Package: arm-mango
Mango: A parallel hyperparameter tuning libraryMangois a python library to find the optimal hyperparameters for machine learning classifiers.
Mango enables parallel optimization over complex search spaces of continuous/discrete/categorical values.Check out the quick 12 seconds demoof Mango approximating a complex decision boundary of SVMMango has the following salient features:Easily define complex search spaces compatible with the scikit-learn.A novel state-of-the-art gradient-free optimizer for continuous/discrete/categorical values.Modular design to schedule objective function on local, cluster,  or cloud infrastructure.Failure detection in the application layer for scalability on commodity hardware.New features are continuously added due to the testing and usage in production settings.IndexInstallationGetting startedHyperparameter tuning exampleSearch space definitionsSchedulerOptional configurationsAdditional featuresCASH featurePlatform-aware neural architecture searchMango introduction slides&Mango production usage slides.Core Mango research papers to citeandnovel applications built over Mango1. InstallationUsingpip:pip install arm-mangoFrom source:$ git clone https://github.com/ARM-software/mango.git
$ cd mango
$ pip3 install .2. Getting StartedMango is straightforward to use. Following example minimizes the quadratic function whose input is an integer between -10 and 10.frommangoimportscheduler,Tuner# Search spaceparam_space=dict(x=range(-10,10))# Quadratic objective Function@scheduler.serialdefobjective(x):returnx*x# Initialize and run Tunertuner=Tuner(param_space,objective)results=tuner.minimize()print(f'Optimal value of parameters:{results["best_params"]}and objective:{results["best_objective"]}')```# => Optimal value of parameters: {'x': 0}  and objective: 03. Hyperparameter Tuning Examplefromsklearnimportdatasetsfromsklearn.neighborsimportKNeighborsClassifierfromsklearn.model_selectionimportcross_val_scorefrommangoimportTuner,scheduler# search space for KNN classifier's hyperparameters# n_neighbors can vary between 1 and 50, with different choices of algorithmparam_space=dict(n_neighbors=range(1,50),algorithm=['auto','ball_tree','kd_tree','brute'])@scheduler.serialdefobjective(**params):X,y=datasets.load_breast_cancer(return_X_y=True)clf=KNeighborsClassifier(**params)score=cross_val_score(clf,X,y,scoring='accuracy').mean()returnscoretuner=Tuner(param_space,objective)results=tuner.maximize()print('best parameters:',results['best_params'])print('best accuracy:',results['best_objective'])# => best parameters: {'algorithm': 'ball_tree', 'n_neighbors': 11}# => best accuracy: 0.9332401800962584Note that best parameters may be different but accuracy should be ~ 0.93. More examples are available
in theexamplesdirectory (Facebook's Prophet,XGBoost,SVM).4. Search SpaceThe search space defines the range and distribution of input parameters to the objective function.
Mango search space is compatible with scikit-learn's parameter space definitions used inRandomizedSearchCVorGridSearchCV.
The search space is defined as a dictionary with keys being the parameter names (string) and values being
list of discreet choices, range of integers or the distributions. Example of some common search spaces are:IntegerFollowing space definesxas an integer parameters with values inrange(-10, 11)(11 is not included):param_space=dict(x=range(-10,11))#=> -10, -9, ..., 10# you can use steps for sparse rangesparam_space=dict(x=range(0,101,10))#=> 0, 10, 20, ..., 100Integers are uniformly sampled from the given range and are assumed to be ordered and treated as continuous variables.CategoricalDiscreet categories can be defined as lists. For example:# stringparam_space=dict(color=['red','blue','green'])# floatparam_space=dict(v=[0.2,0.1,0.3])# mixedparam_space=dict(max_features=['auto',0.2,0.3])Lists are uniformly sampled and are assumed to be unordered. They are one-hot encoded internally.DistributionsAll the distributions supported byscipy.statsare supported.
In general, distributions must provide arvsmethod for sampling.Uniform distributionUsinguniform(loc, scale)one obtains the uniform distribution on[loc, loc + scale].fromscipy.statsimportuniform# uniformly distributed between -1 and 1param_space=dict(a=uniform(-1,2))Log uniform distributionWe have addedloguniformdistribution by extending thescipy.stats.distributionsconstructs.
