Mercurial > repos > iuc > deepmicro
changeset 0:77cbfe3e1b0d draft
planemo upload for repository https://github.com/paulzierep/DeepMicro commit 1bbea291a9d77beafaeba83ab775d870ec24719e
| author | iuc |
|---|---|
| date | Tue, 02 May 2023 17:39:13 +0000 |
| parents | |
| children | c58c1a99578b |
| files | deepmicro.xml macros.xml static/images/ML_Workflow.png test-data/UserDataExample.csv test-data/UserLabelExample.csv |
| diffstat | 5 files changed, 378 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/deepmicro.xml Tue May 02 17:39:13 2023 +0000 @@ -0,0 +1,289 @@ +<tool id="deepmicro" name="DeepMicro" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@"> + <description> + Representation learning and classification framework + </description> + <macros> + <import>macros.xml</import> + </macros> + <expand macro="biotools" /> + <expand macro="requirements" /> + <expand macro="version" /> + <command detect_errors="exit_code"><![CDATA[ + mkdir data && + mkdir results && + ln -s '$features' data/features.csv && + + #if $mode.mode_type == "only_encoding": + + #for $params in $mode.parameter_set: + + #if $params.rl_type.rl_type_choice == "--pca" or $params.rl_type.rl_type_choice == "--rp": + DM.py -r 1 -cd features.csv '$params.rl_type.rl_type_choice' --save_rep --no_clf -t \${GALAXY_SLOTS:-8} && + #else: + DM.py -r 1 -cd features.csv '$params.rl_type.rl_type_choice' -dm '$params.rl_type.dm' --save_rep --no_clf -t \${GALAXY_SLOTS:-8} && + #end if + #end for + + #else: + + ln -s '$mode.class_labels' data/labels.csv && + + #for $params in $mode.parameter_set: + + #if $params.rl_type.rl_type_choice == "--pca" or $params.rl_type.rl_type_choice == "--rp": + DM.py -r 1 -cd features.csv -cl labels.csv '$params.rl_type.rl_type_choice' --save_rep -m '$params.rl_type.classifier' -t \${GALAXY_SLOTS:-8} && + #else: + DM.py -r 1 -cd features.csv -cl labels.csv '$params.rl_type.rl_type_choice' -dm '$params.rl_type.dm' --save_rep -m '$params.rl_type.classifier' -t \${GALAXY_SLOTS:-8} && + #end if + #end for + + #end if + + echo Done ! + ]]> + </command> + <inputs> + <param argument="--features" type="data" format="tabular" label="Feature table" help="Dataset containing the features of samples"/> + <conditional name="mode"> + <param name="mode_type" type="select" label="Mode" help="The tool can either only create a latent + representation of the data or create a latent representation of the data and cross validate a classifier using that encoding."> + <option value="only_encoding">Create only encoding</option> + <option value="e_and_c">Create encoding and cross validate a classifier</option> + </param> + <when value="only_encoding"> + <repeat name="parameter_set" title="Parameter Set"> + <conditional name="rl_type"> + <param name="rl_type_choice" type="select" label="Representation learning type" help="The type of representation learning" > + <option value="--pca">PCA</option> + <option value="--rp">Random Projection</option> + <option value="--ae">Autoencoder or Deep Autoencoder</option> + <option value="--vae">Variational Autoencoder</option> + <option value="--cae">Convolutional Autoencoder</option> + </param> + <when value="--pca"/> + <when value="--rp"/> + <when value="--ae"> + <expand macro="dm" /> + </when> + <when value="--vae"> + <expand macro="dm" /> + </when> + <when value="--cae"> + <expand macro="dm" /> + </when> + </conditional> + </repeat> + </when> + <when value="e_and_c"> + <param argument="--class_labels" type="data" format="tabular" label="Class labels" help="Dataset containing the class labels corresponding to the features"/> + <repeat name="parameter_set" title="Parameter Set"> + <conditional name="rl_type"> + <param name="rl_type_choice" type="select" label="Representation learning type" help="The type of representation learning. `Train on input` trains the classifier on the input features without representation learning" > + <option value="--pca">PCA</option> + <option value="--rp">Random Projection</option> + <option value="--ae">Autoencoder or Deep Autoencoder</option> + <option value="--vae">Variational Autoencoder</option> + <option value="--cae">Convolutional Autoencoder</option> + <option