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+/LightAutoML-0.3.7.3.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-lightautoml
+Version:	0.3.7.3
+Release:	1
+Summary:	Fast and customizable framework for automatic ML model creation (AutoML)
+License:	Apache-2.0
+URL:		https://lightautoml.readthedocs.io/en/latest/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b6/eb/fa7decd357f2a9a8fdff961a20262b5f75737fbeacc22e18611342df7fbd/LightAutoML-0.3.7.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-poetry-core
+Requires:	python3-pandas
+Requires:	python3-pandas
+Requires:	python3-pandas
+Requires:	python3-scikit-learn
+Requires:	python3-lightgbm
+Requires:	python3-catboost
+Requires:	python3-optuna
+Requires:	python3-torch
+Requires:	python3-torch
+Requires:	python3-dataclasses
+Requires:	python3-holidays
+Requires:	python3-networkx
+Requires:	python3-cmaes
+Requires:	python3-pyyaml
+Requires:	python3-tqdm
+Requires:	python3-joblib
+Requires:	python3-importlib-metadata
+Requires:	python3-autowoe
+Requires:	python3-jinja2
+Requires:	python3-json2html
+Requires:	python3-seaborn
+Requires:	python3-gensim
+Requires:	python3-nltk
+Requires:	python3-transformers
+Requires:	python3-albumentations
+Requires:	python3-efficientnet-pytorch
+Requires:	python3-opencv-python
+Requires:	python3-PyWavelets
+Requires:	python3-torchvision
+Requires:	python3-torchvision
+Requires:	python3-featuretools
+Requires:	python3-weasyprint
+Requires:	python3-cffi
+
+%description
+<img src=https://github.com/AILab-MLTools/LightAutoML/raw/master/imgs/LightAutoML_logo_big.png />
+
+# LightAutoML - automatic model creation framework
+
+[![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)
+
+LightAutoML (LAMA) is an AutoML framework which provides automatic model creation for the following tasks:
+- binary classification
+- multiclass  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), Vasilii Bunakov, Rinchin Damdinov, 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)
+
+<a name="toc"></a>
+# Table of Contents
+
+* [Installation LightAutoML from PyPI](#installation)
+* [Quick tour](#quicktour)
+* [Resources](#examples)
+* [Contributing to LightAutoML](#contributing)
+* [License](#apache)
+* [For developers](#developers)
+* [Support and feature requests](#support)
+
+<a name="installation"></a>
+# Installation
+To install LAMA framework on your machine from PyPI, execute following commands:
+```bash
+
+# Install 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
+
+pip install -U lightautoml[nlp]
+
+```
+
+Additionaly, run following commands to enable pdf report generation:
+
+```bash
+# MacOS
+brew install cairo pango gdk-pixbuf libffi
+
+# Debian / Ubuntu
+sudo apt-get install build-essential libcairo2 libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info
+
+# Fedora
+sudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2
+
+# Windows
+# follow this tutorial https://weasyprint.readthedocs.io/en/stable/install.html#windows
+```
+[Back to top](#toc)
+
+<a name="quicktour"></a>
+# 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:
+```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')
+
+automl = TabularAutoML(
+    task = Task(
+        name = 'binary',
+        metric = lambda y_true, y_pred: f1_score(y_true, (y_pred > 0.5)*1))
+)
+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.
+
+[Back to top](#toc)
+
+<a name="examples"></a>
+# Resources
+
+### Kaggle kernel examples of LightAutoML usage:
+
+- [Tabular Playground Series April 2021 competition solution](https://www.kaggle.com/alexryzhkov/n3-tps-april-21-lightautoml-starter)
+- [Titanic competition solution (80% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-titanic-love)
+- [Titanic **12-code-lines** competition solution (78% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-extreme-short-titanic-solution)
+- [House prices competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-houseprices-love)
+- [Natural Language Processing with Disaster Tweets solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-nlp)
+- [Tabular Playground Series March 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-for-tabulardatamarch)
+- [Tabular Playground Series February 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-tabulardata-love)
+- [Interpretable WhiteBox solution](https://www.kaggle.com/simakov/lama-whitebox-preset-example)
+- [Custom ML pipeline elements inside existing ones](https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example)
+
+### Google Colab tutorials and [other examples](examples/):
+
+- [`Tutorial_1_basics.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_1_basics.ipynb) - get started with LightAutoML on tabular data.
