From 4122697c96aa5f776690e7f9eb47c3d2abdbb758 Mon Sep 17 00:00:00 2001 From: dev-rinchin Date: Wed, 7 Aug 2024 14:25:51 +0300 Subject: [PATCH] update readme --- README.md | 226 +++++++++--------- {imgs => docs/imgs}/GENERALL2X2.jpg | Bin {imgs => docs/imgs}/lime.jpg | Bin imgs/LightAutoML_logo_big.png | Bin 1579267 -> 0 bytes imgs/LightAutoML_logo_small.png | Bin 214460 -> 0 bytes imgs/Star_scheme_tables.png | Bin 95611 -> 0 bytes imgs/TabularAutoML_model_descr.png | Bin 116607 -> 0 bytes imgs/TabularUtilizedAutoML_model_descr.png | Bin 433329 -> 0 bytes imgs/autoint.png | Bin 227244 -> 0 bytes imgs/denselight.png | Bin 53933 -> 0 bytes imgs/densenet.png | Bin 111786 -> 0 bytes imgs/fttransformer.png | Bin 68511 -> 0 bytes imgs/node.png | Bin 209702 -> 0 bytes imgs/resnet.png | Bin 70385 -> 0 bytes imgs/swa.png | Bin 672233 -> 0 bytes imgs/tutorial_11_case_problem_statement.png | Bin 60470 -> 0 bytes .../tutorial_11_general_problem_statement.png | Bin 53851 -> 0 bytes imgs/tutorial_11_history_step_params.png | Bin 258063 -> 0 bytes imgs/tutorial_11_transformers_params.png | Bin 280926 -> 0 bytes imgs/tutorial_1_initial_report.png | Bin 181815 -> 0 bytes imgs/tutorial_1_laml_big.png | Bin 564657 -> 0 bytes imgs/tutorial_1_ml_pipeline.png | Bin 356139 -> 0 bytes imgs/tutorial_1_pipeline.png | Bin 664713 -> 0 bytes imgs/tutorial_1_unfolded_report.png | Bin 425286 -> 0 bytes imgs/tutorial_2_initial_report.png | Bin 82734 -> 0 bytes imgs/tutorial_2_pipeline.png | Bin 717360 -> 0 bytes imgs/tutorial_2_unfolded_report.png | Bin 582074 -> 0 bytes imgs/tutorial_3_initial_report.png | Bin 210058 -> 0 bytes imgs/tutorial_3_unfolded_report.png | Bin 632058 -> 0 bytes imgs/tutorial_blackbox_pipeline.png | Bin 1336695 -> 0 bytes imgs/tutorial_whitebox_report_1.png | Bin 234803 -> 0 bytes imgs/tutorial_whitebox_report_2.png | Bin 238847 -> 0 bytes imgs/tutorial_whitebox_report_3.png | Bin 334310 -> 0 bytes imgs/tutorial_whitebox_report_4.png | Bin 168924 -> 0 bytes 34 files changed, 110 insertions(+), 116 deletions(-) rename {imgs => docs/imgs}/GENERALL2X2.jpg (100%) rename {imgs => docs/imgs}/lime.jpg (100%) delete mode 100644 imgs/LightAutoML_logo_big.png delete mode 100644 imgs/LightAutoML_logo_small.png delete mode 100644 imgs/Star_scheme_tables.png delete mode 100644 imgs/TabularAutoML_model_descr.png delete mode 100644 imgs/TabularUtilizedAutoML_model_descr.png delete mode 100644 imgs/autoint.png delete mode 100644 imgs/denselight.png delete mode 100644 imgs/densenet.png delete mode 100644 imgs/fttransformer.png delete mode 100644 imgs/node.png delete mode 100644 imgs/resnet.png delete mode 100644 imgs/swa.png delete mode 100644 imgs/tutorial_11_case_problem_statement.png delete mode 100644 imgs/tutorial_11_general_problem_statement.png delete mode 100644 imgs/tutorial_11_history_step_params.png delete mode 100644 imgs/tutorial_11_transformers_params.png delete mode 100644 imgs/tutorial_1_initial_report.png delete mode 100644 imgs/tutorial_1_laml_big.png delete mode 100644 imgs/tutorial_1_ml_pipeline.png delete mode 100644 imgs/tutorial_1_pipeline.png delete mode 100644 imgs/tutorial_1_unfolded_report.png delete mode 100644 imgs/tutorial_2_initial_report.png delete mode 100644 imgs/tutorial_2_pipeline.png delete mode 100644 imgs/tutorial_2_unfolded_report.png delete mode 100644 imgs/tutorial_3_initial_report.png delete mode 100644 imgs/tutorial_3_unfolded_report.png delete mode 100644 imgs/tutorial_blackbox_pipeline.png