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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit daa111fcd8391d451aab39110251864fd120edf0
author bgruening
date Wed, 07 Aug 2024 10:20:17 +0000
parents 1c5dcef5ce0f
children
comparison
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3:1c5dcef5ce0f 4:f234e2e59d76
1 <tool id="plotly_ml_performance_plots" name="Plot confusion matrix, precision, recall and ROC and AUC curves" version="0.3" profile="22.05"> 1 <tool id="plotly_ml_performance_plots" name="Plot confusion matrix, precision, recall and ROC and AUC curves" version="0.4" profile="22.05">
2 <description>of tabular data</description> 2 <description>of tabular data</description>
3 <requirements> 3 <requirements>
4 <requirement type="package" version="0.10.0">galaxy-ml</requirement> 4 <requirement type="package" version="0.10.0">galaxy-ml</requirement>
5 </requirements> 5 </requirements>
6 <version_command>echo $version</version_command> 6 <version_command>echo $version</version_command>
16 <param name="infile_output" type="data" format="tabular" label="Select predicted data file :" help="Predicted data is a matrix (tabular) where each column is a feature and the last column contains the predicted class labels."/> 16 <param name="infile_output" type="data" format="tabular" label="Select predicted data file :" help="Predicted data is a matrix (tabular) where each column is a feature and the last column contains the predicted class labels."/>
17 <param name="infile_trained_model" type="data" format="h5mlm" label="Select trained model :" help="This file is a final model trained on training data."/> 17 <param name="infile_trained_model" type="data" format="h5mlm" label="Select trained model :" help="This file is a final model trained on training data."/>
18 </inputs> 18 </inputs>
19 19
20 <outputs> 20 <outputs>
21 <data name="output_confusion" format="html" from_work_dir="output_confusion.html" label="Confusion matrix of tabular data on ${on_string}"/> 21 <data name="output_confusion" format="png" from_work_dir="output_confusion.png" label="Confusion matrix of tabular data on ${on_string}"/>
22 <data name="output_prf" format="html" from_work_dir="output_prf.html" label="Precision, recall and f-score of tabular data on ${on_string}"/> 22 <data name="output_prf" format="html" from_work_dir="output_prf.html" label="Precision, recall and f-score of tabular data on ${on_string}"/>
23 <data name="output_roc" format="html" from_work_dir="output_roc.html" label="ROC and AUC curves of tabular data on ${on_string}"/> 23 <data name="output_roc" format="html" from_work_dir="output_roc.html" label="ROC and AUC curves of tabular data on ${on_string}"/>
24 </outputs> 24 </outputs>
25 25
26 <tests> 26 <tests>
28 <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/> 28 <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/>
29 <param name="infile_output" value="binary_prediction_sgd.tabular" ftype="tabular"/> 29 <param name="infile_output" value="binary_prediction_sgd.tabular" ftype="tabular"/>
30 <param name="infile_trained_model" value="model_binary_sgd.h5mlm" ftype="h5mlm"/> 30 <param name="infile_trained_model" value="model_binary_sgd.h5mlm" ftype="h5mlm"/>
31 <output name="output_confusion"> 31 <output name="output_confusion">
32 <assert_contents> 32 <assert_contents>
33 <has_size value="3486809" delta="10000" /> 33 <has_size value="31751" delta="1000" />
34 <has_text text="html" />
35 </assert_contents> 34 </assert_contents>
36 </output> 35 </output>
37 <output name="output_prf"> 36 <output name="output_prf">
38 <assert_contents> 37 <assert_contents>
39 <has_size value="3486974" delta="10000" /> 38 <has_size value="3486974" delta="10000" />
45 <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/> 44 <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/>
