view regression_metrics.xml @ 2:09a0357ce462 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 4ed8c4f6ef9ece81797a398b17a99bbaf49a6978
author bgruening
date Wed, 30 May 2018 08:26:25 -0400
parents e1a494495d9f
children 8437a2320171
line wrap: on
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<tool id="sklearn_regression_metrics" name="Calculate metrics" version="@VERSION@">
    <description>for regression performance</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 "$regression_metrics_script" '$inputs'
        ]]>
    </command>
    <configfiles>
        <inputs name="inputs" />
        <configfile name="regression_metrics_script">
            <![CDATA[
import sys
import json
import pandas
import numpy as np
from sklearn import metrics

@COLUMNS_FUNCTION@

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

header='infer' if params["regression_metrics"]["header1"] else None
y_t = read_columns(
        "$regression_metrics.infile1",
        "$regression_metrics.col1",
        sep='\t',
        header=header,
        parse_dates=True
)

header='infer' if params["regression_metrics"]["header2"] else None
y_p = read_columns(
        "$regression_metrics.infile2",
        "$regression_metrics.col2",
        sep='\t',
        header=header,
        parse_dates=True
)

options = params["regression_metrics"].get("options", {})
if options and options.get('average', '') == 'None':
    options['average'] = None
metric = params["regression_metrics"]["selected_metric"]
metric_function = getattr(metrics, metric)
res = metric_function(y_t,y_p,**options)
res= format(res, '.4f')
with open("$outfile", 'w+') as out_file:
    out_file.write( metric + ' : ' + '\n' + str(res) + '\n' )

            ]]>
        </configfile>
    </configfiles>
    <inputs>
        <conditional name="regression_metrics">
            <param name="selected_metric" type="select" label="Metrics">
                <option value="explained_variance_score" selected="true">explained_variance_score - Explained variance regression score function</option>
                <option value="mean_absolute_error">mean_absolute_error - Mean absolute error regression loss</option>
                <option value="mean_squared_error">mean_squared_error - Mean squared error regression loss</option>
                <option value="mean_squared_log_error">mean_squared_log_error - Mean squared logarithmic error regression loss</option>
                <option value="median_absolute_error">median_absolute_error - Median absolute error regression loss</option>
                <option value="r2_score">r2_score - R^2 (coefficient of determination) regression score function</option>
            </param>
            <when value="explained_variance_score">
                <expand macro="clf_inputs"/>
                <!--section name="options" title="Advanced Options" expanded="False">
                    <!- -sample_weight- ->
                    <!- -multioutput- ->
                </section-->
            </when>
            <when value="mean_absolute_error">
                <expand macro="clf_inputs"/>
                <!--section name="options" title="Advanced Options" expanded="False">
                    <!- -sample_weight- ->
                    <!- -multioutput- ->
                </section-->
            </when>
            <when value="mean_squared_error">
                <expand macro="clf_inputs"/>
                <!--section name="options" title="Advanced Options" expanded="False">
                    <!- -sample_weight- ->
                    <!- -multioutput- ->
                </section-->
            </when>
            <when value="mean_squared_log_error">
                <expand macro="clf_inputs"/>
                <!--section name="options" title="Advanced Options" expanded="False">
                    <!- -sample_weight- ->
                    <!- -multioutput- ->
                </section-->
            </when>
            <when value="median_absolute_error">
                <expand macro="clf_inputs"/>
            </when>
            <when value="r2_score">
                <expand macro="clf_inputs"/>
                <!--section name="options" title="Advanced Options" expanded="False">
                    <!- -sample_weight- ->
                    <!- -multioutput- ->
                </section-->
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="txt" name="outfile"/>
    </outputs>
    <tests>
        <test>
            <param name="selected_metric" value="explained_variance_score"/>
            <param name="infile1" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1"/>
            <param name="infile2" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header2" value="True"/>
            <param name="col2" value="2"/>
            <output name="outfile" file="regression_metrics_result01"/>
        </test>
        <test>
            <param name="selected_metric" value="mean_absolute_error"/>
            <param name="infile1" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1"/>
            <param name="infile2" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header2" value="True"/>
            <param name="col2" value="2"/>
            <output name="outfile" file="regression_metrics_result02"/>
        </test>
        <test>
            <param name="selected_metric" value="mean_squared_error"/>
            <param name="infile1" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1"/>
            <param name="infile2" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header2" value="True"/>
            <param name="col2" value="2"/>
            <output name="outfile" file="regression_metrics_result03"/>
        </test>
        <test>
            <param name="selected_metric" value="mean_squared_log_error"/>
            <param name="infile1" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1"/>
            <param name="infile2" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header2" value="True"/>
            <param name="col2" value="2"/>
            <output name="outfile" file="regression_metrics_result04"/>
        </test>
        <test>
            <param name="selected_metric" value="median_absolute_error"/>
            <param name="infile1" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1"/>
            <param name="infile2" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header2" value="True"/>
            <param name="col2" value="2"/>
            <output name="outfile" file="regression_metrics_result05"/>
        </test>
        <test>
            <param name="selected_metric" value="r2_score"/>
            <param name="infile1" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1"/>
            <param name="infile2" value="regression_test_y.tabular" ftype="tabular"/>
            <param name="header2" value="True"/>
            <param name="col2" value="2"/>
            <output name="outfile" file="regression_metrics_result06"/>
        </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>