tick.robust.ModelLinRegWithIntercepts¶

class tick.robust.ModelLinRegWithIntercepts(fit_intercept: bool = True, n_threads: int = 1)[source]¶

Linear regression model with individual intercepts. This class gives first order information (gradient and loss) for this model

Parameters

fit_intercept : bool, default=`True`

If True, the model uses an intercept

Attributes

features : numpy.ndarray, shape=(n_samples, n_features) (read-only)

The features matrix

labels : numpy.ndarray, shape=(n_samples,) (read-only)

The labels vector

n_samples : int (read-only)

Number of samples

n_features : int (read-only)

Number of features

n_coeffs : int (read-only)

Total number of coefficients of the model

n_threads : int, default=1 (read-only)

Number of threads used for parallel computation.

• if int <= 0: the number of threads available on the CPU

• otherwise the desired number of threads

__init__(fit_intercept: bool = True, n_threads: int = 1)[source]¶

Initialize self. See help(type(self)) for accurate signature.

fit(features, labels)[source]¶

Set the data into the model object

Parameters

features : numpy.ndarray, shape=(n_samples, n_features)

The features matrix

labels : numpy.ndarray, shape=(n_samples,)

The labels vector

Returns

output : ModelLinRegWithIntercepts

The current instance with given data

get_lip_best() → float¶

Returns the best Lipschitz constant, using all samples Warning: this might take some time, since it requires a SVD computation.

Returns

output : float

The best Lipschitz constant

get_lip_max() → float¶

Returns the maximum Lipschitz constant of individual losses. This is particularly useful for step-size tuning of some solvers.

Returns

output : float

The maximum Lipschitz constant

get_lip_mean() → float¶

Returns the average Lipschitz constant of individual losses. This is particularly useful for step-size tuning of some solvers.

Returns

output : float

The average Lipschitz constant

grad(coeffs: numpy.ndarray, out: numpy.ndarray = None) → numpy.ndarray¶

Computes the gradient of the model at coeffs

Parameters

coeffs : numpy.ndarray

Vector where gradient is computed

out : numpy.ndarray or None

If None a new vector containing the gradient is returned, otherwise, the result is saved in out and returned

Returns

output : numpy.ndarray

The gradient of the model at coeffs

Notes

The fit method must be called to give data to the model, before using grad. An error is raised otherwise.

loss(coeffs: numpy.ndarray) → float¶

Computes the value of the goodness-of-fit at coeffs

Parameters

coeffs : numpy.ndarray

The loss is computed at this point

Returns

output : float

The value of the loss

Notes

The fit method must be called to give data to the model, before using loss. An error is raised otherwise.

loss_and_grad(coeffs: numpy.ndarray, out: numpy.ndarray = None) → tuple¶

Computes the value and the gradient of the function at coeffs

Parameters

coeffs : numpy.ndarray

Vector where the loss and gradient are computed

out : numpy.ndarray or None

If None a new vector containing the gradient is returned, otherwise, the result is saved in out and returned

Returns

loss : float

The value of the loss

grad : numpy.ndarray

The gradient of the model at coeffs

Notes

The fit method must be called to give data to the model, before using loss_and_grad. An error is raised otherwise.