" expanded="true" height="60" name="Write Model Validation" width="90" x="720" y="30">
            <parameter key="model_file" value="C:\Projects\RM5\Test\Validation.mod"/>
          </operator>
          <operator activated="true" class="read_performance" expanded="true" height="60" name="Read Performance" width="90" x="715" y="165">
            <parameter key="performance_file" value="C:\Projects\RM5\Test\performance.per"/>
          </operator>
          <connect from_op="Read Excel" from_port="output" to_op="Set ID" to_port="example set input"/>
          <connect from_op="Set ID" from_port="example set output" to_op="Set Label" to_port="example set input"/>
          <connect from_op="Set Label" from_port="example set output" to_op="Set Date" to_port="example set input"/>
          <connect from_op="Set Date" from_port="example set output" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="model" to_op="Write Model Validation" to_port="input"/>
          <connect from_op="Validation" from_port="averagable 1" to_port="result 1"/>
          <connect from_op="Read Performance" from_port="output" to_port="result 2"/>
          <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"/>
        </process>
      </operator>
    </process>







    Reader:

    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="5.0">
      <context>
        <input>
          <location/>
        </input>
        <output>
          <location/>
        </output>
        <macros/>
      </context>
      <operator activated="true" class="process" expanded="true" name="Process">
        <process expanded="true" height="325" width="947">
          <operator activated="true" class="read_excel" expanded="true" height="60" name="Read Excel" width="90" x="45" y="30">
            <parameter key="excel_file" value="C:\Projects\RM5\Test\raw_data.xls"/>
            <parameter key="sheet_number" value="2"/>
          </operator>
          <operator activated="true" class="set_role" expanded="true" height="76" name="Set ID" width="90" x="180" y="30">
            <parameter key="name" value="id"/>
            <parameter key="target_role" value="TheID"/>
          </operator>
          <operator activated="true" class="read_model" expanded="true" height="60" name="Read Model SVM" width="90" x="447" y="255">
            <parameter key="model_file" value="C:\Projects\RM5\Test\SVM_model.mod"/>
          </operator>
          <operator activated="true" class="read_model" expanded="true" height="60" name="Read Model Validation" width="90" x="447" y="165">
            <parameter key="model_file" value="C:\Projects\RM5\Test\Validation.mod"/>
          </operator>
          <operator activated="true" class="set_role" expanded="true" height="76" name="Set Date" width="90" x="315" y="30">
            <parameter key="name" value="date"/>
            <parameter key="target_role" value="date"/>
          </operator>
          <operator activated="true" class="multiply" expanded="true" height="94" name="Multiply" width="90" x="447" y="30"/>
          <operator activated="true" class="apply_model" expanded="true" height="76" name="SVM" width="90" x="648" y="210">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="write_excel" expanded="true" height="60" name="Write Excel (2)" width="90" x="782" y="210">
            <parameter key="excel_file" value="C:\Projects\RM5\Test\SVM.xls"/>
          </operator>
          <operator activated="true" class="apply_model" expanded="true" height="76" name="Validation" width="90" x="648" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="write_excel" expanded="true" height="60" name="Write Excel" width="90" x="782" y="30">
            <parameter key="excel_file" value="C:\Projects\RM5\Test\validation.xls"/>
          </operator>
          <connect from_op="Read Excel" from_port="output" to_op="Set ID" to_port="example set input"/>
          <connect from_op="Set ID" from_port="example set output" to_op="Set Date" to_port="example set input"/>
          <connect from_op="Read Model SVM" from_port="output" to_op="SVM" to_port="model"/>
          <connect from_op="Read Model Validation" from_port="output" to_op="Validation" to_port="model"/>
          <connect from_op="Set Date" from_port="example set output" to_op="Multiply" to_port="input"/>
          <connect from_op="Multiply" from_port="output 1" to_op="Validation" to_port="unlabelled data"/>
          <connect from_op="Multiply" from_port="output 2" to_op="SVM" to_port="unlabelled data"/>
          <connect from_op="SVM" from_port="labelled data" to_op="Write Excel (2)" to_port="input"/>
          <connect from_op="Validation" from_port="labelled data" to_op="Write Excel" to_port="input"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
        </process>
      </operator>
    </process>


