在 Rapidminer 中导出结果

Question

我在Rapid miner中创建了一个神经网络模型，但结果不是我所期望的，结果与某种中间体有关，为了达到最终结果我需要对结果集进行自定义查询由神经网络模型生成，现在的问题是：

1.How can I query the result set? 
2.Or how can I import that result set of neural net in a database then use read database operator to query it. 
3.Or how can I export the neural net model's result set in a csv file so I can Import it into a database for further processing?

Answer 1

训练神经网络时，首先要创建一个模型对象。然后您需要做的是将该模型应用于您的测试数据，该数据不应与用于训练的数据相同。看看下面的示例流程（您也可以将 xml 复制并粘贴到您的 RapidMiner 流程 window 1）：

为了将结果导入数据库或 csv 文件，有特殊的运算符，称为 Write CSV 或 Write Database，对于后者，您还必须先在菜单下定义连接条目 连接 -> 管理数据库连接

您还可以看看 RapidMiner 社区的培训部分，那里有很多培训视频和相关内容 material：Free training material

1:

<?xml version="1.0" encoding="UTF-8"?><process version="8.2.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Process">
<process expanded="true">
  <operator activated="true" class="retrieve" compatibility="8.2.000" expanded="true" height="68" name="Retrieve Weighting" width="90" x="45" y="136">
    <parameter key="repository_entry" value="//Samples/data/Weighting"/>
  </operator>
  <operator activated="true" class="split_data" compatibility="8.2.000" expanded="true" height="103" name="Split Data" width="90" x="246" y="136">
    <enumeration key="partitions">
      <parameter key="ratio" value="0.7"/>
      <parameter key="ratio" value="0.3"/>
    </enumeration>
    <description align="center" color="yellow" colored="true" width="126">Split the data into training and a testing set (ratio 70% and 30%)</description>
  </operator>
  <operator activated="true" class="neural_net" compatibility="8.2.000" expanded="true" height="82" name="Neural Net" width="90" x="447" y="34">
    <list key="hidden_layers"/>
    <description align="center" color="green" colored="true" width="126">Train the neural net here</description>
  </operator>
  <operator activated="true" class="apply_model" compatibility="8.2.000" expanded="true" height="82" name="Apply Model" width="90" x="648" y="136">
    <list key="application_parameters"/>
    <description align="center" color="green" colored="true" width="126">Apply the trained net on the test data</description>
  </operator>
  <operator activated="true" class="performance_classification" compatibility="8.2.000" expanded="true" height="82" name="Performance" width="90" x="841" y="136">
    <list key="class_weights"/>
    <description align="center" color="orange" colored="true" width="126">Check how well the network worked on the data and the see output of classification</description>
  </operator>
  <connect from_op="Retrieve Weighting" from_port="output" to_op="Split Data" to_port="example set"/>
  <connect from_op="Split Data" from_port="partition 1" to_op="Neural Net" to_port="training set"/>
  <connect from_op="Split Data" from_port="partition 2" to_op="Apply Model" to_port="unlabelled data"/>
  <connect from_op="Neural Net" from_port="model" to_op="Apply Model" to_port="model"/>
  <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="result 1"/>
  <connect from_op="Performance" from_port="example set" 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>

在 Rapidminer 中导出结果

Export results in Rapidminer

database

neural-network

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