Mercurial > repos > bgruening > sklearn_model_fit
comparison 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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-1:000000000000 | 0:734c66aa945a |
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1 <tool id="sklearn_model_fit" name="Fit a Pipeline, Ensemble" version="@VERSION@"> | |
2 <description>or other models using a labeled dataset</description> | |
3 <macros> | |
4 <import>main_macros.xml</import> | |
5 <import>keras_macros.xml</import> | |
6 </macros> | |
7 <expand macro="python_requirements"/> | |
8 <expand macro="macro_stdio"/> | |
9 <version_command>echo "@VERSION@"</version_command> | |
10 <command> | |
11 <![CDATA[ | |
12 export HDF5_USE_FILE_LOCKING='FALSE'; | |
13 python '$__tool_directory__/simple_model_fit.py' | |
14 --inputs '$inputs' | |
15 --infile_estimator '$infile_estimator' | |
16 --infile1 '$input_options.infile1' | |
17 --infile2 '$input_options.infile2' | |
18 --out_object '$out_object' | |
19 #if $is_deep_learning == 'booltrue' | |
20 --out_weights '$out_weights' | |
21 #end if | |
22 ]]> | |
23 </command> | |
24 <configfiles> | |
25 <inputs name="inputs" /> | |
26 </configfiles> | |
27 <inputs> | |
28 <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator"/> | |
29 <param name="is_deep_learning" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Is the estimator a deep learning model?"/> | |
30 <conditional name="input_options"> | |
31 <expand macro="data_input_options"/> | |
32 <when value="tabular"> | |
33 <expand macro="samples_tabular" label1="Choose the training dataset containing features" multiple1="true" multiple2="false"/> | |
34 </when> | |
35 <when value="sparse"> | |
36 <expand macro="sparse_target"/> | |
37 </when> | |
38 </conditional> | |
39 </inputs> | |
40 <outputs> | |
41 <data format="zip" name="out_object" label="Fitted model (skeleton) on $(on_string)"/> | |
42 <data format="h5" name="out_weights" label="Weights trained on ${on_string}"> | |
43 <filter>is_deep_learning</filter> | |
44 </data> | |
45 </outputs> | |
46 <tests> | |
47 <test> | |
48 <param name="infile_estimator" value="pipeline05" ftype="zip"/> | |
49 <param name="infile1" value="regression_X.tabular" ftype="tabular"/> | |
50 <param name="header1" value="true" /> | |
51 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/> | |
52 <param name="infile2" value="regression_y.tabular" ftype="tabular"/> | |
53 <param name="header2" value="true"/> | |
54 <param name="col2" value="1"/> | |
55 <output name="out_object" file="model_fit01" compare="sim_size" delta="50"/> | |
56 </test> | |
57 <test> | |
58 <param name="infile_estimator" value="keras_model04"/> | |
59 <param name="is_deep_learning" value="true"/> | |
60 <param name="infile1" value="regression_X.tabular" ftype="tabular"/> | |
61 <param name="header1" value="true" /> | |
62 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/> | |
63 <param name="infile2" value="regression_y.tabular" ftype="tabular"/> | |
64 <param name="header2" value="true"/> | |
65 <param name="col2" value="1"/> | |
66 <output name="out_object" file="model_fit02" compare="sim_size" delta="10"/> | |
67 <output name="out_weights" file="model_fit02.h5" compare="sim_size" delta="10"/> | |
68 </test> | |
69 </tests> | |
70 <help> | |
71 <![CDATA[ | |
72 **What it does** | |
73 | |
74 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. | |
75 | |
76 The supported labeled dataset are the following: | |
77 | |
78 - tabular | |
79 | |
80 - sparse | |
81 | |
82 | |
83 **Output** | |
84 | |
85 - fitted model, a pickled python object in zip format. | |
86 - optional hdf5 file containing weights for deep learning models. | |
87 | |
88 ]]> | |
89 </help> | |
90 <expand macro="sklearn_citation"> | |
91 <expand macro="keras_citation"/> | |
92 <expand macro="selene_citation"/> | |
93 </expand> | |
94 </tool> |