adelie.adelie_core.state.StateGaussianPinNaive32#

class adelie.adelie_core.state.StateGaussianPinNaive32#

Core state class for Gaussian, pin, naive method.

Methods

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

Attributes

`X`	Feature matrix.
`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`.
`resid`	Residual \(y_c - X \beta\) where \(\beta\) is given by `screen_beta`.
`resid_sum`	Weighted (by \(W\)) sum of `resid`.
`rsq`	The change in unnormalized \(R^2\) given by \(\\|y_c-X_c\beta_{\mathrm{old}}\\|_{W}^2 - \\|y_c-X_c\beta_{\mathrm{curr}}\\|_{W}^2\).
`rsqs`	`rsqs[i]` is the unnormalized \(R^2\) at `betas[i]`.
`screen_X_means`	Column means of \(X\) for screen groups (weighted by \(W\)).
`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_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_transforms`	List of \(V_k\) where \(V_k\) is from the SVD of \(\sqrt{W} X_{c,k}\) along the screen groups \(k\) and for possibly column-centered (weighted by \(W\)) \(X_k\).
`screen_vars`	List of \(D_k^2\) where \(D_k\) is from the SVD of \(\sqrt{W} X_{c,k}\) along the screen groups \(k\) and for possibly column-centered (weighted by \(W\)) \(X_k\).
`tol`	Coordinate descent convergence tolerance.
`weights`	Observation weights \(W\).
`y_mean`	Mean of the response vector \(y\) (weighted by \(W\)), i.e. \(\mathbf{1}^\top W y\).
`y_var`	Variance of the response vector \(y\) (weighted by \(W\)), i.e. \(\\|y_c\\|_{W}^2\).

__init__(*args, **kwargs)#

Overloaded function.

__init__(self: adelie.adelie_core.state.StateGaussianPinNaive32, X: adelie.adelie_core.matrix.MatrixNaiveBase32, y_mean: float, y_var: float, 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]], weights: 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_X_means: numpy.ndarray[numpy.float32[1, n]], screen_transforms: adelie.adelie_core.VectorMatrix32, lmda_path: numpy.ndarray[numpy.float32[1, n]], constraint_buffer_size: int, intercept: bool, max_active_size: int, max_iters: int, tol: float, adev_tol: float, ddev_tol: float, newton_tol: float, newton_max_iters: int, n_threads: int, rsq: float, resid: numpy.ndarray[numpy.float32[1, n], flags.writeable], resid_sum: float, screen_beta: 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.StateGaussianPinNaive32, arg0: adelie.adelie_core.state.StateGaussianPinNaive32) -> None

solve(self: adelie.adelie_core.state.StateGaussianPinNaive32) → dict#: Solves the state-specific problem.

X#: Feature matrix.

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.

resid#: Residual \(y_c - X \beta\) where \(\beta\) is given by screen_beta.

Note

This definition is unconventional. This was done deliberately as the algorithm is most efficient when it updates this quantity compared to the conventional quantity \(y_c-X_c \beta\).

resid_sum#: Weighted (by \(W\)) sum of resid.

rsq#: The change in unnormalized \(R^2\) given by \(\|y_c-X_c\beta_{\mathrm{old}}\|_{W}^2 - \|y_c-X_c\beta_{\mathrm{curr}}\|_{W}^2\).

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

screen_X_means#: Column means of \(X\) for screen groups (weighted by \(W\)).

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_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_transforms#: List of \(V_k\) where \(V_k\) is from the SVD of \(\sqrt{W} X_{c,k}\) along the screen groups \(k\) and for possibly column-centered (weighted by \(W\)) \(X_k\). 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 \(D_k\) is from the SVD of \(\sqrt{W} X_{c,k}\) along the screen groups \(k\) and for possibly column-centered (weighted by \(W\)) \(X_k\). 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.

weights#: Observation weights \(W\).

y_mean#: Mean of the response vector \(y\) (weighted by \(W\)), i.e. \(\mathbf{1}^\top W y\).

y_var#: Variance of the response vector \(y\) (weighted by \(W\)), i.e. \(\|y_c\|_{W}^2\).