Mercurial > repos > bgruening > sklearn_feature_selection

comparison feature_selection.xml @ 6:b0d554b38770 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit cd4a8b019168acd5a513c57a1b1f380622f230f6
author bgruening
date Sun, 01 Jul 2018 03:20:10 -0400
parents 2d681d0f9393
children 537c6763c018
comparison
equal deleted inserted replaced
5:2d681d0f9393 6:b0d554b38770
34 features_has_header = params["input_options"]["header1"] 34 features_has_header = params["input_options"]["header1"]
35 input_type = params["input_options"]["selected_input"] 35 input_type = params["input_options"]["selected_input"]
36 if input_type=="tabular": 36 if input_type=="tabular":
37 header = 'infer' if features_has_header else None 37 header = 'infer' if features_has_header else None
38 column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"] 38 column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
39 if column_option in ["by_index_number", "all_but_by_index_number"]: 39 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
40 c = params["input_options"]["column_selector_options_1"]["col1"] 40 c = params["input_options"]["column_selector_options_1"]["col1"]
41 else: 41 else:
42 c = None 42 c = None
43 X, input_df = read_columns( 43 X, input_df = read_columns(
44 "$input_options.infile1", 44 "$input_options.infile1",
53 X = mmread(open("$input_options.infile1", 'r')) 53 X = mmread(open("$input_options.infile1", 'r'))
54 54
55 ## Read labels 55 ## Read labels
56 header = 'infer' if params["input_options"]["header2"] else None 56 header = 'infer' if params["input_options"]["header2"] else None
57 column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"] 57 column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
58 if column_option in ["by_index_number", "all_but_by_index_number"]: 58 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
59 c = params["input_options"]["column_selector_options_2"]["col2"] 59 c = params["input_options"]["column_selector_options_2"]["col2"]
60 else: 60 else:
61 c = None 61 c = None
62 y = read_columns( 62 y = read_columns(
63 "$input_options.infile2", 63 "$input_options.infile2",
210 <param name="infile2" value="regression_y.tabular" ftype="tabular"/> 210 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
211 <param name="col2" value="1"/> 211 <param name="col2" value="1"/>
212 <param name="header2" value="True"/> 212 <param name="header2" value="True"/>
213 <output name="outfile" file="feature_selection_result10"/> 213 <output name="outfile" file="feature_selection_result10"/>
214 </test> 214 </test>
215 <test>
216 <param name="selected_algorithm" value="SelectKBest"/>
217 <param name="k" value="3"/>
218 <param name="infile1" value="test3.tabular" ftype="tabular"/>
219 <param name="header1" value="True"/>
220 <param name="selected_column_selector_option" value="all_but_by_header_name"/>
221 <param name="col1" value="target"/>
222 <param name="infile2" value="test3.tabular" ftype="tabular"/>
223 <param name="header2" value="True"/>
224 <param name="selected_column_selector_option2" value="by_header_name"/>
225 <param name="col2" value="target"/>
226 <output name="outfile" file="feature_selection_result11"/>
227 </test>
215 </tests> 228 </tests>
216 <help> 229 <help>
217 <![CDATA[ 230 <![CDATA[
218 **What it does** 231 **What it does**
219 This tool provides several loss, score, and utility functions to measure classification performance. Some metrics might require probability estimates of the positive class, confidence values, or binary decisions values. This tool is based on 232 This tool provides several loss, score, and utility functions to measure classification performance. Some metrics might require probability estimates of the positive class, confidence values, or binary decisions values. This tool is based on