delete mode 100644 imgs/tutorial_whitebox_report_1.png delete mode 100644 imgs/tutorial_whitebox_report_2.png delete mode 100644 imgs/tutorial_whitebox_report_3.png delete mode 100644 imgs/tutorial_whitebox_report_4.png diff --git a/README.md b/README.md index c5bef1a6..6c81d6b5 100644 --- a/README.md +++ b/README.md @@ -1,59 +1,51 @@ - - -# LightAutoML - automatic model creation framework + +[![GitHub License](https://img.shields.io/github/license/sb-ai-lab/LightAutoML)](https://github.com/sb-ai-lab/RePlay/blob/main/LICENSE) +[![PyPI - Version](https://img.shields.io/pypi/v/lightautoml)](https://pypi.org/project/lightautoml) +![pypi - Downloads](https://img.shields.io/pypi/dm/lightautoml?color=green&label=PyPI%20downloads&logo=pypi&logoColor=green) [![Telegram](https://img.shields.io/badge/chat-on%20Telegram-2ba2d9.svg)](https://t.me/lightautoml) -![PyPI - Downloads](https://img.shields.io/pypi/dm/lightautoml?color=green&label=PyPI%20downloads&logo=pypi&logoColor=orange&style=plastic) -![Read the Docs](https://img.shields.io/readthedocs/lightautoml?style=plastic) -[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +
+[![GitHub Workflow Status (with event)](https://img.shields.io/github/actions/workflow/status/sb-ai-lab/lightautoml/CI.yml)](https://github.com/sb-ai-lab/lightautoml/actions/workflows/CI.yml?query=branch%3Amain) ![Poetry-Lock](https://img.shields.io/github/workflow/status/sb-ai-lab/LightAutoML/Poetry%20run/master?label=Poetry-Lock) - +![Read the Docs](https://img.shields.io/readthedocs/lightautoml) +[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) LightAutoML (LAMA) is an AutoML framework which provides automatic model creation for the following tasks: - binary classification -- multiclass classification +- multiclass classification +- multilabel classification - regression Current version of the package handles datasets that have independent samples in each row. I.e. **each row is an object with its specific features and target**. -Multitable datasets and sequences are a work in progress :) - -**Note**: we use [`AutoWoE`](https://pypi.org/project/autowoe) library to automatically create interpretable models. **Authors**: [Alexander Ryzhkov](https://kaggle.com/alexryzhkov), [Anton Vakhrushev](https://kaggle.com/btbpanda), [Dmitry Simakov](https://kaggle.com/simakov), Rinchin Damdinov, Vasilii Bunakov, Alexander Kirilin, Pavel Shvets. -**Documentation** of LightAutoML is available [here](https://lightautoml.readthedocs.io/), you can also [generate](https://github.com/AILab-MLTools/LightAutoML/blob/master/.github/CONTRIBUTING.md#building-documentation) it. - -# (New features) GPU and Spark pipelines -Full GPU and Spark pipelines for LightAutoML currently available for developers testing (still in progress). The code and tutorials for: -- GPU pipeline is [available here](https://github.com/Rishat-skoltech/LightAutoML_GPU) -- Spark pipeline is [available here](https://github.com/sb-ai-lab/SLAMA) # Table of Contents -* [Installation LightAutoML from PyPI](#installation) +* [Installation](#installation) +* [Documentation](https://lightautoml.readthedocs.io/) * [Quick tour](#quicktour) * [Resources](#examples) -* [Contributing to LightAutoML](#contributing) -* [License](#apache) -* [For developers](#developers) +* [Advanced features](#advancedfeatures) * [Support and feature requests](#support) +* [Contributing to LightAutoML](#contributing) +* [License](#license) + +**Documentation** of