| layout | website | subdomain |
|---|---|---|
subsite-galaxy |
ml |
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{:.rna-intro-right}
The Galaxy Machine Learning workbench is a comprehensive set of data preprocessing, machine learning, deep learning and visualisation tools, consolidated workflows for end-to-end machine learning analysis and training materials to showcase the usage of these tools. The workbench is available on the Galaxy framework{:target="_blank"}, which guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated machine learning analyses independent of command-line knowledge.
The workbench provides you with a Swiss Army knife of scikit-learn{:target="_blank"}, Keras{:target="_blank"} (a deep learning library based on TensorFlow{:target="_blank"}) and various other tools to transform, learn and predict and plot your data.
The workbench is currently developed by the Goecks Lab and the European Galaxy project{:target="_blank"}. The German Network for Bioinformatics Infrastructure (de.NBI){:target="_blank"}, which runs the German ELIXIR Node{:target="_blank"}, provides the necessary compute clusters with CPUs and GPU resources.
The project is a community effort, please jump in, ask questions, and contribute to the development of new tools, workflows or trainings!
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- TOC {:toc}
Are you new to Galaxy, or returning after a long time, and looking for help to get started? Take [a guided tour]({{ page.website }}/tours/core.galaxy_ui){:target="_blank"} through Galaxy's user interface.
We are passionate about training. So we are working in close collaboration with the Galaxy Training Network (GTN){:target="_blank"} to develop training materials of data analyses based on Galaxy {% cite batut2017community %}. These materials hosted on the GTN GitHub repository are available online at https://training.galaxyproject.org{:target="_blank"}.
Want to learn more about machine learning? Take one of our guided tours or check out the following hands-on tutorials, developed together with the GTN community.
In this section we list the most important tools that have been integrated into the Machine Learning workbench. There are many more tools available so please have a more detailed look at the tool panel. For better readability, we have divided them into categories.
Identifying which category an object belongs to.
| Tool | Description | Reference |
|---|---|---|
| {% include tool.html id="sklearn_svm_classifier" %} | Support vector machines (SVMs) for classification | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_nn_classifier" %} | Nearest Neighbors Classification | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_ensemble" %} | Ensemble methods for classification and regression | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_discriminant_classifier" %} | Linear and Quadratic Discriminant Analysis | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_generalized_linear" %} | Generalized linear models for classification and regression | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_clf_metrics" %} | Calculate metrics for classification performance | Pedregosa et al. 2011{:target="_blank"} |
| {: .table.table-striped .tooltable} |
Predicting a continuous-valued attribute associated with an object.
| Tool | Description | Reference |
|---|---|---|
| {% include tool.html id="sklearn_ensemble" %} | Ensemble methods for classification and regression | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_generalized_linear" %} | Generalized linear models for classification and regression | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_regression_metrics" %} | Calculate metrics for regression performance | Pedregosa et al. 2011{:target="_blank"} |
| {: .table.table-striped .tooltable} |
Automatic grouping of similar objects into sets.
| Tool | Description | Reference |
|---|---|---|
| {% include tool.html id="sklearn_numeric_clustering" %} | Different numerical clustering algorithms | Pedregosa et al. 2011{:target="_blank"} |
| {: .table.table-striped .tooltable} |
Building general machine learning models.
| Tool | Description | Reference |
|---|---|---|
| {% include tool.html id="sklearn_estimator_attributes" %} | Estimator attributes to get all attributes from an estimator or scikit object | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_stacking_ensemble_models" %} | Stacking Ensembles to build stacking, voting ensemble models with numerous base options | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_searchcv" %} | Hyperparameter Search performs hyperparameter optimization using various SearchCVs | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_build_pipeline" %} | Pipeline Builder as an all-in-one platform to build pipeline, single estimator, preprocessor and custom wrappers | Pedregosa et al. 2011{:target="_blank"} |
| {: .table.table-striped .tooltable} |
Evaluation, validating and choosing parameters and models.
| Tool | Description | Reference |
|---|---|---|
| {% include tool.html id="sklearn_model_validation" %} | Model Validation includes cross_validate, cross_val_predict, learning_curve, and more | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_pairwise_metrics" %} | Evaluate pairwise distances or compute affinity or kernel for sets of samples | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_train_test_eval" %} | Train, Test and Evaluation to fit a model using part of dataset and evaluate using the rest | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="model_prediction" %} | Model Prediction predicts on new data using a preffited model | Chollet et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_fitted_model_eval" %} | Evaluate a Fitted Model using a new batch of labeled data | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_model_fit" %} | Fit a Pipeline, Ensemble or other models using a labeled dataset | Pedregosa et al. 2011{:target="_blank"} |
| {: .table.table-striped .tooltable} |
Feature selection and preprocessing.
| Tool | Description | Reference |
|---|---|---|
| {% include tool.html id="sklearn_data_preprocess" %} | Preprocess raw feature vectors into standardized datasets | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_feature_selection" %} | Feature Selection module, including univariate filter selection methods and recursive feature elimination algorithm | Pedregosa et al. 2011{:target="_blank"} |
| {: .table.table-striped .tooltable} |
Build and use deep neural networks.
| Tool | Description | Reference |
|---|---|---|
| {% include tool.html id="keras_batch_models" %} | Build Deep learning Batch Training Models with online data generator for Genomic/Protein sequences and images | Chollet et al. 2011{:target="_blank"} |
| {% include tool.html id="keras_model_builder" %} | Create deep learning model with an optimizer, loss function and fit parameters | Chollet et al. 2011{:target="_blank"} |
| {% include tool.html id="keras_model_config" %} | Create a deep learning model architecture using Keras | Chollet et al. 2011{:target="_blank"} |
| {% include tool.html id="keras_train_and_eval" %} | Deep learning training and evaluation either implicitly or explicitly | Chollet et al. 2011{:target="_blank"} |
| {: .table.table-striped .tooltable} |
Plotting and visualization.
| Tool | Description | Reference |
|---|---|---|
| {% include tool.html id="plotly_regression_performance_plots" %} | Plot actual vs predicted curves and residual plots of tabular data | |
| {% include tool.html id="plotly_ml_performance_plots" %} | Plot confusion matrix, precision, recall and ROC and AUC curves of tabular data | |
| {% include tool.html id="ml_visualization_ex" %} | Machine Learning Visualization Extension includes several types of plotting for machine learning | Chollet et al. 2011{:target="_blank"} |
| {: .table.table-striped .tooltable} |
General data and table manipulation tools.
| Tool | Description | Reference |
|---|---|---|
| {% include tool.html id="table_compute" %} | The power of the pandas data library for manipulating and computing expressions upon tabular data and matrices. | |
| {% include tool.html id="datamash_ops" %} | Datamash operations on tabular data | |
| {% include tool.html id="datamash_transpose" %} | Transpose rows/columns in a tabular file | |
| {% include tool.html id="sklearn_sample_generator" %} | Generate random samples with controlled size and complexity | Pedregosa et al. 2011{:target="_blank"} |
| {% include tool.html id="sklearn_train_test_split" %} | Split Dataset into training and test subsets | Pedregosa et al. 2011{:target="_blank"} |
| {: .table.table-striped .tooltable} |
You have done the heavy lifting and now want to use your coding skills inside Jupyter or RStudio? Work on data with the following:
| Tool | Description | Reference |
|---|---|---|
| Jupyter{:target="_blank"} | Jupyter lab | |
| RStudio{:target="_blank"} | RStudio | |
| {: .table.table-striped .tooltable} |