diff --git a/experiments/SIAM/xp_0_balls/make_fig_paper.sh b/experiments/SIAM/xp_0_balls/make_fig_paper.sh
deleted file mode 100755
index 36f8245cdc32c7ea2a508833bdd323d55c3d35a7..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/make_fig_paper.sh
+++ /dev/null
@@ -1 +0,0 @@
-python viz_final.py --noshow --save
\ No newline at end of file
diff --git a/experiments/SIAM/xp_0_balls/poc.py b/experiments/SIAM/xp_0_balls/poc.py
deleted file mode 100644
index ec464e1f27dc3b8b59e4ca3e1d5aead11a7754ba..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/poc.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# -*- coding: utf-8 -*-
-import argparse
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-# Algorithm import
-from src import __version__
-from src.dictionaries import generate_dic
-from src.utils import get_lambda_max, gamma_sequence_generator
-
-# Screening
-from src.screening.singletest import GapSphereSingleTest
-from src.screening.gap_ptest import GAP_Ptest
-from src.screening.gap_rqtest import GAP_RQtest
-from src.screening.kappa_test import Kappa_test
-
-# XP import
-from xps.SIAM.slopepb import SlopePb
-from xps.SIAM.setup import Setup
-from xps.SIAM.xp_1_balls import xpparams
-
-
-parser=argparse.ArgumentParser()
-parser.add_argument('--id', help='setup id', type=str, default="1a")
-args=parser.parse_args()
-
-# -------------------------
-# Load Setup
-# -------------------------
-
-setup = Setup(args.id)
-solutions_filename = f"results/xp_setup{args.id}.npz"
-
-try:
- # Try to load existing results
- load_results = np.load(solutions_filename, allow_pickle=True)
- mat_seed = load_results["mat_seed"]
- mat_pvopt = load_results["mat_pvopt"]
- mat_dvopt = load_results["mat_dvopt"]
-
-except FileNotFoundError:
- print("No result found")
- sys.exit(1)
-
-
-# -------------------------
-# Screening Xp
-# -------------------------
-
-# percentage of detected zero
-n_rep = 5
-mat_nb_zero_detected = np.full(
- (3, xpparams.nb_point, setup.nb_sequence, n_rep),
- np.nan,
- dtype=int
-)
-poc_filename = f"results/poc{args.id}_screening.npz"
-
-i_xp = 0
-
-i_dic = 2
-i_ratio = 1
-ratio = .5
-i_seq = 1
-seq = "OSCAR-1"
-
-# seq_r = 1+np.arange(setup.n, dtype=int)
-seq_r = 1 + np.arange(setup.n, dtype=int) // 5
-
-#1 + (1 + np.arange(setup.n, dtype=int)) // 2
-
-# for rep in range(setup.n_rep):
-for rep in range(n_rep):
-
- np.random.seed(mat_seed[i_dic, i_seq, rep])
-
- # ---- 1. Gen data ----
- matA = generate_dic(
- setup.list_dic[i_dic],
- setup.m,
- setup.n,
- setup.normalize
- )
-
- vecy = np.random.randn(setup.m)
- vecy /= np.linalg.norm(vecy)
-
-
- # ---- 2. Compute parameters ----
- vec_gammas = gamma_sequence_generator(
- setup.m,
- setup.n,
- setup.list_sequence[i_seq][0],
- setup.list_sequence[i_seq][1:]
- )
-
- lbd_max = get_lambda_max(vecy, matA, vec_gammas)
-
- # ---- 3. XP ----
- i_xp += 1
- print(f"xp {i_xp}")
-
- slopePb = SlopePb(matA, vecy, vec_gammas, ratio, lbdmax=lbd_max)
- vecx_hat = mat_pvopt[i_dic, i_seq, i_ratio, rep, :]
