在Rapidminer中對神經網絡輸出施加約束

Question

我正在Rapidminer中開發一個簡單的神經網絡模型來預測每小時通過高速公路的車輛數量。很明顯，在清晨（從凌晨2點到上午6點），很少有汽車在高速公路上行駛，有時我的車型預測汽車數量會減少（如-2或-3），這是在統計上可理解，但當您想在某處報告時並不酷。

我正在尋找一種方法來對模型施加約束，以便它只會預測正數。我怎樣才能做到這一點？

感謝

Answer 1

它總是取決於數據和你想要做的，但一種方法是將數字轉換爲多項式。所以0變成字符串「0」，1變成「1」等等。這迫使神經網絡單獨使用可用值。

下面是使用僞數據的示例過程。

<?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> 

它產生虛擬數據並將數值轉換爲多項式。 Cross Validation的預測示例集合輸出包含多項式，並將它們轉換回數字。

不用說，這可能不適合你想要的東西，但它是一個開始。

Andrew

Answer 2

你必須重新調整你的神經網絡參數，否則你就無法獲得算法在RapidMiner細節。其他的想法是在神經網絡模型之後使用閾值算子，這樣你就可以改變決策的邊界，以便它能夠預測到現在的負值。