Mercurial > repos > bgruening > sklearn_model_fit
diff simple_model_fit.xml @ 0:734c66aa945a draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
author | bgruening |
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date | Fri, 01 Nov 2019 17:18:28 -0400 |
parents | |
children | 26decbf4bdb8 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/simple_model_fit.xml Fri Nov 01 17:18:28 2019 -0400 @@ -0,0 +1,94 @@ +<tool id="sklearn_model_fit" name="Fit a Pipeline, Ensemble" version="@VERSION@"> + <description>or other models using a labeled dataset</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 '$__tool_directory__/simple_model_fit.py' + --inputs '$inputs' + --infile_estimator '$infile_estimator' + --infile1 '$input_options.infile1' + --infile2 '$input_options.infile2' + --out_object '$out_object' + #if $is_deep_learning == 'booltrue' + --out_weights '$out_weights' + #end if + ]]> + </command> + <configfiles> + <inputs name="inputs" /> + </configfiles> + <inputs> + <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator"/> + <param name="is_deep_learning" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Is the estimator a deep learning model?"/> + <conditional name="input_options"> + <expand macro="data_input_options"/> + <when value="tabular"> + <expand macro="samples_tabular" label1="Choose the training dataset containing features" multiple1="true" multiple2="false"/> + </when> + <when value="sparse"> + <expand macro="sparse_target"/> + </when> + </conditional> + </inputs> + <outputs> + <data format="zip" name="out_object" label="Fitted model (skeleton) on $(on_string)"/> + <data format="h5" name="out_weights" label="Weights trained on ${on_string}"> + <filter>is_deep_learning</filter> + </data> + </outputs> + <tests> + <test> + <param name="infile_estimator" value="pipeline05" ftype="zip"/> + <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"/> + <param name="infile2" value="regression_y.tabular" ftype="tabular"/> + <param name="header2" value="true"/> + <param name="col2" value="1"/> + <output name="out_object" file="model_fit01" compare="sim_size" delta="50"/> + </test> + <test> + <param name="infile_estimator" value="keras_model04"/> + <param name="is_deep_learning" value="true"/> + <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"/> + <param name="infile2" value="regression_y.tabular" ftype="tabular"/> + <param name="header2" value="true"/> + <param name="col2" value="1"/> + <output name="out_object" file="model_fit02" compare="sim_size" delta="10"/> + <output name="out_weights" file="model_fit02.h5" compare="sim_size" delta="10"/> + </test> + </tests> + <help> + <![CDATA[ +**What it does** + +This tools takes a pre-built model to simply fit on the provided labeled dataset by calling scikit-learn API `fit`. The model could be built in `build_pipeline` tool, `stacking_ensemble` tool or other places. Limited deep learning model is also supported. + +The supported labeled dataset are the following: + +- tabular + +- sparse + + +**Output** + +- fitted model, a pickled python object in zip format. +- optional hdf5 file containing weights for deep learning models. + + ]]> + </help> + <expand macro="sklearn_citation"> + <expand macro="keras_citation"/> + <expand macro="selene_citation"/> + </expand> +</tool>