Merge branch 'master' of github.com:4paradigm/AutoX

Author: enjoysport2022

README_EN.md

@@ -1,5 +1,5 @@
 English | [简体中文](./README.md)
-
+<img src="./img/logo.png" width = "1500" alt="logo" align=center />
 # What-is-AutoX?
 AutoX is an efficient AutoML tool, and it is designed for the tabular data modelling for real-world datasets.
 Its features include:
@@ -8,35 +8,29 @@ Its features include:
 - Generic & Universal: Supporting tabular data, including binary classification, multi-class classification and regression problems.
 - Auto: Fully automated pipeline without human-intervention.
 - Out of the box: Providing flexible modules which can be used alone.
-- Summary of magics: Organize and publish magics of competitions.
-
-## interpretable-ml
-AutoX covers following interpretable machine learning methods:
-### Golbel interpretation
-- [tree-based model](autox/autox_interpreter/interpreter_demo/global_interpretation/global_surrogate_tree_demo.ipynb)
-
-### Local interpretation
-- [LIME](autox/autox_interpreter/interpreter_demo/local_interpretation/lime_demo.ipynb)
-- [SHAP](autox/autox_interpreter/interpreter_demo/local_interpretation/shap_demo.ipynb)
+- Summary of magics: Organize and publish magics of competitions. 
 
-### Influential interpretation
-- [nn](autox/autox_interpreter/interpreter_demo/influential_instances/influential_interpretation_nn.ipynb)
-- [nn_sgd](autox/autox_interpreter/interpreter_demo/influential_instances/influential_interpretation_nn_sgd.ipynb)
+# What-does-AutoX-contain?
+- autox_competition: mainly for tabular table data mining competitions 
+- autox_server: automl service for online deployment 
+- autox_interpreter: machine learning interpretable function
 
-### Prototypes and Criticisms
-- [MMD-critic](autox/autox_interpreter/interpreter_demo/prototypes_and_criticisms/MMD_demo.ipynb)
-- [ProtoDash algorithm](autox/autox_interpreter/interpreter_demo/prototypes_and_criticisms/ProtodashExplainer.ipynb)
+# Join-the-community
+<img src="./img/qr_code_0429.png" width = "200" height = "200" alt="AutoX Community" align=center />  
 
+# How-to-contribute-for-AutoX
+[how to contribute](./how_to_contribute.md)
 
 # Table-of-Contents
 <!-- TOC -->
 
 - [What is AutoX?](#What-is-AutoX?)
+- [What does AutoX contain?](#What-does-AutoX-contain?)
+- [Join-the-community](#Join-the-community)
+- [How to contribute for AutoX](#How-to-contribute-for-AutoX)
 - [Table of Contents](#Table-of-Contents)
 - [Installation](#Installation)
-- [Architecture](#Architecture)
 - [Quick Start](#Quick-Start)
-- [Summary of Magics](#Summary-of-Magics)
 - [Evaluation](#Evaluation)
 
 <!-- /TOC -->
@@ -47,38 +41,11 @@ AutoX covers following interpretable machine learning methods:
 3. python setup.py install
 ```
 
-# Architecture
-```
-├── autox
-│   ├── ensemble
-│   ├── feature_engineer
-│   ├── feature_selection
-│   ├── file_io
-│   ├── join_tables
-│   ├── metrics
-│   ├── models
-│   ├── process_data
-│   └── util.py
-│   ├── CONST.py
-│   ├── autox.py
-├── run_oneclick.py
-└── demo
-└── test
-├── setup.py
-├── README.md
-```
-
 # Quick-Start
-- Full-Automl
-```
-from autox import AutoX
-path = data_dir
-autox = AutoX(target = 'loss', train_name = 'train.csv', test_name = 'test.csv', 
-               id = ['id'], path = path)
-sub = autox.get_submit()
-sub.to_csv("submission.csv", index = False)
-```
-- Semi-Automl: run_demo.ipynb
+- [autox competition](autox/autox_competition/README_EN.md)
+- [autox server](autox/autox_server/README_EN.md)
+- [autox interpreter](autox/autox_interpreter/README_EN.md)
+
 
 # Evaluation
 | index |data_type | data_name(link)  | metric | AutoX         | AutoGluon   | H2o |
@@ -87,112 +54,3 @@ sub.to_csv("submission.csv", index = False)
 | 2    |regression | [Tabular Playground Series - Aug 2021](https://www.kaggle.com/c/tabular-playground-series-aug-2021)   | rmse | 7.87731 | 10.3944 | 7.8895|
 | 3    |regression | [House Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/)   | rmse | 0.13043 | 0.13104 | 0.13161 |
 | 4    |binary classification | [Titanic](https://www.kaggle.com/c/titanic/)  | accuracy | 0.77751 | 0.78229 | 0.79186 |
-
-# Data type
-- cat: Categorical, Categorical variable without order.
-- ord: Ordinal, Categorical variable with order.
-- num: Numeric, Numeric variable.
-- datetime: Time variable with Datetime format.
-- timestamp: Time variable with Timestamp format.
-
-# Pipeline
-- 1.Initialize AutoX
-```
-1.1 Read data
-1.2 Concat train and test
-1.3 Identify columns type in data
-1.4 Data preprocess
-```
-- 2.Feature engineering
-```
-Every feature engineer class inclues the following features:
-1. auto select columns which will be executed with current operation
-2. review the selected columns
-3. modify the columns
-4. execute the operation, and return features whose samples' number and order are consistent with orginal table.
-```
-- 3. Features combination
-```
-Combine the raw features and derived features, and return wide table.
-```
-- 4. Data split
-```
-Split the table into train and test.
-```
-- 5.Features filter
-```
-Filter the features according to the distribution of train and test.
-```
-- 6.Model training
-```
-Inputs of models are filtered features. 
-model class inclues the following features:
-1. get the default parameters
-2. model training
-3. parameters tuning
-4. get the features importance
-5. prediction
-```
-- 7.Prediction
-
-# AutoX
-## Attributes
-###  info_: Information about the data set.
-- info_['id']: List, unique keys to identify the sample.
-- info_['target']: String, label column.
-- info_['shape_of_train']: Int, the number of samples in the train set.
-- info_['shape_of_test']: Int, the number of samples in the test set.
-- info_['feature_type']: Dict of Dict, data type of the features.
-- info_['train_name']: String, the table name of main table of train.
-- info_['test_name']: String, the table name of main table of test.
-
-### dfs_: dfs_ contains all DataFrames, including raw tables and derived tables.
-- dfs_['train_test']: The combined data of train data and test data.
-- dfs_['FE_feature_name']: Derived tables by feature engineering, such as FE_count, FE_groupby.
-- dfs_['FE_all']: The merged table which contains raw tables and derived tables.
-
-## Methods
-- concat_train_test: concat the train and test data.
-- split_train_test: split train and test data.
-- get_submit: get the submission.
-
-# Details of operations in the pipeline:
-## Data IO
-```
-```
-
-## Data Pre-process
-```
-- extract year, month, day, hour, weekday info from time columns
-- delete invalid(nunique equal to 1) features 
-- delete invalid (label is nan) samples
-```
-
-## Feature Engineer
-
-- count feature
-```
-```
-
-- target encoding feature
-
-
-- shift feature
-```
-```
-
-## Model Fitting
-```
-AutoX supports fellowing models:
-1. Lightgbm
-2. Xgboost
-3. Tabnet
-```
-
-## Ensemble
-```
-AutoX supports two ensemble methods(Bagging will be used in default). 
-1. Stacking；
-2. Bagging。
-```
-