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        "author": "Charles Gobber",
        "author_email": "charles26f@gmail.com",
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            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# sklearn-export\n\nThis package is based on sklearn porter from [https://github.com/nok/sklearn-porter](https://github.com/nok/sklearn-porter).  I choose to build it because sklearn porter saves data in matrix format. However, most popular algebra libraries (e.g., [blas](http://www.netlib.org/blas/) and [lapack](http://www.netlib.org/lapack/)) are used to work with vectors. Then, sklearn-export saves the sklearn model data in Json format (matrices are stored in [column major order](https://en.wikipedia.org/wiki/Row-_and_column-major_order)).  Note that, this is a beta version yet, then only some models and functionalities are supported.\n\n## New features (0.0.7)\n\nThe code was optimized and now it works with sklearn >= 0.24. Some complete examples were added (see Complete Examples section).\n\n## Support\n\n|  Class | Details  |\n| ------------ | ------ |\n| [sklearn.svm.SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)| C-Support Vector Classification. The multiclass support is handled according to a one-vs-one scheme.|\n| [sklearn.svm.NuSVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html) | Nu-Support Vector Classification. Similar to SVC but uses a parameter to control the number of support vectors. |\n|[sklearn.svc.LinearSVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html) | Linear Support Vector Classification.|\n|[sklearn.neural_network.MLPClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)| Multi-layer Perceptron classifier.|\n|[sklearn.neural_network.MLPRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html)|Multi-layer Perceptron regressor.|\n|[sklearn.linear_model.LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)|Logistic Regression (aka logit, MaxEnt) classifier.|\n|[sklearn.linear_model.LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)|Ordinary least squares Linear Regression.|\n|[sklearn.preprocessing.MinMaxScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)|Transforms features by scaling each feature to a given range.|\n|[sklearn.preprocessing.StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)|Standardize features by removing the mean and scaling to unit variance.|\n|[sklearn.svm.SVR](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html)|Epsilon-Support Vector Regression.|\n|[sklearn.svm.LinearSVR](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html)|Linear Support Vector Regression.|\n\n**Observation**: details were extracted from sklearn documentation.\n## Installation\nWe recommend to make a instalation using pip:\n```bash\n$ pip install sklearn_export\n```\nIf you are using jupyter notebooks consider to install sklearn_export through a notebook cell. Then, you can type and execute the following:\n```python\nimport sys\n!{sys.executable} -m pip install sklearn_export\n```\n## Usage\n\nActually sklearn-export can save Classifiers, Regressions and some Scalers (see Support session).\n\n### Saving a Model or Scaler\n\n The basic usage is to save a simple model.\n```python\n# Basic imports\nfrom sklearn.datasets import load_iris\nfrom sklearn_export import Export\nfrom sklearn.neural_network import MLPRegressor\n\n# Load data and train model\nsamples = load_iris()\nX, y = samples.data, samples.target\nclf = MLPRegressor()\nclf.fit(X, y)\n\n# Save using sklearn_export\nexport = Export(clf)\nresult = export.to_json()\n```\nThe result is a Json file that can be loaded in any language.\n\n### Complete examples\n\nSome complete examples are provided [here](https://github.com/gobber/sklearn-export/examples/).\n\n### Saving a Model and a Scaler\nThe sklearn-export can also save more then one class in the same Json. This is usefull to store a Classifier and a Scaler (for example). To be honest, actually is only possible to store a pair Model and Scaler.\n```python\n# Basic imports\nfrom sklearn.datasets import load_iris\nfrom sklearn_export import Export\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neural_network import MLPRegressor\n\n# Load data\nsamples = load_iris()\nX, y = samples.data, samples.target\n\n# Normalize data\nscaler = StandardScaler()\nXz = scaler.fit_transform(X)\n\n# Train model with normalized data\nclf = MLPRegressor()\nclf.fit(Xz, y)\n\n# Save model and scaler using sklearn_export\nexport = Export([scaler, clf])\nresult = export.to_json()\n```\n The result is a Json file that contains information about a Model and a Scaler. The file can be loaded in any programing language.\n\n### Extra options\n\nThe method `to_json()` also support some other parameters:\n\n|  Parameter | Details  | Default |\n| -------- | ------ | ------ |\n| `filename` | Name of the output Json file | `data.json` |\n| `directory` | Path to save the file | `.` |\n| `with_md5_hash` | Include md5 hash in file name | `False` |\n\n## Questions\nIf you have any question please send me a mail <charles26f@gmail.com>.",
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