- vecu_hat = mat_dvopt[i_dic, i_seq, i_ratio, rep, :]
- gap = slopePb.eval_gap(vecx_hat, vecu_hat)
- Atu = matA.T @ vecu_hat
-
- # ---- 3b. Build thin safe ball ----
- rgap = np.sqrt(2 * gap)
-
- # ---- 3c. Testing sphere ----
- list_tests = [
- GapSphereSingleTest(),
- Kappa_test(vec_gammas, np.arange(setup.n, dtype=np.double)),
- GAP_RQtest(vec_gammas, seq_r),
- # GAP_Ptest(vec_gammas),
- ]
-
-
- for i_offset, offset in enumerate(xpparams.vec_offsets):
- for i_test, test in enumerate(list_tests):
- out = test.apply_test(np.abs(Atu), gap, ratio * lbd_max, vec_gammas, offset_radius=offset)
- mat_nb_zero_detected[i_test, i_offset, i_seq, rep] = np.sum(out)
-
-f, ax = plt.subplots(1, 1)
-for i_test in range(3):
- ax.plot(np.mean(mat_nb_zero_detected[i_test, :, i_seq, :n_rep], axis=1))
-
-plt.show()
-
- # # Save
- # np.savez(
- # poc_filename,
- # mat_nb_zero_detected=mat_nb_zero_detected,
- # list_test = [test.get_name() for test in list_tests],
- # version = __version__,
- # allow_pickle=True
- # )
\ No newline at end of file
diff --git a/experiments/SIAM/xp_0_balls/poc_debug.py b/experiments/SIAM/xp_0_balls/poc_debug.py
deleted file mode 100644
index d5c4da9416ba6b42c3097d83179c6d72fd0fd651..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/poc_debug.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# -*- coding: utf-8 -*-
-import argparse
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-# Algorithm import
-from src import __version__
-from src.dictionaries import generate_dic
-from src.utils import get_lambda_max, gamma_sequence_generator
-
-# Screening
-from src.screening.singletest import GapSphereSingleTest
-from src.screening.gap_ptest import GAP_Ptest
-from src.screening.gap_rqtest import GAP_RQtest
-from src.screening.kappa_test import Kappa_test
-
-# XP import
-from xps.SIAM.slopepb import SlopePb
-from xps.SIAM.setup import Setup
-from xps.SIAM.xp_1_balls import xpparams
-
-
-parser=argparse.ArgumentParser()
-parser.add_argument('--id', help='setup id', type=str, default="1a")
-args=parser.parse_args()
-
-# -------------------------
-# Load Setup
-# -------------------------
-
-setup = Setup(args.id)
-solutions_filename = f"results/xp_setup{args.id}.npz"
-
-try:
- # Try to load existing results
- load_results = np.load(solutions_filename, allow_pickle=True)
- mat_seed = load_results["mat_seed"]
- mat_pvopt = load_results["mat_pvopt"]
- mat_dvopt = load_results["mat_dvopt"]
-
-except FileNotFoundError:
- print("No result found")
- sys.exit(1)
-
-
-# -------------------------
-# Screening Xp
-# -------------------------
-
-# percentage of detected zero
-mat_nb_zero_detected = np.full(
- (4, xpparams.nb_point, setup.nb_sequence, setup.n_rep),
- np.nan,
- dtype=int
-)
-poc_filename = f"results/poc{args.id}_screening.npz"
-
-i_xp = 0
-nb_xp = setup.nb_dic * setup.n_rep * setup.nb_sequence * setup.nb_ratio_lbd
-
-i_dic = 2
-i_ratio = 1
-ratio = .5
-rep = 0
-
-f, ax = plt.subplots(1, 3)
-for i_seq, seq in enumerate(setup.list_sequence):
- # for rep in range(setup.n_rep):
-
- np.random.seed(mat_seed[i_dic, i_seq, rep])
-
- # ---- 1. Gen data ----
- matA = generate_dic(
- setup.list_dic[i_dic],
- setup.m,
- setup.n,
- setup.normalize
- )
-
- vecy = np.random.randn(setup.m)
- vecy /= np.linalg.norm(vecy)
-
-
- # ---- 2. Compute parameters ----
- vec_gammas = gamma_sequence_generator(
- setup.m,
- setup.n,
- setup.list_sequence[i_seq][0],
- setup.list_sequence[i_seq][1:]
- )
-
- lbd_max = get_lambda_max(vecy, matA, vec_gammas)
-
- # ---- 3. XP ----
- i_xp += 1
- print(f"xp {i_xp} / {nb_xp}")
-
- slopePb = SlopePb(matA, vecy, vec_gammas, ratio, lbdmax=lbd_max)
- vecx_hat = mat_pvopt[i_dic, i_seq, i_ratio, rep, :]
- vecu_hat = mat_dvopt[i_dic, i_seq, i_ratio, rep, :]
- gap = slopePb.eval_gap(vecx_hat, vecu_hat)
- Atu = matA.T @ vecu_hat
-
- # ---- 3b. Build thin safe ball ----
- rgap = np.sqrt(2 * gap)
-
- # ---- 3c. Testing sphere ----
- list_tests = [
- # --- Lasso like test --
- GapSphereSingleTest(),
- GAP_RQtest(vec_gammas, 1+np.arange(setup.n, dtype=int)),
- # # --- ideal like test ---
- Kappa_test(vec_gammas, np.arange(setup.n, dtype=np.double)),
- GAP_RQtest(vec_gammas, np.ones(setup.n, dtype=int)),
- # --- doing all tets ---
- # GAP_Ptest(vec_gammas),
- ]
-
- for i_offset, offset in enumerate(xpparams.vec_offsets):
- for i_test, test in enumerate(list_tests):
- out = test.apply_test(np.abs(Atu), gap, ratio * lbd_max, vec_gammas, offset_radius=offset)
- mat_nb_zero_detected[i_test, i_offset, i_seq, rep] = np.sum(out)
-
-
- ax[i_seq].plot(mat_nb_zero_detected[0, :, i_seq, rep], linewidth=4)
- ax[i_seq].plot(mat_nb_zero_detected[1, :, i_seq, rep])
-
- ax[i_seq].plot(mat_nb_zero_detected[2, :, i_seq, rep], linewidth=4)
- ax[i_seq].plot(mat_nb_zero_detected[3, :, i_seq, rep])
-
-plt.show()
\ No newline at end of file
diff --git a/experiments/SIAM/xp_0_balls/process_data.py b/experiments/SIAM/xp_0_balls/process_data.py
deleted file mode 100644
index d8a4f63bcd5db7e8f43f704e9275d0e1713c2dd9..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/process_data.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-
-from xps.SIAM.setup import Setup
-from xps.SIAM.xp_1_balls import xpparams
-
-
-def process(setup, log=True):
-
- # ---- load ----
- solutions_filename = f"results/xp_setup{setup.setup_id}.npz"
- solutions = np.load(solutions_filename, allow_pickle=True)
-
- screenings_filename = f"results/xp_setup{setup.setup_id}_screening.npz"
- screenings = np.load(screenings_filename, allow_pickle=True)
-
- # mat_results_gap[1, i_dic, i_seq, rep, i_ratio]
- mat_pvopt = solutions["mat_pvopt"]
-
- mat_nb_zero_detected = screenings['mat_nb_zero_detected']
- nb_test = mat_nb_zero_detected.shape[0]
- list_tests = screenings["list_test"]
-
- # ---- log ----
- if log:
- print("Experiment info")
- # print(f"- run with version {solutions["version"]}")
- print(f"- setup{setup.setup_id}")
- print("")
-
- # ---- processing ----
- mat_pc_detected =np.zeros(