+- [`Tutorial_2_WhiteBox_AutoWoE.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_2_WhiteBox_AutoWoE.ipynb) - creating interpretable models.
+- [`Tutorial_3_sql_data_source.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_3_sql_data_source.ipynb) - shows how to use LightAutoML presets (both standalone and time utilized variants) for solving ML tasks on tabular data from SQL data base instead of CSV.
+- [`Tutorial_4_NLP_Interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_4_NLP_Interpretation.ipynb) - example of using TabularNLPAutoML preset, LimeTextExplainer.
+- [`Tutorial_5_uplift.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_5_uplift.ipynb) - shows how to use LightAutoML for a uplift-modeling task.
+- [`Tutorial_6_custom_pipeline.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_6_custom_pipeline.ipynb) - shows how to create your own pipeline from specified blocks: pipelines for feature generation and feature selection, ML algorithms, hyperparameter optimization etc.
+- [`Tutorial_7_ICE_and_PDP_interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_7_ICE_and_PDP_interpretation.ipynb) - shows how to obtain local and global interpretation of model results using ICE and PDP approaches.
+- [`Tutorial_8_CV_preset.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_8_CV_preset.ipynb) - example of using TabularCVAutoML preset in CV multi-class classification task.
+
+
+**Note 1**: for production you have no need to use profiler (which increase work time and memory consomption), so please do not turn it on - it is in off state by default
+
+**Note 2**: to take a look at this report after the run, please comment last line of demo with report deletion command.
+
+### Courses, videos and papers
+
+* **LightAutoML crash courses**:
+    - (Russian) [AutoML course for OpenDataScience community](https://ods.ai/tracks/automl-course-part1)
+
+* **Video guides**:
+    - (Russian) [LightAutoML webinar for Sberloga community](https://www.youtube.com/watch?v=ci8uqgWFJGg) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov), [Dmitry Simakov](https://kaggle.com/simakov))
+    - (Russian) [LightAutoML hands-on tutorial in Kaggle Kernels](https://www.youtube.com/watch?v=TYu1UG-E9e8) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
+    - (English) [Automated Machine Learning with LightAutoML: theory and practice](https://www.youtube.com/watch?v=4pbO673B9Oo) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
+    - (English) [LightAutoML framework general overview, benchmarks and advantages for business](https://vimeo.com/485383651) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
+    - (English) [LightAutoML practical guide - ML pipeline presets overview](https://vimeo.com/487166940) ([Dmitry Simakov](https://kaggle.com/simakov))
+
+* **Papers**:
+    - Anton Vakhrushev, Alexander Ryzhkov, Dmitry Simakov, Rinchin Damdinov, Maxim Savchenko, Alexander Tuzhilin ["LightAutoML: AutoML Solution for a Large Financial Services Ecosystem"](https://arxiv.org/pdf/2109.01528.pdf). arXiv:2109.01528, 2021.
+
+* **Articles about LightAutoML**:
+    - (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)
+
+<a name="contributing"></a>
+# 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)
+
+<a name="apache"></a>
+# 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)
+
+<a name="developers"></a>
+# 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)
+
+<a name="support"></a>
+# 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).
