Mercurial > repos > goeckslab > ludwig_render_config
diff ludwig_render_config.xml @ 0:ed8a9ea5bc73 draft default tip
planemo upload for repository https://github.com/goeckslab/Galaxy-Ludwig.git commit bdea9430787658783a51cc6c2ae951a01e455bb4
author | goeckslab |
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date | Tue, 07 Jan 2025 22:45:58 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ludwig_render_config.xml Tue Jan 07 22:45:58 2025 +0000 @@ -0,0 +1,509 @@ +<tool id="ludwig_render_config" name="Ludwig Render Config" version="@VERSION@" profile="@PROFILE@"> + <description>renders thefull config based on user input</description> + <macros> + <import>ludwig_macros.xml</import> + </macros> + <expand macro="python_requirements_gpu" /> + <expand macro="macro_stdio" /> + <version_command>echo "@VERSION@"</version_command> + <command> + <![CDATA[ + python '$__tool_directory__/ludwig_render_config.py' '$inputs' '$output' + ]]> + </command> + <configfiles> + <inputs name="inputs" /> + </configfiles> + <inputs> + <section name="input_features" title="Input Features" expanded="true"> + <repeat name="input_feature" min="1" max="30" title="Input Feature"> + <conditional name="input_feature_selector" > + <param name="type" type="select" label="Select an input feature type"> + <option value="number" selected="true">number</option> + <option value="binary">binary</option> + <option value="category">category</option> + <option value="set">set</option> + <option value="bag">bag</option> + <option value="sequence">sequence</option> + <option value="text">text</option> + <option value="timeseries">timeseries</option> + <option value="audio">audio</option> + <option value="image">image</option> + <option value="date">date</option> + <option value="h3">h3</option> + <option value="vector">vector</option> + </param> + <when value="number"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="passthrough" selected="true">passthrough</option> + <option value="dense">dense</option> + <option value="sparse">sparse</option> + </param> + <when value="passthrough" /> + <when value="dense" /> + <when value="sparse" /> + </conditional> + </when> + <when value="binary"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="passthrough" selected="true">passthrough</option> + <option value="dense">dense</option> + </param> + <when value="passthrough" /> + <when value="dense" /> + </conditional> + </when> + <when value="category"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="passthrough" selected="true">passthrough</option> + <option value="dense">dense</option> + <option value="sparse">sparse</option> + </param> + <when value="passthrough" /> + <when value="dense" /> + <when value="sparse" /> + </conditional> + </when> + <when value="set"> + </when> + <when value="bag"> + </when> + <when value="sequence"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="parallel_cnn" selected="true">parallel_cnn</option> + <option value="embed">embed</option> + <option value="stacked_cnn">stacked_cnn</option> + <option value="stacked_parallel_cnn">stacked_parallel_cnn</option> + <option value="rnn">rnn</option> + <option value="cnnrnn">cnnrnn</option> + <option value="transformer">transformer</option> + <option value="passthrough">passthrough</option> + </param> + <when value="parallel_cnn" /> + <when value="embed" /> + <when value="stacked_cnn" /> + <when value="stacked_parallel_cnn" /> + <when value="rnn" /> + <when value="cnnrnn" /> + <when value="transformer" /> + <when value="passthrough" /> + </conditional> + </when> + <when value="text"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="parallel_cnn" selected="true">parallel_cnn</option> + <option value="embed">embed</option> + <option value="stacked_cnn">stacked_cnn</option> + <option value="stacked_parallel_cnn">stacked_parallel_cnn</option> + <option value="rnn">rnn</option> + <option value="cnnrnn">cnnrnn</option> + <option value="transformer">transformer</option> + <option value="passthrough">passthrough</option> + </param> + <when value="parallel_cnn" /> + <when value="embed" /> + <when value="stacked_cnn" /> + <when value="stacked_parallel_cnn" /> + <when value="rnn" /> + <when value="cnnrnn" /> + <when value="transformer" /> + <when value="passthrough" /> + </conditional> + </when> + <when value="timeseries"> + </when> + <when value="audio"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="parallel_cnn" selected="true">parallel_cnn</option> + <option value="embed">embed</option> + <option value="stacked_cnn">stacked_cnn</option> + <option value="stacked_parallel_cnn">stacked_parallel_cnn</option> + <option value="rnn">rnn</option> + <option value="cnnrnn">cnnrnn</option> + <option value="transformer">transformer</option> + <option value="passthrough">passthrough</option> + </param> + <when value="parallel_cnn" /> + <when value="embed" /> + <when value="stacked_cnn" /> + <when value="stacked_parallel_cnn" /> + <when value="rnn" /> + <when value="cnnrnn" /> + <when value="transformer" /> + <when value="passthrough" /> + </conditional> + </when> + <when value="image"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="stacked_cnn" selected="true">stacked_cnn</option> + <option value="resnet">resnet</option> + <option value="mlp_mixer">mlp_mixer</option> + <option value="vit">vit</option> + </param> + <when value="stacked_cnn" /> + <when value="resnet" /> + <when value="mlp_mixer" /> + <when value="vit" /> + </conditional> + </when> + <when value="date"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="embed" selected="true">embed</option> + <option value="wave">wave</option> + </param> + <when value="embed" /> + <when value="wave" /> + </conditional> + </when> + <when value="h3"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="embed" selected="true">embed</option> + <option value="weighted_sum">weighted_sum</option> + <option value="rnn">rnn</option> + </param> + <when value="embed" /> + <when value="weighted_sum" /> + <when value="rnn" /> + </conditional> + </when> + <when value="vector"> + <param argument="name" type="text" value="" label="Name"/> + <conditional name="encoder"> + <param argument="type" type="select" label="Select an encoder"> + <option value="passthrough">passthrough</option> + <option value="dense" selected="true">dense</option> + </param> + <when value="passthrough" /> + <when value="dense" /> + </conditional> + </when> + </conditional> + </repeat> + </section> + <section name="output_features" title="Output Features" expanded="true"> + <repeat name="output_feature" min="1" max="30" title="Output Feature"> + <conditional name="output_feature_selector"> + <param argument="type" type="select" label="Select an ouput feature type"> + <option value="category" selected="true">category</option> + <option value="binary" >binary</option> + <option value="set" >set</option> + <option value="vector" >vector</option> + <option value="number" >number</option> + <option value="sequence" >sequence</option> + <option value="text" >text</option> + </param> + <when value="category"> + <param argument="name" type="text" value="" /> + <conditional name="decoder"> + <param argument="type" type="select" label="Select a decoder type"> + <option value="classifier" selected="true">classifier</option> + <option value="passthrough">passthrough</option> + </param> + <when value="classifier"> + <param argument="num_fc_layers" type="integer" value="0" help="The number of stacked fully connected layers that the input to the feature passes through" min="0" max="20" /> + <param argument="output_size" type="integer" value="256" help="The Size of the output of a fully connected layer." min="1" max="1024" /> + <param argument="fc_dropout" type="float" value="0" help="Dropout rate" min="0" max="1" /> + </when> + <when value="passthrough" /> + </conditional> + <conditional name="loss"> + <param argument="type" type="select" label="Select a loss type"> + <option value="softmax_cross_entropy" selected="true">softmax_cross_entropy</option> + </param> + <when value="softmax_cross_entropy" /> + </conditional> + <param argument="top_k" type="integer" value="3" help="The number of categories to consider when computing the first k measure." min="2" max="10" /> + </when> + <when value="binary"> + <param argument="name" type="text" value="" /> + <conditional name="decoder"> + <param argument="type" type="select" label="Select a decoder type"> + <option value="regressor" selected="true">regressor</option> + <option value="passthrough">passthrough</option> + </param> + <when value="regressor"> + <param argument="num_fc_layers" type="integer" value="0" help="The number of stacked fully connected layers that the input to the feature passes through" min="1" max="20" /> + <param argument="output_size" type="integer" value="256" help="The Size of the output of a fully connected layer." min="1" max="1024" /> + <param argument="fc_dropout" type="float" value="0" help="Dropout rate" min="0" max="1" /> + </when> + <when value="passthrough" /> + </conditional> + <conditional