- (nb_test, xpparams.nb_point, setup.nb_dic, setup.nb_sequence, setup.nb_ratio_lbd, setup.n_rep)
- )
-
- for i_dic in range(setup.nb_dic):
- for i_seq in range(setup.nb_sequence):
- for i_ratio in range(setup.nb_ratio_lbd):
- for rep in range(setup.n_rep):
-
- nb_0 = np.sum(mat_pvopt[i_dic, i_seq, i_ratio, rep, :] == 0)
- if nb_0 > 0:
- mat_detected = mat_nb_zero_detected[:, :, i_dic, i_seq, i_ratio, rep]
- mat_pc_detected[:, :, i_dic, i_seq, i_ratio, rep] = mat_detected / float(nb_0)
-
-
- # ---- return ----
- return {
- "mat_pc_detected": np.mean(mat_pc_detected, axis=5),
- "list_tests": list_tests,
- }
-
- # {
- # "results_mat_nb": results_mat_nb,
- # "results_mat_nbz": results_mat_nbz,
- # }
\ No newline at end of file
diff --git a/experiments/SIAM/xp_0_balls/viz.py b/experiments/SIAM/xp_0_balls/viz.py
deleted file mode 100755
index 05d0832c2788d55cacc8d8e96a33f730a1b16502..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/viz.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# -*- coding: utf-8 -*-
-from decimal import Decimal
-import argparse
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-# Slope import
-from src.utils import gamma_sequence_generator
-
-# XP import
-from xps.SIAM.slopepb import SlopePb
-from xps.SIAM.setup import Setup
-from xps.SIAM.xp_1_balls import xpparams
-from xps.SIAM.xp_1_balls.process_data import process
-
-
-parser=argparse.ArgumentParser()
-parser.add_argument('--noshow', help='do not display figures', action="store_true")
-parser.add_argument('--save', help='save figure', action="store_true")
-parser.add_argument('--id', help='setup id', type=str, default=1)
-args=parser.parse_args()
-
-
-# -------------------------
-# Load Results
-# -------------------------
-
-setup = Setup(args.id)
-dic_process = process(setup)
-
-mat_pc_detected = dic_process["mat_pc_detected"]
-list_tests = dic_process["list_tests"]
-list_legends = ["$r_q=1 \\forall q$", "Testing all $q$", "$r_q=q \\forall q$"]
-
-# -------------------------
-# Plot Results
-# -------------------------
-
-for i_dic in range(setup.nb_dic):
-
- if not np.any(mat_pc_detected[:, :, i_dic, :, :] > 0):
- continue
-
- f, ax = plt.subplots(setup.nb_sequence, setup.nb_ratio_lbd+1)
- f.suptitle(f"{setup.list_dic[i_dic]}")
-
- for i_seq, str_seq in enumerate(setup.list_sequence):
-
- for i_lbd in range(len(setup.list_ratio_lbd)):
-
- if i_seq ==0:
- ax[i_seq, i_lbd].set_title(f"$\\lambda={setup.list_ratio_lbd[i_lbd]}$")
-
- for i_test in [0, 2, 1]:
- ax[i_seq, i_lbd].plot(
- xpparams.vec_offsets,
- 100 * mat_pc_detected[i_test, :, i_dic, i_seq, i_lbd],
- # '-',
- # color="blue",
- label= list_legends[i_test],
- # ,
- # linestyle=style
- )
-
- # if j == 0:
- # ax[j, i_seq].set_title(f"{setup.list_sequence[i_seq]} -- lbd={setup.list_ratio_lbd[j]}")
- # else:
- # ax[j, i_seq].set_title(f"lbd={setup.list_ratio_lbd[j]}")
-
- if i_lbd == 0 and i_seq == 0:
- ax[i_seq, i_lbd].legend()
-
- ax[i_seq, i_lbd].set_xscale('log')
-
- # if i_seq >= 2:
