adelie.adelie_core.state.StateGaussianPinCov32#

class adelie.adelie_core.state.StateGaussianPinCov32#

Core state class for Gaussian, pin, covariance method.

Methods

`__init__`(args, *kwargs)	Overloaded function.
`solve`(self)	Solves the state-specific problem.

Attributes

`A`	Positive semi-definite matrix \(A\).
`active_begins`	List of indices that index a corresponding list of values for each active group.
`active_order`	Ordering such that `groups` is sorted in ascending order for the active groups.
`active_set`	List of indices into `screen_set` that correspond to active groups.
`active_set_size`	Number of active groups.
`adev_tol`	Percent deviance explained tolerance.
`alpha`	Elastic net parameter.
`benchmark_active`	Benchmark time for performing coordinate-descent on the active set for each \(\lambda\).
`benchmark_screen`	Benchmark time for performing coordinate-descent on the screen set for each \(\lambda\).
`betas`	`betas[i]` is the solution at `lmdas[i]`.
`constraint_buffer_size`	Max constraint buffer size.
`constraints`	List of constraints for each group.
`ddev_tol`	Difference in percent deviance explained tolerance.
`group_sizes`	List of group sizes corresponding to each element in `groups`.
`groups`	List of starting indices to each group where G is the number of groups.
`intercept`	`True` if the function should fit with intercept.
`intercepts`	`intercepts[i]` is the intercept at `lmdas[i]`.
`iters`	Number of coordinate descents taken.
`lmda_path`	The regularization path to solve for.
`lmdas`	`lmdas[i]` is the regularization \(\lambda\) used for the `i` th solution.
`max_active_size`	Maximum number of active groups allowed.
`max_iters`	Maximum number of coordinate descents.
`n_threads`	Number of threads.
`newton_max_iters`	Maximum number of iterations for the BCD update.
`newton_tol`	Convergence tolerance for the BCD update.
`penalty`	Penalty factor for each group in the same order as `groups`.
`rdev_tol`	Relative percent deviance explained tolerance.
`rsq`	The change in unnormalized \(R^2\) given by \(2(\ell(\beta_{\mathrm{old}}) - \ell(\beta_{\mathrm{curr}}))\).
`rsqs`	`rsqs[i]` is the unnormalized \(R^2\) at `betas[i]`.
`screen_begins`	List of indices that index a corresponding list of values for each screen group.
`screen_beta`	Coefficient vector on the screen set.
`screen_grad`	Gradient \(v_k - A_{k,\cdot} \beta\) on the screen groups \(k\) where \(\beta\) is given by `screen_beta`.
`screen_is_active`	Boolean vector that indicates whether each screen group in `groups` is active or not.
`screen_set`	List of indices into `groups` that correspond to the screen groups.
`screen_subset_order`	Ordering such that `screen_subset` is sorted in ascending order where `screen_subset` is a list of column indices into \(A\) in the implicit ordering of `screen_grad`.
`screen_subset_ordered`	Ordered `screen_subset` (see `screen_subset_order` for its definition).
`screen_transforms`	List of \(V_k\) where \(V_k\) are the eigenvectors of \(A_{kk}\) along the screen groups \(k\).
`screen_vars`	List of \(D_k^2\) where \(D_k^2\) are the eigenvalues of \(A_{kk}\) along the screen groups \(k\).
`tol`	Coordinate descent convergence tolerance.

__init__(*args, **kwargs)#

Overloaded function.

__init__(self: adelie.adelie_core.state.StateGaussianPinCov32, A: adelie.adelie_core.matrix.MatrixCovBase32, constraints: adelie.adelie_core.constraint.VectorConstraintBase32, groups: numpy.ndarray[numpy.int64[1, n]], group_sizes: numpy.ndarray[numpy.int64[1, n]], alpha: float, penalty: numpy.ndarray[numpy.float32[1, n]], screen_set: numpy.ndarray[numpy.int64[1, n]], screen_begins: numpy.ndarray[numpy.int64[1, n]], screen_vars: numpy.ndarray[numpy.float32[1, n]], screen_transforms: adelie.adelie_core.VectorMatrix32, screen_subset_order: numpy.ndarray[numpy.int64[1, n]], screen_subset_ordered: numpy.ndarray[numpy.int64[1, n]], lmda_path: numpy.ndarray[numpy.float32[1, n]], constraint_buffer_size: int, max_active_size: int, max_iters: int, tol: float, rdev_tol: float, newton_tol: float, newton_max_iters: int, n_threads: int, rsq: float, screen_beta: numpy.ndarray[numpy.float32[1, n], flags.writeable], screen_grad: numpy.ndarray[numpy.float32[1, n], flags.writeable], screen_is_active: numpy.ndarray[bool[1, n], flags.writeable], active_set_size: int, active_set: numpy.ndarray[numpy.int64[1, n], flags.writeable]) -> None
__init__(self: adelie.adelie_core.state.StateGaussianPinCov32, arg0: adelie.adelie_core.state.StateGaussianPinCov32) -> None

solve(self: adelie.adelie_core.state.StateGaussianPinCov32) → dict#: Solves the state-specific problem.

