SCIKIT-LEARN DESIGN

SCIKIT-LEARN DESIGN

Scikit-Learn’s API is remarkably well designed. The main design principles are:

• Consistency. All objects share a consistent and simple interface:
  
  • Estimators. Any object that can estimate some parameters based on a dataset is called an estimator (e.g., an imputer is an estimator). The estimation itself is performed by the fit() method, and it takes only a dataset as a parameter (or two for supervised learning algorithms; the second dataset contains the labels). Any other parameter needed to guide the estimation process is considered a hyperparameter (such as an imputer ’s strategy ), and it must be set as an instance variable (generally via a constructor parameter).
  • Transformers. Some estimators (such as an imputer ) can also transform a dataset; these are called transformers. Once again, the API is quite simple: the transformation is performed by the transform() method with the dataset to transform as a parameter. It returns the transformed dataset. This transformation generally relies on the learned parameters, as is the case for an imputer . All transformers also have a convenience method called fit_transform() that is equivalent to calling fit() and then transform() (but sometimes fit_transform() is optimized and runs much faster).
  • Predictors. Finally, some estimators are capable of making predictions given a dataset; they are called predictors. A predictor has a predict() method that takes a dataset of new instances and returns a dataset of corresponding predictions. It also has a score() method that measures the quality of the predictions given a test set (and the corresponding labels in the case of supervised learning algorithms).
• Inspection. All the estimator’s hyperparameters are accessible directly via public instance variables (e.g., imputer.strategy ), and all the estimator’s learned parameters are also accessible via public instance variables with an underscore suffix (e.g., imputer.statistics_ ).
• Nonproliferation of classes. Datasets are represented as NumPy arrays or SciPy sparse matrices, instead of homemade classes. Hyperparameters are just regular Python strings or numbers.
• Composition. Existing building blocks are reused as much as possible. For example, it is easy to create a Pipeline estimator from an arbitrary sequence of transformers followed by a final estimator, as we will see.
• Sensible defaults. Scikit-Learn provides reasonable default values for most parameters, making it easy to create a baseline working system quickly.