The base_class Module#

Introduction#

The base_class module introduces the Base class and abstract classes defining broad model categories and feature constructors. These abstract classes must inherit from Base.

The Base class is envisioned as the foundational component for any object type (e.g., basis, regression, dimensionality reduction, clustering, observation models, regularizers etc.). In contrast, abstract classes derived from Base define overarching object categories (e.g., base_regressor.BaseRegressor is building block for GLMs, GAMS, etc. while observation_models.Observations is the building block for the Poisson observations, Gamma observations, … etc.).

Designed to be compatible with the scikit-learn API, the class inherits directly from sklearn.BaseEstimator. The class facilitate access to scikit-learn’s robust pipeline and cross-validation modules, while customizing the set_param method for working with NeMoS basis objects. This is achieved while leveraging the accelerated computational capabilities of jax in the backend, which is essential for analyzing extensive neural recordings and fitting large models.

Below a scheme of how we envision the architecture of the NeMoS models.

Abstract Class Base
│
├─ Abstract Subclass BaseRegressor
│   │
│   └─ Concrete Subclass GLM
│       │
│       └─ Concrete Subclass PopulationGLM
│
├─ Abstract Subclass BaseManifold *(not implemented yet)
│   │
│   ...
│
├─ Abstract Subclass Regularizer
│   │
│   ├─ Concrete Subclass UnRegularized
│   │
│   ├─ Concrete Subclass Ridge
│   ...
│
├─ Abstract Subclass Observations
│   │
│   ├─ Concrete Subclass PoissonObservations
│   │
│   ├─ Concrete Subclass GammaObservations
│   ...
│
...

The Class model_base.Base#

The Base class inherits from scikit-learn base.BaseEstimator, but reimplements theset_params method, essential for compatibility with both scikit-learn and NeMoS composite basis. This class is foundational for all model implementations as well as for our basis objects.

For a detailed understanding, consult the scikit-learn API Reference and BaseEstimator.

Public methods#

  • get_params: The get_params method retrieves parameters set during model instance initialization. Opting for a deep inspection allows the method to assess nested object parameters, resulting in a comprehensive parameter dictionary.

  • set_params: The set_params method offers a mechanism to adjust or set an estimator’s parameters. It’s versatile, accommodating both individual estimators and more complex nested structures like pipelines. Feeding an unrecognized parameter will raise a ValueError.

  • get_metadata_routing: Returns a routing object that describes how keyword arguments (metadata) are handled by the estimator’s public methods (e.g., fit, predict). This is used internally by scikit-learn to automatically pass relevant metadata through routers (pipelines, meta-estimators, and cross-validation), ensuring that additional information like score type (\(R^2\) vs log-likelihood) are routed correctly.