46 <param name="infile_output" value="prediction_binary_linearsvm.tabular" ftype="tabular"/> 45 <param name="infile_output" value="prediction_binary_linearsvm.tabular" ftype="tabular"/>
47 <param name="infile_trained_model" value="model_binary_linearsvm.h5mlm" ftype="h5mlm"/> 46 <param name="infile_trained_model" value="model_binary_linearsvm.h5mlm" ftype="h5mlm"/>
48 <output name="output_confusion"> 47 <output name="output_confusion">
49 <assert_contents> 48 <assert_contents>
50 <has_size value="3486810" delta="10000" /> 49 <has_size value="31983" delta="1000" />
51 <has_text text="html" />
52 </assert_contents> 50 </assert_contents>
53 </output> 51 </output>
54 <output name="output_prf"> 52 <output name="output_prf">
55 <assert_contents> 53 <assert_contents>
56 <has_size value="3486973" delta="10000" /> 54 <has_size value="3486973" delta="10000" />
68 <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/> 66 <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/>
69 <param name="infile_output" value="prediction_binary_rfc.tabular" ftype="tabular"/> 67 <param name="infile_output" value="prediction_binary_rfc.tabular" ftype="tabular"/>
70 <param name="infile_trained_model" value="model_binary_rfc.h5mlm" ftype="h5mlm"/> 68 <param name="infile_trained_model" value="model_binary_rfc.h5mlm" ftype="h5mlm"/>
71 <output name="output_confusion"> 69 <output name="output_confusion">
72 <assert_contents> 70 <assert_contents>
73 <has_size value="3486806" delta="10000" /> 71 <has_size value="34096" delta="1000" />
74 <has_text text="html" />
75 </assert_contents> 72 </assert_contents>
76 </output> 73 </output>
77 <output name="output_prf"> 74 <output name="output_prf">
78 <assert_contents> 75 <assert_contents>
79 <has_size value="3486883" delta="10000" /> 76 <has_size value="3486883" delta="10000" />
91 <param name="infile_input" value="binary_test_label_knn.tabular" ftype="tabular"/> 88 <param name="infile_input" value="binary_test_label_knn.tabular" ftype="tabular"/>
92 <param name="infile_output" value="prediction_binary_knn.tabular" ftype="tabular"/> 89 <param name="infile_output" value="prediction_binary_knn.tabular" ftype="tabular"/>
93 <param name="infile_trained_model" value="model_binary_knn.h5mlm" ftype="h5mlm"/> 90 <param name="infile_trained_model" value="model_binary_knn.h5mlm" ftype="h5mlm"/>
94 <output name="output_confusion"> 91 <output name="output_confusion">
95 <assert_contents> 92 <assert_contents>
96 <has_size value="3486856" delta="10000" /> 93 <has_size value="32398" delta="1000" />
97 <has_text text="html" />
98 </assert_contents> 94 </assert_contents>
99 </output> 95 </output>
100 <output name="output_prf"> 96 <output name="output_prf">
101 <assert_contents> 97 <assert_contents>
102 <has_size value="3486928" delta="10000" /> 98 <has_size value="3486928" delta="10000" />
114 <param name="infile_input" value="multi_test_label.tabular" ftype="tabular"/> 110 <param name="infile_input" value="multi_test_label.tabular" ftype="tabular"/>
115 <param name="infile_output" value="prediction_multi_lr.tabular" ftype="tabular"/> 111 <param name="infile_output" value="prediction_multi_lr.tabular" ftype="tabular"/>
116 <param name="infile_trained_model" value="model_multi_lr.h5mlm" ftype="h5mlm"/> 112 <param name="infile_trained_model" value="model_multi_lr.h5mlm" ftype="h5mlm"/>
117 <output name="output_confusion"> 113 <output name="output_confusion">
118 <assert_contents> 114 <assert_contents>
119 <has_size value="3486832" delta="10000" /> 115 <has_size value="34474" delta="1000" />
120 <has_text text="html" />
121 </assert_contents> 116 </assert_contents>
122 </output> 117 </output>
123 <output name="output_prf"> 118 <output name="output_prf">
124 <assert_contents> 119 <assert_contents>
125 <has_size value="3487035" delta="10000" /> 120 <has_size value="3487035" delta="10000" />