+
+
+%package -n python3-lightautoml
+Summary:	Fast and customizable framework for automatic ML model creation (AutoML)
+Provides:	python-lightautoml
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-lightautoml
+<img src=https://github.com/AILab-MLTools/LightAutoML/raw/master/imgs/LightAutoML_logo_big.png />
+
+# LightAutoML - automatic model creation framework
+
+[![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)
+
+LightAutoML (LAMA) is an AutoML framework which provides automatic model creation for the following tasks:
+- binary classification
+- multiclass  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), Vasilii Bunakov, Rinchin Damdinov, 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)
+
+<a name="toc"></a>
+# Table of Contents
+
+* [Installation LightAutoML from PyPI](#installation)
+* [Quick tour](#quicktour)
+* [Resources](#examples)
+* [Contributing to LightAutoML](#contributing)
+* [License](#apache)
+* [For developers](#developers)
+* [Support and feature requests](#support)
+
+<a name="installation"></a>
+# Installation
+To install LAMA framework on your machine from PyPI, execute following commands:
+```bash
+
+# Install 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
+
+pip install -U lightautoml[nlp]
+
+```
+
+Additionaly, run following commands to enable pdf report generation:
+
+```bash
+# MacOS
+brew install cairo pango gdk-pixbuf libffi
+
+# Debian / Ubuntu
+sudo apt-get install build-essential libcairo2 libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info
+
+# Fedora
+sudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2
+
+# Windows
+# follow this tutorial https://weasyprint.readthedocs.io/en/stable/install.html#windows
+```
+[Back to top](#toc)
+
+<a name="quicktour"></a>
+# 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:
+```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')
+
+automl = TabularAutoML(
+    task = Task(
+        name = 'binary',
+        metric = lambda y_true, y_pred: f1_score(y_true, (y_pred > 0.5)*1))
+)
+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.
+
+[Back to top](#toc)
+
+<a name="examples"></a>
+# Resources
+
+### Kaggle kernel examples of LightAutoML usage:
+
+- [Tabular Playground Series April 2021 competition solution](https://www.kaggle.com/alexryzhkov/n3-tps-april-21-lightautoml-starter)
+- [Titanic competition solution (80% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-titanic-love)
+- [Titanic **12-code-lines** competition solution (78% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-extreme-short-titanic-solution)
+- [House prices competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-houseprices-love)
+- [Natural Language Processing with Disaster Tweets solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-nlp)
+- [Tabular Playground Series March 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-for-tabulardatamarch)
+- [Tabular Playground Series February 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-tabulardata-love)
+- [Interpretable WhiteBox solution](https://www.kaggle.com/simakov/lama-whitebox-preset-example)
+- [Custom ML pipeline elements inside existing ones](https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example)
+
+### Google Colab tutorials and [other examples](examples/):
+
+- [`Tutorial_1_basics.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_1_basics.ipynb) - get started with LightAutoML on tabular data.
+- [`Tutorial_2_WhiteBox_AutoWoE.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_2_WhiteBox_AutoWoE.ipynb) - creating interpretable models.
+- [`Tutorial_3_sql_data_source.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_3_sql_data_source.ipynb) - shows how to use LightAutoML presets (both standalone and time utilized variants) for solving ML tasks on tabular data from SQL data base instead of CSV.
+- [`Tutorial_4_NLP_Interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_4_NLP_Interpretation.ipynb) - example of using TabularNLPAutoML preset, LimeTextExplainer.
+- [`Tutorial_5_uplift.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_5_uplift.ipynb) - shows how to use LightAutoML for a uplift-modeling task.
+- [`Tutorial_6_custom_pipeline.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_6_custom_pipeline.ipynb) - shows how to create your own pipeline from specified blocks: pipelines for feature generation and feature selection, ML algorithms, hyperparameter optimization etc.
+- [`Tutorial_7_ICE_and_PDP_interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_7_ICE_and_PDP_interpretation.ipynb) - shows how to obtain local and global interpretation of model results using ICE and PDP approaches.
+- [`Tutorial_8_CV_preset.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_8_CV_preset.ipynb) - example of using TabularCVAutoML preset in CV multi-class classification task.
+
+
+**Note 1**: for production you have no need to use profiler (which increase work time and memory consomption), so please do not turn it on - it is in off state by default
+
+**Note 2**: to take a look at this report after the run, please comment last line of demo with report deletion command.