A#: Positive semi-definite matrix \(A\).

active_begins#: List of indices that index a corresponding list of values for each active group. active_begins[i] is the starting index corresponding to the i th active group.

active_order#: Ordering such that groups is sorted in ascending order for the active groups. groups[screen_set[active_set[active_order[i]]]] is the i th active group in ascending order.

active_set#: 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].

active_set_size#: Number of active groups. active_set[i] is only well-defined for i in the range [0, active_set_size).

adev_tol#: Percent deviance explained tolerance.

alpha#: Elastic net parameter.

benchmark_active#: Benchmark time for performing coordinate-descent on the active set for each \(\lambda\).

benchmark_screen#: Benchmark time for performing coordinate-descent on the screen set for each \(\lambda\).

betas#: betas[i] is the solution at lmdas[i].

constraint_buffer_size#: Max constraint buffer size. Equivalent to np.max([0 if c is None else c.buffer_size() for c in constraints]).

constraints#: List of constraints for each group. constraints[i] is the constraint object corresponding to group i.

ddev_tol#: Difference in percent deviance explained tolerance.

group_sizes#: List of group sizes corresponding to each element in groups. group_sizes[i] is the group size of the i th group.

groups#: 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.

intercept#: True if the function should fit with intercept.

intercepts#: intercepts[i] is the intercept at lmdas[i].

iters#: Number of coordinate descents taken.

lmda_path#: The regularization path to solve for.

lmdas#: lmdas[i] is the regularization \(\lambda\) used for the i th solution.

max_active_size#: Maximum number of active groups allowed.

max_iters#: Maximum number of coordinate descents.

n_threads#: Number of threads.

newton_max_iters#: Maximum number of iterations for the BCD update.

newton_tol#: Convergence tolerance for the BCD update.

penalty#: Penalty factor for each group in the same order as groups.

rdev_tol#: Relative percent deviance explained tolerance.

rsq#: The change in unnormalized \(R^2\) given by \(2(\ell(\beta_{\mathrm{old}}) - \ell(\beta_{\mathrm{curr}}))\).

rsqs#: rsqs[i] is the unnormalized \(R^2\) at betas[i].

screen_begins#: List of indices that index a corresponding list of values for each screen group. screen_begins[i] is the starting index corresponding to the i th screen group. From this index, reading group_sizes[screen_set[i]] number of elements will grab values corresponding to the full i th screen group block.

screen_beta#: 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].

screen_grad#: Gradient \(v_k - A_{k,\cdot} \beta\) on the screen groups \(k\) where \(\beta\) is given by screen_beta. screen_grad[b:b+p] is the gradient for the i th screen group where k = screen_set[i], b = screen_begins[i], and p = group_sizes[k].

screen_is_active#: 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.

screen_set#: List of indices into groups that correspond to the screen groups. screen_set[i] is i th screen group.

screen_subset_order#: Ordering such that screen_subset is sorted in ascending order where screen_subset is a list of column indices into \(A\) in the implicit ordering of screen_grad. screen_subset[screen_subset_order[i]] is the i th column index into \(A\) corresponding to screen_grad[screen_subset_order[i]].

screen_subset_ordered#: Ordered screen_subset (see screen_subset_order for its definition). We have the equivalence screen_subset_ordered[i] == screen_subset[screen_subset_order[i]].

screen_transforms#: List of \(V_k\) where \(V_k\) are the eigenvectors of \(A_{kk}\) along the screen groups \(k\). It only needs to be properly initialized for groups with size > 1. screen_transforms[i] is \(V_k\) for the i th screen group where k = screen_set[i].

screen_vars#: List of \(D_k^2\) where \(D_k^2\) are the eigenvalues of \(A_{kk}\) along the screen groups \(k\). screen_vars[b:b+p] is \(D_k^2\) for the i th screen group where k = screen_set[i], b = screen_begins[i], and p = group_sizes[k].

tol#: Coordinate descent convergence tolerance.