在 Rapidminer 中限制神经网络的输出
Putting constraint on neural network's output in Rapidminer
我正在 Rapidminer 中开发一个简单的神经网络模型来预测每小时在高速公路上通过的汽车数量。很明显，在清晨（从 2:00 上午到 6:00 上午）高速公路上很少有汽车，有时我的模型预测汽车数量为负（如 -2 或 -3 )，这在统计上是可以理解的，但是当你想在某个地方报告它时并不酷。
我正在寻找一种方法来限制模型，使其只能预测正数。我该怎么做？
谢谢
您已重新调整您的神经网络参数，否则您无法访问 RapidMiner 中的算法细节。另一个想法是在神经网络模型之后使用阈值运算符，这样您就可以更改决策的边界，使其预测的负数比现在少。
它总是取决于数据和你想做什么，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。这会强制神经网络单独使用可用值。
这是一个使用虚拟数据的示例过程。
<?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>
它生成虚拟数据并将数值转换为多项式。 Cross Validation 的预测示例集输出包含多项式，这些被转换回数字。
不用说，这可能不适合您想要的，但这是一个开始。
安德鲁
我正在 Rapidminer 中开发一个简单的神经网络模型来预测每小时在高速公路上通过的汽车数量。很明显，在清晨（从 2:00 上午到 6:00 上午）高速公路上很少有汽车，有时我的模型预测汽车数量为负（如 -2 或 -3 )，这在统计上是可以理解的，但是当你想在某个地方报告它时并不酷。
我正在寻找一种方法来限制模型，使其只能预测正数。我该怎么做？
谢谢
您已重新调整您的神经网络参数，否则您无法访问 RapidMiner 中的算法细节。另一个想法是在神经网络模型之后使用阈值运算符，这样您就可以更改决策的边界，使其预测的负数比现在少。
它总是取决于数据和你想做什么，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。这会强制神经网络单独使用可用值。
这是一个使用虚拟数据的示例过程。
<?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>
它生成虚拟数据并将数值转换为多项式。 Cross Validation 的预测示例集输出包含多项式，这些被转换回数字。
不用说，这可能不适合您想要的，但这是一个开始。
安德鲁