alphacsc.BatchCDL#
- class alphacsc.BatchCDL(n_atoms, n_times_atom, reg=0.1, n_iter=60, n_jobs=1, solver_z='lgcd', solver_z_kwargs={}, unbiased_z_hat=False, solver_d='auto', solver_d_kwargs={}, rank1=True, window=False, uv_constraint='auto', lmbd_max='scaled', eps=1e-10, D_init=None, verbose=10, random_state=None, sort_atoms=False)#
Batch algorithm for convolutional dictionary learning
This transformer solves the following problem
\[\min_{D, Z} \sum_{n=1}^N \frac{1}{2} \|X^{(n)} - \sum_{k=1}^K D_k*Z^{(n)}_k\|_2^2 + \lambda\|Z^{(n)}\|_2\]for K = n_atoms and N = n_samples.
- Parameters
- Problem Specs
- n_atomsint
The number of atoms to learn.
- n_times_atomint
The support of the atom.
- rank1boolean
If set to True, learn rank 1 dictionary atoms.
- windowboolean
If set to True, re-parametrizes the atoms with a temporal Tukey window.
- uv_constraint{‘joint’ | ‘separate’ | ‘auto’}
The kind of norm constraint on the atoms if
rank1=True. Ifrank1=False, it must be ‘auto’, else it can be:'joint': the constraint is ||[u, v]||_2 <= 1'separate': the constraint is ||u||_2 <= 1 and ||v||_2 <= 1. This is the default for rank1 with if ‘auto’.
- sort_atomsboolean
If True, the atoms are sorted by explained variances.
- Global algorithm
Batch algorithm
- n_iterint
The number of alternate steps to perform.
- epsfloat
Stopping criterion. If the cost descent after a uv and a z update is smaller than eps, return.
- regfloat
The regularization parameter.
- lmbd_max‘fixed’ | ‘scaled’ | ‘per_atom’ | ‘shared’
If not fixed, adapt the regularization rate as a ratio of lambda_max:
'scaled': the regularization parameter is fixed as a ratio of its maximal value at init i.e. lambda = reg * lmbd_max(uv_init).'shared': the regularization parameter is set at each iteration as a ratio of its maximal value for the current dictionary estimate i.e. lambda = reg * lmbd_max(uv_hat).'per_atom': the regularization parameter is set per atom and at each iteration as a ratio of its maximal value for this atom i.e. lambda[k] = reg * lmbd_max(uv_hat[k]).
- Z-step parameters
- solver_zstr
The solver to use for the z update. Options are {‘l_bfgs’ (default) | ‘lgcd’}.
- solver_z_kwargsdict
Additional keyword arguments to pass to update_z_multi.
- unbiased_z_hatboolean
If set to True, the value of the non-zero coefficients in the returned z_hat are recomputed with reg=0 on the frozen support.
- D-step parameters
- solver_dstr (default: ‘auto’)
The solver to use for the d update. Options are: {‘alternate’, ‘alternate_adaptive’, ‘joint’, ‘fista’, ‘auto’} ‘auto’ amounts to ‘fista’ when
rank1=Falseand ‘alternate_adaptive’ forrank1=True.- solver_d_kwargsdict
Additional keyword arguments to provide to update_d
- D_initstr or array
The initial atoms with shape (n_atoms, n_channels + n_times_atoms) or (n_atoms, n_channels, n_times_atom) or an initialization scheme str in {‘chunk’ | ‘random’ | ‘greedy’}.
- Technical parameters
- n_jobsint
The number of parallel jobs.
- verboseint
The verbosity level.
- callbackfunc
A callback function called at the end of each loop of the coordinate descent.
- random_stateint | None
State to seed the random number generator.
- raise_on_increaseboolean
Raise an error if the objective function increase.
- __init__(n_atoms, n_times_atom, reg=0.1, n_iter=60, n_jobs=1, solver_z='lgcd', solver_z_kwargs={}, unbiased_z_hat=False, solver_d='auto', solver_d_kwargs={}, rank1=True, window=False, uv_constraint='auto', lmbd_max='scaled', eps=1e-10, D_init=None, verbose=10, random_state=None, sort_atoms=False)#
Methods
__init__(n_atoms, n_times_atom[, reg, ...])fit(X[, y])Learn a convolutional dictionary from the set of signals X.
fit_transform(X[, y])Learn a convolutional dictionary and returns sparse codes.
transform(X)Returns sparse codes associated to the signals X for the dictionary.
transform_inverse(z_hat)Reconstruct the signals from the given sparse codes.
Attributes
D_hat_array: dictionary in full rank mode.
pobj_list: Objective function value at each step of the alternate minimization.
times_list: Cumulative time for each iteration of the coordinate descent.
u_hat_array: spatial map of the dictionary.
uv_hat_array: dictionary in rank 1 mode.
v_hat_array: temporal patterns of the dictionary.
z_hat_array: Sparse code associated to the signals used to fit the model.