  • CleoCleo Member Posts: 44 Maven

    Raw Data:
    Trainer:
    id date 1 5 6 7 8 9 Label
    1 2009.04.05 00:00:00 148.8 8169.0 2.7 141.8 104.9 95.7 false
    2 2009.04.06 00:00:00 150.5 46581.0 2.8 142.5 103.4 87.2 false
    3 2009.04.07 00:00:00 147.4 48491.0 2.8 143.0 103.2 71.0 false
    4 2009.04.08 00:00:00 146.9 41437.0 2.7 143.4 104.9 47.9 false
    5 2009.04.09 00:00:00 146.9 39267.0 2.7 143.8 105.5 32.0 false
    6 2009.04.10 00:00:00 146.9 7423.0 2.6 144.1 104.4 21.4 true
    7 2009.04.12 00:00:00 146.9 4208.0 2.5 144.4 104.2 24.8 false
    8 2009.04.13 00:00:00 147.0 25574.0 2.5 144.7 103.6 40.0 true
    9 2009.04.14 00:00:00 148.3 43028.0 2.3 145.0 99.7 39.6 true
    10 2009.04.15 00:00:00 146.4 46816.0 2.4 145.3 100.2 53.1 false
    11 2009.04.16 00:00:00 148.8 43909.0 2.3 145.6 98.3 46.4 false
    12 2009.04.17 00:00:00 147.9 31635.0 2.1 145.7 99.5 49.2 false
    13 2009.04.19 00:00:00 146.8 9367.0 1.9 145.8 99.1 25.3 false
    14 2009.04.20 00:00:00 145.5 41054.0 1.4 145.6 97.3 14.7 false
    15 2009.04.21 00:00:00 142.9 42425.0 1.1 145.4 97.7 17.9 false
    16 2009.04.22 00:00:00 143.6 44563.0 .7 145.1 96.2 17.5 true
    17 2009.04.23 00:00:00 141.3 43898.0 .5 144.9 97.1 22.7 false
    18 2009.04.24 00:00:00 142.8 39469.0 .2 144.6 96.0 23.7 false
    19 2009.04.26 00:00:00 142.0 7828.0 -.1 144.3 96.3 25.7 true
    20 2009.04.27 00:00:00 141.0 41112.0 -.3 144.0 94.5 18.0 true
    21 2009.04.28 00:00:00 140.7 40907.0 -.3 143.7 96.4 30.3 true
    22 2009.04.29 00:00:00 142.5 30449.0 -.2 143.6 98.4 59.8 true
    23 2009.04.30 00:00:00 144.4 32885.0 .0 143.8 100.4 82.7 true
    24 2009.05.01 00:00:00 146.1 19640.0 .4 144.1 103.6 92.7 false
    25 2009.05.03 00:00:00 148.0 5383.0 .7 144.6 103.6 94.5 false
    26 2009.05.04 00:00:00 148.8 24038.0 .8 144.9 104.6 92.7 true
    27 2009.05.05 00:00:00 147.9 28211.0 .9 145.3 103.2 82.3 true
    28 2009.05.06 00:00:00 147.3 30350.0 1.1 145.6 104.5 72.1 true
    29 2009.05.07 00:00:00 148.7 32076.0 1.2 146.0 105.5 59.1 true
    30 2009.05.08 00:00:00 148.7 22138.0 1.4 146.3 106.6 65.1 false
    31 2009.05.10 00:00:00 150.2 6313.0 1.5 146.7 104.9 61.9 false
    32 2009.05.11 00:00:00 149.5 27513.0 1.3 146.8 101.9 50.5 false
    33 2009.05.12 00:00:00 147.1 30191.0 1.2 146.9 100.7 31.3 false
    34 2009.05.13 00:00:00 147.2 36016.0 .9 146.8 97.5 12.7 false
    35 2009.05.14 00:00:00 144.4 44701.0 .8 146.6 98.3 23.1 false
    36 2009.05.15 00:00:00 146.2 37431.0 .5 146.5 97.6 20.8 true
    37 2009.05.17 00:00:00 143.7 8478.0 .2 146.2 97.5 21.4 true
    38 2009.05.18 00:00:00 143.6 39476.0 .4 146.1 99.4 34.5 true
    39 2009.05.19 00:00:00 147.9 46622.0 .5 146.4 99.6 57.9 true
    40 2009.05.20 00:00:00 148.1 47786.0 .7 146.6 99.5 87.2 true
    41 2009.05.21 00:00:00 149.2 51994.0 .8 146.8 99.9 86.0 true
    42 2009.05.22 00:00:00 149.3 35143.0 1.0 147.1 102.5 92.5 false
    43 2009.05.24 00:00:00 150.6 6175.0 1.1 147.4 102.1 90.2 true
    44 2009.05.25 00:00:00 150.2 22616.0 1.2 147.8 104.1 85.6 true
    45 2009.05.26 00:00:00 150.2 42310.0 1.4 148.1 104.2 84.7 true
    46 2009.05.27 00:00:00 152.4 48549.0 1.6 148.5 106.1 89.8 true
    47 2009.05.28 00:00:00 153.1 47781.0 1.8 149.1 107.0 90.9 true
    48 2009.05.29 00:00:00 153.7 38171.0 2.0 149.6 104.2 81.5 true
    49 2009.05.31 00:00:00 154.1 6941.0 2.1 150.1 104.0 74.3 true
    50 2009.06.01 00:00:00 154.1 47195.0 2.5 150.7 106.2 79.6 false
    51 2009.06.02 00:00:00 158.5 47791.0 2.8 151.4 106.2 84.9 false
    52 2009.06.03 00:00:00 158.6 53801.0 2.8 152.1 103.7 73.9 true
    53 2009.06.04 00:00:00 156.4 54965.0 2.8 152.6 103.8 54.6 true
    54 2009.06.05 00:00:00 156.0 41565.0 2.9 153.0 105.1 46.1 false
    55 2009.06.07 00:00:00 157.2 6575.0 2.8 153.4 102.9 44.3 true
    56 2009.06.08 00:00:00 156.9 44823.0 2.8 153.8 102.5 45.0 true
    57 2009.06.09 00:00:00 156.9 46871.0 2.9 154.2 103.7 54.2 true
    58 2009.06.10 00:00:00 159.4 49857.0 3.0 154.8 104.3 73.8 true
    59 2009.06.11 00:00:00 160.6 45543.0 3.2 155.4 105.0 93.8 false
    60 2009.06.12 00:00:00 161.8 36976.0 3.3 156.0 101.9 90.1 false
    61 2009.06.14 00:00:00 161.7 5065.0 3.3 156.6 101.6 85.6 false
    62 2009.06.15 00:00:00 161.1 45319.0 2.9 156.8 100.4 57.1 false
    63 2009.06.16 00:00:00 157.0 48336.0 2.7 157.0 101.7 38.1 false
    64 2009.06.17 00:00:00 158.6 47684.0 2.4 157.0 99.4 19.8 true
    65 2009.06.18 00:00:00 157.0 42714.0 2.2 157.0 100.6 31.6 false
    66 2009.06.19 00:00:00 157.8 31360.0 2.1 157.1 101.1 39.3 false
    67 2009.06.21 00:00:00 158.8 5637.0 1.9 157.3 99.0 50.8 false
    68 2009.06.22 00:00:00 157.8 41347.0 1.6 157.2 96.8 44.3 true
    69 2009.06.23 00:00:00 155.5 46829.0 1.4 157.1 97.2 43.1 true
    70 2009.06.24 00:00:00 157.3 45874.0 1.3 157.1 97.8 49.3 false
    71 2009.06.25 00:00:00 158.0 42608.0 1.2 157.1 97.8 67.0 false
    72 2009.06.26 00:00:00 157.6 32514.0 1.1 157.1 100.2 71.4 true
    73 2009.06.28 00:00:00 157.2 5351.0 1.0 157.1 99.2 71.7 true
    74 2009.06.29 00:00:00 157.2 33749.0 1.1 157.3 101.6 76.5 false
    75 2009.06.30 00:00:00 159.5 43372.0 1.1 157.5 100.9 78.4 false
    76 2009.07.01 00:00:00 159.2 37880.0 1.1 157.6 100.2 77.2 false
    77 2009.07.02 00:00:00 159.1 41510.0 .9 157.6 99.5 53.2 false
    78 2009.07.03 00:00:00 157.1 16873.0 .8 157.6 100.9 30.4 false
    79 2009.07.05 00:00:00 156.9 6332.0 .5 157.4 98.8 11.6 false
    80 2009.07.06 00:00:00 155.3 38447.0 .3 157.1 98.3 19.2 false
    81 2009.07.07 00:00:00 155.3 42313.0 -.2 156.8 96.2 13.6 false
    82 2009.07.08 00:00:00 151.6 52563.0 -.7 156.0 95.2 20.3 false
    83 2009.07.09 00:00:00 149.7 47107.0 -.9 155.5 96.4 27.2 false
    84 2009.07.10 00:00:00 151.6 37966.0 -1.3 155.0 93.9 35.5 true
    85 2009.07.12 00:00:00 149.7 6500.0 -1.5 154.5 93.7 36.5 true
    86 2009.07.13 00:00:00 149.1 46095.0 -1.6 154.0 95.1 42.2 true
