posthoc.Workbench¶

class posthoc.Workbench(model, cov=None, pattern_modifier=None, normalizer_modifier=None)¶

Work bench for post-hoc alteration of a linear model.

Decomposes the .coef_ of a linear model into a covariance matrix, a pattern and a normalizer. These components are then altered by user speficied functions and the linear model is re-assembled.

Parameters:

Parameters:	model : instance of sklearn.linear_model.LinearModel The linear model to alter. cov : instance of CovEstimator \| function \| None The method used to estimate the covariance. Can either be one of the predefined CovEstimator objects, or a function. If a function is used, it must have the signature: `def cov_modifier(cov, X, y)`, take the training data as input and return a matrix that will be added to the emperical covariance matrix. Defaults to None, which means the default empirical estimator of the covariance matrix is used. pattern_modifier : function \| None Function that takes a pattern (an ndarray of shape (n_features, n_targets)) and modifies it. Must have the signature: `def pattern_modifier(pattern, X, y)` and return the modified pattern. Defaults to `None`, which means no modification of the pattern. normalizer_modifier : function \| None Function that takes a normalizer (an ndarray of shape (n_targets, n_targets)) and modifies it. Must have the signature: `def normalizer_modifier(coef, X, y, pattern, coef)` and return the modified normalizer. Defaults to `None`, which means no modification of the normalizer.
Attributes:	coef_ : ndarray, shape (n_targets, n_features) Matrix containing the filter weights. intercept_ : ndarray, shape (n_targets) The intercept of the linear model. pattern_ : ndarray, shape (n_features, n_targets) The altered pattern. normalizer_ : ndarray, shape (n_targets, n_targets) The altered normalizer.

model : instance of sklearn.linear_model.LinearModel: The linear model to alter.
cov : instance of CovEstimator | function | None: The method used to estimate the covariance. Can either be one of the predefined CovEstimator objects, or a function. If a function is used, it must have the signature: def cov_modifier(cov, X, y), take the training data as input and return a matrix that will be added to the emperical covariance matrix. Defaults to None, which means the default empirical estimator of the covariance matrix is used.
pattern_modifier : function | None: Function that takes a pattern (an ndarray of shape (n_features, n_targets)) and modifies it. Must have the signature: def pattern_modifier(pattern, X, y) and return the modified pattern. Defaults to None, which means no modification of the pattern.
normalizer_modifier : function | None: Function that takes a normalizer (an ndarray of shape (n_targets, n_targets)) and modifies it. Must have the signature: def normalizer_modifier(coef, X, y, pattern, coef) and return the modified normalizer. Defaults to None, which means no modification of the normalizer.

Attributes:

coef_ : ndarray, shape (n_targets, n_features): Matrix containing the filter weights.
intercept_ : ndarray, shape (n_targets): The intercept of the linear model.
pattern_ : ndarray, shape (n_features, n_targets): The altered pattern.
normalizer_ : ndarray, shape (n_targets, n_targets): The altered normalizer.

Methods

`fit`(X, y)	Fit the model to the data.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`inverse_predict`(y)	Apply the inverse of the linear model to the targets.
`predict`(X)	Predict using the linear model
`score`(X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Apply the model to the data.

__hash__($self, /)¶: Return hash(self).