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Piskle\n\n![pyversions](https://img.shields.io/pypi/pyversions/piskle) ![wheel](https://img.shields.io/pypi/wheel/piskle) ![license](https://img.shields.io/pypi/l/piskle) ![version](https://img.shields.io/pypi/v/piskle)\n\n`Piskle` allows you to selectively serialize python objects to save on memory and load times. \n\nIt has special support for exporting `scikit-learn`'s  models in an optimized way, \nexporting exactly what's needed to make predictions.\n\n![Banner](https://media.giphy.com/media/QVhHtKMbPZAzoKLUG2/giphy.gif)\n\u003cp\u003e\u003ca href=\"https://giphy.com/gifs/rickandmorty-season-3-adult-swim-rick-and-morty-QVhHtKMbPZAzoKLUG2\"\u003evia GIPHY\u003c/a\u003e\u003c/p\u003e\n\n## Example:\nTo use `piskle`, you first need a model to export. You can use this as an example:\n\n```python\nfrom sklearn import datasets\nfrom sklearn.neural_network import MLPClassifier\n\ndata = datasets.load_iris()\n\nmodel = MLPClassifier().fit(data.data, data.target)\n```\n\nExporting the model is then as easy as the following:\n```python\nimport piskle\n\npiskle.dump(model, 'model.pskl')\n```\n\nLoading it is even easier:\n```python\nmodel = piskle.load('model.pskl')\n```\n\nIf you want even faster serialization, you can disable the `optimize` feature. \nNote that this feature reduces the size of the exported file even further and improves loading time.\n```python\npiskle.dump(model, 'model.pskl', optimize=False)\n```\n\n## Future Improvements\nThis is still an early working version of piskle, there are still a few improvements planned:\n- More thorough testing\n- Version Management: Support for more versions of scikit-learn (earlier versions)\n- Support for more Estimators (Feel free to contact us for a specific request)\n- Support for \"Nested\" Estimators (Pipelines, RandomForests, etc...)\n- Support for other serialization methods (such as joblib, shelve or json...)\n\n## Contribute\nAs this is still a work in progress, while using piskle, you might encounter some bugs.\nIt would be a great help to us, if you could **report them in the github repo**.\n\nFeel free, to share with us any potential improvements you'd like to see in piskle.\n\n\n\nIf you like the project and want to support us, you can buy us a coffee here:\n\n\u003ca href=\"https://www.buymeacoffee.com/amal.hasni\" target=\"_blank\"\u003e\u003cimg src=\"https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png\" alt=\"Buy Me A Coffee\" height=\"41\" width=\"174\"\u003e\u003c/a\u003e\n\n\n\n## Currently Supported Models\n\n### Predictors ( Classifiers, Regressors, ...)\n|       Estimator        |       Reference        |\n| :--------------------: | :--------------------: |\n|       LinearSVC        |      sklearn.svm       |\n|    LinearRegression    |  sklearn.linear_model  |\n|   LogisticRegression   |  sklearn.linear_model  |\n|         Lasso          |  sklearn.linear_model  |\n|         Ridge          |  sklearn.linear_model  |\n|       Perceptron       |  sklearn.linear_model  |\n|       GaussianNB       |  sklearn.naive_bayes   |\n|  KNeighborsRegressor   |   sklearn.neighbors    |\n|  KNeighborsClassifier  |   sklearn.neighbors    |\n|     MLPClassifier      | sklearn.neural_network |\n|      MLPRegressor      | sklearn.neural_network |\n| DecisionTreeClassifier |      sklearn.tree      |\n| DecisionTreeRegressor  |      sklearn.tree      |\n|         KMeans         |    sklearn.cluster     |\n|    GaussianMixture     |    sklearn.mixture     |\n### Transformers\n|    Estimator    |            Reference            |\n| :-------------: | :-----------------------------: |\n|       PCA       |      sklearn.decomposition      |\n|     FastICA     |      sklearn.decomposition      |\n| CountVectorizer | sklearn.feature_extraction.text |\n| TfidfVectorizer | sklearn.feature_extraction.text |\n|  SimpleImputer  |         sklearn.impute          |\n| StandardScaler  |      sklearn.preprocessing      |\n|  LabelEncoder   |      sklearn.preprocessing      |\n|  OneHotEncoder  |      sklearn.preprocessing      |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhmiladhia%2Fpiskle","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhmiladhia%2Fpiskle","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhmiladhia%2Fpiskle/lists"}