Package: xingu
Xingu for automated ML model trainingXingu is a framework of a few classes that helps on full industrialization of
your machine learning training and deployment pipelines. Just write your
DataProvider class, mostly in a declarative way, that completely controls
your training and deployment pipeline.Notebooks are useful in EDA time, but when the modeling is ready to become
a product, use Xingu proposed classes to organize interactions with DB
(queries), data cleanup, feature engineering, hyper-parameters optimization,
training algorithm, general and custom metrics computation, estimation
post-processing.Don’t save a pickle at the end of your EDA, let Xingu organize a versioned
inventory of saved models (PKLs) linked and associated to commit hashes and
branches of your code.Don’t save metrics manually and in an informal way. Metrics are first class
citizens, so use Xingu to write methods that compute metrics and let it
store metrics in an organized database that can be queried and compared.Don’t make ad-hoc plots to understand your data. Plots are important assets
to measure the quality of your model, so use Xingu to write methods that
formaly generate versioned plots.Do not worry or even write code that loads pre-req models, use Xingu pre-req
architecture to load pre-req models for you and package them together.Don’t save ad-hoc hypermaters after optimizations. Let Xingu store and manage
those for you in a way that can be reused in future trains.Don’t change your code if you want different functionality. Use Xingu
environment variables or command line parameters to strategize your trains.Don’t manually copy PKLs to production environments on S3 or other object
storage. Use Xingu’s deployment tools to automate the deployment step.Don’t write database integration code. Just provide your queries and Xingu
will give you the data. Xingu will also maintain a local cache of your data
so you won’t hammer your database across multiple retrains. Do the same with
static data files with parquet, CSV, on local filesystem or object storage.Xingu can run anyware, from your laptop, with a plain SQLite database, to
large scale cloud-powered training pipelines with GitOps, Jenkins, Docker
etc. Xingu’s database is used only to cellect training information, it isn´t
required later when model is used to predict.Installpipinstallhttps://github.com/avibrazil/xinguorpipinstallxinguUse to Train a ModelCheck your project has the necessary files and folders:$find
dataproviders/
dataproviders/my_dataprovider.py
estimators/
estimators/myrandomestimator.py
models/
data/
plots/Train with DataProvidersid_of_my_dataprovider1andid_of_my_dataprovider2, both defined indataproviders/my_dataprovider.py:$xingu\--dpsid_of_my_dataprovider1,id_of_my_dataprovider2\--databasesathena"awsathena+rest://athena.us..."\--query-cache-pathdata\--trained-models-pathmodels\--debugUse the APISee theproof of concept notebookwith vairous usage scenarios:POC 1. Train some ModelsPOC 2. Use Pre-Trained Models for Batch PredictPOC 3. Assess Metrics and create Comparative ReportsPOC 4. Check and report how Metrics evolvedPOC 5. Play with Xingu barebonesPOC 6. Play with theConfigManagerPOC 7. Xingu Estimators in the Command LinePOC 8. Deploy Xingu Data and Estimators between environments (laptop, staging, production etc)Procedures defined by XinguXingu classes do all the heavy lifting while you focus on your machine learning
code only.ClassCoachis responsible of coordinating the training process of one or
multiple models. You control parallelism via command line or environment
variables.ClassModelimplements a standard pipelines for train, train with hyperparam
optimization, load and save pickles, database access etc. These pipelines are
is fully controlled by your DataProvider or the environment.ClassDataProvideris a base class that is constantly queried by theModelto determine how theModelshould operate. Your should create a class derived
fromDataProviderand reimplement whatever you want to change. This will
completely change behaviour ofModeloperation in a way that you´ll get a
completelly different model.It is yourDataProviderthat defines the source of training data as SQL
queries or URLs of parquets, CSVs, JSONsIt is yourDataProviderthat defines how multi-source data should be
integratedIt is yourDataProviderthat defines how data should be split into train
and test setsYourDataProviderdefines whichEstimatorclass to useYourDataProviderdefines how theEstimatorshould be initialized and
optimizedYourDataProviderdefines which metrics should be computed, how to
compute them and against which datasetYourDataProviderdefines which plots should be created and against
which datasetSee below when and how each method of yourDataProviderwill be called
