Mercurial > repos > florianbegusch > qiime2_suite
comparison qiime2/qiime_sample-classifier_fit-regressor.xml @ 14:a0a8d77a991c draft
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author | florianbegusch |
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date | Thu, 03 Sep 2020 09:51:29 +0000 |
parents | f190567fe3f6 |
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13:887cd4ad8e16 | 14:a0a8d77a991c |
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1 <?xml version="1.0" ?> | 1 <?xml version="1.0" ?> |
2 <tool id="qiime_sample-classifier_fit-regressor" name="qiime sample-classifier fit-regressor" version="2019.7"> | 2 <tool id="qiime_sample-classifier_fit-regressor" name="qiime sample-classifier fit-regressor" |
3 <description> - Fit a supervised learning regressor.</description> | 3 version="2020.8"> |
4 <requirements> | 4 <description>Fit a supervised learning regressor.</description> |
5 <requirement type="package" version="2019.7">qiime2</requirement> | 5 <requirements> |
6 </requirements> | 6 <requirement type="package" version="2020.8">qiime2</requirement> |
7 <command><![CDATA[ | 7 </requirements> |
8 <command><![CDATA[ | |
8 qiime sample-classifier fit-regressor | 9 qiime sample-classifier fit-regressor |
9 | 10 |
10 --i-table=$itable | 11 --i-table=$itable |
11 --m-metadata-column="$mmetadatacolumn" | 12 # if $input_files_mmetadatafile: |
13 # def list_dict_to_string(list_dict): | |
14 # set $file_list = list_dict[0]['additional_input'].__getattr__('file_name') | |
15 # for d in list_dict[1:]: | |
16 # set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name') | |
17 # end for | |
18 # return $file_list | |
19 # end def | |
20 --m-metadata-file=$list_dict_to_string($input_files_mmetadatafile) | |
21 # end if | |
12 | 22 |
13 #if str($pstep): | 23 #if '__ob__' in str($mmetadatacolumn): |
14 --p-step=$pstep | 24 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__ob__', '[') |
25 #set $mmetadatacolumn = $mmetadatacolumn_temp | |
26 #end if | |
27 #if '__cb__' in str($mmetadatacolumn): | |
28 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__cb__', ']') | |
29 #set $mmetadatacolumn = $mmetadatacolumn_temp | |
30 #end if | |
31 #if 'X' in str($mmetadatacolumn): | |
32 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('X', '\\') | |
33 #set $mmetadatacolumn = $mmetadatacolumn_temp | |
34 #end if | |
35 #if '__sq__' in str($mmetadatacolumn): | |
36 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__sq__', "'") | |
37 #set $mmetadatacolumn = $mmetadatacolumn_temp | |
38 #end if | |
39 #if '__db__' in str($mmetadatacolumn): | |
40 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__db__', '"') | |
41 #set $mmetadatacolumn = $mmetadatacolumn_temp | |
15 #end if | 42 #end if |
16 | 43 |
17 #if str($pcv): | 44 --m-metadata-column=$mmetadatacolumn |
18 --p-cv=$pcv | 45 |
19 #end if | 46 |
47 --p-step=$pstep | |
48 | |
49 --p-cv=$pcv | |
20 | 50 |
21 #if str($prandomstate): | 51 #if str($prandomstate): |
22 --p-random-state="$prandomstate" | 52 --p-random-state=$prandomstate |
23 #end if | 53 #end if |
54 --p-n-jobs=$pnjobs | |
24 | 55 |
25 #set $pnjobs = '${GALAXY_SLOTS:-4}' | 56 --p-n-estimators=$pnestimators |
26 #if str($pnjobs): | |
27 --p-n-jobs="$pnjobs" | |
28 #end if | |
29 | |
30 | |
31 #if str($pnestimators): | |
32 --p-n-estimators=$pnestimators | |
33 #end if | |
34 | 57 |
35 #if str($pestimator) != 'None': | 58 #if str($pestimator) != 'None': |
36 --p-estimator=$pestimator | 59 --p-estimator=$pestimator |
37 #end if | 60 #end if |
38 | 61 |
39 #if $poptimizefeatureselection: | 62 #if $poptimizefeatureselection: |
40 --p-optimize-feature-selection | 63 --p-optimize-feature-selection |
41 #end if | 64 #end if |
43 #if $pparametertuning: | 66 #if $pparametertuning: |
44 --p-parameter-tuning | 67 --p-parameter-tuning |
45 #end if | 68 #end if |
46 | 69 |
47 #if str($pmissingsamples) != 'None': | 70 #if str($pmissingsamples) != 'None': |
48 --p-missing-samples=$pmissingsamples | 71 --p-missing-samples=$pmissingsamples |
49 #end if | 72 #end if |
50 | 73 |
74 --o-sample-estimator=osampleestimator | |
51 | 75 |
76 --o-feature-importance=ofeatureimportance | |
52 | 77 |
53 | 78 #if str($examples) != 'None': |
54 #if $metadatafile: | 79 --examples=$examples |
55 --m-metadata-file=$metadatafile | |
56 #end if | 80 #end if |
57 | 81 |
82 ; | |
83 cp ofeatureimportance.qza $ofeatureimportance | |
