Modifications experiments

experiments/SIAM/xp_2_bench_time/make_figs.sh

-python viz_paper.py --id 1a --precision 8 --noshow --save
-python viz_paper_it.py --id 1a --precision 8 --noshow --save
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experiments/SIAM/xp_2_bench_time/viz_paper_it.py

-# -*- coding: utf-8 -*-
-import argparse
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-from xps.SIAM.setup import Setup
-from process_data import process
-from get_algs_params import get_alg_params, get_nb_algs
-from src.utils import gamma_sequence_generator
-
-
-parser=argparse.ArgumentParser()
-parser.add_argument('--id', help='setup id', type=str)
-parser.add_argument('--precision', help='stop when gap reaches 1e-precision', default=8)
-parser.add_argument('--exact', action="store_true")
-parser.add_argument('--noshow', help='show plots', action="store_true")
-parser.add_argument('--save', help='save figure', action="store_true")
-args=parser.parse_args()
-
-# -------------------------
-#       Load  results
-# -------------------------
-
-folder = f'results/1e-{args.precision}'
-folder += 'exact/' if args.exact else 'gersh/'
-
-setup = Setup(args.id)
-_, list_names = get_alg_params(setup, np.zeros(5), args.exact)
-dic_process = process(folder, True, setup)
-
-vec_tau            = dic_process["vec_tau"]
-results_rho        = dic_process["results_rho"]
-
-
-# -------------------------
-#       Plot  results
-# -------------------------
-
-f, ax = plt.subplots(1, setup.nb_dic, figsize=(16, 4.),
-   sharex=True, sharey=True,
-   gridspec_kw = {'wspace':.05, 'hspace':.05}
-)
-
-list_algnames = ["PG", "PG$_s$", "PG$_p$"]
-list_dicname  = ["Gaussian", "Uniform", "toeplitz"]
-i_lbd = 1
-i_dic = 0
-
-print("ploting it fig for")
-print(f" - {list_dicname[i_dic]} dictionary")
-print(f" - lbd / lbd_max = {setup.list_ratio_lbd[i_lbd]}")
-
-for i_seq in range(setup.nb_sequence):
-
-   list_colors = ["tab:blue", "tab:orange", "tab:green"]
-   for i_alg in range(len(list_names)):
-
-      ax[i_seq].plot(
-         vec_tau, 
-         100. * results_rho[i_alg, i_dic, i_seq, i_lbd],
-         linewidth=3.,
-         label=list_algnames[i_alg],
-         color=list_colors[i_alg],
-         linestyle='-.'
-      )
-
-   ax[i_seq].set_title(f"OSCAR-{i_seq+1}", fontsize=16)
-
-   ax[i_seq].set_xscale('log')
-
-   ax[i_seq].set_xlim([5e-15, 5e-5])
-   ax[i_seq].set_ylim([-2, 101])
-   ax[i_seq].grid()
-
-   if i_seq == 0 :
-      ax[i_seq].legend()   
-
-   ax[i_seq].set_xlabel("$\\tau$ (Dual gap)", fontsize=14)
-
-   if i_seq == 0:
-      ax[i_seq].set_ylabel("$\\rho_s(\\tau)$", fontsize=14)
-
-
-
-if args.save:
-   fig_name = f"figs/setup{args.id}_it"
-
-
-   plt.savefig(fig_name + ".pdf", bbox_inches='tight')
-
-if not args.noshow:
-   plt.show()

experiments/utils/increasingball.py

+import time
+
+import numpy as np
+
+from src.utils import get_lambda_max
+
+
+def test_increasing_ball(list_tests, vec_offsets, slopepb, vecx):
+
+   nb_test = len(list_tests)
+   mat_out = np.zeros((nb_test, vec_offsets.size))
+
+   vecu, Atu, gap = slopepb.make_screening_quanties(
+      vecx
+   )
+
+   list_time = [0 for i in range(nb_test)]
+
+   # Boucle
+   for i_offset, offset in enumerate(vec_offsets):
+      for i_test, test in enumerate(list_tests):
+         # Test
+         t_start = time.time()
+         out = test.apply_test(
+            np.abs(Atu), 
+            gap, 
+            slopepb.lbd, 
+            slopepb.vec_gammas, 
+            offset_radius=offset
+         )
+         list_time[i_test] += time.time() - t_start
+         
+         # save
+         mat_out[i_test, i_offset] = np.sum(out)
+
+   # out
+   return mat_out, list_time
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experiments/utils/slopepb.py

+import numpy as np
+
+from src.slope import primal_func, dual_func
+from src.utils import get_lambda_max
+
+
+class SlopePb(object):
+   """docstring for SlopePb"""
+   def __init__(self, matA, vecy, vec_gammas, ratio_lbd, lbdmax=None):
+      super(SlopePb, self).__init__()
+      self.matA = matA
+      self.vecy = vecy
+      self.vec_gammas = vec_gammas
+      self.ratio_lbd = ratio_lbd
+      
+      if lbdmax is None:
+         self.lbdmax = get_lambda_max(vecy, matA, vec_gammas)
+      else:
+         self.lbdmax = lbdmax
+
+      self.lbd = ratio_lbd * self.lbdmax
+
+
+   def make_dual_scaling(self, vecr):
+
+      beta_dual = np.sort(np.abs(self.matA.T @ vecr))[::-1]
+      beta_dual = np.cumsum(beta_dual) / \
+         np.cumsum(self.lbd * self.vec_gammas)
+
+      return vecr / np.max(beta_dual)
+
+
+   def make_screening_quanties(self, vecx):
+      """
+      """
+
+      # residual error
+      vecu = self.vecy - self.matA @ vecx
+
+         # dual scaling
+      vecu = self.make_dual_scaling(vecu)
+
+      pval = primal_func(self.vecy, self.matA @ vecx, vecx, 
+         self.lbd, self.vec_gammas
+      )
+      dval = dual_func(self.vecy, 
+         np.linalg.norm(self.vecy, 2)**2, vecu
+      )
+
+      gap = np.abs(pval - dval)
+
+      Atu = self.matA.T @ vecu
+
+      return vecu, Atu, gap
+
+
+   def eval_gap(self, vecx):
+      """
+      """
+
+      # residual error
+      vecu = self.vecy - self.matA @ vecx
+
+         # dual scaling
+      vecu = self.make_dual_scaling(vecu)
+
+      pval = primal_func(self.vecy, self.matA @ vecx, vecx, 
+         self.lbd, self.vec_gammas
+      )
+      dval = dual_func(self.vecy, 
+         np.linalg.norm(self.vecy, 2)**2, vecu
+      )
+
+      return np.abs(pval - dval)
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