Fitting

This module defines the linear readout used to decode the target functions from the networks’ state. This module is based on scikit-learn’s linear model module and can be extended to other (possibly non-linear) regression methods as needed.

class hetero.fitting.Decoder(readout_params, ncpu=-1, **kwargs)

Bases: object

A decoder class for fitting and predicting using various regression methods.

Parameters:

• readout_params (dict) –

  Dictionary containing readout parameters:

  • ’readout_method’: str, regression method (‘ridge’, ‘lasso’, ‘linreg’, ‘logreg’)

  • ’readout_has_bias’: bool, whether to include bias term

  • ’readout_has_bias’: float, regularization parameter (for ridge and lasso)

• ncpu (int, optional) – Number of CPU cores to use, by default -1 (all cores)

Warning

The readout method must be compatible with the problem at hand. No sanity checks are performed to assess that is the case or not.

fit(X, y)

Fit the model using features X and target y.

predict(X)

Predict using fitted model on features X.

score(yhat, y)

Compute score between predictions yhat and true values y.

property coeff

Get the model coefficients.

hetero.fitting.det_coeff(yhat, y, eps=1e-09)

Calculate the coefficient of determination (R-squared) score.

Handles both single and multi-dimensional targets, with built-in outlier handling. For multi-dimensional data, computes R-squared separately for each dimension. Relevant from regression problems.

Parameters:

• yhat (array-like) – Predicted values

• y (array-like) – True values

• eps (float, optional) – Small constant to avoid division by zero, default 1e-9

Returns:

R-squared score(s). Returns array for multi-dimensional targets.

Return type:

float or array-like

hetero.fitting.conf_mat(yhat, y)

Compute confusion matrix between predicted and true labels. Relevant from classification problems.

Parameters:

• yhat (array-like) – Predicted values

• y (array-like) – True values

Returns:

Confusion matrix

Return type:

2d array