{
    "info": {
        "author": "Charles Lai",
        "author_email": "lai.bluejay@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\n\n# Diego\n\nDiego: Data in,  IntElliGence Out.\n\n[\u7b80\u4f53\u4e2d\u6587](README_zh_CN.md)\n\nA 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.\n\nInspired by [Fast.ai](https://github.com/fastai/fastai) and [MicroSoft nni](https://github.com/Microsoft/nni).\n\n[![Build Status](https://travis-ci.org/lai-bluejay/diego.svg?branch=master)](https://travis-ci.org/lai-bluejay/diego)\n![PyPI](https://img.shields.io/pypi/v/diego.svg?style=flat)\n![GitHub](https://img.shields.io/github/license/lai-bluejay/diego.svg)\n![GitHub code size in bytes](https://img.shields.io/github/languages/code-size/lai-bluejay/diego.svg)\n\n- [x] the classifier trained by a Study.\n- [x] AutoML classifier with support for scikit-learn api. Support for exporting models and use them directly.\n- [x] Hyperparametric optimization using Bayesian optimization and genetic algorithms\n- [x] Supports bucketing/binning algorithm and LUS sampling method for preprocessing\n- [ ] Supports scikit-learn api classifier custom classifier for parameter search and super parameter optimization\n\n\n## Installation\n\nYou need to install swig first, and some rely on C/C++ interface compilation. Recommended to use conda installation\n\n```shell\nconda install --yes pip gcc swig libgcc=5.2.0\npip install diego\n```\n\nAfter installation, start with 6 lines of code to solve a machine learning classification problem.\n\n## Usage\n\nEach task is considered to be a `Study`, and each Study consists of multiple `Trial`.\nIt is recommended to create a Study first and then generate a Trial from the Study:\n\n```python\nfrom diego.study import create_study\nimport sklearn.datasets\ndigits = sklearn.datasets.load_digits()\nX_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)\n\ns = create_study(X_train, y_train)\n# can use default trials in Study\n\n# or generate one\n# s.generate_trials(mode='fast')\ns.optimize(X_test, y_test)\n# all_trials = s.get_all_trials()\n# for t in all_trials:\n#     print(t.__dict__)\n#     print(t.clf.score(X_test, y_test))\n\n```\n\n## RoadMap\nideas for releases in the future\n- [ ] \u56de\u5f52\u3002\n- [ ] add documents.\n- [ ] \u4e0d\u540c\u7c7b\u578b\u7684Trial\u3002TPE\uff0c BayesOpt\uff0c RandomSearch\n- [ ] \u81ea\u5b9a\u4e49\u7684Trial\u3002Trials by custom Classifier (like sklearn, xgboost)\n- [ ] \u6a21\u578b\u4fdd\u5b58\u3002model persistence\n- [ ] \u6a21\u578b\u8f93\u51fa\u3002model output\n- [ ] basic Classifier\n- [ ] fix mac os hanged in optimize pipeline\n- [ ] add preprocessor\n- [ ] add FeatureTools for automated feature engineering\n\n\n## \n\n## Project Structure\n\n### study, trials\nStudy: \n\nTrial:\n\n### \u5982\u679c\u5728OS X\u6216\u8005Linux\u591a\u8fdb\u7a0b\u88ab hang/crash/freeze\n\nSince 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)\n\nIn 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.\n\n```python\nimport multiprocessing\n# other imports, custom code, load data, define model...\nif __name__ == '__main__':\n    multiprocessing.set_start_method('forkserver')\n\n    # call scikit-learn utils with n_jobs > 1 here\n```\n\nmore info :[multiprocessing document](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods)\n\n### core\n\n#### storage\n\nFor 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.\n\n#### update result\n\nWhen 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`.\n\n## auto ml \u8865\u5b8c\u8ba1\u5212\n\n[overview](https://hackernoon.com/a-brief-overview-of-automatic-machine-learning-solutions-automl-2826c7807a2a)\n\n### bayes opt\n\n1. [fmfn/bayes](https://github.com/fmfn/BayesianOptimization)\n2. [auto-sklearn](https://github.com/automl/auto-sklearn)\n\n### grid search\n\n1. H2O.ai\n\n### tree parzen\n\n1. hyperopt\n2. mlbox\n\n### metaheuristics grid search\n\n1. pybrain\n\n### generation\n\n1.tpot\n\n### dl\n\n1. ms nni\n\n## issues\n\n## updates\n\n### TODO \u6587\u6863\u66f4\u65b0\u3002\n\n\n\n",
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