    87 2009.07.14 00:00:00 151.3 45251.0 -1.5 153.8 97.3 62.3 true
    88 2009.07.15 00:00:00 152.8 42123.0 -1.3 153.8 98.3 88.6 false
    89 2009.07.16 00:00:00 154.2 42772.0 -1.1 153.8 98.7 86.5 true
    90 2009.07.17 00:00:00 153.3 31508.0 -1.0 153.8 99.3 84.7 true
    91 2009.07.19 00:00:00 154.0 7589.0 -.8 153.9 102.4 85.5 false
    92 2009.07.20 00:00:00 155.2 39221.0 -.6 154.0 103.6 84.6 true
    93 2009.07.21 00:00:00 155.0 36478.0 -.6 154.1 101.3 64.9 true
    94 2009.07.22 00:00:00 153.6 71587.0 -.4 154.0 103.6 55.0 true
    95 2009.07.23 00:00:00 155.2 72190.0 -.2 154.3 104.6 57.4 false
    96 2009.07.24 00:00:00 156.0 50149.0 -.1 154.4 102.9 67.1 true
    97 2009.07.26 00:00:00 156.1 7118.0 .0 154.6 102.2 67.7 false
    98 2009.07.27 00:00:00 156.1 55048.0 .2 154.8 101.6 72.1 false
    99 2009.07.28 00:00:00 156.7 66385.0 .2 154.9 101.0 62.6 true
    100 2009.07.29 00:00:00 154.8 74955.0 .2 154.9 100.8 52.9 true
    101 2009.07.30 00:00:00 155.3 61819.0 .4 155.1 101.5 51.6 true
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    Reader:
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  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Hi Cleo,
    the Validation's subprocesses are executed several times. Each one on a different SUBset of the data. When writing results inside the validation, you are simply overwriting all of them again and again, until the last round. So the model and performance are both generated on a subset of the data! If you retrieve the performance and model from the validation itself, the model is rebuilt from all training data and the performance is averaged. So this both vectors and models must differ!

    Greetings,
      Sebastian
  • CleoCleo Member Posts: 44 Maven
    Hello Sebastian,

    Thank you for clarifying the sliding window operator for me.  I added an iterating macro and followed your advice and the process is a little clearer for me.  I would like to ask a couple of follow up questions.

    1) Should the training window width = the test window width?
    2) If I add a threshold finder/threshold applier and model applier to the Testing portion of the Sliding Window Validation, does it get included in the “mod”?  When I have added the threshold I get much better predictions, however when I use the model on unseen data I get a uniform prediction of false.  Any suggestions on how to use the threshold operator with the sliding windows.

    Thanks again,
    Cleo
  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Hi Cleo,
    if you don't have the same window with, won't you have different attributes? You cannot apply the trained models anyway then.
    The threshold operator will not be included into the model automatically. You will have to add an Threshold Applier on the testing side and forward the Threshold.

    Greetings,
      Sebastian
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