annotate feature_selection.xml @ 17:2bbbac61e48d draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d

<tool id="sklearn_feature_selection" name="Feature Selection" version="@VERSION@.1">
    <description>module, including univariate filter selection methods and recursive feature elimination algorithm</description>
    <macros>
        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        python "$feature_selection_script" '$inputs'
        ]]>
    </command>
    <configfiles>
        <inputs name="inputs" />
        <configfile name="feature_selection_script">
            <![CDATA[
import json
import sklearn.feature_selection

with open('$__tool_directory__/sk_whitelist.json', 'r') as f:
    sk_whitelist = json.load(f)
exec(open('$__tool_directory__/utils.py').read(), globals())

warnings.simplefilter('ignore')

safe_eval = SafeEval()

input_json_path = sys.argv[1]
with open(input_json_path, 'r') as param_handler:
    params = json.load(param_handler)

#handle cheetah
#if $fs_algorithm_selector.selected_algorithm == 'SelectFromModel'\
        and $fs_algorithm_selector.model_inputter.input_mode == 'prefitted':
params['fs_algorithm_selector']['model_inputter']['fitted_estimator'] =\
        '$fs_algorithm_selector.model_inputter.fitted_estimator'
#end if

#if $fs_algorithm_selector.selected_algorithm == 'SelectFromModel'\
        and $fs_algorithm_selector.model_inputter.input_mode == 'new'\
        and $fs_algorithm_selector.model_inputter.estimator_selector.selected_module == 'customer_estimator':
params['fs_algorithm_selector']['model_inputter']['estimator_selector']['c_estimator'] =\
        '$fs_algorithm_selector.model_inputter.estimator_selector.c_estimator'
#end if

#if $fs_algorithm_selector.selected_algorithm in ['RFE', 'RFECV']\
        and $fs_algorithm_selector.estimator_selector.selected_module == 'customer_estimator':
params['fs_algorithm_selector']['estimator_selector']['c_estimator'] =\
        '$fs_algorithm_selector.estimator_selector.c_estimator'
#end if

# Read features
features_has_header = params['input_options']['header1']
input_type = params['input_options']['selected_input']
if input_type == 'tabular':
    header = 'infer' if features_has_header else None
    column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option']
    if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
        c = params['input_options']['column_selector_options_1']['col1']
    else:
        c = None
    X, input_df = read_columns(
            '$input_options.infile1',
            c = c,
            c_option = column_option,
            return_df = True,
            sep='\t',
            header=header,
            parse_dates=True
    )
else:
    X = mmread('$input_options.infile1')

# Read labels
header = 'infer' if params['input_options']['header2'] else None
column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2']
if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
    c = params['input_options']['column_selector_options_2']['col2']
else:
    c = None
y = read_columns(
        '$input_options.infile2',
        c = c,
        c_option = column_option,
        sep='\t',
        header=header,
        parse_dates=True
)
y=y.ravel()

# Create feature selector
new_selector = feature_selector(params['fs_algorithm_selector'])
if params['fs_algorithm_selector']['selected_algorithm'] != 'SelectFromModel'\
        or params['fs_algorithm_selector']['model_inputter']['input_mode'] != 'prefitted' :
    new_selector.fit(X, y)

## Transform to select features
selected_names = None

res = new_selector.transform(X)
if features_has_header:
    selected_names = input_df.columns[new_selector.get_support(indices=True)]
res = pandas.DataFrame(res, columns = selected_names)
res.to_csv(path_or_buf='$outfile', sep='\t', index=False)

#if $save:
with open('$outfile_selector', 'wb') as output_handler:
    pickle.dump(new_selector, output_handler, pickle.HIGHEST_PROTOCOL)
#end if

            ]]>
        </configfile>
    </configfiles>
    <inputs>
        <expand macro="feature_selection_fs"/>
        <param name="save" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Save the fitted selector?"/>
        <expand macro="sl_mixed_input"/>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile" />
        <data format="zip" name="outfile_selector" label="${fs_algorithm_selector.selected_algorithm}">
            <filter>save</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="selected_algorithm" value="SelectFromModel"/>
            <param name="input_mode" value="new"/>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="n_estimators=10, random_state=10"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="header1" value="false"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="6"/>
            <param name="header2" value="false"/>
            <output name="outfile" file="feature_selection_result01"/>
        </test>
        <test>
            <param name="selected_algorithm" value="GenericUnivariateSelect"/>
            <param name="param" value="20"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result02"/>
        </test>
        <test>
            <param name="selected_algorithm" value="SelectPercentile"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result03"/>
        </test>
        <test>
            <param name="selected_algorithm" value="SelectKBest"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result04"/>
        </test>
        <test>
            <param name="selected_algorithm" value="SelectFpr"/>
            <param name="alpha" value="0.05"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result05"/>
        </test>
        <test>
            <param name="selected_algorithm" value="SelectFdr"/>
            <param name="alpha" value="0.05"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result06"/>
        </test>
        <test>
            <param name="selected_algorithm" value="SelectFwe"/>
            <param name="alpha" value="0.05"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result07"/>
        </test>
        <test>
            <param name="selected_algorithm" value="RFE"/>
            <param name="input_mode" value="new"/>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="n_estimators=10, random_state=10"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="header1" value="false"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="6"/>
            <param name="header2" value="false"/>
            <output name="outfile" file="feature_selection_result08"/>
        </test>
        <test>
            <param name="selected_algorithm" value="RFECV"/>
            <param name="input_mode" value="new"/>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="n_estimators=10, random_state=10"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="header1" value="false"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="6"/>
            <param name="header2" value="false"/>
            <output name="outfile" file="feature_selection_result09"/>
        </test>
        <test>
            <param name="selected_algorithm" value="VarianceThreshold"/>
            <param name="threshold" value="0.1"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result10"/>
        </test>
        <test>
            <param name="selected_algorithm" value="SelectKBest"/>
            <param name="k" value="3"/>
            <param name="infile1" value="test3.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="selected_column_selector_option" value="all_but_by_header_name"/>
            <param name="col1" value="target"/>
            <param name="infile2" value="test3.tabular" ftype="tabular"/>
            <param name="header2" value="True"/>
            <param name="selected_column_selector_option2" value="by_header_name"/>
            <param name="col2" value="target"/>
            <output name="outfile" file="feature_selection_result11"/>
        </test>
        <test>
            <param name="selected_algorithm" value="SelectFromModel"/>
            <param name="input_mode" value="prefitted"/>
            <param name="fitted_estimator" value="rfr_model01" ftype="zip"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="header1" value="false"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="false"/>
            <output name="outfile" file="feature_selection_result12"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**
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
sklearn.metrics package.
For information about classification metric functions and their parameter settings please refer to `Scikit-learn classification metrics`_.

.. _`Scikit-learn classification metrics`: http://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics
        ]]>
    </help>
    <expand macro="sklearn_citation">
        <expand macro="skrebate_citation"/>
        <expand macro="xgboost_citation"/>
    </expand>
</tool>