Merge pull request #26 from Alex-Lekov/develop

v1.3.1

.gitignore

@@ -137,5 +137,12 @@ alexautoml/.DS_Store
 .catboost_info
 *.catboost_info/
 catboost_info/*
+*/catboost_info
 *.pkl
-de.pkl
+test_de.zip
+.gitignore
+dask-worker-space/*
+de.zip
+.automl-alex_tmp
+test_save.zip
+examples/prod_sample/catboost_info/*

CHANGELOG.md

@@ -5,6 +5,20 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 
+## [1.3.1]
+### Fix
+- Fix import - add loguru and psutil in requirements.txt
+
+
+## [1.2.28]
+### ADD
+- Advanced Logging (logs in .automl-alex_tmp/log.log)
+- Class Optimizer
+- Pruner in optimizer
+- connection with [optuna-dashboard](https://github.com/optuna/optuna-dashboard) (run > optuna-dashboard sqlite:///db.sqlite3 )
+- NumericInteractionFeatures Class in data_prepare
+
+
 ## [1.2.25]
 ### Fix
 - Fix save & load in AutoML

README.md

@@ -17,9 +17,18 @@
 
 <p align="center"> State-of-the art Automated Machine Learning python library for Tabular Data</p>
 
-<img width=800 src="https://github.com/Alex-Lekov/AutoML-Benchmark/blob/master/img/Total_SUM.png" alt="bench">
+## Works with Tasks:
+
+-   [x] Binary Classification
+
+-   [x] Regression
+
+-   [ ] Multiclass Classification (in progress...)
 
+### Benchmark Results
+<img width=800 src="https://github.com/Alex-Lekov/AutoML-Benchmark/blob/master/img/Total_SUM.png" alt="bench">
 
+The bigger, the better   
 From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/) 
 
 ### Scheme
@@ -35,7 +44,8 @@ From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/)
 - Feature Selection
 - Models Selection
 - Cross Validation
-- Timelimit and EarlyStoping
+- Optimization Timelimit and EarlyStoping
+- Save and Load (Predict new data)
 
 
 # Installation
@@ -74,12 +84,28 @@ 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)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
 
 
 
@@ -117,19 +143,33 @@ It integrates many popular frameworks:
 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)
+
+<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/dashboard.gif" alt="Dashboard">
+
+<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/dashboard_2.gif" alt="Dashboard_2">
+
+Run
+```console
+$ optuna-dashboard sqlite:///db.sqlite3
+```
+
 # Road Map
 
 -   [x] Feature Generation
 
 -   [x] Save/Load and Predict on New Samples
 
--   [ ] Advanced Logging
+-   [x] Advanced Logging
+
+-   [x] Add opt Pruners
 
 -   [ ] DL Encoders
 
 -   [ ] Add More libs (NNs)
 
--   [ ] Add opt Pruners
+-   [ ] Multiclass Classification
 
 -   [ ] Build pipelines
 

automl_alex/__init__.py

@@ -1,6 +1,9 @@
 from .models import *
+from .base import *
 from .automl_alex import *
 from .data_prepare import *
 from .encoders import *
 from .cross_validation import *
+from .optimizer import *
+from .logger import *
 from .__version__ import __version__

automl_alex/__version__.py

@@ -1 +1 @@
-__version__ = "1.2.25"
+__version__ = "1.3.1"