comparison keras_batch_models.xml @ 5:79efb5472c2e draft

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

4:ca0719aa1de3

          <param name="generator_type" type="select" label="Select a batch data generator">
            <option value="FastaDNABatchGenerator" selected="true">FastaDNABatchGenerator -- Online transformation of DNA sequences</option>
            <option value="FastaRNABatchGenerator">FastaRNABatchGenerator -- Online transformation of RNA sequences</option>
            <option value="FastaProteinBatchGenerator">FastaProteinBatchGenerator -- Online transformation of Protein sequences</option>
            <option value="GenomicIntervalBatchGenerator">GenomicIntervalBatchGenerator - Online transformation of genomic sequences from a reference genome and intervals</option>
            <!--option value="ImageBatchGenerator">ImageBatchGenerator - Online transformation of images</option-->
          </param>
          <when value="FastaDNABatchGenerator">
            <expand macro="params_fasta_dna_batch_generator"/>
          </when>
          <when value="FastaRNABatchGenerator">

            <expand macro="params_fasta_protein_batch_generator"/>
          </when>
          <when value="GenomicIntervalBatchGenerator">
            <expand macro="params_genomic_interval_batch_generator"/>
          </when>
          <!--when value="ImageBatchGenerator">
            <expand macro="params_image_batch_generator"/>
          </when-->
        </conditional>
        <expand macro="keras_compile_params_section"/>
        <expand macro="keras_fit_params_section"/>
        <param name="class_positive_factor" type="float" value="1" optional="true" label="class_positive_factor" help="For binary classification only. If int, like 5, will convert to class_weight {0: 1, 1: 5}. If float, 0.2, corresponds to class_weight {0: 1/0.2, 1: 1}"/>
        <param name="prediction_steps" type="integer" value="" optional="true" label="prediction_steps" help="Prediction steps. Optional. If None, it equals number of samples divided by `batch_size`."/>

    <data format="tabular" name="outfile_params" label="get_params for Keras Batch Classifier on ${on_string}">
      <filter>get_params</filter>
    </data>
  </outputs>
  <tests>
    <test>
      <conditional name="mode_selection">
        <param name="infile_json" value="deepsear_1feature.json" ftype="json"/>
        <param name="learning_type" value="KerasGBatchClassifier"/>
        <conditional name="generator_selection">

5:79efb5472c2e

          <param name="generator_type" type="select" label="Select a batch data generator">
            <option value="FastaDNABatchGenerator" selected="true">FastaDNABatchGenerator -- Online transformation of DNA sequences</option>
            <option value="FastaRNABatchGenerator">FastaRNABatchGenerator -- Online transformation of RNA sequences</option>
            <option value="FastaProteinBatchGenerator">FastaProteinBatchGenerator -- Online transformation of Protein sequences</option>
            <option value="GenomicIntervalBatchGenerator">GenomicIntervalBatchGenerator - Online transformation of genomic sequences from a reference genome and intervals</option>
            <option value="none">To be determined</option>
          </param>
          <when value="FastaDNABatchGenerator">
            <expand macro="params_fasta_dna_batch_generator"/>
          </when>
          <when value="FastaRNABatchGenerator">

            <expand macro="params_fasta_protein_batch_generator"/>
          </when>
          <when value="GenomicIntervalBatchGenerator">
            <expand macro="params_genomic_interval_batch_generator"/>
          </when>
          <when value="none"/>
        </conditional>
        <expand macro="keras_compile_params_section"/>
        <expand macro="keras_fit_params_section"/>
        <param name="class_positive_factor" type="float" value="1" optional="true" label="class_positive_factor" help="For binary classification only. If int, like 5, will convert to class_weight {0: 1, 1: 5}. If float, 0.2, corresponds to class_weight {0: 1/0.2, 1: 1}"/>
        <param name="prediction_steps" type="integer" value="" optional="true" label="prediction_steps" help="Prediction steps. Optional. If None, it equals number of samples divided by `batch_size`."/>

    <data format="tabular" name="outfile_params" label="get_params for Keras Batch Classifier on ${on_string}">
      <filter>get_params</filter>
    </data>
  </outputs>
  <tests>
    <test>
      <conditional name="mode_selection">
        <param name="infile_json" value="keras01.json" ftype="json"/>
        <param name="learning_type" value="KerasGBatchClassifier"/>
        <conditional name="generator_selection">
          <param name="generator_type" value="none"/>
        </conditional>
        <section name="fit_params">
          <param name="epochs" value="100"/>
          <repeat name="callbacks">
            <conditional name="callback_selection">
              <param name="callback_type" value="None"/>
            </conditional>
          </repeat>
        </section>
      </conditional>
      <param name="get_params" value="true" />
      <output name="outfile" file="keras_batch_model04" compare="sim_size" delta="5"/>
      <output name="outfile_params" file="keras_batch_params04.tabular" />
    </test>
    <test>
      <conditional name="mode_selection">
        <param name="infile_json" value="deepsear_1feature.json" ftype="json"/>
        <param name="learning_type" value="KerasGBatchClassifier"/>
        <conditional name="generator_selection">