<macros>
    <token name="@LUDWIG_VERSION@">0.10.1</token>

    <token name="@SUFFIX@">0</token>

    <token name="@VERSION@">@LUDWIG_VERSION@+@SUFFIX@</token>

    <token name="@PROFILE@">21.05</token>

    <xml name="python_requirements_gpu">
        <requirements>
            <container type="docker">quay.io/goeckslab/galaxy-ludwig-gpu:latest</container>
        </requirements>
    </xml>

    <xml name="python_requirements">
        <requirements>
            <container type="docker">quay.io/goeckslab/galaxy-ludwig:0.10.3</container>
        </requirements>
    </xml>

    <xml name="macro_stdio">
        <stdio>
            <exit_code range="137" level="fatal_oom" description="Out of Memory" />
            <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error" />
        </stdio>
    </xml>

    <xml name="macro_citations">
        <citations>
            <citation type="bibtex">
@misc{https://doi.org/10.48550/arxiv.1909.07930,
    doi = {10.48550/ARXIV.1909.07930},
    url = {https://arxiv.org/abs/1909.07930},
    author = {Molino, Piero and Dudin, Yaroslav and Miryala, Sai Sumanth},
    title = {Ludwig: a type-based declarative deep learning toolbox},
    publisher = {arXiv},
    year = {2019},
    copyright = {arXiv.org perpetual, non-exclusive license}
}
            </citation>
        </citations>
    </xml>

    <xml name="encoder_parameters">
        <param argument="name" type="text" value="" label="Name of the column containing the input feature" />
        <param argument="norm" type="select" label="Select the norm mode">
            <option value="none" selected="true">Null</option>
            <option value="batch">batch</option>
            <option value="layer">layer</option>
        </param>
        <param argument="tied_weights" type="text" value="" optional="true" label="Name of the input feature to tie the weights of the encoder with" help="It needs to be the name of a feature of the same type and with the same encoder parameters. Optional" />
        <yield />
    </xml>

    <xml name="visualize_file_format">
        <param type="select" name="file_format" label="Choose the output format">
            <option value="pdf" selected="true">pdf</option>
            <option value="png">png</option>
        </param>
    </xml>

    <xml name="visualize_output_feature_name">
        <param argument="output_feature_name" type="text" value="" optional="true" label="Name of the output feature" help="If `None`, use all output features." />
    </xml>

    <xml name="visualize_training_statistics">
        <param argument="training_statistics" type="data" format="html,json" multiple="true" label="Training statistics" />
    </xml>

    <xml name="visualize_test_statistics">
        <param argument="test_statistics" type="data" format="html,json" multiple="true" label="Choose experiment test statistics file(s)" />
    </xml>

    <xml name="visualize_hyperopt_stats_path">
        <param argument="hyperopt_stats_path" type="data" format="json" label="Select the hyperopt result (JSON)" />
    </xml>

    <xml name="visualize_model_names">
        <param argument="model_names" type="text" value="" optional="true" label="Model name(s) to use as labels" help="Comma delimited for multiple." />
    </xml>

    <xml name="visualize_probabilities">
        <param argument="probabilities" type="data" format="html" multiple="true" label="Choose the prediction results to extract probabilities from" />
    </xml>

    <xml name="visualize_ground_truth_metadata">
        <param argument="ground_truth_metadata" type="data" format="ludwig_model,json" label="Choose the file containing feature metadata json file created during training" />
    </xml>

    <xml name="visualize_split_file">
        <param argument="split_file" type="data" format="csv" optional="true" label="Choose the file containing split values" />
    </xml>

    <xml name="visualize_ground_truth_split">
        <param argument="ground_truth_split" type="select" label="Select the ground truth split" >
            <option value="0">0 -- training</option>
            <option value="1">1 -- validation</option>
            <option value="2">2 -- test</option>
        </param>
    </xml>

    <xml name="visualize_ground_truth">
        <param argument="ground_truth" type="text" value="" label="Choose the ground truth file" />
    </xml>

    <xml name="visualize_predictions">
        <param argument="predictions" type="data" format="html" multiple="true" label="Choose the prediction result files" />
    </xml>

    <xml name="visualize_top_n_classes">
        <param argument="top_n_classes" type="text" value="" label="Type in the list containing numbers of classes to plot" />
    </xml>

    <xml name="visualize_threshold_output_feature_names">
        <param argument="threshold_output_feature_names" type="text" value="" label="Type in the list containing two output feature names for visualization" />
    </xml>

    <xml name="visualize_labels_limit">
        <param argument="labels_limit" type="integer" value="" optional="true" label="Set the upper limit on the numeric encoded label value" help="Encoded numeric label values in dataset that are higher than `label_limit` are considered to be 'rare' labels." min="1" max="1000"/>
    </xml>

    <xml name="visualize_metrics">
        <param argument="metrics" type="select" multiple="true" label="Select the metrics to display" >
            <option value="f1" selected="true">f1</option>
            <option value="precision">precision</option>
            <option value="recall">recall</option>
            <option value="accuracy">accuracy</option>
        </param>
    </xml>

    <xml name="visualize_positive_label">
        <param argument="positive_label" type="integer" value="1" label="Numeric encoded value for the positive class" min="1" max="1000" />
    </xml>

    <xml name="visualize_ground_truth_apply_idx">
        <param argument="ground_truth_apply_idx" type="boolean" checked="true" label="Whether to use metadata['str2idx'] in np.vectorize?" />
    </xml>

    <xml name="visualize_normalize">
        <param argument="normalize" type="boolean" checked="false" label="Whether to normalize rows in confusion matrix?" />
    </xml>
</macros>