Mercurial > repos > bgruening > model_prediction

comparison model_prediction.xml @ 15:3bb1b688b0e4 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
author bgruening
date Wed, 09 Aug 2023 13:06:25 +0000
parents 4aa701f5a393
children
comparison
equal deleted inserted replaced
14:29e4122c90de 15:3bb1b688b0e4
1 <tool id="model_prediction" name="Model Prediction" version="@VERSION@" profile="20.05"> 1 <tool id="model_prediction" name="Model Prediction" version="@VERSION@" profile="@PROFILE@">
2 <description>predicts on new data using a preffited model</description> 2 <description>predicts on new data using a preffited model</description>
3 <macros> 3 <macros>
4 <import>main_macros.xml</import> 4 <import>main_macros.xml</import>
5 <import>keras_macros.xml</import> 5 <import>keras_macros.xml</import>
6 </macros> 6 </macros>
12 export HDF5_USE_FILE_LOCKING='FALSE'; 12 export HDF5_USE_FILE_LOCKING='FALSE';
13 python '$__tool_directory__/model_prediction.py' 13 python '$__tool_directory__/model_prediction.py'
14 --inputs '$inputs' 14 --inputs '$inputs'
15 --infile_estimator '$infile_estimator' 15 --infile_estimator '$infile_estimator'
16 --outfile_predict '$outfile_predict' 16 --outfile_predict '$outfile_predict'
17 --infile_weights '$infile_weights'
18 #if $input_options.selected_input == 'seq_fasta' 17 #if $input_options.selected_input == 'seq_fasta'
19 --fasta_path '$input_options.fasta_path' 18 --fasta_path '$input_options.fasta_path'
20 #elif $input_options.selected_input == 'variant_effect' 19 #elif $input_options.selected_input == 'variant_effect'
21 --ref_seq '$input_options.ref_genome_file' 20 --ref_seq '$input_options.ref_genome_file'
22 --vcf_path '$input_options.vcf_file' 21 --vcf_path '$input_options.vcf_file'
27 </command> 26 </command>
28 <configfiles> 27 <configfiles>
29 <inputs name="inputs" /> 28 <inputs name="inputs" />
30 </configfiles> 29 </configfiles>
31 <inputs> 30 <inputs>
32 <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object" /> 31 <param name="infile_estimator" type="data" format="h5mlm" label="Choose the dataset containing pipeline/estimator object" />
33 <param name="infile_weights" type="data" format="h5" optional="true" label="Choose the dataset containing weights for the estimator above" help="Optional. For deep learning only." />
34 <param argument="method" type="select" label="Select invocation method"> 32 <param argument="method" type="select" label="Select invocation method">
35 <option value="predict" selected="true">predict</option> 33 <option value="predict" selected="true">predict</option>
36 <option value="predict_proba">predict_proba</option> 34 <option value="predict_proba">predict_proba</option>
37 </param> 35 </param>
38 <conditional name="input_options"> 36 <conditional name="input_options">
76 <outputs> 74 <outputs>
77 <data format="tabular" name="outfile_predict" /> 75 <data format="tabular" name="outfile_predict" />
78 </outputs> 76 </outputs>
79 <tests> 77 <tests>
80 <test> 78 <test>
81 <param name="infile_estimator" value="best_estimator_.zip" ftype="zip" /> 79 <param name="infile_estimator" value="best_estimator_.h5mlm" ftype="h5mlm" />
82 <param name="method" value="predict" /> 80 <param name="method" value="predict" />
83 <param name="infile1" value="regression_X.tabular" ftype="tabular" /> 81 <param name="infile1" value="regression_X.tabular" ftype="tabular" />
84 <param name="header1" value="true" /> 82 <param name="header1" value="true" />
85 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" /> 83 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" />
86 <output name="outfile_predict" file="model_pred01.tabular" /> 84 <output name="outfile_predict" file="model_pred01.tabular" />
87 </test> 85 </test>
88 <test> 86 <test>
89 <param name="infile_estimator" value="keras_model04" ftype="zip" /> 87 <param name="infile_estimator" value="train_test_eval_model01" ftype="h5mlm" />
90 <param name="infile_weights" value="train_test_eval_weights02.h5" ftype="h5" />
91 <param name="method" value="predict" /> 88 <param name="method" value="predict" />
92 <param name="infile1" value="regression_X.tabular" ftype="tabular" /> 89 <param name="infile1" value="regression_X.tabular" ftype="tabular" />
93 <param name="header1" value="true" /> 90 <param name="header1" value="true" />
94 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" /> 91 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" />
95 <output name="outfile_predict"> 92 <output name="outfile_predict" >
96 <assert_contents> 93 <assert_contents>
97 <has_n_columns n="1" /> 94 <has_n_columns n="1" />
98 <has_text text="70.2" /> 95 <has_text text="71.0" />
99 <has_text text="61.2" /> 96 <has_text text="61.3" />
100 <has_text text="74.2" /> 97 <has_text text="83.7" />
101 <has_text text="65.9" /> 98 <has_text text="69.2" />
102 <has_text text="52.9" /> 99 <has_text text="51.8" />
103 </assert_contents> 100 </assert_contents>
104 </output> 101 </output>
105 </test> 102 </test>
106 </tests> 103 </tests>
107 <help> 104 <help>
108 <![CDATA[ 105 <![CDATA[
109 **What it does** 106 **What it does**
110 107
111 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. 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.
112 109
113 For estimator, this tool supports fitted sklearn estimators (pickled) and trained deep learning models (model skeleton + weights). It predicts on three different dataset inputs, 110 For estimator, this tool supports fitted sklearn estimators and trained deep learning models. It predicts on three different dataset inputs,
114 111
115 - tabular 112 - tabular
116 113
117 - sparse 114 - sparse
118 115