{ "info": { "author": "tvdboom", "author_email": "m.524687@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "
\r\n

\r\n\t\"ATOM\"\r\n

\r\n\r\n# Automated Tool for Optimized Modelling\r\n### A Python package for fast exploration of machine learning pipelines\r\n
\r\n\r\n

\r\n\r\n\r\n\r\n\ud83d\udcdc Overview \r\n-----------\r\n\r\n

\r\n\"Author\" Mavs\r\n    \r\n\"Email\" m.524687@gmail.com\r\n    \r\n\"Documentation\" Documentation\r\n    \r\n\"Slack\" Slack\r\n

\r\n\r\n
\r\n\r\n**General Information** | |\r\n--- | ---\r\n**Repository** | [![Project Status: Active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Conda Recipe](https://img.shields.io/badge/recipe-atom--ml-green.svg)](https://anaconda.org/conda-forge/atom-ml) [![License: MIT](https://img.shields.io/github/license/tvdboom/ATOM)](https://opensource.org/licenses/MIT) [![Downloads](https://pepy.tech/badge/atom-ml)](https://pepy.tech/project/atom-ml)\r\n**Release** | [![PyPI version](https://img.shields.io/pypi/v/atom-ml)](https://pypi.org/project/atom-ml/) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/atom-ml.svg)](https://anaconda.org/conda-forge/atom-ml) [![DOI](https://zenodo.org/badge/195069958.svg)](https://zenodo.org/badge/latestdoi/195069958)\r\n**Compatibility** | [![Python 3.7\\|3.8\\|3.9\\|3.10](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue?logo=python)](https://www.python.org) [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/atom-ml.svg)](https://anaconda.org/conda-forge/atom-ml)\r\n**Build status** | [![Build Status](https://github.com/tvdboom/ATOM/workflows/ATOM/badge.svg)](https://github.com/tvdboom/ATOM/actions) [![Azure Pipelines](https://dev.azure.com/conda-forge/feedstock-builds/_apis/build/status/atom-ml-feedstock?branchName=master)](https://dev.azure.com/conda-forge/feedstock-builds/_build/latest?definitionId=10822&branchName=master) [![codecov](https://codecov.io/gh/tvdboom/ATOM/branch/master/graph/badge.svg)](https://codecov.io/gh/tvdboom/ATOM)\r\n**Code analysis** | [![PEP8](https://img.shields.io/badge/code%20style-pep8-orange.svg)](https://www.python.org/dev/peps/pep-0008/) [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/tvdboom/ATOM.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/tvdboom/ATOM/context:python) [![Total alerts](https://img.shields.io/lgtm/alerts/g/tvdboom/ATOM.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/tvdboom/ATOM/alerts/)\r\n\r\n\r\n

\r\n\r\n\r\n\r\n\ud83d\udca1 Introduction \r\n---------------\r\n\r\nDuring the exploration phase of a machine learning project, a data\r\nscientist tries to find the optimal pipeline for his specific use case.\r\nThis usually involves applying standard data cleaning steps, creating\r\nor selecting useful features, trying out different models, etc. Testing\r\nmultiple pipelines requires many lines of code, and writing it all in\r\nthe same notebook often makes it long and cluttered. On the other hand,\r\nusing multiple notebooks makes it harder to compare the results and to\r\nkeep an overview. On top of that, refactoring the code for every test\r\ncan be quite time-consuming. How many times have you conducted the same\r\naction to pre-process a raw dataset? How many times have you\r\ncopy-and-pasted code from an old repository to re-use it in a new use\r\ncase?\r\n\r\nATOM is here to help solve these common issues. The package acts as\r\na wrapper of the whole machine learning pipeline, helping the data\r\nscientist to rapidly find a good model for his problem. Avoid\r\nendless imports and documentation lookups. Avoid rewriting the same\r\ncode over and over again. With just a few lines of code, it's now\r\npossible to perform basic data cleaning steps, select relevant\r\nfeatures and compare the performance of multiple models on a given\r\ndataset, providing quick insights on which pipeline performs best\r\nfor the task at hand.\r\n\r\nExample steps taken by ATOM's pipeline:\r\n\r\n1. Data Cleaning\r\n\t* Handle missing values\r\n\t* Encode categorical features\r\n * Detect and remove outliers\r\n\t* Balance the training set\r\n2. Feature engineering\r\n * Create new non-linear features\r\n\t* Remove multi-collinear features\r\n\t* Remove features with too low variance\r\n\t* Select the most promising features\r\n3. Train and validate multiple models\r\n\t* Select hyperparameters using a Bayesian Optimization approach\r\n\t* Train and test the models on the provided data\r\n\t* Assess the robustness of the output using a bootstrap algorithm\r\n4. Analyze the results\r\n * Get the model scores on various metrics\r\n * Make plots to compare the model performances\r\n\r\n\r\n

\r\n\r\n\"diagram_pipeline\"\r\n\r\n

\r\n\r\n\r\n\ud83d\udee0\ufe0f Installation\r\n---------------\r\n\r\nInstall ATOM's newest release easily via `pip`:\r\n\r\n $ pip install -U atom-ml\r\n\r\n\r\nor via `conda`:\r\n\r\n $ conda install -c conda-forge atom-ml\r\n\r\n

