[英]rapidminer process in java code
I am working on sentiment analysis using rapidminer now i want to convert my rapidminer result into the java code using rapidminer api .please help me out from this issue i have stuck into the using .rmp file in process . 我正在使用Rapidminer进行情感分析,现在我想使用Rapidminer api将我的Rapidminer结果转换为Java代码。请帮助我摆脱这个问题,我一直在使用过程中使用.rmp文件。
i have use preprocessing task within it i have use the document process operate which also contain the other four sub process like tokenize , filter token by length ,stem , and filter stopword . 我已经在其中使用了预处理任务,我已经在使用文档流程操作,该文档流程还包含其他四个子流程,例如标记化,按长度过滤标记,词干和过滤停用词。 enter code here 在这里输入代码
this is my rapidminer xml code and its result:
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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.3.000" expanded="true" name="Process">
<process expanded="true" height="636" width="668">
<operator activated="true" class="text:process_document_from_file" compatibility="5.3.002" expanded="true" height="76" name="Process Documents from Files" width="90" x="112" y="30">
<list key="text_directories">
<parameter key="negative" value="F:\fOR_FINAL_EXAM\dataset\for_database\Apex AD2600 Progressive-scan DVD player"/>
<parameter key="positive" value="F:\fOR_FINAL_EXAM\dataset\for_database\Canon G3"/>
</list>
<parameter key="encoding" value="windows-1253"/>
<parameter key="prune_method" value="percentual"/>
<parameter key="prune_above_percent" value="95.0"/>
<process expanded="true" height="490" width="673">
<operator activated="true" class="text:tokenize" compatibility="5.3.002" expanded="true" height="60" name="Tokenize" width="90" x="112" y="75"/>
<operator activated="true" class="text:filter_by_length" compatibility="5.3.002" expanded="true" height="60" name="Filter Tokens (by Length)" width="90" x="216" y="162"/>
<operator activated="true" class="text:stem_porter" compatibility="5.3.002" expanded="true" height="60" name="Stem (Porter)" width="90" x="292" y="247"/>
<operator activated="true" class="text:filter_stopwords_english" compatibility="5.3.002" expanded="true" height="60" name="Filter Stopwords (English)" width="90" x="421" y="338"/>
<connect from_port="document" to_op="Tokenize" to_port="document"/>
<connect from_op="Tokenize" from_port="document" to_op="Filter Tokens (by Length)" to_port="document"/>
<connect from_op="Filter Tokens (by Length)" from_port="document" to_op="Stem (Porter)" to_port="document"/>
<connect from_op="Stem (Porter)" from_port="document" to_op="Filter Stopwords (English)" to_port="document"/>
<connect from_op="Filter Stopwords (English)" from_port="document" to_port="document 1"/>
<portSpacing port="source_document" spacing="0"/>
<portSpacing port="sink_document 1" spacing="0"/>
<portSpacing port="sink_document 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="x_validation" compatibility="5.3.000" expanded="true" height="112" name="Validation" width="90" x="313" y="30">
<process expanded="true" height="472" width="311">
<operator activated="true" class="support_vector_machine_linear" compatibility="5.3.000" expanded="true" height="76" name="SVM (Linear)" width="90" x="112" y="30"/>
<operator activated="true" class="k_nn" compatibility="5.3.000" expanded="true" height="76" name="k-NN" width="90" x="112" y="165"/>
<connect from_port="training" to_op="SVM (Linear)" to_port="training set"/>
<connect from_op="SVM (Linear)" from_port="model" to_port="model"/>
<connect from_op="SVM (Linear)" from_port="exampleSet" to_op="k-NN" to_port="training set"/>
<connect from_op="k-NN" from_port="exampleSet" to_port="through 1"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
<portSpacing port="sink_through 2" spacing="0"/>
</process>
<process expanded="true" height="472" width="311">
<operator activated="true" class="apply_model" compatibility="5.3.000" expanded="true" height="76" name="Apply Model" width="90" x="51" y="56">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance" compatibility="5.3.000" expanded="true" height="76" name="Performance" width="90" x="99" y="165"/>
<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="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="source_through 2" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<operator activated="false" class="store" compatibility="5.3.000" expanded="true" height="60" name="Store" width="90" x="447" y="165">
<parameter key="repository_entry" value="store"/>
</operator>
<operator activated="true" class="store" compatibility="5.3.000" expanded="true" height="60" name="Store (3)" width="90" x="112" y="300">
<parameter key="repository_entry" value="data"/>
</operator>
<connect from_op="Process Documents from Files" from_port="example set" to_op="Validation" to_port="training"/>
<connect from_op="Process Documents from Files" from_port="word list" to_op="Store (3)" to_port="input"/>
<connect from_op="Validation" from_port="model" to_port="result 1"/>
<connect from_op="Validation" from_port="averagable 1" 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>
enter image description here]
this is my rapidminer result . 这是我的迅捷的结果。 as shown in images i wana get this result in my java code 如图片所示,我要在我的Java代码中获得此结果
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