Usingloguniform(loc, scale)one obtains the loguniform distribution on[10loc, 10loc + scale].frommango.domain.distributionimportloguniform# log uniformly distributed between 10^-3 and 10^-1param_space=dict(learning_rate=loguniform(-3,2))Hyperparameter search space examplesExample hyperparameter search space forRandom Forest Classifier:param_space=dict(max_features=['sqrt','log2',.1,.3,.5,.7,.9],n_estimators=range(10,1000,50),# 10 to 1000 in steps of 50bootstrap=[True,False],max_depth=range(1,20),min_samples_leaf=range(1,10))Example search space forXGBoost Classifier:fromscipy.statsimportuniformfrommango.domain.distributionimportloguniformparam_space={'n_estimators':range(10,2001,100),# 10 to 2000 in steps of 100'max_depth':range(1,15),# 1 to 14'reg_alpha':loguniform(-3,6),# 10^-3 to 10^3'booster':['gbtree','gblinear'],'colsample_bylevel':uniform(0.05,0.95),# 0.05 to 1.0'colsample_bytree':uniform(0.05,0.95),# 0.05 to 1.0'learning_rate':loguniform(-3,3),# 0.001 to 1'reg_lambda':loguniform(-3,6),# 10^-3 to 10^3'min_child_weight':loguniform(0,2),# 1 to 100'subsample':uniform(0.1,0.89)# 0.1 to 0.99}Example search space forSVM:fromscipy.statsimportuniformfrommango.domain.distributionimportloguniformparam_dict={'kernel':['rbf','sigmoid'],'gamma':uniform(0.1,4),# 0.1 to 4.1'C':loguniform(-7,8)# 10^-7 to 10}5. SchedulerMango is designed to take advantage of distributed computing. The objective function can be scheduled to
run locally or on a cluster with parallel evaluations. Mango is designed to allow the use of any distributed
computing framework (like Celery or Kubernetes). Theschedulermodule comes with some pre-defined
schedulers.Serial schedulerSerial scheduler runs locally with one objective function evaluation at a timefrommangoimportscheduler@scheduler.serialdefobjective(x):returnx*xParallel schedulerParallel scheduler runs locally and usesjoblibto evaluate the objective functions in parallelfrommangoimportscheduler@scheduler.parallel(n_jobs=2)defobjective(x):returnx*xn_jobsspecifies the number of parallel evaluations.n_jobs = -1uses all the available cpu cores
on the machine. Seesimple_parallelfor full working example.Custom distributed schedulerUsers can define their own distribution strategies usingcustomscheduler. To do so, users need to define
an objective function that takes a list of parameters and returns the list of results:frommangoimportscheduler@scheduler.custom(n_jobs=4)defobjective(params_batch):""" Template for custom distributed objective functionArgs:params_batch (list): Batch of parameter dictionaries to be evaluated in parallelReturns:list: Values of objective function at given parameters"""# evaluate the objective on a distributed framework...returnresultsFor example the following snippet usesCelery:importceleryfrommangoimportTuner,scheduler# connect to celery backendapp=celery.Celery('simple_celery',backend='rpc://')# remote celery task@app.taskdefremote_objective(x):returnx*x@scheduler.custom(n_jobs=4)defobjective(params_batch):jobs=celery.group(remote_objective.s(params['x'])forparamsinparams_batch)()returnjobs.get()param_space=dict(x=range(-10,10))tuner=Tuner(param_space,objective)results=tuner.minimize()A working example to tune hyperparameters of KNN using Celery ishere.6. Optional configurationsThe default configuration parameters used by the Mango as below:{'param_dict':...,'userObjective':...,'domain_size':5000,'initial_random':1,'num_iteration':20,'batch_size':1}The configuration parameters are:domain_size: The size which is explored in each iteration by the gaussian process. Generally, a larger size is preferred if higher dimensional functions are optimized. More on this will be added with details about the internals of bayesian optimization.initial_random: The number of random samples tried. Note: Mango returns all the random samples together. Users can exploit this to parallelize the random runs without any constraint.num_iteration: The total number of iterations used by Mango to find the optimal value.batch_size: The size of args_list passed to the objective function for parallel evaluation. For larger batch sizes, Mango internally uses intelligent sampling to decide the optimal samples to evaluate.early_stopping: A Callable to specify custom stopping criteria. The callback has the following signature:defearly_stopping(results):'''results is the same as dict returned by tunerkeys available: params_tries, objective_values,best_objective, best_params'''...returnTrue/FalseEarly stopping is one of Mango's important features that allow to early terminate the current parallel search
based on the custom user-designed criteria, such as the total optimization time spent, current validation accuracy
achieved, or improvements in the past few iterations. For usage see early stopping examplesnotebook.constraint: A callable to specify constraints on parameter space. It has the following
signature:defconstraint(samples:List[dict])->List[bool]:'''Given a list of samples (each sample is a dict with parameter names as keys)Returns a list of True/False elements indicating whether the corresponding samplesatisfies the constraints or not'''Seethisnotebook for an example.initial_custom: A list of initial evaluation points to warm up the optimizer instead of random sampling.
