adelie.state.gaussian_pin_naive#

adelie.state.gaussian_pin_naive(*, X: MatrixNaiveBase32 | MatrixNaiveBase64, y_mean: float, y_var: float, constraints: list[ConstraintBase32 | ConstraintBase64], groups: ndarray, alpha: float, penalty: ndarray, weights: ndarray, screen_set: ndarray, lmda_path: ndarray, rsq: float, resid: ndarray, screen_beta: ndarray, screen_is_active: ndarray, active_set_size: int, active_set: ndarray, intercept: bool = True, max_active_size: int | None = None, max_iters: int = 100000, tol: float = 1e-07, adev_tol: float = 0.9, ddev_tol: float = 0, newton_tol: float = 1e-12, newton_max_iters: int = 1000, n_threads: int = 1)[source]#

Creates a Gaussian, pin, naive method state object.

Define the following quantities:

\(X_c\) as \(X\) if intercept is False and otherwise the column-centered version.

\(y_c\) as \(y\) if intercept is False and otherwise the centered version.

Parameters:

XUnion[MatrixNaiveBase32, MatrixNaiveBase64]: Feature matrix. It is typically one of the matrices defined in adelie.matrix submodule.
y_meanfloat: Mean of the response vector \(y\) (weighted by \(W\)), i.e. \(\mathbf{1}^\top W y\).
y_varfloat: \(\ell_2\) norm squared (weighted by \(W\)) of \(y_c\), i.e. \(\|y_c\|_{W}^2\). Variance of the response vector \(y\) (weighted by \(W\)), i.e. \(\|y_c\|_{W}^2\). This is only used to check convergence as a relative measure, i.e. this quantity is the “null” model MSE.
constraints(G,) list[Union[ConstraintBase32, ConstraintBase64]]: List of constraints for each group. constraints[i] is the constraint object corresponding to group i. If constraints[i] is None, then the i th group is unconstrained. If None, every group is unconstrained.
groups(G,) ndarray: List of starting indices to each group where G is the number of groups. groups[i] is the starting index of the i th group.
alphafloat: Elastic net parameter. It must be in the range \([0,1]\).
penalty(G,) ndarray: Penalty factor for each group in the same order as groups. It must be a non-negative vector.
weights(n,) ndarray: Observation weights \(W\). The weights must sum to 1.
screen_set(s,) ndarray: List of indices into groups that correspond to the screen groups. screen_set[i] is i th screen group. screen_set must contain at least the true (optimal) active groups when the regularization is given by lmda.
lmda_path(L,) ndarray: The regularization path to solve for. It is recommended that the path is sorted in decreasing order.
rsqfloat: The change in unnormalized \(R^2\) given by \(\|y_c-X_c\beta_{\mathrm{old}}\|_{W}^2 - \|y_c-X_c\beta_{\mathrm{curr}}\|_{W}^2\). Usually, \(\beta_{\mathrm{old}} = 0\) and \(\beta_{\mathrm{curr}}\) is given by screen_beta.
resid(n,) ndarray: Residual \(y_c - X \beta\) where \(\beta\) is given by screen_beta.
screen_beta(ws,) ndarray: Coefficient vector on the screen set. screen_beta[b:b+p] is the coefficient for the i th screen group where k = screen_set[i], b = screen_begins[i], and p = group_sizes[k]. The values can be arbitrary but it is recommended to be close to the solution at lmda.
screen_is_active(s,) ndarray: Boolean vector that indicates whether each screen group in groups is active or not. screen_is_active[i] is True if and only if screen_set[i] is active.
active_set_sizeint: Number of active groups. active_set[i] is only well-defined for i in the range [0, active_set_size).
active_set(G,) ndarray: List of indices into screen_set that correspond to active groups. screen_set[active_set[i]] is the i th active group. An active group is one with non-zero coefficient block, that is, for every i th active group, screen_beta[b:b+p] == 0 where j = active_set[i], k = screen_set[j], b = screen_begins[j], and p = group_sizes[k].
interceptbool, optional: True to fit with intercept. Default is True.
max_active_sizeint, optional: Maximum number of active groups allowed. The function will return a valid state and guarantees to have active set size less than or equal to max_active_size. If None, it will be set to the total number of groups. Default is None.
max_itersint, optional: Maximum number of coordinate descents. Default is int(1e5).
tolfloat, optional: Coordinate descent convergence tolerance. Default is 1e-7.
adev_tolfloat, optional: Percent deviance explained tolerance. If the training percent deviance explained exceeds this quantity, then the solver terminates. Default is 0.9.
ddev_tolfloat, optional: Difference in percent deviance explained tolerance. If the difference of the last two training percent deviance explained exceeds this quantity, then the solver terminates. Default is 0.
newton_tolfloat, optional: Convergence tolerance for the BCD update. Default is 1e-12.
newton_max_itersint, optional: Maximum number of iterations for the BCD update. Default is 1000.
n_threadsint, optional: Number of threads. Default is 1.

Returns:

wrap: Wrapper state object.