weevenetwork/scikit-learn repository overview

⁠Scikit Learn

• Scikit Learn⁠
  • Description⁠
  • Environment Variables⁠
    • Module Specific⁠
    • Set by the weeve Agent on the edge-node⁠
  • Dependencies⁠
  • Input⁠
  • Output⁠

⁠Description

Use your pre-trained scikit-learn model with weeve modules. The model should be available to the module via a downloadable URL or it should be stored in the edge device local filesystem. The module will take input data and the compose a numpy array with data in the order assigned to the Ordered Labels environment variable. Later, the module will input that array into the model.

⁠Environment Variables

⁠Module Specific

The following module configurations can be provided in a data service designer section on weeve platform:

⁠Set by the weeve Agent on the edge-node

Other features required for establishing the inter-container communication between modules in a data service are set by weeve agent.

⁠Dependencies

bottle
requests
scikit-learn

⁠Input

Input to this module is:

• JSON body single object, example:

{
    "temperature": 12,
    "volume": 1.3,
    "pressure": 0.32
}

• array of JSON body objects, example:

[
    {
        "temperature": 12,
        "volume": 1.3,
        "pressure": 0.32
    },
    {
        "temperature": 13,
        "volume": 2.1,
        "pressure": 0.34
    }
]

⁠Output

Output of this module is

• JSON body single object, example:

{
    "prediction": 14.323
}

• array of JSON body objects, example:

[
    {
        "prediction": 14.323
    },
    {
        "prediction": 13.45
    }
]