- # ax[i_seq, i_lbd].set_xlim([0, .5])
- # if i_seq >= 3:
- # ax[i_seq, i_lbd].set_xlim([0, .05])
-
- vec_gammas = gamma_sequence_generator(
- setup.m,
- setup.n,
- setup.list_sequence[i_seq][0],
- setup.list_sequence[i_seq][1:],
- log=True
- )
-
- ax[i_seq, len(setup.list_ratio_lbd)].plot(vec_gammas)
-
- if args.save:
- filename = f"figs/setup{args.id}_{setup.list_dic[i_dic]}.pdf"
- plt.savefig(filename, bbox_inches='tight')
-
-if not args.noshow:
- plt.show()
\ No newline at end of file
diff --git a/experiments/SIAM/xp_0_balls/viz_final.py b/experiments/SIAM/xp_0_balls/viz_final.py
deleted file mode 100644
index 49b42e0af0329ff1d2cd601d40ff6456763b8911..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/viz_final.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# -*- coding: utf-8 -*-
-from decimal import Decimal
-import json, argparse
-from prettytable import PrettyTable
-
-import numpy as np
-
-# XP import
-from xps.SIAM.slopepb import SlopePb
-from xps.SIAM.setup import Setup
-from xps.SIAM.xp_1_balls import xpparams
-from xps.SIAM.xp_1_balls.process_data import process
-
-
-parser=argparse.ArgumentParser()
-parser.add_argument('--noshow', help='do not display figures', action="store_true")
-parser.add_argument('--save', help='save figure', action="store_true")
-# parser.add_argument('--id', help='setup id', type=str, default=1)
-args=parser.parse_args()
-
-import matplotlib
-if args.noshow:
- matplotlib.use('PS')
-else:
- matplotlib.use("TkAgg")
-
-import matplotlib.pyplot as plt
-from matplotlib.legend_handler import HandlerBase
-import matplotlib.font_manager as font_manager
-
-# -------------------------
-# Font stuff
-# -------------------------
-
-fs = 20
-
-font_math = font_manager.FontProperties(
- fname='../fonts/cmunrm.ttf',
- # weight='normal',
- # style='normal',
- math_fontfamily="cm",
- size=fs+2
-)
-
-font_text = font_manager.FontProperties(
- fname='../fonts/cmunrm.ttf',
- # weight='normal',
- # style='normal',
- math_fontfamily="cm",
- size=fs+2
-)
-
-font_ttt = font_manager.FontProperties(
- # fname='../fonts/ectt1000.ttf',
- fname='../fonts/computer-modern/cmuntt.ttf',
- weight='bold',
- style='normal',
- size=fs
-)
-
-
-# -------------------------
-# Load Results
-# -------------------------
-
-# OSCAR
-setup_oscar = Setup("1a")
-dic_process_oscar = process(setup_oscar)
-
-mat_pc_detected_oscar = dic_process_oscar["mat_pc_detected"]
-list_tests_oscar = dic_process_oscar["list_tests"]
-
-# EXP
-setup_exp = Setup("1b")
-dic_process_exp = process(setup_exp)
-
-mat_pc_detected_exp = dic_process_exp["mat_pc_detected"]
-list_tests_exp = dic_process_exp["list_tests"]
-
-
-# -------------------------
-# Plot Results
-# -------------------------
-
-i_dic = 2
-i_lbd = 1
-
-fs=22
-fs_ylabels = 20
-list_colors = ["tab:blue", "tab:orange", "tab:green"]
-list_legends = ["$r_q=q\\;\\forall q$", "best performance", "$r_q=1\\;\\forall q$"]
-# "best $r_q \\;\\forall q$ "
-
-print("printing xp_0_ball parameters with")
-print(" - OSCAR sequence")
