Package: atom-ml
Automated Tool for Optimized ModellingA Python package for fast exploration of machine learning pipelines📜 OverviewMavsm.524687@gmail.comDocumentationSlackGeneral InformationRepositoryReleaseCompatibilityBuild statusCode analysis💡 IntroductionDuring the exploration phase of a machine learning project, a data
scientist tries to find the optimal pipeline for his specific use case.
This usually involves applying standard data cleaning steps, creating
or selecting useful features, trying out different models, etc. Testing
multiple pipelines requires many lines of code, and writing it all in
the same notebook often makes it long and cluttered. On the other hand,
using multiple notebooks makes it harder to compare the results and to
keep an overview. On top of that, refactoring the code for every test
can be quite time-consuming. How many times have you conducted the same
action to pre-process a raw dataset? How many times have you
copy-and-pasted code from an old repository to re-use it in a new use
case?ATOM is here to help solve these common issues. The package acts as
a wrapper of the whole machine learning pipeline, helping the data
scientist to rapidly find a good model for his problem. Avoid
endless imports and documentation lookups. Avoid rewriting the same
code over and over again. With just a few lines of code, it's now
possible to perform basic data cleaning steps, select relevant
features and compare the performance of multiple models on a given
dataset, providing quick insights on which pipeline performs best
for the task at hand.Example steps taken by ATOM's pipeline:Data CleaningHandle missing valuesEncode categorical featuresDetect and remove outliersBalance the training setFeature engineeringCreate new non-linear featuresSelect the most promising featuresTrain and validate multiple modelsApply hyperparameter tuningFit the models on the training setEvaluate the results on the test setAnalyze the resultsGet the scores on various metricsMake plots to compare the model performances🛠️ InstallationInstall ATOM's newest release easily viapip:$ pip install -U atom-mlor viaconda:$ conda install -c conda-forge atom-ml⚡ UsageATOM contains a variety of classes and functions to perform data cleaning,
feature engineering, model training, plotting and much more. The easiest
way to use everything ATOM has to offer is through one of the main classes:ATOMClassifierfor binary or multiclass classification tasks.ATOMRegressorfor regression tasks.Let's walk you through an example. Click on the SageMaker Studio Lab badge
on top of this section to run this example yourself.Make the necessary imports and load the data.import pandas as pdfrom atom import ATOMClassifier# Load the Australian Weather datasetX = pd.read_csv("https://raw.githubusercontent.com/tvdboom/ATOM/master/examples/datasets/weatherAUS.csv")X.head()Initialize the ATOMClassifier or ATOMRegressor class. These two classes
are convenient wrappers for the whole machine learning pipeline. Contrary
to sklearn's API, they are initialized providing the data you want to
manipulate.atom = ATOMClassifier(X, y="RainTomorrow", n_rows=1000, verbose=2)Data transformations are applied through atom's methods. For example,
calling theimputemethod will initialize anImputerinstance, fit it on the training set and transform the whole dataset.
The transformations are applied immediately after calling the method
(no fit and transform commands necessary).atom.impute(strat_num="median", strat_cat="most_frequent")atom.encode(strategy="Target", max_onehot=8)Similarly, models aretrained and evaluatedusing therunmethod. Here, we fit both aLinearDiscriminantAnalysisandAdaBoostmodel,
and applyhyperparameter tuning.atom.run(models=["LDA", "AdaB"], metric="auc", n_trials=10)And lastly, analyze the results.atom.evaluate()DocumentationRelevant links⭐AboutLearn more about the package.🚀Getting startedNew to ATOM? Here's how to get you started!📢Release historyWhat are the new features of the latest release?👨‍💻User guideHow to use ATOM and its features.🎛️API ReferenceThe detailed reference for ATOM's API.📋ExamplesExample notebooks show you what can be done and how.❔FAQGet answers to frequently asked questions.🔧ContributingDo you wan to contribute to the project? Read this before creating a PR.🌳DependenciesWhich other packages does ATOM depend on?📃LicenseCopyright and permissions under the MIT license.