\r\n\r\n\r\n\u26a1 Usage\r\n-------\r\n\r\n[![Colab](https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/1PnYfycwdmKw8dGyygwh7F0S3A4Rc47lI?usp=sharing)\r\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tvdboom/ATOM/HEAD)\r\n\r\nATOM contains a variety of classes and functions to perform data cleaning,\r\nfeature engineering, model training, plotting and much more. The easiest\r\nway to use everything ATOM has to offer is through one of the main classes:\r\n\r\n* [ATOMClassifier](https://tvdboom.github.io/ATOM/latest//API/ATOM/atomclassifier) for binary or multiclass classification tasks.\r\n* [ATOMRegressor](https://tvdboom.github.io/ATOM/latest//API/ATOM/atomregressor) for regression tasks.\r\n\r\nLet's walk you through an example. Click on the Google Colab badge on top\r\nof this section to run this example yourself.\r\n\r\nMake the necessary imports and load the data.\r\n\r\n```python\r\nimport pandas as pd\r\nfrom atom import ATOMClassifier\r\n\r\n# Load the Australian Weather dataset\r\nX = pd.read_csv(\"https://raw.githubusercontent.com/tvdboom/ATOM/master/examples/datasets/weatherAUS.csv\")\r\nX.head()\r\n```\r\n\r\nInitialize the ATOMClassifier or ATOMRegressor class. These two classes\r\nare convenient wrappers for the whole machine learning pipeline. Contrary\r\nto sklearn's API, they are initialized providing the data you want to\r\nmanipulate.\r\n\r\n```python\r\natom = ATOMClassifier(X, y=\"RainTomorrow\", test_size=0.3, verbose=2)\r\n```\r\n\r\nData transformations are applied through atom's methods. For example,\r\ncalling the [impute](https://tvdboom.github.io/ATOM/latest/API/ATOM/atomclassifier/#impute)\r\nmethod will initialize an [Imputer](https://tvdboom.github.io/ATOM/latest/API/data_cleaning/imputer)\r\ninstance, fit it on the training set and transform the whole dataset.\r\nThe transformations are applied immediately after calling the method\r\n(no fit and transform commands necessary).\r\n\r\n```python\r\natom.impute(strat_num=\"median\", strat_cat=\"most_frequent\") \r\natom.encode(strategy=\"LeaveOneOut\", max_onehot=8)\r\n```\r\n\r\nSimilarly, models are [trained and evaluated](https://tvdboom.github.io/ATOM/latest/user_guide/training)\r\nusing the [run](https://tvdboom.github.io/ATOM/latest/API/ATOM/atomclassifier/#run)\r\nmethod. Here, we fit both a [Random Forest](https://tvdboom.github.io/ATOM/latest/API/models/rf)\r\nand [AdaBoost](https://tvdboom.github.io/ATOM/latest/API/models/adab) model,\r\nand apply [hyperparameter tuning](https://tvdboom.github.io/ATOM/latest/user_guide/training/#hyperparameter-tuning).\r\n\r\n```python\r\natom.run(models=[\"RF\", \"AdaB\"], metric=\"auc\", n_calls=10, n_initial_points=4)\r\n```\r\n\r\nLastly, visualize the result using the integrated [plots](https://tvdboom.github.io/ATOM/latest/user_guide/plots).\r\n\r\n```python\r\natom.plot_roc()\r\natom.rf.plot_confusion_matrix(normalize=True)\r\n```\r\n\r\n

\r\n\r\n\r\n\"Documentation\" Documentation\r\n----------------\r\n \r\n**Relevant links** | |\r\n--- | ---\r\n\u2b50 **[About](https://tvdboom.github.io/ATOM/latest/release_history/)** | Learn more about the package.\r\n\ud83d\ude80 **[Getting started](https://tvdboom.github.io/ATOM/latest/getting_started/)** | New to ATOM? Here's how to get you started!\r\n\ud83d\udce2 **[Release history](https://tvdboom.github.io/ATOM/latest/release_history/)** | What are the new features of the latest release?\r\n\ud83d\udc68\u200d\ud83d\udcbb **[User guide](https://tvdboom.github.io/ATOM/latest/user_guide/introduction/)** | How to use ATOM and its features.\r\n\ud83c\udf9b\ufe0f **[API Reference](https://tvdboom.github.io/ATOM/latest/API/ATOM/atomclassifier/)** | The detailed reference for ATOM's API.\r\n\ud83d\udccb **[Examples](https://tvdboom.github.io/ATOM/latest/examples/binary_classification/)** | Example notebooks show you what can be done and how.\r\n\u2754 **[FAQ](https://tvdboom.github.io/ATOM/latest/faq/)** | Get answers to frequently asked questions.\r\n\ud83d\udd27 **[Contributing](https://tvdboom.github.io/ATOM/latest/contributing/)** | Do you wan to contribute to the project? 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