Mercurial > repos > goeckslab > pycaret_predict

comparison pycaret_predict.xml @ 6:a32ff7201629 draft default tip

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planemo upload for repository https://github.com/goeckslab/gleam commit 06c0da44ac93256dfb616a6b40276b5485a71e8e
author goeckslab
date Wed, 02 Jul 2025 19:00:03 +0000
parents afd0864d18b6
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5:c846405830eb 6:a32ff7201629
33 <param name="input_model" value="expected_model_classification.h5" /> 33 <param name="input_model" value="expected_model_classification.h5" />
34 <param name="input_dataset" value="pcr.tsv" /> 34 <param name="input_dataset" value="pcr.tsv" />
35 <param name="model_type" value="classification" /> 35 <param name="model_type" value="classification" />
36 <param name="target_feature" value="11" /> 36 <param name="target_feature" value="11" />
37 <output name="prediction" file="predictions_classification.csv" /> 37 <output name="prediction" file="predictions_classification.csv" />
38 <output name="report" file="evaluation_report_classification.html" compare="sim_size" /> 38 <output name="report">
39 <assert_contents>
40 <has_text text="Metrics" />
41 <has_text text="Plots" />
42 </assert_contents>
43 </output>
39 </test> 44 </test>
40 <test expect_num_outputs="2"> 45 <test expect_num_outputs="2">
41 <param name="input_model" value="expected_model_regression.h5" /> 46 <param name="input_model" value="expected_model_regression.h5" />
42 <param name="input_dataset" value="auto-mpg.tsv" /> 47 <param name="input_dataset" value="auto-mpg.tsv" />
43 <param name="model_type" value="regression" /> 48 <param name="model_type" value="regression" />
44 <param name="target_feature" value="1" /> 49 <param name="target_feature" value="1" />
45 <output name="prediction" file="predictions_regression.csv" /> 50 <output name="prediction" file="predictions_regression.csv" />
46 <output name="report" file="evaluation_report_regression.html" compare="sim_size" /> 51 <output name="report">
52 <assert_contents>
53 <has_text text="Metrics" />
54 <has_text text="Plots" />
55 </assert_contents>
56 </output>
47 </test> 57 </test>
48 </tests> 58 </tests>
49 <help> 59 <help>
50 This tool uses PyCaret to evaluate a machine learning model or do prediction. 60 This tool uses PyCaret to evaluate a machine learning model or do prediction.
51 61