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comparison qiime2/qiime_sample-classifier_classify-samples.xml @ 0:51b9b6b57732 draft

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author florianbegusch
date Thu, 24 May 2018 05:21:07 -0400
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1 <?xml version="1.0" ?>
2 <tool id="qiime_sample-classifier_classify-samples" name="qiime sample-classifier classify-samples" version="2018.4">
3 <description> - Supervised learning classifier.</description>
4 <requirements>
5 <requirement type="package" version="2018.4">qiime2</requirement>
6 </requirements>
7 <command>
8 <![CDATA[
9 qiime sample-classifier classify-samples --i-table=$itable
10
11 #def list_dict_to_string(list_dict):
12 #set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
13 #for d in list_dict[1:]:
14 #set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name')
15 #end for
16 #return $file_list
17 #end def
18
19 --m-metadata-file=$list_dict_to_string($input_files_mmetadatafile) --m-metadata-column="$mmetadatacolumn"
20 #if $pstep:
21 --p-step=$pstep
22 #end if
23
24 #if $poptimizefeatureselection:
25 --p-optimize-feature-selection
26 #else
27 --p-no-optimize-feature-selection
28 #end if
29
30 #if $ptestsize:
31 --p-test-size=$ptestsize
32 #end if
33
34 #if str($cmdconfig) != 'None':
35 --cmd-config=$cmdconfig
36 #end if
37 --o-visualization=ovisualization
38 #if str($pestimator) != 'None':
39 --p-estimator=$pestimator
40 #end if
41
42 #if $pnestimators:
43 --p-n-estimators=$pnestimators
44 #end if
45
46 #set $pnjobs = '${GALAXY_SLOTS:-4}'
47
48 #if str($pnjobs):
49 --p-n-jobs="$pnjobs"
50 #end if
51
52
53 #if $pcv:
54 --p-cv=$pcv
55 #end if
56
57 #if str($ppalette) != 'None':
58 --p-palette=$ppalette
59 #end if
60
61 #if $pparametertuning:
62 --p-parameter-tuning
63 #else
64 --p-no-parameter-tuning
65 #end if
66
67 #if str($prandomstate):
68 --p-random-state="$prandomstate"
69 #end if
70 ;
71 qiime tools export ovisualization.qzv --output-dir out && mkdir -p '$ovisualization.files_path'
72 && cp -r out/* '$ovisualization.files_path'
73 && mv '$ovisualization.files_path/index.html' '$ovisualization'
74 ]]>
75 </command>
76 <inputs>
77 <param format="qza,no_unzip.zip" label="--i-table: FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]" name="itable" optional="False" type="data"/>
78
79 <repeat name="input_files_mmetadatafile" optional="False" title="--m-metadata-file">
80 <param label="--m-metadata-file: Metadata file or artifact viewable as metadata. This option may be supplied multiple times to merge metadata. [required]" name="additional_input" type="data" format="tabular,qza,no_unzip.zip" />
81 </repeat>
82 <param label="--m-metadata-column: MetadataColumn[Categorical] Column from metadata file or artifact viewable as metadata. Categorical metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text"/>
83
84 <param label="--p-test-size: Fraction of input samples to exclude from training set and use for classifier testing. [default: 0.2]" name="ptestsize" optional="True" type="float" value="0.2"/>
85 <param label="--p-step: 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"/>
86 <param label="--p-cv: Number of k-fold cross-validations to perform. [default: 5]" name="pcv" optional="True" type="integer" value="5"/>
87
88 <param label="--p-random-state: Seed used by random number generator. [optional]" name="prandomstate" optional="True" type="text"/>
89
90 <param label="--p-n-estimators: 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"/>
91 <param label="--p-estimator: Estimator method to use for sample
92 prediction. [default:
93 RandomForestClassifier]" name="pestimator" optional="True" type="select">
94 <option selected="True" value="None">Selection is Optional</option>
95 <option value="LinearSVC">LinearSVC</option>
96 <option value="RandomForestClassifier">RandomForestClassifier</option>
97 <option value="SVC">SVC</option>
98 <option value="AdaBoostClassifier">AdaBoostClassifier</option>
99 <option value="GradientBoostingClassifier">GradientBoostingClassifier</option>
100 <option value="ExtraTreesClassifier">ExtraTreesClassifier</option>
101 <option value="KNeighborsClassifier">KNeighborsClassifier</option>
102 </param>
103
104 <param label="--p-optimize-feature-selection: --p-no-optimize-feature-selection Automatically optimize input feature selection using recursive feature elimination. [default: False]" name="poptimizefeatureselection" checked="False" type="boolean"/>
105
106 <param label="--p-parameter-tuning: --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]" name="pparametertuning" checked="False" type="boolean"/>
107
108 <param label="--p-palette: The color palette to use for plotting.
