RapidMiner:过滤出n>3的n-gram
我有大约200万条消息和数据表。 我想过滤掉包含频繁X-gram的消息，而X>3。 频率（占所有消息的百分比） 例如：RapidMiner:过滤出n>3的n-gram,rapidminer,Rapidminer,我有大约200万条消息和数据表。 我想过滤掉包含频繁X-gram的消息，而X>3。 频率（占所有消息的百分比） 例如： Message 1 = "1 2 3 4 5" Message 2 = "1 2 3 4 6" Message 3 = "1 2 3" M1和M2都有4克1_2_3_4，因此我想排除它们，因此结果只能剩下M3。您可以使用文本处理扩展来查找n克，计算长度超过3克的数量，并将该数字添加到示例集中，以允许后续过滤。您还可以保留原始数据 下面是一个您可以复制的示例注意，您必须
Message 1 = "1 2 3 4 5"
Message 2 = "1 2 3 4 6"
Message 3 = "1 2 3"
M1和M2都有4克1_2_3_4，因此我想排除它们，因此结果只能剩下M3。您可以使用文本处理扩展来查找n克，计算长度超过3克的数量，并将该数字添加到示例集中，以允许后续过滤。您还可以保留原始数据 下面是一个您可以复制的示例注意，您必须从RapidMiner marketplace安装文本挖掘扩展
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="6.5.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="6.5.000" expanded="true" name="Process">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<parameter key="parallelize_main_process" value="false"/>
<process expanded="true">
<operator activated="true" class="generate_data_user_specification" compatibility="6.5.000" expanded="true" height="60" name="Generate Data by User Specification" width="90" x="45" y="75">
<list key="attribute_values">
<parameter key="message" value=""1 2 3 4""/>
</list>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="generate_data_user_specification" compatibility="6.5.000" expanded="true" height="60" name="Generate Data by User Specification (2)" width="90" x="45" y="165">
<list key="attribute_values">
<parameter key="message" value=""1 2 3 4 5""/>
</list>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="generate_data_user_specification" compatibility="6.5.000" expanded="true" height="60" name="Generate Data by User Specification (3)" width="90" x="45" y="255">
<list key="attribute_values">
<parameter key="message" value=""1 2 3""/>
</list>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="append" compatibility="6.5.000" expanded="true" height="112" name="Append" width="90" x="246" y="75">
<parameter key="datamanagement" value="double_array"/>
<parameter key="merge_type" value="all"/>
</operator>
<operator activated="true" class="nominal_to_text" compatibility="6.5.000" expanded="true" height="76" name="Nominal to Text" width="90" x="380" y="75">
<parameter key="attribute_filter_type" value="all"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="nominal"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="file_path"/>
<parameter key="block_type" value="single_value"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="single_value"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
</operator>
<operator activated="true" class="text:process_document_from_data" compatibility="6.5.000" expanded="true" height="76" name="Process Documents from Data" width="90" x="514" y="75">
<parameter key="create_word_vector" value="false"/>
<parameter key="vector_creation" value="Term Occurrences"/>
<parameter key="add_meta_information" value="true"/>
<parameter key="keep_text" value="true"/>
<parameter key="prune_method" value="none"/>
<parameter key="prune_below_percent" value="3.0"/>
<parameter key="prune_above_percent" value="30.0"/>
<parameter key="prune_below_rank" value="0.05"/>
<parameter key="prune_above_rank" value="0.95"/>
<parameter key="datamanagement" value="double_sparse_array"/>
<parameter key="select_attributes_and_weights" value="false"/>
<list key="specify_weights"/>
<parameter key="parallelize_vector_creation" value="false"/>
<process expanded="true">
<operator activated="true" class="multiply" compatibility="6.5.000" expanded="true" height="94" name="Multiply" width="90" x="44" y="30"/>
<operator activated="true" class="text:tokenize" compatibility="6.5.000" expanded="true" height="60" name="Tokenize" width="90" x="179" y="30">
<parameter key="mode" value="regular expression"/>
<parameter key="characters" value=".:"/>
<parameter key="expression" value="\s"/>
<parameter key="language" value="English"/>
<parameter key="max_token_length" value="3"/>
</operator>
<operator activated="true" class="text:generate_n_grams_terms" compatibility="6.5.000" expanded="true" height="60" name="Generate n-Grams (Terms)" width="90" x="179" y="120">
<parameter key="max_length" value="4"/>
</operator>
<operator activated="true" class="text:filter_tokens_by_content" compatibility="6.5.000" expanded="true" height="60" name="Filter Tokens (by Content)" width="90" x="179" y="210">
<parameter key="condition" value="contains match"/>
<parameter key="regular_expression" value="(_.){3,}"/>
<parameter key="case_sensitive" value="false"/>
<parameter key="invert condition" value="false"/>
</operator>
<operator activated="true" class="text:extract_token_number" compatibility="6.5.000" expanded="true" height="60" name="Extract Token Number" width="90" x="179" y="300">
<parameter key="metadata_key" value="numberOfNGramsGT3"/>
<parameter key="condition" value="all"/>
<parameter key="case_sensitive" value="false"/>
<parameter key="invert_condition" value="false"/>
</operator>
<connect from_port="document" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Tokenize" to_port="document"/>
<connect from_op="Multiply" from_port="output 2" to_port="document 1"/>
<connect from_op="Tokenize" from_port="document" to_op="Generate n-Grams (Terms)" to_port="document"/>
<connect from_op="Generate n-Grams (Terms)" from_port="document" to_op="Filter Tokens (by Content)" to_port="document"/>
<connect from_op="Filter Tokens (by Content)" from_port="document" to_op="Extract Token Number" to_port="document"/>
<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="filter_examples" compatibility="6.5.000" expanded="true" height="94" name="Filter Examples" width="90" x="782" y="75">
<parameter key="parameter_expression" value=""/>
<parameter key="condition_class" value="custom_filters"/>
<parameter key="invert_filter" value="false"/>
<list key="filters_list">
<parameter key="filters_entry_key" value="numberOfNGramsGT3.eq.0"/>
</list>
<parameter key="filters_logic_and" value="true"/>
<parameter key="filters_check_metadata" value="true"/>
</operator>
<connect from_op="Generate Data by User Specification" from_port="output" to_op="Append" to_port="example set 1"/>
<connect from_op="Generate Data by User Specification (2)" from_port="output" to_op="Append" to_port="example set 2"/>
<connect from_op="Generate Data by User Specification (3)" from_port="output" to_op="Append" to_port="example set 3"/>
<connect from_op="Append" from_port="merged set" to_op="Nominal to Text" to_port="example set input"/>
<connect from_op="Nominal to Text" from_port="example set output" to_op="Process Documents from Data" to_port="example set"/>
<connect from_op="Process Documents from Data" from_port="example set" to_op="Filter Examples" to_port="example set input"/>
<connect from_op="Filter Examples" from_port="example set output" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
在上面的示例中，为每个令牌序列添加n>3的n克数量，然后过滤掉它们。这并没有回答我的问题：只过滤掉频繁的一次。也就是说：1234和1235将导致空集，尽管每4克只出现50%的次数。例如，我想过滤掉>60%的数据。我试过了。也许你可以更清楚地解释你想要什么。