value="no_rl">Train on input</option> + </param> + <when value="no_rl"> + <expand macro="clfs" /> + </when> + <when value="--pca"> + <expand macro="clfs" /> + </when> + <when value="--rp"> + <expand macro="clfs" /> + </when> + <when value="--ae"> + <expand macro="dm" /> + <expand macro="clfs" /> + </when> + <when value="--vae"> + <expand macro="dm" /> + <expand macro="clfs" /> + </when> + <when value="--cae"> + <expand macro="dm" /> + <expand macro="clfs" /> + </when> + </conditional> + </repeat> + </when> + </conditional> + </inputs> + <outputs> + <data name="results" format="tabular" from_work_dir="./results/*_result.txt" label="${tool.name} on ${on_string}: Results"> + <!-- results are only for classifiers --> + <filter>mode["mode_type"] == "e_and_c"</filter> + </data> + <collection name="encoded_features" type="list" label="Encoded Features"> + <!-- the encoded features generated by the tool are only for the training set, this is not very useful, therefore omitting + todo change tool do export features complete dataset also when classification is performed --> + <filter>mode["mode_type"] == "only_encoding"</filter> + <discover_datasets directory="results" pattern="(?P<designation>.*)_rep\.csv" format="tabular" visible="false" /> + </collection> + </outputs> + <tests> + + <!-- only encoding --> + <!-- test one parameter sets --> + + <test expect_num_outputs="1"> + <param name="mode_type" value="only_encoding" /> + <param name="features" value="UserDataExample.csv" /> + <param name="rl_type_choice" value="--ae" /> + <param name="dm" value="40" /> + <output_collection name="encoded_features" type="list"> + <!-- output is non determinisitc --> + <element name="AE[40]_features" ftype="tabular" > + <assert_contents> + <has_n_lines n="20"/> + <!-- <has_n_columns n="40" sep="," /> --> + </assert_contents> + </element> + </output_collection> + </test> + + <test expect_num_outputs="1"> + <param name="mode_type" value="only_encoding" /> + <param name="features" value="UserDataExample.csv" /> + <param name="rl_type_choice" value="--pca" /> + <output_collection name="encoded_features" type="list"> + <element name="PCA_features" ftype="tabular" > + <assert_contents> + <has_n_lines n="20"/> + <!-- <has_n_columns n="40" sep="," /> --> + </assert_contents> + </element> + </output_collection> + </test> + + <!-- test multiple parameter sets --> + <test expect_num_outputs="1"> + <param name="features" value="UserDataExample.csv" /> + <conditional name="mode"> + <param name="mode_type" value="only_encoding" /> + + <repeat name="parameter_set"> + <conditional name="rl_type"> + <param name="rl_type_choice" value="--pca" /> + </conditional> + </repeat> + + <repeat name="parameter_set"> + <conditional name="rl_type"> + <param name="rl_type_choice" value="--ae" /> + <param name="dm" value="40" /> + </conditional> + </repeat> + + </conditional> + + <output_collection name="encoded_features" type="list"> + <element name="AE[40]_features" ftype="tabular" > + <assert_contents> + <has_n_lines n="20"/> + <!-- <has_n_columns n="40" sep="," /> --> + </assert_contents> + </element> + <element name="PCA_features" ftype="tabular" > + <assert_contents> + <has_n_lines n="20"/> + <!-- <has_n_columns n="40" sep="," /> --> + </assert_contents> + </element> + </output_collection> + + </test> + + <!-- encoding and clf --> + <!-- test one parameter set --> + + <test expect_num_outputs="1"> + <param name="features" value="UserDataExample.csv" /> + <param name="mode_type" value="e_and_c" /> + <param name="class_labels" value="UserLabelExample.csv" /> + <param name="rl_type_choice" value="--vae" /> + <param name="dm" value="40" /> + <param name="classifier" value="rf" /> + <output ftype="tabular" name="results" > + <assert_contents> + <has_text text="VAE[40]_rf" /> + </assert_contents> + </output> + + </test> + + <!