Mercurial > repos > bgruening > sklearn_mlxtend_association_rules

annotate association_rules.xml @ 1:77f046dad222 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d970dae980fbe349414fc0889f719d875d999c5b"
author bgruening
date Fri, 27 Aug 2021 09:37:58 +0000
parents af2624d5ab32
children
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af2624d5ab32 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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1 <tool id="sklearn_mlxtend_association_rules" name="Association rules" version="@VERSION@">
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2 <description>Extract frequent itemsets and generate association rules</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 </macros>
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6 <expand macro="python_requirements"/>
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7 <expand macro="macro_stdio"/>
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8 <version_command>echo "@VERSION@"</version_command>
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9 <command detect_errors="exit_code"><![CDATA[
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10 python '$__tool_directory__/association_rules.py'
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11 --inputs '$inputs'
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12 --infile '$infile'
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13 --outfile '$outfile'
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14 #if $support
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15 --support '$support'
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16 #end if
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17 #if $confidence
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18 --confidence '$confidence'
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19 #end if
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20 #if $lift
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21 --lift '$lift'
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22 #end if
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23 #if $conviction
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24 --conviction '$conviction'
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25 #end if
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26 #if $length
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27 --length '$length'
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28 #end if
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29 ]]>
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30 </command>
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31 <configfiles>
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32 <inputs name="inputs" />
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33 </configfiles>
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34 <inputs>
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35 <param name="infile" type="data" format="tabular" label="Input file"/>
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36 <param name="header0" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Does the dataset contain header?"/>
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37 <param name="support" type="float" optional="true" label="Minimum support"/>
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38 <param name="confidence" type="float" optional="true" label="Minimum confidence"/>
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39 <param name="lift" type="float" optional="true" label="Minimum lift"/>
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40 <param name="conviction" type="float" optional="true" label="Minimum conviction"/>
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41 <param name="length" type="integer" optional="true" label="Maximum length"/>
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42 </inputs>
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43 <outputs>
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44 <data name="outfile" format="tabular"/>
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45 </outputs>
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46 <tests>
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47 <test>
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48 <param name="infile" value="mba_input_str_w.tabular" ftype="tabular"/>
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49 <param name="header0" value="true"/>
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50 <param name="support" value="0.5"/>
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51 <param name="confidence" value="0.5"/>
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52 <param name="lift" value="1.1"/>
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53 <param name="conviction" value="1.1"/>
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54 <param name="length" value="5"/>
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55 <output name="outfile" file="mba_out_str.tabular" ftype="tabular"/>
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56 </test>
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57 <test>
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58 <param name="infile" value="mba_input_int_w.tabular" ftype="tabular"/>
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59 <param name="header0" value="true"/>
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60 <param name="support" value="0.5"/>
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61 <param name="confidence" value="0.5"/>
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62 <param name="lift" value="1.1"/>
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63 <param name="conviction" value="1.1"/>
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64 <param name="length" value="5"/>
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65 <output name="outfile" file="mba_output_int.tabular" ftype="tabular"/>
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66 </test>
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67 <test>
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68 <param name="infile" value="mba_input_str_wo.tabular" ftype="tabular"/>
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69 <param name="header0" value="false"/>
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70 <param name="support" value="0.5"/>
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71 <param name="confidence" value="0.5"/>
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72 <param name="lift" value="1.1"/>
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73 <param name="conviction" value="1.1"/>
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74 <param name="length" value="5"/>
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75 <output name="outfile" file="mba_output_str.tabular" ftype="tabular"/>
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76 </test>
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77 <test>
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78 <param name="infile" value="mba_input_int_wo.tabular" ftype="tabular"/>
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79 <param name="header0" value="false"/>
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80 <param name="support" value="0.5"/>
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81 <param name="confidence" value="0.5"/>
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82 <param name="lift" value="1.1"/>
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83 <param name="conviction" value="1.1"/>
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84 <param name="length" value="5"/>
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85 <output name="outfile" file="mba_output_int.tabular" ftype="tabular"/>
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86 </test>
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87 </tests>
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88 <help><![CDATA[
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89 **What it does**
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90
af2624d5ab32 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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91 Extract frequent itemsets and generate association rules
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92
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93 from mlxtend.frequent_patterns import fpgrowth
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94
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95 Extracts frequent itemsets for association rule mining. An itemset is considered as "frequent" if it
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96 meets a user-specified support threshold. For instance, if the support threshold is set to 0.5 (50%),
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97 a frequent itemset is defined as a set of items that occur together in at least 50% of all transactions
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98 in the database. We can only get itemsets that have a maximum number of items via length input parameter.
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99
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100 from mlxtend.frequent_patterns import association_rules
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101
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102 Generates association rules from frequent itemsets. Rule generation is a common task in the mining of
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103 frequent patterns. An association rule is an implication expression of the form X->Y, where X and Y
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104 are disjoint itemsets. A more concrete example based on consumer behaviour would be {Diapers}->{Beer}
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105 suggesting that people who buy diapers are also likely to buy beer. To evaluate the "interest" of
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106 such an association rule, different metrics have been developed, e.g., confidence, lift, and conviction.
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107
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108 Arguments
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109
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110 infile: Each line in infile contains (tab-separated) items in a tranasaction. Different lines/transactions
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111 can have differnt/varying number of items.
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112
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113 Returns
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114
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115 outfile: A tab separated file, that has an association rule on each line, with various metrics listed.
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116
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117 ]]></help>
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118 <expand macro="sklearn_citation"/>
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119 </tool>