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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
author bgruening
date Sun, 05 Nov 2023 15:23:41 +0000
parents 3bb1b688b0e4
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1 <tool id="model_prediction" name="Model Prediction" version="@VERSION@" profile="@PROFILE@">
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2 <description>predicts on new data using a preffited model</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 <import>keras_macros.xml</import>
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6 </macros>
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7 <expand macro="python_requirements" />
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8 <expand macro="macro_stdio" />
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9 <version_command>echo "@VERSION@"</version_command>
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10 <command>
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11 <![CDATA[
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12 export HDF5_USE_FILE_LOCKING='FALSE';
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13 python '$__tool_directory__/model_prediction.py'
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14 --inputs '$inputs'
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15 --infile_estimator '$infile_estimator'
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16 --outfile_predict '$outfile_predict'
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17 #if $input_options.selected_input == 'seq_fasta'
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18 --fasta_path '$input_options.fasta_path'
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19 #elif $input_options.selected_input == 'variant_effect'
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20 --ref_seq '$input_options.ref_genome_file'
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21 --vcf_path '$input_options.vcf_file'
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22 #else
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23 --infile1 '$input_options.infile1'
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24 #end if
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25 ]]>
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26 </command>
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27 <configfiles>
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28 <inputs name="inputs" />
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29 </configfiles>
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30 <inputs>
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31 <param name="infile_estimator" type="data" format="h5mlm" label="Choose the dataset containing pipeline/estimator object" />
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32 <param argument="method" type="select" label="Select invocation method">
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33 <option value="predict" selected="true">predict</option>
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34 <option value="predict_proba">predict_proba</option>
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35 </param>
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36 <conditional name="input_options">
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37 <param name="selected_input" type="select" label="Select input data type for prediction">
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38 <option value="tabular" selected="true">tabular data</option>
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39 <option value="sparse">sparse matrix</option>
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40 <option value="seq_fasta">sequnences in a fasta file</option>
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41 <option value="variant_effect">reference genome and variant call file</option>
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42 </param>
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43 <when value="tabular">
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44 <param name="infile1" type="data" format="tabular" label="Training samples dataset:" />
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45 <param name="header1" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
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46 <conditional name="column_selector_options_1">
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47 <expand macro="samples_column_selector_options" multiple="true" />
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48 </conditional>
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49 </when>
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50 <when value="sparse">
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51 <param name="infile1" type="data" format="txt" label="Select a sparse matrix" help="" />
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52 </when>
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53 <when value="seq_fasta">
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54 <param name="fasta_path" type="data" format="fasta" label="Dataset containing fasta genomic/protein sequences" help="Sequences will be one-hot encoded to arrays." />
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55 <param name="seq_type" type="select" label="Sequence type">
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56 <option value="FastaDNABatchGenerator">DNA</option>
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57 <option value="FastaRNABatchGenerator">RNA</option>
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58 <option value="FastaProteinBatchGenerator">Protein</option>
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59 </param>
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60 </when>
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61 <when value="variant_effect">
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62 <param name="ref_genome_file" type="data" format="fasta" label="Dataset containing reference genomic sequence" help="fasta" />
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63 <param name="blacklist_regions" type="select" label="blacklist regioins" help="A pre-loaded list of blacklisted intervals.Refer to `selene` for details.">
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64 <option value="none" selected="true">None</option>
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65 <option value="hg38">hg38</option>
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66 <option value="hg19">hg19</option>
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67 </param>
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68 <param name="vcf_file" type="data" format="vcf" label="Dataset containing sequence variations" help="vcf" />
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69 <param name="seq_length" type="integer" value="1000" label="Encoding seqence length" help="A stretch of sequence surrounding the variation position on the reference genome." />
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70 <param name="output_reference" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Predict the reference sequence?" help="If False, predict on the variant sequence." />
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71 </when>
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72 </conditional>
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73 </inputs>
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74 <outputs>
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75 <data format="tabular" name="outfile_predict" />
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76 </outputs>
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77 <tests>
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78 <test>
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79 <param name="infile_estimator" value="best_estimator_.h5mlm" ftype="h5mlm" />
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80 <param name="method" value="predict" />
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81 <param name="infile1" value="regression_X.tabular" ftype="tabular" />
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82 <param name="header1" value="true" />
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83 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" />
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84 <output name="outfile_predict" file="model_pred01.tabular" />
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85 </test>
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86 <test>
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87 <param name="infile_estimator" value="train_test_eval_model01" ftype="h5mlm" />
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88 <param name="method" value="predict" />
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89 <param name="infile1" value="regression_X.tabular" ftype="tabular" />
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90 <param name="header1" value="true" />
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91 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" />
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92 <output name="outfile_predict" >
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93 <assert_contents>
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94 <has_n_columns n="1" />
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95 <has_text text="71.0" />
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96 <has_text text="61.3" />
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97 <has_text text="83.7" />
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98 <has_text text="69.2" />
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99 <has_text text="51.8" />
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100 </assert_contents>
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101 </output>
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102 </test>
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103 </tests>
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104 <help>
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105 <![CDATA[
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106 **What it does**
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107
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108 Given a fitted estimator and new data sets, this tool outpus the prediction results on the data sets via invoking the estimator's `predict` or `predict_proba` method.
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109
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110 For estimator, this tool supports fitted sklearn estimators and trained deep learning models. It predicts on three different dataset inputs,
0
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111
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112 - tabular
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113
db511406350a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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114 - sparse
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115
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116 - bio-sequences in a fasta file
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117
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118 - reference genome and variant call file
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119
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120 ]]>
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121 </help>
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122 <expand macro="sklearn_citation">
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123 <expand macro="keras_citation" />
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124 <expand macro="selene_citation" />
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125 </expand>
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126 </tool>