-print(f"- {setup_oscar.list_dic[i_dic]} dictionary")
-print(f"- lbd / lbd_max = {setup_oscar.list_ratio_lbd[i_lbd]}")
-
-for i_seq in range(2):
-
- f, ax = plt.subplots(1, 1, figsize=(.7*16, .6*9), sharex=True, sharey=True)
-
- # ax.set_title(f"OSCAR-{i_seq+1}", fontsize=fs+2)
- ax.set_xlabel(
- r"$R$",
- fontsize=fs+2,
- fontproperties=font_math,
- )
- ax.set_ylabel(
- "% of zero entries detected",
- fontsize=fs+2,
- fontproperties=font_text
- )
-
- for tick in ax.xaxis.get_major_ticks():
- tick.label.set_fontproperties(font_math)
- tick.label.set_fontsize(20)
-
- for tick in ax.yaxis.get_major_ticks():
- tick.label.set_fontproperties(font_math)
- tick.label.set_fontsize(20)
-
- for i_test in [2, 0, 1]:
- ax.plot(
- xpparams.vec_offsets,
- 100 * mat_pc_detected_exp[i_test, :, i_dic, i_seq, i_lbd],
- label = list_legends[i_test],
- linewidth=4.,
- alpha=.9,
- color=list_colors[i_test]
- )
-
- if i_seq == 0:
- ax.legend(
- fontsize=fs-2,
- prop=font_math
- )
-
- ax.set_xscale("log")
- ax.set_xlim([1e-6, 1e0])
- ax.set_ylim([-2, 102])
-
- if args.save:
- filename = f"figs/xp_illustration_screening{i_seq}.eps"
- plt.savefig(filename, bbox_inches='tight')
-
-if not args.noshow:
- plt.show()
\ No newline at end of file
diff --git a/experiments/SIAM/xp_0_balls/xp_a_accuracy_sol.py b/experiments/SIAM/xp_0_balls/xp_a_accuracy_sol.py
deleted file mode 100755
index ddb328dc42d27bf9847aaacf702ab9985e61bd4e..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/xp_a_accuracy_sol.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# -*- coding: utf-8 -*-
-import time, argparse, sys
-
-import numpy as np
-
-# Algorithm import
-from src import __version__
-from src.parameters import SlopeParameters, EnumLipchitzOptions
-from src.slope import primal_func, dual_func, slope_gp
-
-# Generative models
-from src.utils import get_lambda_max, gamma_sequence_generator
-from src.dictionaries import generate_dic
-
-# Screening
-from src.screening.singletest import GapSphereSingleTest
-from src.screening.gap_ptest import GAP_Ptest
-
-# XP import
-from xps.SIAM.slopepb import SlopePb
-from xps.SIAM.setup import Setup
-from xps.SIAM.xp_1_balls import xpparams
-
-parser=argparse.ArgumentParser()
-parser.add_argument('--erase', help='erase existing results', action="store_true")
-parser.add_argument('--id', help='setup id', type=str)
-args=parser.parse_args()
-
-
-# -------------------------
-# Load Setup
-# -------------------------
-
-setup = Setup(args.id)
-out_file_name = f"results/xp_setup{args.id}.npz"
-
-mat_seed = np.random.randint(
- 0, 2**32-1,
- size=(setup.nb_dic, setup.nb_sequence, setup.n_rep),
-)
-
-mat_pvopt = np.zeros(
- (setup.nb_dic, setup.nb_sequence, setup.nb_ratio_lbd, setup.n_rep, setup.n)
-)
-
-mat_dvopt = np.zeros(
- (setup.nb_dic, setup.nb_sequence, setup.nb_ratio_lbd, setup.n_rep, setup.m)
-)
-
-
-try:
- if args.erase:
- raise FileNotFoundError
-
- # Try to load existing results
- load_results = np.load(out_file_name, allow_pickle=True)
