comparison ml_visualization_ex.xml @ 4:6b94d76a1397 draft

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

3:72c0d2747dc9

    <description>includes several types of plotting for machine learning</description>
    <macros>
        <import>main_macros.xml</import>
        <import>keras_macros.xml</import>
    </macros>
    <expand macro="python_requirements">
        <requirement type="package" version="3.1.1">plotly</requirement>
        <requirement type="package" version="2.40.1">graphviz</requirement>
        <requirement type="package" version="1.4.1">pydot</requirement>
    </expand>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        python '$__tool_directory__/ml_visualization_ex.py' 

            </param>
            <when value="learning_curve">
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing values for plotting learning curve." help="This dataset should be the output of tool model_validation->learning_curve."/>
                <param name="plot_std_err" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" label="Whether to plot standard error bar?"/>
                <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/>
            </when>
            <when value="pr_curve">
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing true labels." help="No headers. For 2-class, single column contains both class labels (e.g. True and False). For multi-label, each column, hot-encoded, corresponds to one label."/>
                <param name="infile2" type="data" format="tabular" label="Select the dataset containing predicted probabilities." help="No headers. For 2-class, sinle column or the first column contains scores for the positive label. For multi-label, each column corresponds to one label."/>
                <param name="pos_label" type="text" value="" optional="true" label="pos_label" help="The label of positive class. If not specified, it will be 1 by default."/>
                <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/>
            </when>
            <when value="roc_curve">
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing true labels." help="No headers. For 2-class, single column contains both class labels (e.g. True and False). For multi-label, each column, hot-encoded, corresponds to one label."/>
                <param name="infile2" type="data" format="tabular" label="Select the dataset containing predicted probabilities." help="No headers. For 2-class, sinle column or the first column contains scores for the positive label. For multi-label, each column corresponds to one label."/>
                <param name="pos_label" type="text" value="" optional="true" label="pos_label" help="The label of positive class. If not specified, it will be 1 by default."/>
                <param name="drop_intermediate" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true" label="drop_intermediate" help="Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve."/>
                <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/>
            </when>
            <when value="rfecv_gridscores">
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing grid_scores from a fitted RFECV object." help="Headered. Single column. Could be Output from `estimator_attributes->grid_scores_`."/>
                <param name="steps" type="text" value="" optional="true" label="Step IDs" help="List, containing hover labels for each grid_score. For example: `list(range(10)) + [15, 20]`."/>
                <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/>
            </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."/>
                <param name="title" type="text" value="" optional="true" label="Custom plot title" help="Optional."/>
            </when>
            <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."/>
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data name="output" format="html" from_work_dir="output" label="${plotting_selection.plot_type} plot on ${on_string}">
            <change_format>
                <when input="plotting_selection.plot_type" value="keras_plot_model" format="png"/>
            </change_format>
        </data>
    </outputs>
    <tests>
        <test>

4:6b94d76a1397

    <description>includes several types of plotting for machine learning</description>
    <macros>
        <import>main_macros.xml</import>
        <import>keras_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        python '$__tool_directory__/ml_visualization_ex.py' 

            </param>
            <when value="learning_curve">
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing values for plotting learning curve." help="This dataset should be the output of tool model_validation->learning_curve."/>
                <param name="plot_std_err" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" label="Whether to plot standard error bar?"/>
                <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/>
                <param name="plot_format" type="select" label="The output format and library">
                    <option value="html" selected="true">An interactive html by plotly</option>
                    <!--<option value="png">PNG image by matplotlib</option> TODO-->
                </param>
            </when>
            <when value="pr_curve">
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing true labels." help="y_true. Headered. For 2-class, single column contains both class labels (e.g. True and False). For multi-label, each column, hot-encoded, corresponds to one label."/>
                <param name="infile2" type="data" format="tabular" label="Select the dataset containing predicted probabilities." help="y_preds. Headered. For 2-class, sinle column or the first column contains scores for the positive label. For multi-label, each column corresponds to one label."/>
                <param name="pos_label" type="text" value="" optional="true" label="pos_label" help="The label of positive class. If not specified, it will be 1 by default."/>
                <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/>
                <param name="report_minimum_n_positives" type="integer" value="" optional="true" label="Report minimum number of positives" help="For mulitple label binary classifications, whose number of true postives is less than the threhold will be ignored."/>
                <param name="plot_format" type="select" label="The output format and library">
                    <option value="plotly_html" selected="true">An interactive html by plotly</option>
                    <option value="matplotlib_svg">SVG image by matplotlib</option>
                </param>
            </when>
            <when value="roc_curve">
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing true labels." help="y_true. Headered. For 2-class, single column contains both class labels (e.g. True and False). For multi-label, each column, hot-encoded, corresponds to one label."/>
                <param name="infile2" type="data" format="tabular" label="Select the dataset containing predicted probabilities." help="y_preds. Headered. For 2-class, sinle column or the first column contains scores for the positive label. For multi-label, each column corresponds to one label."/>
                <param name="pos_label" type="text" value="" optional="true" label="pos_label" help="The label of positive class. If not specified, it will be 1 by default."/>
                <param name="drop_intermediate" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true" label="drop_intermediate" help="Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve."/>
                <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/>
                <param name="report_minimum_n_positives" type="integer" value="" optional="true" label="Report minimum number of positives" help="For mulitple label binary classifications, whose number of true postives is less than the threhold will be ignored."/>
                <param name="plot_format" type="select" label="The output format and library">
                    <option value="plotly_html" selected="true">An interactive html by plotly</option>
                    <option value="matplotlib_svg">SVG image by matplotlib</option>
                </param>
            </when>
            <when value="rfecv_gridscores">
                <param name="infile1" type="data" format="tabular" label="Select the dataset containing grid_scores from a fitted RFECV object." help="Headered. Single column. Could be Output from `estimator_attributes->grid_scores_`."/>
                <param name="steps" type="text" value="" optional="true" label="Step IDs" help="List, containing hover labels for each grid_score. For example: `list(range(10)) + [15, 20]`."/>
                <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/>
                <param name="plot_format" type="select" label="The output format and library">
                    <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."/>
                <param name="title" type="text" value="" optional="true" label="Custom plot title" help="Optional."/>
                <param name="plot_format" type="select" label="The output format and library">
                    <option value="html" selected="true">An interactive html by plotly</option>
                    <!--<option value="png">PNG image by matplotlib</option> TODO-->
                </param>
            </when>
            <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>
        </conditional>
    </inputs>
    <outputs>
        <data name="output" format="html" from_work_dir="output" label="${plotting_selection.plot_type} plot on ${on_string}">
            <change_format>
                <when input="plotting_selection.plot_format" value="png" format="png"/>
            </change_format>
        </data>
    </outputs>
    <tests>
        <test>