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https://github.com/lai-bluejay/diego
Diego: Data in, IntElliGence Out. A fast framework that supports the rapid construction of automated learning tasks. Simply create an automated learning study (Study) and generate correlated trials (Trial). Then run the code and get a machine learning model. Implemented using Scikit-learn API glossary, using Bayesian optimization and genetic algorithms for automated machine learning. Inspired by [Fast.ai](https://github.com/fastai/fastai).
https://github.com/lai-bluejay/diego
automl autosklearn bayesian-optimization generation-algorithms hyperparameter-optimization machine-learning scikit-learn
Last synced: 22 days ago
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Diego: Data in, IntElliGence Out. A fast framework that supports the rapid construction of automated learning tasks. Simply create an automated learning study (Study) and generate correlated trials (Trial). Then run the code and get a machine learning model. Implemented using Scikit-learn API glossary, using Bayesian optimization and genetic algorithms for automated machine learning. Inspired by [Fast.ai](https://github.com/fastai/fastai).
- Host: GitHub
- URL: https://github.com/lai-bluejay/diego
- Owner: lai-bluejay
- License: mit
- Created: 2019-03-05T14:10:06.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-02-03T07:35:23.000Z (almost 4 years ago)
- Last Synced: 2024-10-01T15:44:18.101Z (about 1 month ago)
- Topics: automl, autosklearn, bayesian-optimization, generation-algorithms, hyperparameter-optimization, machine-learning, scikit-learn
- Language: Python
- Homepage:
- Size: 177 KB
- Stars: 8
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Diego
Diego: Data in, IntElliGence Out.
[简体中文](README_zh_CN.md)
A fast framework that supports the rapid construction of automated learning tasks. Simply create an automated learning study (`Study`) and generate correlated trials (`Trial`). Then run the code and get a machine learning model. Implemented using Scikit-learn API [glossary](https://scikit-learn.org/stable/glossary.html), using Bayesian optimization and genetic algorithms for automated machine learning.
Inspired by [Fast.ai](https://github.com/fastai/fastai) and [MicroSoft nni](https://github.com/Microsoft/nni).
[](https://travis-ci.org/lai-bluejay/diego)
- [x] the classifier trained by a Study.
- [x] AutoML classifier with support for scikit-learn api. Support for exporting models and use them directly.
- [x] Hyperparametric optimization using Bayesian optimization and genetic algorithms
- [x] Supports bucketing/binning algorithm and LUS sampling method for preprocessing
- [ ] Supports scikit-learn api classifier custom classifier for parameter search and super parameter optimization
## Installation
You need to install swig first, and some rely on C/C++ interface compilation. Recommended to use conda installation
```shell
conda install --yes pip gcc swig libgcc=5.2.0
pip install diego
```
After installation, start with 6 lines of code to solve a machine learning classification problem.
## Usage
Each task is considered to be a `Study`, and each Study consists of multiple `Trial`.
It is recommended to create a Study first and then generate a Trial from the Study:
```python
from diego.study import create_study
import sklearn.datasets
digits = sklearn.datasets.load_digits()
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(digits.data, digits.target,train_size=0.75, test_size=0.25)
s = create_study(X_train, y_train)
# can use default trials in Study
# or generate one
# s.generate_trials(mode='fast')
s.optimize(X_test, y_test)
# all_trials = s.get_all_trials()
# for t in all_trials:
# print(t.__dict__)
# print(t.clf.score(X_test, y_test))
```
## RoadMap
ideas for releases in the future
- [ ] 回归。
- [ ] add documents.
- [ ] 不同类型的Trial。TPE， BayesOpt， RandomSearch
- [ ] 自定义的Trial。Trials by custom Classifier (like sklearn, xgboost)
- [ ] 模型保存。model persistence
- [ ] 模型输出。model output
- [ ] basic Classifier
- [ ] fix mac os hanged in optimize pipeline
- [ ] add preprocessor
- [ ] add FeatureTools for automated feature engineering
##
## Project Structure
### study, trials
Study:
Trial:
### 如果在OS X或者Linux多进程被 hang/crash/freeze
Since n_jobs>1 may get stuck during parallelization. Similar problems may occur in [scikit-learn] (https://scikit-learn.org/stable/faq.html#why-do-i-sometime-get-a-crash-freeze-with-n -jobs-1-under-osx-or-linux)
In Python 3.4+, one solution is to directly configure `multiprocessing` to use `forkserver` or `spawn` to start process pool management (instead of the default `fork`). For example, the `forkserver` mode is enabled globally directly in the code.
```python
import multiprocessing
# other imports, custom code, load data, define model...
if __name__ == '__main__':
multiprocessing.set_start_method('forkserver')
# call scikit-learn utils with n_jobs > 1 here
```
more info :[multiprocessing document](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods)
### core
#### storage
For each study, the data storage and parameters, and the model is additionally stored in the `Storage` object, which ensures that Study only controls trials, and each Trial updates the results in the storage after updating, and updates the best results.
#### update result
When creating `Study`, you need to specify the direction of optimization `maximize` or `minimize`. Also specify the metrics for optimization when creating `Trials`. The default is `maximize accuracy`.
## auto ml 补完计划
[overview](https://hackernoon.com/a-brief-overview-of-automatic-machine-learning-solutions-automl-2826c7807a2a)
### bayes opt
1. [fmfn/bayes](https://github.com/fmfn/BayesianOptimization)
2. [auto-sklearn](https://github.com/automl/auto-sklearn)
### grid search
1. H2O.ai
### tree parzen
1. hyperopt
2. mlbox
### metaheuristics grid search
1. pybrain
### generation
1.tpot
### dl
1. ms nni
## issues
## updates
### TODO 文档更新。