name="loss"> + <param argument="type" type="select" label="Select a loss type"> + <option value="binary_weighted_cross_entropy" selected="true">binary_weighted_cross_entropy</option> + </param> + <when value="binary_weighted_cross_entropy"> + <param argument="positive_class_weight" type="integer" value="1" help="Multiplies the loss for the positive class, increasing its importance" min="1" max="100" /> + </when> + </conditional> + <param argument="threshold" type="float" value="0.5" help="The threshold above which the predicted output of the sigmoid will be mapped to 1." min="0" max="1.0" /> + </when> + <when value="set"> + <param argument="name" type="text" value="" /> + <conditional name="decoder"> + <param argument="type" type="select" label="Select a decoder type"> + <option value="classifier" selected="true">classifier</option> + <option value="passthrough">passthrough</option> + </param> + <when value="classifier"> + <param argument="num_fc_layers" type="integer" value="0" help="The number of stacked fully connected layers that the input to the feature passes through" min="1" max="20" /> + <param argument="output_size" type="integer" value="256" help="The Size of the output of a fully connected layer." min="1" max="1024" /> + <param argument="fc_dropout" type="float" value="0" help="Dropout rate" min="0" max="1" /> + </when> + <when value="passthrough" /> + </conditional> + <conditional name="loss"> + <param argument="type" type="select" label="Select a loss type"> + <option value="sigmoid_cross_entropy" selected="true">sigmoid_cross_entropy</option> + </param> + <when value="sigmoid_cross_entropy" /> + </conditional> + </when> + <when value="vector"> + <param argument="name" type="text" value="" /> + <conditional name="decoder"> + <param argument="type" type="select" label="Select a decoder type"> + <option value="projector" selected="true">projector</option> + <option value="passthrough">passthrough</option> + </param> + <when value="projector"> + <param argument="softmax" type="boolean" value="false" help="Whether to apply a softmax at the end of the decoder?" /> + <param argument="num_fc_layers" type="integer" value="0" help="The number of stacked fully connected layers that the input to the feature passes through" min="0" max="20" /> + <param argument="output_size" type="integer" value="256" help="The Size of the output of a fully connected layer." min="1" max="1024" /> + <param argument="fc_dropout" type="float" value="0" help="Dropout rate" min="0" max="1" /> + </when> + <when value="passthrough" /> + </conditional> + <conditional name="loss"> + <param argument="type" type="select" label="Select a loss type"> + <option value="mean_squared_error" selected="true">mean_squared_error</option> + <option value="mean_absolute_error">mean_absolute_error</option> + <option value="softmax_cross_entropy">softmax_cross_entropy (use this only if softmax is true)</option> + </param> + <when value="mean_squared_error" /> + <when value="mean_absolute_error" /> + <when value="softmax_cross_entropy" /> + </conditional> + </when> + <when value="number"> + <param argument="name" type="text" value="" /> + <conditional name="decoder"> + <param argument="type" type="select" label="Select a decoder type"> + <option value="regressor" selected="true">regressor</option> + <option value="passthrough">passthrough</option> + </param> + <when value="regressor"> + <param argument="num_fc_layers" type="integer" value="0" help="The number of stacked fully connected layers that the input to the feature passes through" min="1" max="20" /> + <param argument="output_size" type="integer" value="256" help="The Size of the output of a fully connected layer." min="1" max="1024" /> + <param argument="fc_dropout" type="float" value="0" help="Dropout rate" min="0" max="1" /> + </when> + <when value="passthrough" /> + </conditional> + <conditional name="loss"> + <param argument="type" type="select" label="Select a loss type"> + <option value="mean_squared_error" selected="true">mean_squared_error</option> + <option value="mean_absolute_error">mean_absolute_error</option> + <option value="root_mean_squared_error">root_mean_squared_error</option> + <option value="root_mean_squared_percentage_error">root_mean_squared_percentage_error</option> + </param> + <when value="mean_squared_error" /> + <when value="mean_absolute_error" /> + <when value="root_mean_squared_error" /> + <when value="root_mean_squared_percentage_error" /> + </conditional> + </when> + <when