58 | 84 |
85 ]]></command> | |
86 <inputs> | |
87 <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]" name="itable" optional="False" type="data" /> | |
88 <repeat name="input_files_mmetadatafile" optional="True" title="--m-metadata-file"> | |
89 <param format="tabular,qza,no_unzip.zip" label="--m-metadata-file: METADATA" name="additional_input" optional="True" type="data" /> | |
90 </repeat> | |
91 <param label="--m-metadata-column: COLUMN MetadataColumn[Numeric] Numeric metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text" /> | |
92 <param exclude_min="True" label="--p-step: PROPORTION Range(0.0, 1.0, inclusive_start=False) If optimize-feature-selection is True, step is the percentage of features to remove at each iteration. [default: 0.05]" max="1.0" min="0.0" name="pstep" optional="True" type="float" value="0.05" /> | |
93 <param label="--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]" min="1" name="pcv" optional="True" type="integer" value="5" /> | |
94 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="False" type="text" /> | |
95 <param label="--p-n-estimators: INTEGER Range(1, None) Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting. [default: 100]" min="1" name="pnestimators" optional="True" type="integer" value="100" /> | |
96 <param label="--p-estimator: " name="pestimator" optional="True" type="select"> | |
97 <option selected="True" value="None">Selection is Optional</option> | |
98 <option value="RandomForestRegressor">RandomForestRegressor</option> | |
99 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option> | |
100 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option> | |
101 <option value="AdaBoostRegressor">AdaBoostRegressor</option> | |
102 <option value="ElasticNet">ElasticNet</option> | |
103 <option value="Ridge">Ridge</option> | |
104 <option value="Lasso">Lasso</option> | |
105 <option value="KNeighborsRegressor">KNeighborsRegressor</option> | |
106 <option value="LinearSVR">LinearSVR</option> | |
107 <option value="SVR">SVR</option> | |
108 </param> | |
109 <param label="--p-optimize-feature-selection: --p-optimize-feature-selection: / --p-no-optimize-feature-selection Automatically optimize input feature selection using recursive feature elimination. [default: False]" name="poptimizefeatureselection" selected="False" type="boolean" /> | |
110 <param label="--p-parameter-tuning: --p-parameter-tuning: / --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]" name="pparametertuning" selected="False" type="boolean" /> | |
111 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select"> | |
112 <option selected="True" value="None">Selection is Optional</option> | |
113 <option value="error">error</option> | |
114 <option value="ignore">ignore</option> | |
115 </param> | |
116 <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" /> | |
117 | |
118 </inputs> | |
59 | 119 |
60 --o-sample-estimator=osampleestimator | 120 <outputs> |
61 --o-feature-importance=ofeatureimportance | 121 <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator" /> |
62 ; | 122 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance" /> |
63 cp osampleestimator.qza $osampleestimator; | 123 |
64 cp ofeatureimportance.qza $ofeatureimportance | 124 </outputs> |
65 ]]></command> | |
66 <inputs> | |
67 <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]" name="itable" optional="False" type="data"/> | |
68 <param label="--m-metadata-column: COLUMN MetadataColumn[Numeric] Numeric metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text"/> | |
69 <param label="--p-step: PROPORTION Range(0.0, 1.0, inclusive_start=False) If optimize-feature-selection is True, step is the percentage of features to remove at each iteration. [default: 0.05]" name="pstep" optional="True" type="float" value="0.05" min="0" max="1" exclusive_start="True"/> | |
70 <param label="--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]" name="pcv" optional="True" type="integer" value="5" min="1"/> | |
71 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="True" type="integer"/> | |
72 <param label="--p-n-estimators: INTEGER Range(1, None) Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting. [default: 100]" name="pnestimators" optional="True" type="integer" value="100" min="1"/> | |
73 <param label="--p-estimator: " name="pestimator" optional="True" type="select"> | |
