Advanced use

How to define a new machine learning task?

The definition of a new Machine Learning task in Cardea can be made in four simple steps:

1. Go to the problem_definition directory and create a file with a class specifically for your problem. This class should extend the ProblemDefinition class and overwrites accordingly the necessary attributes and methods as needed. Usually, you should pay special attention to the generate_target_label(...) and generate_cutoff_times(...) methods as you might need to extend them or re-implemented in some cases.

2. Expose your new class definition in the init file inside the problem_definition directory

3. If you will be using a dataset in a different format that the expected by Cardea (CSV files), then you will need to provide a specific loading dataset method for your data in the EntitySetLoader class, where you will be creating your collection of entities and relationships between them using the featuretools.EntitySet class.

4. Finally, you need to update the Cardea class to support the new problem definition and be able to instantiate the proper class when it is necessary in the Cardea.select_problem(...) method.

Features, primitives and AutoML integration

Once you have defined your problem, following the four steps in the previous section, you will be able to perform featurization and run different primitives using the AutoML tool as follows:

from cardea import Cardea
cardea = Cardea()
cardea.load_your_custom_data()
problem = cardea.select_problem('YourCustomProblemDefinition')
feature_matrix = cardea.generate_features(problem[:1000])  # a subset
feature_matrix = feature_matrix.sample(frac=1)  # shuffle
y = list(feature_matrix.pop('label'))
X = feature_matrix.values
pipeline = [
    ['sklearn.ensemble.RandomForestClassifier'],
    ['sklearn.naive_bayes.MultinomialNB'],
    ['sklearn.neighbors.KNeighborsClassifier']
]
result = cardea.execute_model(feature_matrix=X, target=y, primitives=pipeline)

Auditing API Reference