Function

This page explains how to design functions with SmartPredict.

Functions are created by defining the expected behavior of your custom modules, their roles and location. They contain sections to insert the right code lines according to a predefined template.

Function template

# ---------------------------------------------------- IMPORTS ---------------------------------------------------------
# Your import statements can be written here.
# All builtins python package can be imported.
# Popular libraries for ML like tensorflow, sklearn,
# pandas, nltk are also supported.
# ----------------------------------------------------------------------------------------------------------------------


# ------------------------------------------------- CONFIGURATION ------------------------------------------------------
# Follow this convention when specifying the configuration of your module.
# The name of each input and output are up to you, but the structure of each one of them should be the same.
from logging import Logger

configuration = {
    # You can create as many inputs as you want.
    'in': {
        'input_1': {
            'label': 'Input 1',
            'type': 'int',
            'order': 1,
            'description': 'This is the first input.',
        },
        'input_2': {
            'label': 'Input 2',
            'type': 'int',
            'order': 2,
            'description': 'This is the second input.',
        },
    },

    # You can also create as many outputs as you want
    'out': {
        'result': {
            'label': 'Result of the operation',
            'type': 'int',
            'order': 1,
            'description': 'This is the result of the operation.',
        }
    },

    # The parameters are shown when double-clicking the module or using the menu on its right while in edit mode.
    'params': {
        # Read the documentation for more details on the kind of parameters and options available in SmartPredict.
        'operation': {
            'label': 'Operation',
            'type': 'str',
            'default': 'Addition',
            'choices': ['Addition', 'Subtraction', 'Multiplication', 'Division'],
            'category': 'basic',
            'input-type': 'drop-down',
            'description': 'Choose the operation to be done.'
        }
    }
}


# ----------------------------------------------------------------------------------------------------------------------

# -------------------------------------------- CUSTOM MODULE FUNCTION --------------------------------------------------

# This is a normalized function, it must take "in_data" , "param", "logger" as parameters.
# The name of this function should also contain "custom_module" in it.
def custom_module_function(in_data: dict, param: dict, logger: Logger) -> dict:
    # Retrieve the input, and the parameters of the module as follows :
    first_number = in_data['input_1']
    second_number = in_data['input_2']
    operation = param['operation']
    logger.debug('Computing the operation...')

    # Create the logic of the custom module, this is just an example.
    result_switcher = {
        'Addition': first_number + second_number,
        'Subtraction': first_number - second_number,
        'Multiplication': first_number * second_number,
        'Division': first_number / second_number
    }
    result = result_switcher[operation]
    logger.debug('Finished computing the operation.')
    logger.info('Hey, this is the result : {} '.format(result))

    # The output of your function must be an "out_data" mapping dictionary, each key is as specified in configuration
    out_data = {
        'result': result,
    }

    # Return this map of output data
    return out_data
# ----------------------------------------------------------------------------------------------------------------------

The official Python documentation provides a complete explanation of functions.