74 <option selected="True" value="None">Selection is Optional</option> | |
75 <option value="RandomForestRegressor">RandomForestRegressor</option> | |
76 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option> | |
77 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option> | |
78 <option value="AdaBoostRegressor">AdaBoostRegressor</option> | |
79 <option value="ElasticNet">ElasticNet</option> | |
80 <option value="Ridge">Ridge</option> | |
81 <option value="Lasso">Lasso</option> | |
82 <option value="KNeighborsRegressor">KNeighborsRegressor</option> | |
83 <option value="LinearSVR">LinearSVR</option> | |
84 <option value="SVR">SVR</option> | |
85 </param> | |
86 <param label="--p-optimize-feature-selection: --p-no-optimize-feature-selection Automatically optimize input feature selection using recursive feature elimination. [default: False]" name="poptimizefeatureselection" selected="False" type="boolean"/> | |
87 <param label="--p-parameter-tuning: --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]" name="pparametertuning" selected="False" type="boolean"/> | |
88 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select"> | |
89 <option selected="True" value="None">Selection is Optional</option> | |
90 <option value="error">error</option> | |
91 <option value="ignore">ignore</option> | |
92 </param> | |
93 | 125 |
94 <param label="--m-metadata-file METADATA" name="metadatafile" type="data" format="tabular,qza,no_unzip.zip" /> | 126 <help><![CDATA[ |
95 | |
96 </inputs> | |
97 <outputs> | |
98 <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator"/> | |
99 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance"/> | |
100 </outputs> | |
101 <help><![CDATA[ | |
102 Fit a supervised learning regressor. | 127 Fit a supervised learning regressor. |
103 #################################### | 128 ############################################################### |
104 | 129 |
105 Fit a supervised learning regressor. Outputs the fit estimator (for | 130 Fit a supervised learning regressor. Outputs the fit estimator (for |
106 prediction of test samples and/or unknown samples) and the relative | 131 prediction of test samples and/or unknown samples) and the relative |
107 importance of each feature for model accuracy. Optionally use k-fold cross- | 132 importance of each feature for model accuracy. Optionally use k-fold cross- |
108 validation for automatic recursive feature elimination and hyperparameter | 133 validation for automatic recursive feature elimination and hyperparameter |
120 features to remove at each iteration. | 145 features to remove at each iteration. |
121 cv : Int % Range(1, None), optional | 146 cv : Int % Range(1, None), optional |
122 Number of k-fold cross-validations to perform. | 147 Number of k-fold cross-validations to perform. |
123 random_state : Int, optional | 148 random_state : Int, optional |
124 Seed used by random number generator. | 149 Seed used by random number generator. |
150 n_jobs : Int, optional | |
151 Number of jobs to run in parallel. | |
125 n_estimators : Int % Range(1, None), optional | 152 n_estimators : Int % Range(1, None), optional |
126 Number of trees to grow for estimation. More trees will improve | 153 Number of trees to grow for estimation. More trees will improve |
127 predictive accuracy up to a threshold level, but will also increase | 154 predictive accuracy up to a threshold level, but will also increase |
128 time and memory requirements. This parameter only affects ensemble | 155 time and memory requirements. This parameter only affects ensemble |
129 estimators, such as Random Forest, AdaBoost, ExtraTrees, and | 156 estimators, such as Random Forest, AdaBoost, ExtraTrees, and |
142 retained. | 169 retained. |
143 | 170 |
144 Returns | 171 Returns |
145 ------- | 172 ------- |
146 sample_estimator : SampleEstimator[Regressor] | 173 sample_estimator : SampleEstimator[Regressor] |
147 \ | |
148 feature_importance : FeatureData[Importance] | 174 feature_importance : FeatureData[Importance] |
149 Importance of each input feature to model accuracy. | 175 Importance of each input feature to model accuracy. |
150 ]]></help> | 176 ]]></help> |
151 <macros> | 177 <macros> |
152 <import>qiime_citation.xml</import> | 178 <import>qiime_citation.xml</import> |
153 </macros> | 179 </macros> |
154 <expand macro="qiime_citation"/> | 180 <expand macro="qiime_citation"/> |
155 </tool> | 181 </tool> |