Package: photonai
PHOTONAI is a high level python API for designing and optimizing machine learning pipelines.We've created a system in which you can easily select and combine both pre-processing and learning algorithms from
state-of-the-art machine learning toolboxes,
and arrange them in simple or parallel pipeline data streams.In addition, you can parametrize your training and testing
workflow choosing cross-validation schemes, performance metrics and hyperparameter
optimization metrics from a list of pre-registered options.Importantly, you can integrate custom solutions into your data processing pipeline,
but also for any part of the model training and evaluation process including custom
hyperparameter optimization strategies.For a detailed description,visit our website and read the documentationor you can read our paper inPLOS ONEGetting StartedIn order to use PHOTONAI you only need to have your favourite Python IDE ready.
Then install the latest stable version simply via pippip install photonai
# Or try out the latest features if you don't rely on a stable version, using:
pip install --upgrade git+https://github.com/wwu-mmll/photonai.git@developYou can setup a full stack machine learning pipeline in a few lines of code:fromsklearn.datasetsimportload_breast_cancerfromsklearn.model_selectionimportKFoldfromphotonaiimportHyperpipe,PipelineElement,FloatRange,Categorical,IntegerRange# DESIGN YOUR PIPELINEmy_pipe=Hyperpipe('basic_svm_pipe',# the name of your pipeline# which optimizer PHOTONAI shall useoptimizer='sk_opt',optimizer_params={'n_configurations':25},# the performance metrics of your interestmetrics=['accuracy','precision','recall','balanced_accuracy'],# after hyperparameter optimization, this metric declares the winner configbest_config_metric='accuracy',# repeat hyperparameter optimization three timesouter_cv=KFold(n_splits=3),# test each configuration five times respectively,inner_cv=KFold(n_splits=5),verbosity=1,project_folder='./tmp/')# first normalize all featuresmy_pipe.add(PipelineElement('StandardScaler'))# then do feature selection using a PCAmy_pipe+=PipelineElement('PCA',hyperparameters={'n_components':IntegerRange(5,20)},test_disabled=True)# engage and optimize the good old SVM for Classificationmy_pipe+=PipelineElement('SVC',hyperparameters={'kernel':Categorical(['rbf','linear']),'C':FloatRange(0.5,2)},gamma='scale')# train pipelineX,y=load_breast_cancer(return_X_y=True)my_pipe.fit(X,y)FeaturesEasy access to established ML implementationsWe pre-registered diverse preprocessing and learning algorithms from
state-of-the-art toolboxes e.g. scikit-learn, keras and imbalanced learn to
rapidly build custom pipelinesHyperparameter OptimizationWith PHOTONAI you can seamlessly switch between diverse hyperparameter
optimization strategies, such as (random) grid-search
or bayesian optimization (scikit-optimize, smac3).Extended ML PipelineYou can build custom sequences of processing and learning algorithms with a simple syntax.
PHOTONAI offers extended pipeline functionality such as parallel sequences, custom callbacks in-between pipeline
elements, AND- and OR- Operations, as well as the possibility to flexibly position data augmentation, class balancing
or learning algorithms anywhere in the pipeline.Model SharingPHOTONAI provides a standardized format for sharing and loading optimized pipelines across
platforms with only one line of code.AutomationWhile you concentrate on selecting appropriate processing steps, learning algorithms, hyperparameters and
training parameters, PHOTONAI automates the nested cross-validated optimization and evaluation loop for any custom pipeline.Results VisualizationPHOTONAI comes with extensive logging of all information in the training, testing and hyperparameter
optimization process. In addition, optimum performances and the hyperparameter optimization progress
are visualized in thePHOTONAI Explorer.For more use cases, examples, contribution guidelines and API details visit our websitewww.photon-ai.com