okridge.solvers
Module Contents
Classes
Functions
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Returns the approximate memory footprint an object and all of its contents. |
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- okridge.solvers.total_size(o, handlers={}, verbose=False)
Returns the approximate memory footprint an object and all of its contents.
Automatically finds the contents of the following builtin containers and their subclasses: tuple, list, deque, dict, set and frozenset. To search other containers, add handlers to iterate over their contents:
- handlers = {SomeContainerClass: iter,
OtherContainerClass: OtherContainerClass.get_elements}
- okridge.solvers.calculate_size(tmp_dict)
- class okridge.solvers.linRegModel_unnormalized_big_n(data)
- warm_start_from_betas(betas)
Warm start the model from a given betas
- Parameters:
betas (np.array) – 1D array of coefficients
- get_betas()
Get the current betas
- Returns:
1D array of coefficients
- Return type:
np.array
- get_betas_r()
Get the current betas, and r
- Returns:
1D array of coefficients np.array: 1D array of intermediate values - XTX_lambda2.dot(betas)
- Return type:
np.array
- get_betas_r_loss()
Get the current betas, r, and loss
- Returns:
1D array of coefficients np.array: 1D array of intermediate values - XTX_lambda2.dot(betas) float: loss
- Return type:
np.array
- finetune_on_current_support(supp_mask)
Finetune the current solution on a given support
- Parameters:
supp_mask (np.array) – 1D array of boolean values indicating the support
- Returns:
1D array of coefficients on the support np.array: 1D array of intermediate values - XTX_lambda2.dot(betas) float: loss
- Return type:
np.array
- class okridge.solvers.sparseLogRegModel_big_n(data, parent_size=10, child_size=10, allowed_supp_mask=None, max_memory_GB=50)
Bases:
linRegModel_unnormalized_big_n- reset_fixed_supp_and_allowed_supp(fixed_supp_mask, allowed_supp_mask)
Reset the fixed support and allowed support
- Parameters:
fixed_supp_mask (np.array) – 1D array of boolean values indicating the fixed support
allowed_supp_mask (np.array) – 1D array of boolean values indicating the allowed support
- get_sparse_sol_via_brute_force(k)
Get sparse solution through brute force
- Parameters:
k (int) – cardinality of the final sparse solution
- get_sparse_sol_via_OMP(k)
Get sparse solution through beam search and orthogonal matching pursuit (OMP), for level i, each parent solution generates [child_size] child solutions, so there will be [parent_size] * [child_size] number of total child solutions. However, only the top [parent_size] child solutions are retained as parent solutions for the next level i+1
- Parameters:
k (int) – cardinality of the final sparse solution
- beamSearch_multipleSupports_via_OMP_by_1()
Each parent solution generates [child_size] child solutions, so there will be [parent_size] * [child_size] number of total child solutions. However, only the top [parent_size] child solutions are retained as parent solutions for the next level i+1.
- expand_parent_i_support_via_OMP_by_1(i)
For parent solution i, generate [child_size] child solutions
- Parameters:
i (int) – index of the parent solution