skelm.BatchCholeskySolver¶

class skelm.BatchCholeskySolver(alpha: float = 1e-07)[source]¶

__init__(alpha: float = 1e-07)[source]¶

Methods

`__init__`([alpha])
`compute_output_weights`()
`fit`(X, y)	Solves an L2-regularized linear system like Ridge regression, overwrites any previous solutions.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit`(X, y[, forget, ...])	Update model with a new batch of data.
`predict`(X)
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_params`(**params)	Set the parameters of this estimator.

Attributes

`XtX_`
`XtY_`
`coef_`
`intercept_`

fit(X, y)[source]¶: Solves an L2-regularized linear system like Ridge regression, overwrites any previous solutions.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

partial_fit(X, y, forget=False, compute_output_weights=True) → BatchCholeskySolver[source]¶

Update model with a new batch of data.

Output weight computation can be temporary turned off for faster processing. This will mark model as not fit. Enable compute_output_weights in the final call to partial_fit.

Parameters:

X ({array-like, sparse matrix}, shape=[n_samples, n_features]) – Training input samples
y (array-like, shape=[n_samples, n_targets]) – Training targets
forget (boolean, default False) – Performs a negative update, effectively removing the information given by training samples from the model. Output weights need to be re-computed after forgetting data.
compute_output_weights (boolean, optional, default True) –
Whether to compute new output weights (coef_, intercept_). Disable this in intermediate partial_fit steps to run computations faster, then enable in the last call to compute the new solution.

Note

Solution can be updated without extra data by setting X=None and y=None.

score(X, y, sample_weight=None)¶

Return the coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$ , where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – $R^2$ of self.predict(X) w.r.t. y.

Return type:

float

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance