Mercurial > repos > tduigou > icfree_learner

annotate learner.xml @ 7:cac9eb21fb69 draft

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planemo upload for repository https://github.com/brsynth/icfree-ml commit 7a51fc3ee7c0eb4b07d47772340ff1d3dab5e7aa-dirty
author tduigou
date Thu, 06 Feb 2025 15:11:19 +0000
parents 09df65e2df97
children fad19e8ec225
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1 <tool id="icfree_learner" name="iCFree learner" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" license="MIT">
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2 <description>Active learning and model training</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements"/>
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7 <command detect_errors="exit_code"><![CDATA[
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8 cp '$input_param_tsv' 'param.tsv' &&
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9 mkdir 'indir' &&
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10 #for $i, $input in enumerate($input_data_csv)
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11 #if $input
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12 cp '$input' 'indir/plate.${i}.csv' &&
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13 #end if
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14 #end for
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15 python -m icfree.learner
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16 --data_folder 'indir'
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17 --parameter_file 'param.tsv'
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18 --output_folder 'outdir'
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19 #if str($adv.name_list) != ''
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20 --name_list '$adv.name_list'
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21 #end if
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22 #if str($adv.test) == 'true'
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23 --test
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24 #end if
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25 --nb_rep '$adv.nb_rep'
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26 #if str($adv.flatten) != ''
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27 --flatten
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28 #end if
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29 #if str($adv.seed_cond.seed_param) == 'not_random'
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30 --seed '$adv.seed_cond.seed'
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31 #end if
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32 --nb_new_data_predict '$adv.nb_new_data_predict'
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33 --nb_new_data '$adv.nb_new_data'
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34 --parameter_step '$adv.parameter_step'
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35 --n_group '$adv.n_group'
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36 --km '$adv.km'
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37 --ks '$adv.ks'
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38 --save_plot
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39 --verbose && ls 'outdir'
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40 ]]></command>
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41 <environment_variables>
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42 <environment_variable name="MPLCONFIGDIR">.matplotlib</environment_variable>
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43 </environment_variables>
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44 <inputs>
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45 <param name="input_data_csv" type="data" format="csv" multiple="true" label="Input data files" help="Input data files" />
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46 <param name="input_param_tsv" type="data" format="tabular" label="Parameter values for the experiments" help="Parameter values for the experiment"/>
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47 <section name="adv" title="Advanced Options" expanded="false">
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48 <param name="name_list" type="text" value="" label="Labels of the feature list" help="A comma-separated string of column names or identifiers, converted to a list of strings representing columns that contain labels (y). This separates y columns from the rest (X features). (Default: Yield1,Yield2,Yield3,Yield4,Yield5)" />
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49 <param name="test" type="boolean" label="Validate the model" help="A flag for validating the model; not required to run inside the active learning loop. If not set, skip the validating step" checked="false" />
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50 <param name="nb_rep" type="integer" value="100" label="Number of test repetitions for validation the model behavior" help="The number of test repetitions for validating the model behavior. 80% of data is randomly separated for training, and 20% is used for testing." />
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51 <param name="flatten" type="boolean" label="Flattent feature data" help="A flag to indicate whether to flatten Y data. If set, treats each repetition in the same experiment independently; multiple same X values with different y outputs are modeled. Else, calculates the average of y across repetitions and only model with y average." />
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52 <param name="nb_new_data_predict" type="integer" value="1000" label="Number of new data points generated" help="The number of new data points sampled from all possible cases." />
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53 <param name="nb_new_data" type="integer" value="50" label="Number of new data points used" help="The number of new data points selected from the generated ones. These are the data points labeled after active learning loops. `nb_new_data_predict` must be greater than `nb_new_data` to be meaningful." />
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54 <param name="parameter_step" type="integer" value="10" label="Step size used to decrement the maximum predefined concentration sequentially" help="The step size used to decrement the maximum predefined concentration sequentially. For example, if the maximum concentration is `max`, the sequence of concentrations is calculated as: `max - 1 * parameter_step`, `max - 2 * parameter_step`, `max - 3 * parameter_step`, and so on. Each concentration is a candidate for experimental testing. Smaller steps result in more possible combinations to sample." />
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55 <param name="n_group" type="integer" value="15" label="Number of clusters" help="Parameter for the cluster margin algorithm, specifying the number of groups into which generated data will be clustered." />
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56 <param name="km" type="integer" value="50" label="Number of data points for the first selection" help="Parameter for the cluster margin algorithm, specifying the number of data points for the first selection. Ensure `nb_new_data_predict > ks > km`." />
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57 <param name="ks" type="integer" value="20" label="Number of data points for the second selection" help="Parameter for the cluster margin algorithm, specifying the number of data points for the second selection. This is also similar to `nb_new_data`." />
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58 <!-- Seed -->
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59 <conditional name="seed_cond">
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60 <param name="seed_param" type="select" label="Seed" help="Choose a seed or let it as random">
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61 <option value="not_random" selected="true">fixed</option>
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62 <option value="random">random</option>
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63 </param>
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64 <when value="random"/>
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65 <when value="not_random">
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66 <param name="seed" type="text" value="85" label="Seed value" help="Only integer allowed">
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67 <validator type="empty_field" message="Not empty, select random"/>
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68 <validator type="regex" message="Only integer allowed">^(?:\d+)$</validator>
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69 </param>
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70 </when>
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71 </conditional>
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72 </section>
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73 </inputs>
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74 <outputs>
2
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75 <data name="output_csv" format="csv" label="${tool.name} - Data">
7
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76 <discover_datasets pattern="(?P&lt;designation&gt;.*)\.csv" ext="csv" directory="outdir" />
2
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77 </data>
0
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78 <collection name="output_png" type="list" label="${tool.name} - Plot">
7
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79 <discover_datasets pattern="(?P&lt;name&gt;.*)\.png" format="png" directory="outdir" />
0
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80 </collection>
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81 </outputs>
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82 <tests>
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83 <test>
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84 <!-- python -m icfree.learner -data_folder learner -parameter_file learner.input.param.tsv -output_folder tmp -save_plot -verbose -seed 85 -->
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85 <param name="input_data_csv" value="learner.input.data.1.csv,learner.input.data.2.csv" />
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86 <param name="input_param_tsv" value="learner.input.param.tsv" />
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87 <output name="output_csv">
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88 <discovered_dataset designation="next_sampling_ei50" ftype="csv">
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89 <assert_contents>
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90 <has_n_lines n="51" />
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91 </assert_contents>
2
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92 </discovered_dataset>
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93 </output>
0
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94 <output_collection name="output_png" type="list" count="4">
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95 <element name="EI selected">
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96 <assert_contents>
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97 <has_size value="77k" delta="1k"/>
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98 </assert_contents>
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99 </element>
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100 <element name="EI">
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101 <assert_contents>
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102 <has_size value="36k" delta="1k"/>
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103 </assert_contents>
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104 </element>
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105 <element name="Train_Test">
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106 <assert_contents>
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107 <has_size value="64k" delta="1k"/>
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108 </assert_contents>
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109 </element>
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110 <element name="Yield evolution through each active learning query">
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111 <assert_contents>
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112 <has_size value="19k" delta="1k"/>
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113 </assert_contents>
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114 </element>
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115 </output_collection>
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116 </test>
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117 <test>
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118 <!-- python -m icfree.learner -data_folder learner -parameter_file learner.input.param.tsv -output_folder tmp2 -name_list "Yield1,Yield2" -nb_rep 5 -flatten -seed 85 -nb_new_data_predict 20 -nb_new_data 2 -parameter_step 2 -n_group 3 -km 5 -ks 2 -save_plot -verbose -->
2
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119 <param name="input_data_csv" value="learner.input.data.1.csv,learner.input.data.2.csv" />
0
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120 <param name="input_param_tsv" value="learner.input.param.tsv" />
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121 <param name="name_list" value="Yield1,Yield2" />
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122 <param name="nb_rep" value="5" />
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123 <param name="flatten" value="true" />
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124 <param name="nb_new_data_predict" value="20" />
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125 <param name="nb_new_data" value="2" />
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126 <param name="parameter_step" value="2" />
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127 <param name="n_group" value="3" />
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128 <param name="km" value="5" />
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129 <param name="ks" value="2" />
2
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130 <!-- <element name="next_sampling_ei5" file="learner.output.data.2.csv" ftype="csv" > -->
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131 <output name="output_csv">
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132 <discovered_dataset designation="next_sampling_ei5" ftype="csv">
0
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133 <assert_contents>
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134 <has_n_lines n="6" />
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135 </assert_contents>
2
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136 </discovered_dataset>
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137 </output>
0
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138 <output_collection name="output_png" type="list" count="4">
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139 <element name="EI selected">
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140 <assert_contents>
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141 <has_size value="24k" delta="1k"/>
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142 </assert_contents>
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143 </element>
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144 <element name="EI">
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145 <assert_contents>
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146 <has_size value="36k" delta="1k"/>
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147 </assert_contents>
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148 </element>
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149 <element name="Train_Test">
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150 <assert_contents>
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151 <has_size value="60k" delta="1k"/>
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152 </assert_contents>
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153 </element>
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154 <element name="Yield evolution through each active learning query">
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155 <assert_contents>
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156 <has_size value="19k" delta="1k"/>
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157 </assert_contents>
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158 </element>
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159 </output_collection>
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160 </test>
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161 </tests>
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162 <help><![CDATA[
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163 Learner
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164 =======
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165 Active learning and model training
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166
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167 ]]></help>
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168 <expand macro="creator"/>
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169 <expand macro="citation"/>
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170 </tool>