model.workflow

Model - Pipeline

ModelWorkflow

ModelWorkflow class that holds DataSet and ModelSet.

Warning

This class is a proposed framework. There are many member functions to be implemented if you are using the ModelWorkflow class.

In most projects, we do not need to use this class. We have a class ModelWorkflowX which is a more detailed implementation in our model.pipeline module.

ModelWorkflow takes a few arguments to instantiate. To run the workflow, use the train method.

Parameters:

Name	Type	Description	Default
config	dict	a dictionary that contains the configs.	required
dataset	haferml.model.DataSet	a DataSet object that contains the data and provides a create_train_test_datasets method.	required
modelset	haferml.model.ModelSet	a ModelSet object that contains the model as well as the hyperparameters and a create_model.	required
base_folder	str	working directory where all the artifacts are being perserved.	required

export_results(self)

export_results saves the necessary artifacts

Warning

Please implement this method.

Source code in haferml/model/workflow.py

def export_results(self):
    """
    export_results saves the necessary artifacts

    !!! warning
        Please implement this method.
    """
    ...

fit_and_report(self)

_fit_and_report fits the model using input data and generate reports.

Warning

Please implement this method.

Source code in haferml/model/workflow.py

def fit_and_report(self):
    """
    `_fit_and_report` fits the model using input data and generate reports.

    !!! warning
        Please implement this method.
    """
    ...

train(self, dataset)

train connects the training workflow and executes the workflow step by step.

Source code in haferml/model/workflow.py

def train(self, dataset):
    """
    train connects the training workflow and executes the workflow step by step.
    """

    logger.info("1. Create train test datasets")
    self.DataSet.create_train_test_datasets(dataset)
    logger.info("2. Create model")
    self.ModelSet.create_model()
    logger.info("3. Fit model and report")
    self.fit_and_report()
    logger.info("4. Export results")
    self.export_results()