-- test multiple parameter sets --> + <test expect_num_outputs="1"> + <param name="features" value="UserDataExample.csv" /> + <conditional name="mode"> + <param name="mode_type" value="e_and_c" /> + <param name="class_labels" value="UserLabelExample.csv" /> + + <repeat name="parameter_set"> + <conditional name="rl_type"> + <param name="rl_type_choice" value="--cae" /> + <param name="dm" value="20" /> + <param name="classifier" value="rf" /> + </conditional> + </repeat> + + <repeat name="parameter_set"> + <conditional name="rl_type"> + <param name="rl_type_choice" value="--vae" /> + <param name="dm" value="40" /> + <param name="classifier" value="mlp" /> + </conditional> + </repeat> + + </conditional> + + <output ftype="tabular" name="results" > + <assert_contents> + <has_text text="CAE[20]_rf" /> + <has_text text="VAE[40]_mlp" /> + </assert_contents> + </output> + + </test> + + </tests> + <help> + <![CDATA[ +DeepMicro is a deep representation learning framework exploiting various autoencoders +to learn robust low-dimensional representations from high-dimensional data and training +classification models based on the learned representation. + +====================================== +Option 1) Only representation learning +====================================== + +The representation learning does not require class labels and can be learned from the features alone. +The wrapper allows to explore multiple paramertes (i.e. different modes to +generate the features, please refer to the publication for details), for each +added parameter the encoded features are generated. Those features can then be passed to subsequent ML tools, +such as `Ensemble methods for classification and regression` or `Split Dataset into training and test subsets` + +===================================================== +Option 2) Representation learning and classification +===================================================== + +The tool itself can also evaluate the performance of the generated representation learning for different +classifiers internally using 5 fold CV. The wrapper allows to explore multiple paramertes and clfs. +Each parameter run will be stored as a line to the result file. If this option is chosen the latent representation is not +exported as output. To create the latent representation of the complete feature set, run the tool again +with the same parameters using the `Only representation learning` option. +The header of the result file is: + +'{Encoding}_{classifier}, AUC, ACC, Recall, Precision, F1_score, time-end, runtime(sec), classfication time(sec), best hyper-parameter' + +The overall schema of the tool is shown in: + +.. image:: ML_Workflow.png + ]]> + </help> + <expand macro="citations" /> + <expand macro="creator" /> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Tue May 02 17:39:13 2023 +0000 @@ -0,0 +1,49 @@ +<?xml version="1.0"?> +<macros> + <token name="@TOOL_VERSION@">1.4</token> + <token name="@VERSION_SUFFIX@">0</token> + <token name="@PROFILE@">22.05</token> + <xml name="biotools"> + <xrefs> + <xref type="bio.tools"> + DeepMicro + </xref> + </xrefs> + </xml> + <xml name="requirements"> + <requirements> + <requirement type="package" version="@TOOL_VERSION@">deepmicro</requirement> + </requirements> + </xml> + <xml name="version"> + <version_command><![CDATA[ + echo "@TOOL_VERSION@" + ]]></version_command> + </xml> + <xml name="citations"> + <citations> + <citation type="doi"> + 10.1038/s41598-020-63159-5 + </citation> + </citations> + </xml> + <xml name="creator"> + <creator> + <person givenName="Paul" familyName="Zierep" email="zierep@informatik.uni-freiburg.de" /> + </creator> + </xml> + <xml name="dm"> + <param name="dm" type="text" value="40" label="Dimensions for deep representation learning" help="Comma-separated dimensions for deep representation learning e.g. [50,30,20]"> + <validator type="regex" message="Use only numbers and commas for this input">^(\d+|\,)+$</validator> + </param > + + </xml> + <xml name="clfs"> + <param name="classifier" type="select" label="Classifier for the cross validation" help="The classifier used for the cross validation (`all` will create a run for each classifier)"> + <option value="svm">svm</option> + <option value="mlp">mlp</option> + <option value="rf">rf</option> + <option value="all">all</option> + </param> + </xml> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/UserDataExample.csv Tue May 02 17:39:13 2023 +0000 @@ -0,0 +1,20 @@ 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