It can be either:A list of dict with parameters. For example, for a search space with two parametersx1andx2the input could be:[{'x1': 10, 'x2': -5}, {'x1': 0, 'x2': 10}].A list of tuple with parameters and objective function values. For example, if the objective function is to addx1andx2the input could be:[({'x1': 10, 'x2': -5}, 5), ({'x1': 0, 'x2': 10}, 10)].This allows the user to customize the initial evaluation points and therefore guide the optimization process.
It also enables starting the optimizer from the results of a previous tuner run (seethisnotebook for a working example).
Note that ifinitial_customoption is given theninitial_randomis ignored.The default configuration parameters can be modified, as shown below. Only the parameters whose values need to adjusted can be passed as the dictionary.conf_dict=dict(num_iteration=40,domain_size=10000,initial_random=3)tuner=Tuner(param_dict,objective,conf_dict)7. Additional FeaturesHandling runtime failed evaluationAt runtime, failed evaluations are widespread in production deployments. Mango abstractions enable users to make progress even in the presence of failures by only using the correct evaluations. The syntax can return the successful evaluation, and the user can flexibly keep track of failures, for example, using timeouts. Examples showing the usage of Mango in the presence of failures:serial executionandparallel executionNeural Architecture SearchMango can also do an efficient neural architecture search. An example on the MNIST dataset to search for optimal filter sizes, the number of filters, etc., isavailable.More extensive examples are available in theTHIN-Bayesfolder doingNeural Architecture Searchfor a class of neural networks and classical models for different regression and classification tasks.8. Combiner Classifier Selection and Optimization (CASH)Mango now provides a novel functionality of combined classifier selection and optimization. It allows developers to directly specify a set of classifiers along with their different hyperparameter spaces. Mango internally finds the best classifier along with the optimal parameters with the least possible number of overall iterations. The examples are  availablehereThe important parts in the skeletion code are as below.frommangoimportMetaTuner#define search spaces and objective functions as done for tuner.param_space_list=[param_space1,param_space2,param_space3,param_space4,..]objective_list=[objective_1,objective_2,objective_3,objective_4,..]metatuner=MetaTuner(param_space_list,objective_list)results=metatuner.run()print('best_objective:',results['best_objective'])print('best_params:',results['best_params'])print('best_objective_fid:',results['best_objective_fid'])ParticipateCore Papers to Cite MangoMore technical details are available in theMango paper-1 (ICASSP 2020)andMango paper-2 (CogMI 2021)Please cite them as:@inproceedings{sandha2020mango,
  title={Mango: A Python Library for Parallel Hyperparameter Tuning},
  author={Sandha, Sandeep Singh and Aggarwal, Mohit and Fedorov, Igor and Srivastava, Mani},
  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={3987--3991},
  year={2020},
  organization={IEEE}
}@inproceedings{sandha2021mango,
  title={Enabling Hyperparameter Tuning of Machine Learning Classifiers in Production},
  author={Sandha, Sandeep Singh and Aggarwal, Mohit and Saha, Swapnil Sayan and Srivastava, Mani},
  booktitle={CogMI 2021, IEEE International Conference on Cognitive Machine Intelligence},
  year={2021},
  organization={IEEE}
}Novel Applications built over Mango@article{saha2022auritus,
  title={Auritus: An open-source optimization toolkit for training and development of human movement models and filters using earables},
  author={Saha, Swapnil Sayan and Sandha, Sandeep Singh and Pei, Siyou and Jain, Vivek and Wang, Ziqi and Li, Yuchen and Sarker, Ankur and Srivastava, Mani},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume={6},
  number={2},
  pages={1--34},
  year={2022},
  publisher={ACM New York, NY, USA}
}@article{saha2022tinyodom,
  title={Tinyodom: Hardware-aware efficient neural inertial navigation},
  author={Saha, Swapnil Sayan and Sandha, Sandeep Singh and Garcia, Luis Antonio and Srivastava, Mani},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume={6},
  number={2},
  pages={1--32},
  year={2022},
  publisher={ACM New York, NY, USA}
}@article{saha2022thin,
  title={THIN-Bayes: Platform-Aware Machine Learning for Low-End IoT Devices},
  author={Saha, Swapnil Sayan and Sandha, Sandeep Singh and Aggarwal, Mohit and Srivastava, Mani},
  year={2022}
}SlidesSlides explaining Mango abstractions and design choices are available.Mango Slides-1,Mango Slides-2.ContributePlease take a look atopen issuesif you are looking for areas to contribute to.QuestionsFor any questions feel free to reach out by creating an issuehere.