Galaxy-ML

Galaxy-ML is a web machine learning end-to-end pipeline building framework, with special support to biomedical data. Under the management of unified scikit-learn APIs, cutting-edge machine learning libraries are combined together to provide thousands of different pipelines suitable for various needs. In the form of Galalxy tools, Galaxy-ML provides scalabe, reproducible and transparent machine learning computations.

Key features

• simple web UI
• no coding or minimum coding requirement
• fast model deployment and model selection, specialized in hyperparameter tuning using GridSearchCV
• high level of parallel and automated computation

Supported modules

A typic machine learning pipeline is composed of a main estimator/model and optional preprocessing component(s).

Model

• scikit-learn
  • sklearn.ensemble
  • sklearn.linear_model
  • sklearn.naive_bayes
  • sklearn.neighbors
  • sklearn.svm
  • sklearn.tree
• xgboost
  • XGBClassifier
  • XGBRegressor

• mlxtend

  • StackingCVClassifier
  • StackingClassifier
  • StackingCVRegressor
  • StackingRegressor

• Keras (Deep learning models are re-implemented to fully support sklearn APIs. Supports parameter, including layer subparameter, swaps or searches. Supports callbacks)

  • KerasGClassifier
  • KerasGRegressor
  • KerasGBatchClassifier (works best with online data generators, processing images, genomic sequences and so on)

• BinarizeTargetClassifier/BinarizeTargetRegressor

• IRAPSClassifier

Preprocessor

• scikit-learn
  • sklearn.preprocessing
  • sklearn.feature_selection
  • sklearn.decomposition
  • sklearn.kernel_approximation
  • sklearn.cluster
• imblanced-learn
  • imblearn.under_sampling
  • imblearn.over_sampling
  • imblearn.combine
• skrebate
  • ReliefF
  • SURF
  • SURFstar
  • MultiSURF
  • MultiSURFstar
• TDMScaler
• DyRFE/DyRFECV
• Z_RandomOverSampler
• GenomeOneHotEncoder
• ProteinOneHotEncoder
• FastaDNABatchGenerator
• FastaRNABatchGenerator
• FastaProteinBatchGenerator
• GenomicIntervalBatchGenerator
• GenomicVariantBatchGenerator
• ImageDataFrameBatchGenerator

Installation

APIs for models, preprocessors and utils implemented in Galaxy-ML can be installed separately.

Installing using anaconda (recommended)

conda install -c bioconda -c conda-forge Galaxy-ML

Installing using pip

pip install -U Galaxy-ML

Installing from source

python setup.py install

Using source code inplace

python install -e .

To install Galaxy-ML tools in Galaxy, please refer to https://galaxyproject.org/admin/tools/add-tool-from-toolshed-tutorial/.

Running the tests

Before running the tests, run the following commands:

conda create --name galaxy_ml python=3.9 conda activate galaxy_ml pip install -e . pip install nose nose-htmloutput pytest cd galaxy_ml

To run all tests and generate an HTML report: nosetests ./tests --with-html --html-file=./report.html

To run tests in a specific file (e.g., testkerasgalaxy.py file) and generate an HTML report nosetests ./tests/test_keras_galaxy.py --with-html --html-file=./report.html

To run a specific test in a specific file (e.g., testmultidimensionaloutput test in testkerasgalaxy.py file) and generate an HTML report ``` nosetests ./tests/testkerasgalaxy.py:testmultidimensionaloutput --with-html --html-file=./report.html ```

Examples for using Galaxy-ML custom models

```

handle imports

from keras.models import Sequential from keras.layers import Dense, Activation from sklearn.modelselection import GridSearchCV from galaxyml.kerasgalaxymodels import KerasGClassifier

build a DNN classifier

model = Sequential() model.add(Dense(64)) model.add(Activation(relu')) model.add((Dense(1, activation=sigmoid))) config = model.get_config()

classifier = KerasGClassifier(config, random_state=42)

clone a classifier

clf = clone(classifier)

Get parameters

params = clf.get_params()

Set parameters

newparams = dict( epochs=60, lr=0.01, layers1Denseconfigkernelinitializerconfigseed=999, layers0Denseconfigkernelinitializerconfigseed=999 ) clf.setparams(**new_params)

model evaluation using GridSearchCV

grid = GridSearchCV(clf, paramgrid={}, scoring=rocauc, cv=5, n_jobs=2) grid.fit(X, y) ```

Example for using Galaxy-ML to persist a sklearn/keras model

``` from galaxyml.modelpersist import (dumpmodeltoh5, loadmodelfromh5)

dump model to hdf5

dumpmodeltoh5(model, `savepath`, store_hyperparameter=True)

load model from hdf5

model = loadmodelfromh5(`pathto_hdf5) ``

Performance comparison

Galaxy-ML's HDF5 saving utils perform faster than cPickle for large, array-rich models.

``` Loading model using pickle... (1.2471628189086914 s)

Dumping model using pickle... (3.6942389011383057 s) File size: 930712861

Dumping model to hdf5... (3.006715774536133 s) File size: 930729696

Loading model from hdf5... (0.6420958042144775 s)

Pipeline(memory=None, steps=[('robustscaler', RobustScaler(copy=True, quantilerange=(25.0, 75.0), withcentering=True, withscaling=True)), ('kneighborsclassifier', KNeighborsClassifier(algorithm='auto', leafsize=30, metric='minkowski', metricparams=None, njobs=1, n_neighbors=100, p=2, weights='uniform'))], verbose=False) ```

Publication

Gu Q, Kumar A, Bray S, Creason A, Khanteymoori A, Jalili V, et al. (2021) Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine. PLoS Comput Biol 17(6): e1009014. https://doi.org/10.1371/journal.pcbi.1009014