109 [default: sirocco]" name="ppalette" optional="True" type="select">
110 <option selected="True" value="None">Selection is Optional</option>
111 <option value="plasma">plasma</option>
112 <option value="inferno">inferno</option>
113 <option value="BluePurple">BluePurple</option>
114 <option value="summer">summer</option>
115 <option value="magma">magma</option>
116 <option value="drifting">drifting</option>
117 <option value="sirocco">sirocco</option>
118 <option value="enigma">enigma</option>
119 <option value="YellowOrangeRed">YellowOrangeRed</option>
120 <option value="GreenBlue">GreenBlue</option>
121 <option value="deepblue">deepblue</option>
122 <option value="ambition">ambition</option>
123 <option value="melancholy">melancholy</option>
124 <option value="PurpleRed">PurpleRed</option>
125 <option value="greyscale">greyscale</option>
126 <option value="dandelions">dandelions</option>
127 <option value="YellowOrangeBrown">YellowOrangeBrown</option>
128 <option value="verve">verve</option>
129 <option value="viridis">viridis</option>
130 <option value="OrangeRed">OrangeRed</option>
131 <option value="mysteriousstains">mysteriousstains</option>
132 <option value="spectre">spectre</option>
133 <option value="solano">solano</option>
134 <option value="daydream">daydream</option>
135 <option value="eros">eros</option>
136 <option value="RedPurple">RedPurple</option>
137 <option value="PurpleBlue">PurpleBlue</option>
138 <option value="YellowGreen">YellowGreen</option>
139 <option value="copper">copper</option>
140 <option value="navarro">navarro</option>
141 </param>
142
143 <param label="--cmd-config: Use config file for command options" name="cmdconfig" optional="True" type="data"/>
144 </inputs>
145 <outputs>
146 <data format="html" label="${tool.name} on ${on_string}: visualization.qzv" name="ovisualization"/>
147 </outputs>
148 <help>
149 <![CDATA[
150 Supervised learning classifier.
151 --------------------------------
152
153 Predicts a categorical sample metadata column using a supervised learning
154 classifier. Splits input data into training and test sets. The training set
155 is used to train and test the estimator using a stratified k-fold cross-
156 validation scheme. This includes optional steps for automated feature
157 extraction and hyperparameter optimization. The test set validates
158 classification accuracy of the optimized estimator. Outputs classification
159 results for test set. For more details on the learning algorithm, see
160 http://scikit-learn.org/stable/supervised_learning.html
161
162 Parameters
163 ----------
164 table : FeatureTable[Frequency]
165 Feature table containing all features that should be used for target
166 prediction.
167 metadata : MetadataColumn[Categorical]
168 Categorical metadata column to use as prediction target.
169 test_size : Float % Range(0.0, 1.0, inclusive_start=False), optional
170 Fraction of input samples to exclude from training set and use for
171 classifier testing.
172 step : Float % Range(0.0, 1.0, inclusive_start=False), optional
173 If optimize_feature_selection is True, step is the percentage of
174 features to remove at each iteration.
175 cv : Int % Range(1, None), optional
176 Number of k-fold cross-validations to perform.
177 random_state : Int, optional
178 Seed used by random number generator.
179 n_estimators : Int % Range(1, None), optional
180 Number of trees to grow for estimation. More trees will improve
181 predictive accuracy up to a threshold level, but will also increase
182 time and memory requirements. This parameter only affects ensemble
183 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
184 GradientBoosting.
185 estimator : Str % Choices({'AdaBoostClassifier', 'ExtraTreesClassifier', 'GradientBoostingClassifier', 'KNeighborsClassifier', 'LinearSVC', 'RandomForestClassifier', 'SVC'}), optional
186 Estimator method to use for sample prediction.
187 optimize_feature_selection : Bool, optional
188 Automatically optimize input feature selection using recursive feature
189 elimination.
190 parameter_tuning : Bool, optional
191 Automatically tune hyperparameters using random grid search.
192 palette : Str % Choices({'BluePurple', 'GreenBlue', 'OrangeRed', 'PurpleBlue', 'PurpleRed', 'RedPurple', 'YellowGreen', 'YellowOrangeBrown', 'YellowOrangeRed', 'ambition', 'copper', 'dandelions', 'daydream', 'deepblue', 'drifting', 'enigma', 'eros', 'greyscale', 'inferno', 'magma', 'melancholy', 'mysteriousstains', 'navarro', 'plasma', 'sirocco', 'solano', 'spectre', 'summer', 'verve', 'viridis'}), optional
193 The color palette to use for plotting.
194
195 Returns
196 -------
197 visualization : Visualization
198 \
199 ]]>
200 </help>
201 <macros>
202 <import>qiime_citation.xml</import>
203 </macros>
204 <expand macro="qiime_citation" />
205 </tool>