comparison model_validation.xml @ 24:a5aed87b2cc0 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"

23:56f6ebf69ddc

<tool id="sklearn_model_validation" name="Model Validation" version="@VERSION@">
    <description>evaluates estimator performances without changing parameters</description>
    <macros>
        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>

            <![CDATA[
import imblearn
import joblib
import json
import numpy as np
import pandas as pd
import pickle
import pprint
import skrebate
import sys

from galaxy_ml.model_validations import _fit_and_score


setattr(_validation, '_fit_and_score', _fit_and_score)

N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
CACHE_DIR = './cached'
ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
                     'CSVLogger', 'None')

warnings.filterwarnings('ignore')

            </param>
            <when value="cross_validate">
                <section name="options" title="Other Options" expanded="false">
                    <expand macro="scoring_selection"/>
                    <expand macro="model_validation_common_options"/>
                    <!--param argument="return_train_score" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="Whether to include train scores."/> -->
                    <!--param argument="return_estimator" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to return the estimators fitted on each split."/> -->
                    <!--param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/> -->
                    <!--fit_params-->
                    <expand macro="pre_dispatch"/>
                </section>

    </outputs>
    <tests>
        <test>
            <param name="infile_estimator" value="pipeline02"/>
            <param name="selected_function" value="cross_validate"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="6"/>
            <output name="outfile">

24:a5aed87b2cc0

<tool id="sklearn_model_validation" name="Model Validation" version="@VERSION@">
    <description>includes cross_validate, cross_val_predict, learning_curve, and more</description>
    <macros>
        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>

            <![CDATA[
import imblearn
import joblib
import json
import numpy as np
import os
import pandas as pd
import pickle
import pprint
import skrebate
import sys

from galaxy_ml.model_validations import _fit_and_score


setattr(_validation, '_fit_and_score', _fit_and_score)

N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
CACHE_DIR = os.path.join(os.getcwd(), 'cached')
del os
ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
                     'CSVLogger', 'None')

warnings.filterwarnings('ignore')

            </param>
            <when value="cross_validate">
                <section name="options" title="Other Options" expanded="false">
                    <expand macro="scoring_selection"/>
                    <expand macro="model_validation_common_options"/>
                    <param argument="return_train_score" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to include train scores."/>
                    <!--param argument="return_estimator" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to return the estimators fitted on each split."/> -->
                    <!--param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/> -->
                    <!--fit_params-->
                    <expand macro="pre_dispatch"/>
                </section>

    </outputs>
    <tests>
        <test>
            <param name="infile_estimator" value="pipeline02"/>
            <param name="selected_function" value="cross_validate"/>
            <param name="return_train_score" value="True"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="6"/>
            <output name="outfile">