Metadata-Version: 2.1
Name: sclblpy
Version: 0.1.1
Summary: Python package for uploading models to Scailable toolchain.
Home-page: https://github.com/scailable/sclblpy/
Author: Maurits Kaptein
Author-email: maurits.kaptein@scailable.net
License: UNKNOWN
Description: # sclblpy
        
        sclblpy is the core python package provided by Scailable to convert models fit in python to WebAssembly and
        open them up as a REST endpoint. 
        
        sclblpy is only functional in combination with a valid Scailable user account.
        
        - **Website:** [https://www.scailable.net](https://www.scailable.net)
        - **Docs:** [https://docs.sclbl.net/sclblpy](https://docs.sclbl.net/sclblpy)
        - **Get an account:** [https://admin.sclbl.net](https://admin.sclbl.net/signup.html)
        - **Source:**[https://github.com/scailable/sclblpy/](https://github.com/scailable/sclblpy/)
        
        ## Background
        The sclblpy package allows users with a valid scailable account (see [https://admin.sclbl.net](https://admin.sclbl.net))
        to upload fitted ML / AI models to the Scailable toolchain server. This will result in:
        
        1. The model being tested on the client side.
        2. The model being uploaded to Scailable, tested again, and if all test pass it will be converted to [WebAssembly](https://webassembly.org).
        3. The model being made available as an easy to access REST endpoint.
        
        ## Getting started
        After installing the package using `pip install sclblpy` you can easily fit a ML / AI model using your preferred tools and
        upload it to our toolchains. The following code block provides a simple example:
        
        ````
        # Neccesary imports:
        import sclblpy as sp
        
        from sklearn import svm
        from sklearn import datasets
        
        # Start fitting a simple model:
        clf = svm.SVC()
        X, y = datasets.load_iris(return_X_y=True)
        clf.fit(X, y)
        
        # Create an example feature vector (required):
        row = X[130, :]
        
        # Create documentation (optional, but useful):
        docs = {}
        docs['name'] = "My first fitted model"
        docs['documentation'] = "Any documentation you would like to provide."
        
        # Upload the model:
        sp.upload(clf, row, docs=docs)
        ````
        
        The call to `sp.upload()` will upload the fitted model, after running a number of local tests, to the 
        Scailable toolchain server and create an associated REST endpoint. Limited user feedback will be printed to show progress,
        and you will receive an email at the email address associated with your account when the conversion is fully completed (which might take a few minutes).
        This email also contains further details regarding the usage of your created endpoint.
        
        Note that upon first upload you will be prompted to provide your Scailable username and password; you can choose to
        store the provided credentials locally to enable easy login on subsequently uploads. (users can signup for an account at
         [https://admin.sclbl.net](https://admin.sclbl.net/signup.html)).
         
        ## Examples
        > These examples are merely intended to show the desired syntax for the various packages; we do not intend to fit models
        > that actually have a good predictive performance in these examples.
        
        Currently we support uploading `sklearn`, `statsmodels`, and `xgboost` models (run `sp.list_models()` to print an overview of all supported models). 
        Here we provide an example for each of these.
        
        ### sklearn: the elastic net
        
        The [elastic net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) provides a flexible regularized model that is 
        useful for many supervised learning tasks.
        
        ````
        import sclblpy as sp
        from sklearn import linear_model  # Import linear_model
        from sklearn import datasets  # Import sklearn datasets
        
        iris_data = datasets.load_iris(return_X_y=True)
        X, y = iris_data
        
        mod = linear_model.ElasticNet()  # Instantiate the model
        
        mod.fit(X, y)  # Fit the model
        
        fv = X[0, :]  # An example feature vector
        docs = {'name': "ElasticNet example model", 'documentation' : "Documentation for this model."}
        
        sp.upload(mod, fv, docs)
        
        ````
        
        ### statsmodels: OLS regression
        
        The statsmodels package provides a number of regression models with flexible error functions. We provide a simple OLS
        example:
        
        ```` 
        import sclblpy as sp
        
        import statsmodels.api as sm  # Import statsmodels
        from sklearn import datasets  # Import sklearn datasets
        
        iris_data = datasets.load_iris(return_X_y=True)
        X, y = iris_data
        
        est = sm.OLS(y, X)  # Specify the model
        mod = est.fit()  # Fit the model; note that we send the fitted model to scailable
        
        fv = X[0, :]  # An example feature vector
        docs = {'name': "OLS example model"}
        
        sp.upload(mod, fv, docs=docs)
        ````
        
        ### xgboost: tree boosting
        
        The xgboost package provides flexibly tree boosting models (see [xgboost](https://dl.acm.org/doi/abs/10.1145/2939672.2939785)).
        This model often performs very well "off the shelf" for many supervised learning tasks.
        
        ```` 
        import sclblpy as sp
        
        from xgboost import XGBClassifier  # Import xgboost classifier
        from sklearn import datasets  # Import sklearn datasets
        
        iris_data = datasets.load_iris(return_X_y=True)
        X, y = iris_data
        
        mod = XGBClassifier()  # Instantiate the model
        mod.fit(X, y)  # Fit the model
        
        fv = X[0, :]  # An example feature vector
        docs = {'name': "XGBoost example model"}
        
        sp.upload(mod, fv, docs=docs)
        ````
        
        ## Additional functionality
        Next to the main ``upload()`` function, the package also exposes the following functions to administer endpoints:
        
        ````
        # List all endpoints owned by the current user
        sp.endpoints()
        
        # Remove an endpoint
        sp.delete_endpoint("cfid-cfid-cfid")
        ````
        
        Additionally, the following methods are available:
        
        ````
        # List all models currently supported by our toolchains:
        sp.list_models()  
        
        # Prevent any user feedback from being printed:
        sp.stop_print()  
        
        # Turn user feedback back on:
        sp.start_print()  
        
        # Remove locally stored user credentials:
        sp.remove_credentials()
        
        ````
        
        
        ## Dependencies
        
        sclblpy needs python 3, and has been tested on python `> 3.7`. Furthermore, dependent on usage, sclblpy will import
        the following packages:
        
        * `numpy`
        * `requests`
        * `uuid`
        * `sklearn`
        
        The `statsmodels` and `xgboost` packages are imported when used.
        
        ## Notes:
        
        * We try to stick to the naming conventions in [http://google.github.io/styleguide/pyguide.html](http://google.github.io/styleguide/pyguide.html).
        * The methods `_set_toolchain_URL(string)` and `_set_admin_URL(string)` can be used to change the default location of
        the toolchain and user-management function. These are useful when running the Scailable stack locally. Also the method `_toggle_debug_mode()` can
        be used for troubleshooting (this will raise exceptions and provide a trace upon errors).
        
        For more information please contact us at [go@scailable.net](mailto:go@scailable.net).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
