view feature_selection.xml @ 2:5a06c81f044d draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 79fe42239dcf077b13f85cbcd6c6e30d7e1e4832

<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 sys
import json
import pandas
import pickle
import numpy as np
import sklearn.feature_selection
from sklearn import svm, linear_model, ensemble

@COLUMNS_FUNCTION@

input_json_path = sys.argv[1]
params = json.load(open(input_json_path, "r"))

## 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
    header = 'infer' if params["input_options"]["header1"] else None
    X, input_df = read_columns(
            "$input_options.infile1",
            "$input_options.col1",
            return_df = True,
            sep='\t',
            header=header,
            parse_dates=True
    )
else:
    X = mmread(open("$input_options.infile1", 'r'))

## Read labels
header = 'infer' if params["input_options"]["header2"] else None
y = read_columns(
        "$input_options.infile2",
        "$input_options.col2",
        sep='\t',
        header=header,
        parse_dates=True
)
y=y.ravel()

## Create feature selector
selector = params["feature_selection_algorithms"]["selected_algorithm"]
selector = getattr(sklearn.feature_selection, selector)
options = params["feature_selection_algorithms"]["options"]

if params['feature_selection_algorithms']['selected_algorithm'] == 'SelectFromModel':
    if not options['threshold'] or options['threshold'] == 'None':
        options['threshold'] = None
        if 'extra_estimator' in params['feature_selection_algorithms'] and params['feature_selection_algorithms']['extra_estimator']['has_estimator'] == 'no_load':
            fitted_estimator = pickle.load(open("params['feature_selection_algorithms']['extra_estimator']['fitted_estimator']", 'r'))
            new_selector = selector(fitted_estimator, prefit=True, **options)
        else:
            estimator=params["feature_selection_algorithms"]["estimator"]
            if params["feature_selection_algorithms"]["extra_estimator"]["has_estimator"]=='no':
                estimator=params["feature_selection_algorithms"]["extra_estimator"]["new_estimator"]
            estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'"))
            new_selector = selector(estimator, **options)
            new_selector.fit(X, y)

elif params['feature_selection_algorithms']['selected_algorithm'] in ['RFE', 'RFECV']:
    if 'scoring' in options and (not options['scoring'] or options['scoring'] == 'None'):
        options['scoring'] = None
    estimator=params["feature_selection_algorithms"]["estimator"]
    if params["feature_selection_algorithms"]["extra_estimator"]["has_estimator"]=='no':
        estimator=params["feature_selection_algorithms"]["extra_estimator"]["new_estimator"]
    estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'"))
    new_selector = selector(estimator, **options)
    new_selector.fit(X, y)

elif params['feature_selection_algorithms']['selected_algorithm'] == "VarianceThreshold":
    new_selector = selector(**options)
    new_selector.fit(X, y)

else:
    score_func = params["feature_selection_algorithms"]["score_func"]
    score_func = getattr(sklearn.feature_selection, score_func)
    new_selector = selector(score_func, **options)
    new_selector.fit(X, y)

## Transform to select features
selected_names = None
if "$select_methods.selected_method" == "fit_transform":
    res = new_selector.transform(X)
    if features_has_header:
        selected_names = input_df.columns[new_selector.get_support(indices=True)]
else:
    res = new_selector.get_support(params["select_methods"]["indices"])

res = pandas.DataFrame(res, columns = selected_names)
res.to_csv(path_or_buf="$outfile", sep='\t', index=False)


            ]]>
        </configfile>
    </configfiles>
    <inputs>
        <expand macro="feature_selection_all" />
        <expand macro="feature_selection_methods" />
        <expand macro="sl_mixed_input"/>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile"/>
    </outputs>
    <tests>
        <test>
            <param name="selected_algorithm" value="SelectFromModel"/>
            <param name="has_estimator" value="no"/>
            <param name="new_estimator" value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)"/>
            <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_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="has_estimator" value="no"/>
            <param name="new_estimator" value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)"/>
            <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_result08"/>
        </test>
        <test>
            <param name="selected_algorithm" value="RFECV"/>
            <param name="has_estimator" value="no"/>
            <param name="new_estimator" value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)"/>
            <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_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>
    </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"/>
</tool>