- mat_seed = load_results["mat_seed"]
- mat_pvopt = load_results["mat_pvopt"]
- mat_dvopt = load_results["mat_dvopt"]
-
-except FileNotFoundError:
- pass
-
-
-# -------------------------
-# Xp
-# -------------------------
-
-# For each trial
-# 1. Compute high accuracy solution
-# 2. Create ideal safe ball
-# 3. increase radius
-
-nb_xp = setup.nb_dic * setup.n_rep * setup.nb_sequence * setup.nb_ratio_lbd
-i_xp = 0
-for i_dic in range(setup.nb_dic):
- for i_seq, seq in enumerate(setup.list_sequence):
- for rep in range(setup.n_rep):
-
- np.random.seed(mat_seed[i_dic, i_seq, rep])
-
- # ---- 1. Gen data ----
- matA = generate_dic(
- setup.list_dic[i_dic],
- setup.m,
- setup.n,
- setup.normalize
- )
-
- lip = np.linalg.norm(matA, ord=2)**2
- vecy = np.random.randn(setup.m)
- vecy /= np.linalg.norm(vecy, 2)
-
-
- # ---- 2. Compute parameters ----
- vec_gammas = gamma_sequence_generator(
- setup.m,
- setup.n,
- setup.list_sequence[i_seq][0],
- setup.list_sequence[i_seq][1:]
- )
-
- lbd_max = get_lambda_max(vecy, matA, vec_gammas)
-
-
- # ---- 3. XP ----
- for i_ratio, ratio in enumerate(setup.list_ratio_lbd):
- i_xp += 1
-
- slopePb = SlopePb(matA, vecy, vec_gammas, ratio, lbdmax=lbd_max)
- vecx_hat = mat_pvopt[i_dic, i_seq, i_ratio, rep, :]
- vecu_hat = mat_dvopt[i_dic, i_seq, i_ratio, rep, :]
-
- gap = slopePb.eval_gap(vecx_hat, vecu_hat)
- print(f"xp {i_xp} / {nb_xp} --- (gap={gap})")
-
- if (gap > xpparams.stopping_gap):
- gap_old = gap
-
- # ---- 3a. Find solution ----
- params = SlopeParameters()
- params.vecx_init = np.copy(vecx_hat)
- params.lipchitz_constant = lip
- params.lipchitz_update = EnumLipchitzOptions.EXACT
- params.max_it = 1e7
- params.gap_stopping = xpparams.stopping_gap
- params.time_stopping = np.inf
- params.screening1 = GapSphereSingleTest()
- params.screening2 = GAP_Ptest(vec_gammas)
- params.screening_it_div = 2.
- params.accelerated = False
- params.verbose = False
-
- out_slope = slope_gp(vecy, matA, ratio * lbd_max, vec_gammas, params)
-
- gap = slopePb.eval_gap(out_slope["sol"], out_slope["dualsol"])
-
- if gap <= gap_old:
- mat_pvopt[i_dic, i_seq, i_ratio, rep, :] = out_slope["sol"]
- mat_dvopt[i_dic, i_seq, i_ratio, rep, :] = out_slope["dualsol"]
-
- # Save
- np.savez(out_file_name,
- mat_seed=mat_seed,
- mat_pvopt=mat_pvopt,
- mat_dvopt=mat_dvopt,
- version = __version__,
- allow_pickle=True
- )
-
- # if i_xp ==3:
- # exit()
\ No newline at end of file
diff --git a/experiments/SIAM/xp_0_balls/xp_b_screening.py b/experiments/SIAM/xp_0_balls/xp_b_screening.py
deleted file mode 100644
index d100e8bf8777cdc90d3aa9e226a7f42c0d8f6ed1..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/xp_b_screening.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# -*- coding: utf-8 -*-
-import argparse
-
-import numpy as np
-
-# Algorithm import
-from src import __version__
-from src.dictionaries import generate_dic
-from src.utils import get_lambda_max, gamma_sequence_generator
-
-# Screening