value="sequence"> + <param argument="name" type="text" value="" /> + <conditional name="decoder"> + <param argument="type" type="select" label="Select a decoder type"> + <option value="generator" selected="true">generator</option> + <option value="tagger">tagger</option> + <option value="passthrough">passthrough</option> + </param> + <when value="generator"> + <param argument="cell_type" type="select" label="Selct the type of recurrent cell to use" > + <option value="gru" selected="true">gru</option> + <option value="rnn">rnn</option> + <option value="lstm">lstm</option> + </param> + <param argument="num_fc_layers" type="integer" value="0" help="The number of stacked fully connected layers that the input to the feature passes through" min="1" max="20" /> + <param argument="output_size" type="integer" value="256" help="The Size of the output of a fully connected layer." min="1" max="1024" /> + <param argument="fc_dropout" type="float" value="0" help="Dropout rate" min="0" max="1" /> + </when> + <when value="tagger"> + <param argument="use_attention" type="boolean" value="false" help="Whether to apply a multi-head self attention layer before prediction?" /> + <param argument="use_bias" type="boolean" value="true" help="whether the layer uses a bias vector?" /> + <param argument="attention_embedding_size" type="integer" value="256" help="The embedding size of the multi-head self attention layer." min="1" max="1024" /> + <param argument="attention_num_heads" type="integer" value="8" help="The number of attention heads in the multi-head self attention layer." min="1" max="16"/> + <param argument="num_fc_layers" type="integer" value="0" help="The number of stacked fully connected layers that the input to the feature passes through" min="1" max="20" /> + <param argument="output_size" type="integer" value="256" help="The Size of the output of a fully connected layer." min="1" max="1024" /> + <param argument="fc_dropout" type="float" value="0" help="Dropout rate" min="0" max="1" /> + </when> + <when value="passthrough" /> + </conditional> + <conditional name="loss"> + <param argument="type" type="select" label="Select a loss type"> + <option value="sequence_softmax_cross_entropy" selected="true">sequence_softmax_cross_entropy</option> + </param> + <when value="sequence_softmax_cross_entropy" /> + </conditional> + </when> + <when value="text"> + <param argument="name" type="text" value="" /> + <conditional name="decoder"> + <param argument="type" type="select" label="Select a decoder type"> + <option value="generator" selected="true">generator</option> + <option value="tagger">tagger</option> + <option value="passthrough">passthrough</option> + </param> + <when value="generator"> + <param argument="cell_type" type="select" label="Selct the type of recurrent cell to use" > + <option value="gru" selected="true">gru</option> + <option value="rnn">rnn</option> + <option value="lstm">lstm</option> + </param> + <param argument="num_fc_layers" type="integer" value="0" help="The number of stacked fully connected layers that the input to the feature passes through" min="1" max="20" /> + <param argument="output_size" type="integer" value="256" help="The Size of the output of a fully connected layer." min="1" max="1024" /> + <param argument="fc_dropout" type="float" value="0" help="Dropout rate" min="0" max="1" /> + </when> + <when value="tagger"> + <param argument="use_attention" type="boolean" value="false" help="Whether to apply a multi-head self attention layer before prediction?" /> + <param argument="use_bias" type="boolean" value="true" help="whether the layer uses a bias vector?" /> + <param argument="attention_embedding_size" type="integer" value="256" help="The embedding size of the multi-head self attention layer." min="1" max="1024" /> + <param argument="attention_num_heads" type="integer" value="8" help="The number of attention heads in the multi-head self attention layer." min="1" max="16"/> <param argument="num_fc_layers" type="integer" value="0" help="The number of stacked fully connected layers that the input to the feature passes through" min="1" max="20" /> + <param argument="output_size" type="integer" value="256" help="The Size of the output of a fully connected layer." min="1" max="1024" /> + <param argument="fc_dropout" type="float" value="0" help="Dropout rate" min="0" max="1" /> + </when> + <when value="passthrough" /> + </conditional> + <conditional name="loss"> + <param argument="type" type="select" label="Select a loss type"> + <option