byxingu.ModelClassEstimatoris another base class (that you can reimplement) to contain
estimator-specific affairs. There will be anEstimator-derived class for an
XGBoostRegressor, other for a CatBoostClassifier, other for a
SciKit-Learn-specific algorithm, including hyperparam optimization logic and
libraries. A concreteEstimatorclass can and should be reused across multiple
different models.The hierarchical diagrams below expose complete Xingu pipelines with all their
steps. Steps marked with 💫 are were you put your code. All the rest is Xingu
boilerplate code ready to use.Coach.team_train():Train various Models, all possible in parallel.Coach.team_train_parallel()(background, parallelism controled byPARALLEL_TRAIN_MAX_WORKERS):Coach.team_load()(for pre-req models not trained in this session)Per DataProvider requested to be trained:Coach.team_train_member()(background):Model.fit()calls:💫DataProvider.get_dataset_sources_for_train()return dict of queries and/or URLsModel.data_sources_to_data(sources)💫DataProvider.clean_data_for_train(dict of DataFrames)💫DataProvider.feature_engineering_for_train(DataFrame)💫DataProvider.last_pre_process_for_train(DataFrame)💫DataProvider.data_split_for_train(DataFrame)return tuple of dataframesModel.hyperparam_optimize()(decide origin of hyperparam)💫DataProvider.get_estimator_features_list()💫DataProvider.get_target()💫DataProvider.get_estimator_optimization_search_space()💫DataProvider.get_estimator_hyperparameters()💫Estimator.hyperparam_optimize()(SKOpt, GridSearch et all)💫Estimator.hyperparam_exchange()💫DataProvider.post_process_after_hyperparam_optimize()💫Estimator.fit()💫DataProvider.post_process_after_train()Coach.post_train_parallel()(background, only ifPOST_PROCESS=true):Per trained Model (parallelism controled byPARALLEL_POST_PROCESS_MAX_WORKERS):Model.save()(PKL save in background)Model.trainsets_save()(save the train datasets, background)Model.trainsets_predict():Model.predict_proba()orModel.predict()(seebelow)💫DataProvider.pre_process_for_trainsets_metrics()Model.compute_and_save_metrics(channel=trainsets)(seebelow)💫DataProvider.post_process_after_trainsets_metrics()Coach.single_batch_predict()(seebelow)Coach.team_batch_predict():Load from storage and use various pre-trained Models to estimate data from a pre-defined SQL query.
The batch predict SQL query is defined into the DataProvider and this process will query the database
to get it.Coach.team_load()(for all requested DPs and their pre-reqs)Per loaded model:Coach.single_batch_predict()(background)Model.batch_predict()💫DataProvider.get_dataset_sources_for_batch_predict()Model.data_sources_to_data()💫DataProvider.clean_data_for_batch_predict()💫DataProvider.feature_engineering_for_batch_predict()💫DataProvider.last_pre_process_for_batch_predict()Model.predict_proba()orModel.predict()(seebelow)Model.compute_and_save_metrics(channel=batch_predict)(seebelow)Model.save_batch_predict_estimations()Model.predict()andModel.predict_proba():Model.generic_predict()💫DataProvider.pre_process_for_predict()orDataProvider.pre_process_for_predict_proba()💫DataProvider.get_estimator_features_list()💫Estimator.predict()orEstimator.predict_proba()💫DataProvider.post_process_after_predict()orDataProvider.post_process_after_predict_proba()Model.compute_and_save_metrics():Sub-system to compute various metrics, graphics and transformations over
a facet of the data.This is executed right after a Model was trained and also during a batch predict.Predicted data is computed beforeModel.compute_and_save_metrics()is called.
ByModel.trainsets_predict()andModel.batch_predict().Model.save_model_metrics()calls:Model.compute_model_metrics()calls:Model.compute_trainsets_model_metrics()calls:AllModel.compute_trainsets_model_metrics_{NAME}()All 💫DataProvider.compute_trainsets_model_metrics_{NAME}()Model.compute_batch_model_metrics()calls:AllModel.compute_batch_model_metrics_{NAME}()All 💫DataProvider.compute_batch_model_metrics_{NAME}()Model.compute_global_model_metrics()calls:AllModel.compute_global_model_metrics_{NAME}()All 💫DataProvider.compute_global_model_metrics_{NAME}()Model.render_model_plots()calls:Model.render_trainsets_model_plots()calls:AllModel.render_trainsets_model_plots_{NAME}()All 💫DataProvider.render_trainsets_model_plots_{NAME}()Model.render_batch_model_plots()calls:AllModel.render_batch_model_plots_{NAME}()All 💫DataProvider.render_batch_model_plots_{NAME}()Model.render_global_model_plots()calls:AllModel.render_global_model_plots_{NAME}()All 💫DataProvider.render_global_model_plots_{NAME}()Model.save_estimation_metrics()calls:Model.compute_estimation_metrics()calls:AllModel.compute_estimation_metrics_{NAME}()All 💫DataProvider.compute_estimation_metrics_{NAME}()