在Rapidminer中对神经网络输出施加约束

Question

我正在Rapidminer中开发一个简单的神经网络模型来预测每小时通过高速公路的车辆数量。很明显，在清晨（从凌晨2点到上午6点），很少有汽车在高速公路上行驶，有时我的车型预测汽车数量会减少（如-2或-3），这是在统计上可理解，但当您想在某处报告时并不酷。

我正在寻找一种方法来对模型施加约束，以便它只会预测正数。我怎样才能做到这一点？

感谢

Answer 1

它总是取决于数据和你想要做的，但一种方法是将数字转换为多项式。所以0变成字符串“0”，1变成“1”等等。这迫使神经网络单独使用可用值。

下面是使用伪数据的示例过程。

<?xml version="1.0" encoding="UTF-8"?><process version="7.3.001"> 
    <context> 
    <input/> 
    <output/> 
    <macros/> 
    </context> 
    <operator activated="true" class="process" compatibility="7.3.001" expanded="true" name="Process"> 
    <process expanded="true"> 
     <operator activated="true" class="subprocess" compatibility="7.3.001" expanded="true" height="82" name="Subprocess" width="90" x="246" y="34"> 
     <process expanded="true"> 
      <operator activated="true" class="generate_data" compatibility="7.3.001" expanded="true" height="68" name="Generate Data" width="90" x="45" y="34"> 
      <parameter key="target_function" value="polynomial"/> 
      <parameter key="attributes_lower_bound" value="0.0"/> 
      <parameter key="attributes_upper_bound" value="3.0"/> 
      </operator> 
      <operator activated="true" class="normalize" compatibility="7.3.001" expanded="true" height="103" name="Normalize" width="90" x="179" y="34"> 
      <parameter key="attribute_filter_type" value="single"/> 
      <parameter key="attribute" value="label"/> 
      <parameter key="include_special_attributes" value="true"/> 
      <parameter key="method" value="range transformation"/> 
      <parameter key="max" value="4.99"/> 
      </operator> 
      <operator activated="true" class="real_to_integer" compatibility="7.3.001" expanded="true" height="82" name="Real to Integer" width="90" x="313" y="34"> 
      <parameter key="attribute_filter_type" value="single"/> 
      <parameter key="attribute" value="label"/> 
      <parameter key="include_special_attributes" value="true"/> 
      </operator> 
      <connect from_op="Generate Data" from_port="output" to_op="Normalize" to_port="example set input"/> 
      <connect from_op="Normalize" from_port="example set output" to_op="Real to Integer" to_port="example set input"/> 
      <connect from_op="Real to Integer" from_port="example set output" to_port="out 1"/> 
      <portSpacing port="source_in 1" spacing="0"/> 
      <portSpacing port="sink_out 1" spacing="0"/> 
      <portSpacing port="sink_out 2" spacing="0"/> 
     </process> 
     </operator> 
     <operator activated="true" class="numerical_to_polynominal" compatibility="7.3.001" expanded="true" height="82" name="Numerical to Polynominal" width="90" x="380" y="34"> 
     <parameter key="attribute_filter_type" value="single"/> 
     <parameter key="attribute" value="label"/> 
     <parameter key="include_special_attributes" value="true"/> 
     </operator> 
     <operator activated="true" class="concurrency:cross_validation" compatibility="7.3.001" expanded="true" height="145" name="Validation" width="90" x="514" y="34"> 
     <parameter key="sampling_type" value="shuffled sampling"/> 
     <process expanded="true"> 
      <operator activated="true" class="neural_net" compatibility="7.3.001" expanded="true" height="82" name="Neural Net" width="90" x="323" y="34"> 
      <list key="hidden_layers"/> 
      </operator> 
      <connect from_port="training set" to_op="Neural Net" to_port="training set"/> 
      <connect from_op="Neural Net" from_port="model" to_port="model"/> 
      <portSpacing port="source_training set" spacing="0"/> 
      <portSpacing port="sink_model" spacing="0"/> 
      <portSpacing port="sink_through 1" spacing="0"/> 
     </process> 
     <process expanded="true"> 
      <operator activated="true" class="apply_model" compatibility="7.3.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34"> 
      <list key="application_parameters"/> 
      </operator> 
      <operator activated="true" class="performance" compatibility="7.3.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34"/> 
      <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="performance 1"/> 
      <connect from_op="Performance" from_port="example set" to_port="test set results"/> 
      <portSpacing port="source_model" spacing="0"/> 
      <portSpacing port="source_test set" spacing="0"/> 
      <portSpacing port="source_through 1" spacing="0"/> 
      <portSpacing port="sink_test set results" spacing="0"/> 
      <portSpacing port="sink_performance 1" spacing="0"/> 
      <portSpacing port="sink_performance 2" spacing="0"/> 
     </process> 
     </operator> 
     <operator activated="true" class="nominal_to_numerical" compatibility="7.3.001" expanded="true" height="103" name="Nominal to Numerical (2)" width="90" x="715" y="136"> 
     <parameter key="attribute_filter_type" value="subset"/> 
     <parameter key="attribute" value="label"/> 
     <parameter key="attributes" value="prediction(label)|label"/> 
     <parameter key="include_special_attributes" value="true"/> 
     <parameter key="coding_type" value="unique integers"/> 
     <list key="comparison_groups"/> 
     </operator> 
     <connect from_op="Subprocess" from_port="out 1" to_op="Numerical to Polynominal" to_port="example set input"/> 
     <connect from_op="Numerical to Polynominal" from_port="example set output" to_op="Validation" to_port="example set"/> 
     <connect from_op="Validation" from_port="model" to_port="result 1"/> 
     <connect from_op="Validation" from_port="example set" to_port="result 2"/> 
     <connect from_op="Validation" from_port="test result set" to_op="Nominal to Numerical (2)" to_port="example set input"/> 
     <connect from_op="Validation" from_port="performance 1" to_port="result 4"/> 
     <connect from_op="Nominal to Numerical (2)" from_port="example set output" 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"/> 
     <portSpacing port="sink_result 5" spacing="0"/> 
    </process> 
    </operator> 
</process> 

它产生虚拟数据并将数值转换为多项式。 Cross Validation的预测示例集合输出包含多项式，并将它们转换回数字。

不用说，这可能不适合你想要的东西，但它是一个开始。

Andrew

Answer 2

你必须重新调整你的神经网络参数，否则你就无法获得算法在RapidMiner细节。其他的想法是在神经网络模型之后使用阈值算子，这样你就可以改变决策的边界，以便它能够预测到现在的负值。