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comparison pycaret_predict.xml @ 0:1f20fe57fdee draft

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planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
author goeckslab
date Wed, 11 Dec 2024 04:59:43 +0000
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1 <tool id="pycaret_predict" name="PyCaret Predictor/Evaluator" version="@VERSION@" profile="@PROFILE@">
2 <description>predicts/evaluates your pycaret ML model on a dataset. </description>
3 <macros>
4 <import>pycaret_macros.xml</import>
5 </macros>
6 <expand macro="python_requirements" />
7 <command>
8 <![CDATA[
9 echo $target_feature &&
10 python $__tool_directory__/pycaret_predict.py --model_path $input_model --data_path $input_dataset --task $model_type
11 #if $target_feature
12 --target $target_feature
13 #end if
14 ]]>
15 </command>
16 <inputs>
17 <param name="input_model" type="data" format="h5" label="Model you want to use to predict/evaluate:" />
18 <param name="input_dataset" type="data" format="csv,tabular" label="Dataset you use to predict/evaluate" />
19 <param name="model_type" type="select" label="Task">
20 <option value="classification">classification</option>
21 <option value="regression">regression</option>
22 </param>
23 <param name="target_feature" multiple="false" type="data_column" use_header_names="true" data_ref="input_dataset" optional="true" label="Does your uploaded data include a target column? If so, please select the target column:" />
24 </inputs>
25 <outputs>
26 <data name="prediction" format="csv" from_work_dir="predictions.csv" label="${tool.name} prediction results on ${on_string}" />
27 <data name="report" format="html" from_work_dir="evaluation_report.html" label="${tool.name} evaluation report on ${on_string}">
28 <filter>target_feature</filter>
29 </data>
30 </outputs>
31 <tests>
32 <test expect_num_outputs="2">
33 <param name="input_model" value="expected_model_classification.h5" />
34 <param name="input_dataset" value="pcr.tsv" />
35 <param name="model_type" value="classification" />
36 <param name="target_feature" value="11" />
37 <output name="prediction" file="predictions_classification.csv" />
38 <output name="report" file="evaluation_report_classification.html" compare="sim_size" />
39 </test>
40 <test expect_num_outputs="2">
41 <param name="input_model" value="expected_model_regression.h5" />
42 <param name="input_dataset" value="auto-mpg.tsv" />
43 <param name="model_type" value="regression" />
44 <param name="target_feature" value="1" />
45 <output name="prediction" file="predictions_regression.csv" />
46 <output name="report" file="evaluation_report_regression.html" compare="sim_size" />
47 </test>
48 </tests>
49 <help>
50 This tool uses PyCaret to evaluate a machine learning model or do prediction.
51
52 **Outputs**:
53
54 - **prediction**: The prediction results on the dataset in a csv format.
55
56 - **report**: The evaluation report is generated in HTML format.
57 if you upload a dataset with a target column and select the target column in the target_feature input field.
58
59 </help>
60 <expand macro="macro_citations" />
61 </tool>