value="sequence_softmax_cross_entropy" selected="true">sequence_softmax_cross_entropy</option> + </param> + <when value="sequence_softmax_cross_entropy" /> + </conditional> + </when> + </conditional> + </repeat> + </section> + <section name="combiner" title="Combiner" expanded="false"> + <param name="type" type="select" label="Select a combiner type"> + <option value="concat" selected="true">concat</option> + <option value="sequence_concat">sequence_concat</option> + <option value="sequence">sequence</option> + <option value="tabnet">tabnet</option> + <option value="transformer">transformer</option> + <option value="tabtransformer">tabtransformer</option> + <option value="comparator">comparator</option> + <option value="project_aggregate">project_aggregate</option> + </param> + </section> + <section name="trainer" title="Trainer" expanded="true"> + <conditional name="trainer" > + <param name="model_type" type="select" label="Select the model type"> + <option value="ecd" selected="true">ecd</option> + <option value="gbm">gbm</option> + </param> + <when value="ecd"> + <param argument="batch_size" type="integer" value="128" help="Batch size" min="1" max="4096" /> + <param argument="epochs" type="integer" value="100" help="Number of epochs" min="1" max="1000" /> + <conditional name="optimizer"> + <param name="type" type="select" label="Select an optimizer"> + <option value="adam" selected="true">adam</option> + <option value="sgd">sgd</option> + <option value="adadelta">adadelta</option> + <option value="adamw">adamw</option> + <option value="adagrad">adagrad</option> + <option value="adamax">adamax</option> + <option value="ftrl">ftrl</option> + <option value="nadam">nadam</option> + <option value="rmsprop">rmsprop</option> + </param> + <when value="adam" /> + <when value="sgd" /> + <when value="adadelta" /> + <when value="adamw" /> + <when value="adagrad" /> + <when value="adamax" /> + <when value="ftrl" /> + <when value="nadam" /> + <when value="rmsprop" /> + </conditional> + <param argument="learning_rate" type="float" value="0.001" help="Learning rate" min="0" max="1.0" /> + <param argument="early_stop" type="integer" value="5" help="Number of epochs of patience without an improvement on the validation measure before the training is stopped." min="1" max="20"/> + </when> + <when value="gbm"> + <param argument="boosting_type" type="select" label="Select the type of boosting algorithm" > + <option value="gbdt" selected="true">gbdt</option> + <option value="rf">rf`</option> + <option value="dart">dart</option> + <option value="goss">goss</option> + </param> + <param argument="num_boost_round" type="integer" value="1000" help="Number of boosting rounds to perform." min="1" max="5000" /> + <param argument="learning_rate" type="float" value="0.03" help="Learning rate." min="0.0001" max="1.0" /> + <param argument="lambda_l1" type="float" value="0.25" help="L1 regularization factor." min="0" max="10.0" /> + <param argument="lambda_l2" type="float" value="0.2" help="L2 regularization factor." min="0" max="10.0" /> + <param argument="max_depth" type="integer" value="18" help="Maximum depth of a tree. A negative value means no limit." min="3" max="50" /> + <param argument="early_stop" type="integer" value="5" help="Number of epochs of patience without an improvement on the validation measure before the training is stopped." min="1" max="20" /> + </when> + </conditional> + </section> + <!-- <section name="preprocessing" title="Preprocessing" expanded="false"> + </section> --> + <conditional name="hyperopt"> + <param name="do_hyperopt" type="select" label="Whether to do hyperparameter optimization?"> + <option value="false" selected="true">No</option> + <option value="true">Yes</option> + </param> + <when value="true"> + <section name="hyperopt" title="Hyperparameter Optimization configuration" expanded="false"> + <conditional name="executor"> + <param argument="type" type="select" label="Select the executor type" > + <option value="ray" selected="true">ray</option> + </param> + <when value="ray" /> + </conditional> + </section> + </when> + <when value="false" /> + </conditional> + </inputs> + <outputs> + <data format="yaml" name="output" label="${tool.name}" /> + </outputs> + <tests> + <test expect_num_outputs="1"> + <output name="output" file="render_config01.yml" /> + </test> + </tests> + <help> + <![CDATA[ +**What it does** +Render a Ludwig config template. + + +**Output** +A yaml file. + + ]]> + </help> + <expand macro="macro_citations" /> +</tool>