LightAutoML is available [here](https://lightautoml.readthedocs.io/), you can also [generate](https://github.com/AILab-MLTools/LightAutoML/blob/master/.github/CONTRIBUTING.md#building-documentation) it. + # Installation -To install LAMA framework on your machine from PyPI, execute following commands: +To install LAMA framework on your machine from PyPI: ```bash - -# Install base functionality: - +# Base functionality: pip install -U lightautoml -# For partial installation use corresponding option. -# Extra dependecies: [nlp, cv, report] -# Or you can use 'all' to install everything - +# For partial installation use corresponding option +# Extra dependecies: [nlp, cv, report] or use 'all' to install all dependecies pip install -U lightautoml[nlp] - ``` Additionally, run following commands to enable pdf report generation: @@ -77,7 +69,7 @@ sudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2 # Quick tour Let's solve the popular Kaggle Titanic competition below. There are two main ways to solve machine learning problems using LightAutoML: -* Use ready preset for tabular data: +### Use ready preset for tabular data ```python import pandas as pd from sklearn.metrics import f1_score @@ -105,9 +97,82 @@ pd.DataFrame({ }).to_csv('submit.csv', index = False) ``` -LighAutoML framework has a lot of ready-to-use parts and extensive customization options, to learn more check out the [resources](#Resources) section. +### LightAutoML as a framework: build your own custom pipeline -[Back to top](#toc) +```python +import pandas as pd +from sklearn.metrics import f1_score + +from lightautoml.automl.presets.tabular_presets import TabularAutoML +from lightautoml.tasks import Task + +df_train = pd.read_csv('../input/titanic/train.csv') +df_test = pd.read_csv('../input/titanic/test.csv') +N_THREADS = 4 + +reader = PandasToPandasReader(Task("binary"), cv=5, random_state=42) + +# create a feature selector +selector = ImportanceCutoffSelector( + LGBSimpleFeatures(), + BoostLGBM( + default_params={'learning_rate': 0.05, 'num_leaves': 64, + 'seed': 42, 'num_threads': N_THREADS} + ), + ModelBasedImportanceEstimator(), + cutoff=0 +) + +# build first level pipeline for AutoML +pipeline_lvl1 = MLPipeline([ + # first model with hyperparams tuning + ( + BoostLGBM( + default_params={'learning_rate': 0.05, 'num_leaves': 128, + 'seed': 1, 'num_threads': N_THREADS} + ), + OptunaTuner(n_trials=20, timeout=30) + ), + # second model without hyperparams tuning + BoostLGBM( + default_params={'learning_rate': 0.025, 'num_leaves': 64, + 'seed': 2, 'num_threads': N_THREADS} + ) +], pre_selection=selector, features_pipeline=LGBSimpleFeatures(), post_selection=None) + +# build second level pipeline for AutoML +pipeline_lvl2 = MLPipeline( + [ + BoostLGBM( + default_params={'learning_rate': 0.05, 'num_leaves': 64, + 'max_bin': 1024, 'seed': 3, 'num_threads': N_THREADS}, + freeze_defaults=True + ) + ], + pre_selection=None, + features_pipeline=LGBSimpleFeatures(), + post_selection=None +) + +# build AutoML pipeline +automl = AutoML(reader, [ + [pipeline_lvl1], + [pipeline_lvl2], + ], + skip_conn=False +) + +# train AutoML and get predictions +oof_pred = automl.fit_predict(df_train, roles = {'target': 'Survived', 'drop': ['PassengerId']}) +test_pred = automl.predict(df_test) + +pd.DataFrame({ + 'PassengerId':df_test.PassengerId, + 'Survived': (test_pred.data[:, 0] > 0.5)*1 +}).to_csv('submit.csv', index = False) +``` + +LighAutoML framework has a lot of ready-to-use parts and extensive customization options, to learn more check out the [resources](#Resources) section. # Resources @@ -165,96 +230,25 @@ LighAutoML framework has a lot of ready-to-use parts and extensive customization - (English) [LightAutoML vs Titanic: 80% accuracy in several lines of code (Medium)](https://alexmryzhkov.medium.com/lightautoml-preset-usage-tutorial-2cce7da6f936) - (English) [Hands-On Python Guide to LightAutoML – An Automatic ML Model Creation Framework (Analytic Indian Mag)](https://analyticsindiamag.com/hands-on-python-guide-to-lama-an-automatic-ml-model-creation-framework/?fbclid=IwAR0f0cVgQWaLI60m1IHMD6VZfmKce0ZXxw-O8VRTdRALsKtty8a-ouJex7g) -[Back to top](#toc) + +# Advanced features +### GPU and Spark pipelines +Full GPU and Spark pipelines for LightAutoML currently available for developers testing (still in progress). The code and tutorials for: +- GPU pipeline is [available here](https://github.com/Rishat-skoltech/LightAutoML_GPU) +- Spark pipeline is [available here](https://github.com/sb-ai-lab/SLAMA) # Contributing to LightAutoML If you are interested in contributing to LightAutoML, please read the [Contributing Guide](.github/CONTRIBUTING.md) to get started. -[Back to top](#toc) - - -# License -This project is licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/AILab-MLTools/LightAutoML/blob/master/LICENSE) file for more details. - -[Back to top](#toc) - - -# For developers - -## Build your own custom pipeline: - -```python -import pandas as pd -from sklearn.metrics import f1_score - -from lightautoml.automl.presets.tabular_presets import TabularAutoML -from lightautoml.tasks import Task - -df_train = pd.read_csv('../input/titanic/train.csv') -df_test = pd.read_csv('../input/titanic/test.csv') - -# define that machine learning problem is binary classification -task = Task("binary") - -reader = PandasToPandasReader(task, cv=N_FOLDS, random_state=RANDOM_STATE) - -# create a feature selector -model0 = BoostLGBM( - default_params={'learning_rate': 0.05, 'num_leaves': 64, - 'seed': 42, 'num_threads': N_THREADS} -) -pipe0 = LGBSimpleFeatures() -mbie = ModelBasedImportanceEstimator() -selector = ImportanceCutoffSelector(pipe0, model0, mbie, cutoff=0) - -# build first level pipeline for AutoML -pipe = LGBSimpleFeatures() -# stop after 20 iterations or after 30 seconds -params_tuner1 = OptunaTuner(n_trials=20, timeout=30) -model1 = BoostLGBM( - default_params={'learning_rate': 0.05, 'num_leaves': 128, - 'seed': 1, 'num_threads': N_THREADS} -) -model2 = BoostLGBM( - default_params={'learning_rate': 0.025, 'num_leaves': 64, - 'seed': 2, 'num_threads': N_THREADS} -) -pipeline_lvl1 = MLPipeline([ - (model1, params_tuner1), - model2 -], pre_selection=selector, features_pipeline=pipe, post_selection=None) - -# build second level pipeline for AutoML -pipe1 = LGBSimpleFeatures() -model = BoostLGBM( - default_params={'learning_rate': 0.05, 'num_leaves': 64, - 'max_bin': 1024, 'seed': 3, 'num_threads': N_THREADS}, - freeze_defaults=True -) -pipeline_lvl2 = MLPipeline([model], pre_selection=None, features_pipeline=pipe1, - post_selection=None) - -# build AutoML pipeline -automl = AutoML(reader, [ - [pipeline_lvl1], - [pipeline_lvl2], -], skip_conn=False) - -# train AutoML and get predictions -oof_pred = automl.fit_predict(df_train, roles = {'target': 'Survived', 'drop': ['PassengerId']}) -test_pred = automl.predict(df_test) - -pd.DataFrame({ - 'PassengerId':df_test.PassengerId, - 'Survived': (test_pred.data[:, 0] > 0.5)*1 -}).to_csv('submit.csv', index = False) -``` - -[Back to top](#toc) - # Support and feature requests Seek prompt advice at [Telegram group](https://t.me/lightautoml). Open bug reports and feature requests on GitHub [issues](https://github.com/AILab-MLTools/LightAutoML/issues). + + +# License +This project is licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/AILab-MLTools/LightAutoML/blob/master/LICENSE) file for more details. + +[Back to top](#toc) diff --git a/imgs/GENERALL2X2.jpg b/docs/imgs/GENERALL2X2.jpg similarity index 100% rename from imgs/GENERALL2X2.jpg rename to docs/imgs/GENERALL2X2.jpg diff --git a/imgs/lime.jpg b/docs/imgs/lime.jpg similarity index 100% rename from imgs/lime.jpg rename to docs/imgs/lime.jpg diff --git a/imgs/LightAutoML_logo_big.png b/imgs/LightAutoML_logo_big.png deleted file mode 100644 index 2e7999562380b401b18304182cdec2c0d8570af0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1579267 zcmbTe2{@GP+dr;-k@lz|l#py$hQ`_xrR-6bvBlWOk{LtR@>HZk_I*ppGQ(gn_M)*5 zi5bfvVU%HnkhT9k>UrO<`Ffw@`28IR=Dug{>pHJ<`JA8gye@)uwbj@Tf)28M&i(20&uy+7UN?0|?XebpR!+K( zUHb6%^P=Z_I4`+g+gY*FvP4{JPgb@gu4P+Oht9CL_b*EtbLX;Nttq+6&2#KMC^3O> z9>sFK@*19B1+T?V3hid${dUyu;HkfT zpCz)?z1Sn^wS%QW_ib5<9! z@91<1@HR;2<(#uue$;#SQq;{Iof40(=<-ro&uKq-VC27X;Z({l?5>j7zp8dwc~A|L z`hI=x;2N8lFW!fhEAT%Tq<*qUVWZ?%vGgyKx%sGFN>leTg2%&WbJr}{3dXgc>sf*t z%(~j*jP9b(`3oM)r5$EbKPAi;*}eES=jyws3%fL*nLSzY!}r{G%UzzeH~!1?wVbvBUy$-8 z?XySB_rKo3@nk2v?)#Vn0s&mS4%4|af7f>NjXXLs#>@Ha(Tf{Wm-#Xusvb-|khXt~ zEqwpQy%SG=ggw7{uKOXVJ2zE~H}7Fd;lmqZ5B9q$J=^K^y{zbAjC-ev^t&z zN_ARHUa(2DJZ*Rk68Fv!OzypHDs<6iKY!ep@6(#jg3bkhvERL3iwTn>wY|$l>{F{* z=vdDsUh7r!g9nQ?ELsW{X7mb8@fsX4>OGdfL;l5Cy^(j|A88NS*dDx?2@~X)yIC{e zNd9@}uHFcAwEx@6ZK=HnRrOi#aK4nkC8H(Q%+67G<-0Ji)$E(UN9v8}7`h9~+Zv%s za<{PUud5ZR|+cjz)l)Fa8lL-~7Y zbpnNZrdVnpRi|-t?N)oo^ZC$~BVvye@2|T*%1+~~+EKm#?^j3V-d}xrjJ1K??FQwT z%<*SW&V9V85b@lax9{1>+vlZTdEGoyw9`1q=|*n2Mfk3!+hZrY0!`KE=aZW>#&$hV zm+oc(t4wzuyU017cH92E9C!N-!Hnx)ecca00}RqDJLP^{^xj+jvNS{IBln?u(9R2@NW5uQvwHwY{Vf74vUpcPxe$P*9U-p{Q z7C|$0wD0U|=lwZm2+AkE?$-ZX>RnhJ-%Eb$6In;q_m4kuezN{#^u8?W=+Mz%Re_Hw zUw3)JE=dGkNiL5o4=7LURdwPU5?p+B^8zeFO6bu^jW3Z0Z}Pvvy)k>k_lEV&;V$_i z_P)QTv=0krhi8siAs}9Y%a@mTE+50kzSh8I{=97^bgf0YMX$v`oo}2g^Kq9(Pe#A~ zuD8M6T$84bdvGz{N!~A3)HO6()mshWFJn~Up$1P$+~`QCCvND|4+m{&G126#H=^mz z+2D_uPv#%LxyyTBRtzz}YH_U~izj0^z4nu>{Sh+NffZ*5H*_RBSakYlseHutjC|GX z6zUQp_hlosvo&I0!?Rm^#%aVZbhl!f&)qV$u=I&6j=b1{G9%aQA))HVl?#6I>zDn~ zH+yDKW;sGnJQ5#87d`r=-J-Iy$X-H9!oy9I0R*@U|V2QWQ%0W1{r|3 zC7tZ7H01@t;u}p{D6LiR*Dg0W?YDbo-}L$%PDSjH7hJmgV((4sJB}p-UyDBOgT=VT zxnX7zqu!$f0_DMx&8H)U1UR)q6W!Usrze%~TrP3{I+QM}d0Sub(}PdnvP?8P5*Fj) z6R*cLw~-RgO3@_k680rd#1%*?I}JI!E8Ji5$|%3+iq-5Bo2D;?b4I7~*$v7vb8OyZ zb|H#w@@xvCtM5kK#rC-54Uq1@OHnZkmuR*$7f#m1k1|qDj!rJUdQN?%Rs-NtL8syV zzJbhM(ZakkA17Dqj$-km0&MO0RnPs1xGCN7`_18Bn>^Z3l+O=_4xr``$K~(JB_nSn zS10omIgPl70TKbZZ#GSYFQ_vKAatZzf`u~f5a z(|OafiXrz;o}A+bQ>3qpU4zYLpUuDdH$SEyqe~2Ayc>O*U7X#7%$NX+2a@dz_L#Su zr&jq@jSu05l9gnX&M49RFhy*=Zc`toB5QUJA06(i+2=RB>a}|6=k$UPedbFlt@hKe z*_*53uft8$hK>{-{JhWUu>BG1J+`|yPWEzJpJ_Ofd$5AT>8!}1A@;RrqfN~tQ(qKV z-Ppo|rjB^EE{qb#WS&~3A4|_nM_aB~-m;{k2a6+x2anHnKP=Rbvgc(>`13- za`0O4s^tDNLTBWql;&W|ch^!h&8aJGE;|ehZ+yRVTEvup>g1kh2G6){_E@8vypztp zs+O$O=ei)q;d!e(0u{jl35O&ihNuLpOUHDpM)N?j4zZ(2nYSmFs%|0>uah8`qeT@F z4#(*%CRTYP$`sUVv;r;?gh2-neI8lQ@A_-`@tlk0JxiOBN}*=`MXjF1OUd3I37<-? 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