+
+### Courses, videos and papers
+
+* **LightAutoML crash courses**:
+    - (Russian) [AutoML course for OpenDataScience community](https://ods.ai/tracks/automl-course-part1)
+
+* **Video guides**:
+    - (Russian) [LightAutoML webinar for Sberloga community](https://www.youtube.com/watch?v=ci8uqgWFJGg) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov), [Dmitry Simakov](https://kaggle.com/simakov))
+    - (Russian) [LightAutoML hands-on tutorial in Kaggle Kernels](https://www.youtube.com/watch?v=TYu1UG-E9e8) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
+    - (English) [Automated Machine Learning with LightAutoML: theory and practice](https://www.youtube.com/watch?v=4pbO673B9Oo) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
+    - (English) [LightAutoML framework general overview, benchmarks and advantages for business](https://vimeo.com/485383651) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
+    - (English) [LightAutoML practical guide - ML pipeline presets overview](https://vimeo.com/487166940) ([Dmitry Simakov](https://kaggle.com/simakov))
+
+* **Papers**:
+    - Anton Vakhrushev, Alexander Ryzhkov, Dmitry Simakov, Rinchin Damdinov, Maxim Savchenko, Alexander Tuzhilin ["LightAutoML: AutoML Solution for a Large Financial Services Ecosystem"](https://arxiv.org/pdf/2109.01528.pdf). arXiv:2109.01528, 2021.
+
+* **Articles about LightAutoML**:
+    - (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)
+
+<a name="contributing"></a>
+# 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)
+
+<a name="apache"></a>
+# 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)
+
+<a name="developers"></a>
+# 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)
+
+<a name="support"></a>
+# 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).
+
+
+%package help
+Summary:	Development documents and examples for lightautoml
+Provides:	python3-lightautoml-doc
+%description help
+<img src=https://github.com/AILab-MLTools/LightAutoML/raw/master/imgs/LightAutoML_logo_big.png />
+
+# LightAutoML - automatic model creation framework
+
+[![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)
+
+LightAutoML (LAMA) is an AutoML framework which provides automatic model creation for the following tasks:
+- binary classification
+- multiclass  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), Vasilii Bunakov, Rinchin Damdinov, 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)
+
+<a name="toc"></a>
+# Table of Contents
+
+* [Installation LightAutoML from PyPI](#installation)
+* [Quick tour](#quicktour)
+* [Resources](#examples)
+* [Contributing to LightAutoML](#contributing)
+* [License](#apache)
+* [For developers](#developers)
+* [Support and feature requests](#support)
+
+<a name="installation"></a>
+# Installation
+To install LAMA framework on your machine from PyPI, execute following commands:
+```bash
+
+# Install 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
+
+pip install -U lightautoml[nlp]
+
+```
+
+Additionaly, run following commands to enable pdf report generation:
+
+```bash
+# MacOS
+brew install cairo pango gdk-pixbuf libffi
+
+# Debian / Ubuntu
+sudo apt-get install build-essential libcairo2 libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info
+
+# Fedora
+sudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2
+
+# Windows
+# follow this tutorial https://weasyprint.readthedocs.io/en/stable/install.html#windows
+```
+[Back to top](#toc)
+
+<a name="quicktour"></a>
+# 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:
+```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')
+
+automl = TabularAutoML(
+    task = Task(
+        name = 'binary',
+        metric = lambda y_true, y_pred: f1_score(y_true, (y_pred > 0.5)*1))
+)
+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.
