comparison ml_visualization_ex.xml @ 12:6c787aeeac06 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a

11:840ef9fe2482

<tool id="ml_visualization_ex" name="Machine Learning Visualization Extension" version="@VERSION@" profile="20.05">
    <description>includes several types of plotting for machine learning</description>
    <macros>
        <import>main_macros.xml</import>
        <import>keras_macros.xml</import>
    </macros>

                    <option value="html" selected="true">An interactive html by plotly</option>
                    <!--<option value="png">PNG image by matplotlib</option> TODO-->
                </param>
            </when>
            <when value="feature_importances">
                <param name="infile_estimator" type="data" format="zip" label="Select the dataset containing fitted estimator/pipeline" />
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing feature names" help="Make sure the headers (first row) are feature names." />
                <conditional name="column_selector_options">
                    <expand macro="samples_column_selector_options" multiple="true" />
                </conditional>
                <param name="threshold" type="float" value="" optional="true" min="0." label="Threshold value" help="Features with importance below the threshold value will be ignored." />

            <when value="keras_plot_model">
                <param name="infile_model_config" type="data" format="json" label="Select the JSON dataset containing configuration for a neural network model" />
                <param name="title" type="hidden" value="" optional="true" label="Plot title" help="Optional. If change is desired." />
                <param name="plot_format" type="hidden" value="png" label="The output format and library" />
            </when>

            <when value="classification_confusion_matrix">
                <param name="infile_true" type="data" format="tabular" label="Select dataset containing true labels" />
                <param name="header_true" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
                <conditional name="column_selector_options_true">
                    <expand macro="samples_column_selector_options" multiple="true" column_option="selected_column_selector_option" col_name="col1" infile="infile_true" />

            <param name="infile2" value="y_score.tabular" ftype="tabular" />
            <output name="output" file="ml_vis04.html" compare="sim_size" />
        </test>
        <test>
            <param name="plot_type" value="feature_importances" />
            <param name="infile_estimator" value="best_estimator_.zip" ftype="zip" />
            <param name="infile1" value="regression_X.tabular" ftype="tabular" />
            <param name="selected_column_selector_option" value="all_columns" />
            <output name="output" file="ml_vis01.html" compare="sim_size" />
        </test>
        <test>

12:6c787aeeac06

<tool id="ml_visualization_ex" name="Machine Learning Visualization Extension" version="@VERSION@" profile="@PROFILE@">
    <description>includes several types of plotting for machine learning</description>
    <macros>
        <import>main_macros.xml</import>
        <import>keras_macros.xml</import>
    </macros>

                    <option value="html" selected="true">An interactive html by plotly</option>
                    <!--<option value="png">PNG image by matplotlib</option> TODO-->
                </param>
            </when>
            <when value="feature_importances">
                <param name="infile_estimator" type="data" format="h5mlm" label="Select the dataset containing fitted estimator/pipeline" />
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing feature names" help="Make sure the headers (first row) are feature names." />
                <conditional name="column_selector_options">
                    <expand macro="samples_column_selector_options" multiple="true" />
                </conditional>
                <param name="threshold" type="float" value="" optional="true" min="0." label="Threshold value" help="Features with importance below the threshold value will be ignored." />

            <when value="keras_plot_model">
                <param name="infile_model_config" type="data" format="json" label="Select the JSON dataset containing configuration for a neural network model" />
                <param name="title" type="hidden" value="" optional="true" label="Plot title" help="Optional. If change is desired." />
                <param name="plot_format" type="hidden" value="png" label="The output format and library" />
            </when>
            <when value="classification_confusion_matrix">
                <param name="infile_true" type="data" format="tabular" label="Select dataset containing true labels" />
                <param name="header_true" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
                <conditional name="column_selector_options_true">
                    <expand macro="samples_column_selector_options" multiple="true" column_option="selected_column_selector_option" col_name="col1" infile="infile_true" />

            <param name="infile2" value="y_score.tabular" ftype="tabular" />
            <output name="output" file="ml_vis04.html" compare="sim_size" />
        </test>
        <test>
            <param name="plot_type" value="feature_importances" />
            <param name="infile_estimator" value="best_estimator_.h5mlm" ftype="h5mlm" />
            <param name="infile1" value="regression_X.tabular" ftype="tabular" />
            <param name="selected_column_selector_option" value="all_columns" />
            <output name="output" file="ml_vis01.html" compare="sim_size" />
        </test>
        <test>