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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a
author | bgruening |
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date | Mon, 02 Oct 2023 10:26:06 +0000 |
parents | 3bb1b688b0e4 |
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<tool id="model_prediction" name="Model Prediction" version="@VERSION@" profile="@PROFILE@"> <description>predicts on new data using a preffited model</description> <macros> <import>main_macros.xml</import> <import>keras_macros.xml</import> </macros> <expand macro="python_requirements" /> <expand macro="macro_stdio" /> <version_command>echo "@VERSION@"</version_command> <command> <![CDATA[ export HDF5_USE_FILE_LOCKING='FALSE'; python '$__tool_directory__/model_prediction.py' --inputs '$inputs' --infile_estimator '$infile_estimator' --outfile_predict '$outfile_predict' #if $input_options.selected_input == 'seq_fasta' --fasta_path '$input_options.fasta_path' #elif $input_options.selected_input == 'variant_effect' --ref_seq '$input_options.ref_genome_file' --vcf_path '$input_options.vcf_file' #else --infile1 '$input_options.infile1' #end if ]]> </command> <configfiles> <inputs name="inputs" /> </configfiles> <inputs> <param name="infile_estimator" type="data" format="h5mlm" label="Choose the dataset containing pipeline/estimator object" /> <param argument="method" type="select" label="Select invocation method"> <option value="predict" selected="true">predict</option> <option value="predict_proba">predict_proba</option> </param> <conditional name="input_options"> <param name="selected_input" type="select" label="Select input data type for prediction"> <option value="tabular" selected="true">tabular data</option> <option value="sparse">sparse matrix</option> <option value="seq_fasta">sequnences in a fasta file</option> <option value="variant_effect">reference genome and variant call file</option> </param> <when value="tabular"> <param name="infile1" type="data" format="tabular" label="Training samples dataset:" /> <param name="header1" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" /> <conditional name="column_selector_options_1"> <expand macro="samples_column_selector_options" multiple="true" /> </conditional> </when> <when value="sparse"> <param name="infile1" type="data" format="txt" label="Select a sparse matrix" help="" /> </when> <when value="seq_fasta"> <param name="fasta_path" type="data" format="fasta" label="Dataset containing fasta genomic/protein sequences" help="Sequences will be one-hot encoded to arrays." /> <param name="seq_type" type="select" label="Sequence type"> <option value="FastaDNABatchGenerator">DNA</option> <option value="FastaRNABatchGenerator">RNA</option> <option value="FastaProteinBatchGenerator">Protein</option> </param> </when> <when value="variant_effect"> <param name="ref_genome_file" type="data" format="fasta" label="Dataset containing reference genomic sequence" help="fasta" /> <param name="blacklist_regions" type="select" label="blacklist regioins" help="A pre-loaded list of blacklisted intervals.Refer to `selene` for details."> <option value="none" selected="true">None</option> <option value="hg38">hg38</option> <option value="hg19">hg19</option> </param> <param name="vcf_file" type="data" format="vcf" label="Dataset containing sequence variations" help="vcf" /> <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." /> <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." /> </when> </conditional> </inputs> <outputs> <data format="tabular" name="outfile_predict" /> </outputs> <tests> <test> <param name="infile_estimator" value="best_estimator_.h5mlm" ftype="h5mlm" /> <param name="method" value="predict" /> <param name="infile1" value="regression_X.tabular" ftype="tabular" /> <param name="header1" value="true" /> <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" /> <output name="outfile_predict" file="model_pred01.tabular" /> </test> <test> <param name="infile_estimator" value="train_test_eval_model01" ftype="h5mlm" /> <param name="method" value="predict" /> <param name="infile1" value="regression_X.tabular" ftype="tabular" /> <param name="header1" value="true" /> <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" /> <output name="outfile_predict" > <assert_contents> <has_n_columns n="1" /> <has_text text="71.0" /> <has_text text="61.3" /> <has_text text="83.7" /> <has_text text="69.2" /> <has_text text="51.8" /> </assert_contents> </output> </test> </tests> <help> <![CDATA[ **What it does** 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. For estimator, this tool supports fitted sklearn estimators and trained deep learning models. It predicts on three different dataset inputs, - tabular - sparse - bio-sequences in a fasta file - reference genome and variant call file ]]> </help> <expand macro="sklearn_citation"> <expand macro="keras_citation" /> <expand macro="selene_citation" /> </expand> </tool>