+
+[Back to top](#toc)
+
+<a name="examples"></a>
+# Resources
+
+### Kaggle kernel examples of LightAutoML usage:
+
+- [Tabular Playground Series April 2021 competition solution](https://www.kaggle.com/alexryzhkov/n3-tps-april-21-lightautoml-starter)
+- [Titanic competition solution (80% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-titanic-love)
+- [Titanic **12-code-lines** competition solution (78% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-extreme-short-titanic-solution)
+- [House prices competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-houseprices-love)
+- [Natural Language Processing with Disaster Tweets solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-nlp)
+- [Tabular Playground Series March 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-for-tabulardatamarch)
+- [Tabular Playground Series February 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-tabulardata-love)
+- [Interpretable WhiteBox solution](https://www.kaggle.com/simakov/lama-whitebox-preset-example)
+- [Custom ML pipeline elements inside existing ones](https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example)
+
+### Google Colab tutorials and [other examples](examples/):
+
+- [`Tutorial_1_basics.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_1_basics.ipynb) - get started with LightAutoML on tabular data.
+- [`Tutorial_2_WhiteBox_AutoWoE.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_2_WhiteBox_AutoWoE.ipynb) - creating interpretable models.
+- [`Tutorial_3_sql_data_source.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_3_sql_data_source.ipynb) - shows how to use LightAutoML presets (both standalone and time utilized variants) for solving ML tasks on tabular data from SQL data base instead of CSV.
+- [`Tutorial_4_NLP_Interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_4_NLP_Interpretation.ipynb) - example of using TabularNLPAutoML preset, LimeTextExplainer.
+- [`Tutorial_5_uplift.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_5_uplift.ipynb) - shows how to use LightAutoML for a uplift-modeling task.
+- [`Tutorial_6_custom_pipeline.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_6_custom_pipeline.ipynb) - shows how to create your own pipeline from specified blocks: pipelines for feature generation and feature selection, ML algorithms, hyperparameter optimization etc.
+- [`Tutorial_7_ICE_and_PDP_interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_7_ICE_and_PDP_interpretation.ipynb) - shows how to obtain local and global interpretation of model results using ICE and PDP approaches.
+- [`Tutorial_8_CV_preset.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_8_CV_preset.ipynb) - example of using TabularCVAutoML preset in CV multi-class classification task.
+
+
+**Note 1**: for production you have no need to use profiler (which increase work time and memory consomption), so please do not turn it on - it is in off state by default
+
+**Note 2**: to take a look at this report after the run, please comment last line of demo with report deletion command.
+
+### Courses, videos and papers
+
+* **LightAutoML crash courses**:
+    - (Russian) [AutoML course for OpenDataScience community](https://ods.ai/tracks/automl-course-part1)
+
+* **Video guides**:
+    - (Russian) [LightAutoML webinar for Sberloga community](https://www.youtube.com/watch?v=ci8uqgWFJGg) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov), [Dmitry Simakov](https://kaggle.com/simakov))
+    - (Russian) [LightAutoML hands-on tutorial in Kaggle Kernels](https://www.youtube.com/watch?v=TYu1UG-E9e8) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
+    - (English) [Automated Machine Learning with LightAutoML: theory and practice](https://www.youtube.com/watch?v=4pbO673B9Oo) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
+    - (English) [LightAutoML framework general overview, benchmarks and advantages for business](https://vimeo.com/485383651) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
+    - (English) [LightAutoML practical guide - ML pipeline presets overview](https://vimeo.com/487166940) ([Dmitry Simakov](https://kaggle.com/simakov))
+
+* **Papers**:
+    - Anton Vakhrushev, Alexander Ryzhkov, Dmitry Simakov, Rinchin Damdinov, Maxim Savchenko, Alexander Tuzhilin ["LightAutoML: AutoML Solution for a Large Financial Services Ecosystem"](https://arxiv.org/pdf/2109.01528.pdf). arXiv:2109.01528, 2021.
+
+* **Articles about LightAutoML**:
+    - (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)
+
+<a name="contributing"></a>
+# 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)
+
+<a name="apache"></a>
+# 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)
+
+<a name="developers"></a>
+# 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)
+
+<a name="support"></a>
+# 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).
+
+
+%prep
+%autosetup -n lightautoml-0.3.7.3
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-lightautoml -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.7.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..2d775dd
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+ce0719dc7e6fcba0fe2ca16ef7fa679a  LightAutoML-0.3.7.3.tar.gz