Auto - ML

Cardea makes use of two packages to automate and simplify the modeling step in the Machine Learning tasks: MLPrimitives and MLBlocks.

MLBlocks is a simple framework for seamlessly combining any possible set of Machine Learning tools developed in Python, whether they are custom developments or belong to third party libraries, and build Pipelines out of them that can be fitted and then used to make predictions. This is achieved by providing a simple and intuitive annotation language that allows the user to specify how to integrate with each tool, called primitives, in order to provide a common uniform interface to each one of them.

In the other hand, MLPrimitives is a repository containing primitive annotations to be used by the MLBlocks library.

Thanks to the use of these two packages, the Machine Learning algorithm selection and the hyper-parameter tuning steps can be done easily using JSON annotations as follow:

pipeline = [
    ['sklearn.ensemble.RandomForestClassifier'],
    ['sklearn.naive_bayes.MultinomialNB'],
    ['sklearn.neighbors.KNeighborsClassifier']
]
result = cardea.execute_model(..., primitives=pipeline)

Where, for example, the sklearn.naive_bayes.MultinomialNB primitive is defined in the MLPrimitives package, with the following structure:

{
    "name": "sklearn.naive_bayes.MultinomialNB",
    "contributors": [...],
    "documentation": "...",
    "description": "...",
    "classifiers": {
        "type": "estimator",
        "subtype": "classifier"
    },
    "modalities": ["text"],
    "primitive": "sklearn.naive_bayes.MultinomialNB",
    "fit": {
        "method": "fit",
        "args": [
            {
                "name": "X",
                "type": "ndarray"
            },
            {
                "name": "y",
                "type": "array"
            }
        ]
    },
    "produce": {
        "method": "predict",
        "args": [
            {
                "name": "X",
                "type": "ndarray"
            }
        ],
        "output": [
            {
                "name": "y",
                "type": "array"
            }
        ]
    },
    "hyperparameters": {
        "fixed": {
            "fit_prior": {
                "type": "bool",
                "default": true
            },
            "class_prior": {
                "type": "iterable",
                "default": null
            }
        },
        "tunable": {
            "alpha": {
                "type": "float",
                "default": 1.0,
                "range": [0.0, 1.0]
            }
        }
    }
}

Auto - Featurization Auditing