State-of-the art Automated Machine Learning python library for Tabular Data
## Works with Tasks: - [x] Binary Classification - [x] Regression - [ ] Multiclass Classification (in progress...) ### Benchmark Results
The bigger, the better
From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/)
### Scheme
# Features
- Automated Data Clean (Auto Clean)
- Automated **Feature Engineering** (Auto FE)
- Smart Hyperparameter Optimization (HPO)
- Feature Generation
- Feature Selection
- Models Selection
- Cross Validation
- Optimization Timelimit and EarlyStoping
- Save and Load (Predict new data)
# Installation
```python
pip install automl-alex
```
# Docs
[DocPage](https://alex-lekov.github.io/AutoML_Alex/)
# 🚀 Examples
Classifier:
```python
from automl_alex import AutoMLClassifier
model = AutoMLClassifier()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```
Regression:
```python
from automl_alex import AutoMLRegressor
model = AutoMLRegressor()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```
DataPrepare:
```python
from automl_alex import DataPrepare
de = DataPrepare()
X_train = de.fit_transform(X_train)
X_test = de.transform(X_test)
```
Simple Models Wrapper:
```python
from automl_alex import LightGBMClassifier
model = LightGBMClassifier()
model.fit(X_train, y_train)
predicts = model.predict_proba(X_test)
model.opt(X_train, y_train,
timeout=600, # optimization time in seconds,
)
predicts = model.predict_proba(X_test)
```
More examples in the folder ./examples:
- [01_Quick_Start.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb)
- [02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb)
- [03_Models.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb)
- [04_ModelsReview.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb)
- [05_BestSingleModel.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb)
- [Production Docker template](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/prod_sample)
# What's inside
It integrates many popular frameworks:
- scikit-learn
- XGBoost
- LightGBM
- CatBoost
- Optuna
- ...
# Works with Features
- [x] Categorical Features
- [x] Numerical Features
- [x] Binary Features
- [ ] Text
- [ ] Datetime
- [ ] Timeseries
- [ ] Image
# Note
- **With a large dataset, a lot of memory is required!**
Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory.
# Realtime Dashboard
Works with [optuna-dashboard](https://github.com/optuna/optuna-dashboard)
Run
```console
$ optuna-dashboard sqlite:///db.sqlite3
```
# Road Map
- [x] Feature Generation
- [x] Save/Load and Predict on New Samples
- [x] Advanced Logging
- [x] Add opt Pruners
- [ ] Docs Site
- [ ] DL Encoders
- [ ] Add More libs (NNs)
- [ ] Multiclass Classification
- [ ] Build pipelines
# Contact
[Telegram Group](https://t.me/automlalex)
%package -n python3-automl-alex
Summary: State-of-the art Automated Machine Learning python library for Tabular Data
Provides: python-automl-alex
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-automl-alex
State-of-the art Automated Machine Learning python library for Tabular Data
## Works with Tasks: - [x] Binary Classification - [x] Regression - [ ] Multiclass Classification (in progress...) ### Benchmark Results
The bigger, the better
From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/)
### Scheme
# Features
- Automated Data Clean (Auto Clean)
- Automated **Feature Engineering** (Auto FE)
- Smart Hyperparameter Optimization (HPO)
- Feature Generation
- Feature Selection
- Models Selection
- Cross Validation
- Optimization Timelimit and EarlyStoping
- Save and Load (Predict new data)
# Installation
```python
pip install automl-alex
```
# Docs
[DocPage](https://alex-lekov.github.io/AutoML_Alex/)
# 🚀 Examples
Classifier:
```python
from automl_alex import AutoMLClassifier
model = AutoMLClassifier()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```
Regression:
```python
from automl_alex import AutoMLRegressor
model = AutoMLRegressor()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```
DataPrepare:
```python
from automl_alex import DataPrepare
de = DataPrepare()
X_train = de.fit_transform(X_train)
X_test = de.transform(X_test)
```
Simple Models Wrapper:
```python
from automl_alex import LightGBMClassifier
model = LightGBMClassifier()
model.fit(X_train, y_train)
predicts = model.predict_proba(X_test)
model.opt(X_train, y_train,
timeout=600, # optimization time in seconds,
)
predicts = model.predict_proba(X_test)
```
More examples in the folder ./examples:
- [01_Quick_Start.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb)
- [02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb)
- [03_Models.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb)
- [04_ModelsReview.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb)
- [05_BestSingleModel.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb)
- [Production Docker template](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/prod_sample)
# What's inside
It integrates many popular frameworks:
- scikit-learn
- XGBoost
- LightGBM
- CatBoost
- Optuna
- ...
# Works with Features
- [x] Categorical Features
- [x] Numerical Features
- [x] Binary Features
- [ ] Text
- [ ] Datetime
- [ ] Timeseries
- [ ] Image
# Note
- **With a large dataset, a lot of memory is required!**
Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory.
# Realtime Dashboard
Works with [optuna-dashboard](https://github.com/optuna/optuna-dashboard)
Run
```console
$ optuna-dashboard sqlite:///db.sqlite3
```
# Road Map
- [x] Feature Generation
- [x] Save/Load and Predict on New Samples
- [x] Advanced Logging
- [x] Add opt Pruners
- [ ] Docs Site
- [ ] DL Encoders
- [ ] Add More libs (NNs)
- [ ] Multiclass Classification
- [ ] Build pipelines
# Contact
[Telegram Group](https://t.me/automlalex)
%package help
Summary: Development documents and examples for automl-alex
Provides: python3-automl-alex-doc
%description help
State-of-the art Automated Machine Learning python library for Tabular Data
## Works with Tasks: - [x] Binary Classification - [x] Regression - [ ] Multiclass Classification (in progress...) ### Benchmark Results
The bigger, the better
From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/)
### Scheme
# Features
- Automated Data Clean (Auto Clean)
- Automated **Feature Engineering** (Auto FE)
- Smart Hyperparameter Optimization (HPO)
- Feature Generation
- Feature Selection
- Models Selection
- Cross Validation
- Optimization Timelimit and EarlyStoping
- Save and Load (Predict new data)
# Installation
```python
pip install automl-alex
```
# Docs
[DocPage](https://alex-lekov.github.io/AutoML_Alex/)
# 🚀 Examples
Classifier:
```python
from automl_alex import AutoMLClassifier
model = AutoMLClassifier()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```
Regression:
```python
from automl_alex import AutoMLRegressor
model = AutoMLRegressor()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```
DataPrepare:
```python
from automl_alex import DataPrepare
de = DataPrepare()
X_train = de.fit_transform(X_train)
X_test = de.transform(X_test)
```
Simple Models Wrapper:
```python
from automl_alex import LightGBMClassifier
model = LightGBMClassifier()
model.fit(X_train, y_train)
predicts = model.predict_proba(X_test)
model.opt(X_train, y_train,
timeout=600, # optimization time in seconds,
)
predicts = model.predict_proba(X_test)
```
More examples in the folder ./examples:
- [01_Quick_Start.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb)
- [02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb)
- [03_Models.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb)
- [04_ModelsReview.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb)
- [05_BestSingleModel.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb)
- [Production Docker template](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/prod_sample)
# What's inside
It integrates many popular frameworks:
- scikit-learn
- XGBoost
- LightGBM
- CatBoost
- Optuna
- ...
# Works with Features
- [x] Categorical Features
- [x] Numerical Features
- [x] Binary Features
- [ ] Text
- [ ] Datetime
- [ ] Timeseries
- [ ] Image
# Note
- **With a large dataset, a lot of memory is required!**
Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory.
# Realtime Dashboard
Works with [optuna-dashboard](https://github.com/optuna/optuna-dashboard)
Run
```console
$ optuna-dashboard sqlite:///db.sqlite3
```
# Road Map
- [x] Feature Generation
- [x] Save/Load and Predict on New Samples
- [x] Advanced Logging
- [x] Add opt Pruners
- [ ] Docs Site
- [ ] DL Encoders
- [ ] Add More libs (NNs)
- [ ] Multiclass Classification
- [ ] Build pipelines
# Contact
[Telegram Group](https://t.me/automlalex)
%prep
%autosetup -n automl-alex-2023.3.11
%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-automl-alex -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot