Package: alchemite-apiclient
alchemite-apiclientThis is a client for interacting with Alchemite Analytics, an applied machine learning platform to accelerate industrial
R&D and optimise manufacturing by extracting information from sparce or noisy datasets.
To obtain a licence for this product, pleasecontact Intellegensfor more information.API version: 0.70.0Requirements.Python >=3.8Installation & Usagepip installEither you can install this from the public pip repository using:pipinstallalchemite-apiclientAlternatively, you can install it from a zip archive using:pipinstall./api_client_python-version.zip(you may need to runpipwith root permission:sudo pip install ./api_client_python-version.zip)Then import the package:importalchemite_apiclientGetting StartedPlease follow theinstallation procedure.Examples can be found in the source distribution, downloadable fromhttps://pypi.org/project/alchemite-apiclient/#filesThen place yourcredentials.jsonfile in the "example" directory and runpythonexample_connect.pyThis should connect to the API server and, if successful, print something like this to the terminal (the numbers you see may be different):------ API version -----
{'alchemite_version': '20200414',
 'api_application_version': '0.15.3',
 'api_definition_version': '0.14.3'}If instead you encounter an error at this stage please contact Intellegens for further guidance.Next, look through and try running example/example_basic.py.  This will upload a small dataset, train a basic model with the default hyperparameters and predict the missing values from a dataset.Examples of other functionality possible through the Alchemite API are given by:example/example_hyperopt.py train an optimal model using hyperparameter optimization and impute the training datasetexample/example_chunk.py upload a larger dataset in chunksexample/example_delete.py delete models and datasetsexample/example_optimize.py search the model's parameter space for parameters predicted to meet certain targetsexample/example_outliers.py find outliers in the model's training datasetexample/example_preload.py preload a model into memory to make predictions for larger models fasterCredentialsThe credentials.json file requires the following elements:host: The base uri of the Alchemite api you are attempting to use. (Ordinarilyhttps://alchemiteapi.intellegens.ai/v0)client_id: The client id to use for authentication. (OrdinarilyPythonClient)grant_type: One ofpassword,client_credentials,authorization_code.Grant types each have additional elements:Authorization Code:This will open a browser to prompt for user credentials for using the API. This is the recommended way of authenticating.offline(optional): If true, the client will attempt to acquire an offline token to persist user authentication between sessions. This token is stored in a.alchemite_tokenfile in the working directory.Password:This will use credentials collected from the commandline to authenticate with the API.username(optional): The username to log in with. If omitted the user will be prompted to enter itpassword(optional): The password to log in with. If omitted the user will be prompted to enter itoffline(optional): If true, the client will attempt to acquire an offline token to persist user authentication between sessions. This token is stored in a.alchemite_tokenfile in the working directory.Client Credentials:Attempts to authenticate using a client secret.client_secret: The client secret to use for authentication.Offline tokensOffline tokens persist indefinitely, but will expire if unused for more than 30 days.
In the event that the token is lost or stolen, it can be revoked from your profile page in the Applications tab.Reference documentation corresponding to each API endpoint can be found in the docs directory of the source distribution.This Python package is automatically generated by theOpenAPI Generatorproject.