-from src.screening.singletest import GapSphereSingleTest
-from src.screening.gap_ptest import GAP_Ptest
-from src.screening.kappa_test import Kappa_test
-
-# XP import
-from xps.SIAM.slopepb import SlopePb
-from xps.SIAM.setup import Setup
-from xps.SIAM.xp_1_balls import xpparams
-
-
-parser=argparse.ArgumentParser()
-parser.add_argument('--id', help='setup id', type=str, default=1)
-args=parser.parse_args()
-
-# -------------------------
-# Load Setup
-# -------------------------
-
-setup = Setup(args.id)
-solutions_filename = f"results/xp_setup{args.id}.npz"
-
-try:
- # Try to load existing results
- load_results = np.load(solutions_filename, allow_pickle=True)
- mat_seed = load_results["mat_seed"]
- mat_pvopt = load_results["mat_pvopt"]
- mat_dvopt = load_results["mat_dvopt"]
-
-except FileNotFoundError:
- print("No result found")
- sys.exit(1)
-
-
-# -------------------------
-# Screening Xp
-# -------------------------
-
-# percentage of detected zero
-mat_nb_zero_detected = np.full(
- (3, xpparams.nb_point, setup.nb_dic, setup.nb_sequence, setup.nb_ratio_lbd, setup.n_rep),
- np.nan,
- dtype=int
-)
-screening_filename = f"results/xp_setup{args.id}_screening.npz"
-
-i_xp = 0
-nb_xp = setup.nb_dic * setup.n_rep * setup.nb_sequence * setup.nb_ratio_lbd
-for i_dic in range(setup.nb_dic):
- for i_seq, seq in enumerate(setup.list_sequence):
- for rep in range(setup.n_rep):
-
- np.random.seed(mat_seed[i_dic, i_seq, rep])
-
- # ---- 1. Gen data ----
- matA = generate_dic(
- setup.list_dic[i_dic],
- setup.m,
- setup.n,
- setup.normalize
- )
-
- vecy = np.random.randn(setup.m)
- vecy /= np.linalg.norm(vecy)
-
-
- # ---- 2. Compute parameters ----
- vec_gammas = gamma_sequence_generator(
- setup.m,
- setup.n,
- setup.list_sequence[i_seq][0],
- setup.list_sequence[i_seq][1:]
- )
-
- lbd_max = get_lambda_max(vecy, matA, vec_gammas)
-
- # ---- 3. XP ----
- for i_ratio, ratio in enumerate(setup.list_ratio_lbd):
- i_xp += 1
- print(f"xp {i_xp} / {nb_xp}")
-
- slopePb = SlopePb(matA, vecy, vec_gammas, ratio, lbdmax=lbd_max)
- vecx_hat = mat_pvopt[i_dic, i_seq, i_ratio, rep, :]
- vecu_hat = mat_dvopt[i_dic, i_seq, i_ratio, rep, :]
- gap = slopePb.eval_gap(vecx_hat, vecu_hat)
- Atu = matA.T @ vecu_hat
-
- # ---- 3b. Build thin safe ball ----
- rgap = np.sqrt(2 * gap)
-
- # ---- 3c. Testing sphere ----
- list_tests = [
- GapSphereSingleTest(),
- GAP_Ptest(vec_gammas),
- Kappa_test(vec_gammas, np.arange(setup.n, dtype=np.double)),
- ]
-
-
- for i_offset, offset in enumerate(xpparams.vec_offsets):
- for i_test, test in enumerate(list_tests):
- out = test.apply_test(np.abs(Atu), gap, ratio * lbd_max, vec_gammas, offset_radius=offset)
- mat_nb_zero_detected[i_test, i_offset, i_dic, i_seq, i_ratio, rep] = np.sum(out)
-
-
- # Save
- np.savez(
- screening_filename,
- mat_nb_zero_detected=mat_nb_zero_detected,
- list_test = [test.get_name() for test in list_tests],
- version = __version__,
- allow_pickle=True
- )
\ No newline at end of file
diff --git a/experiments/SIAM/xp_0_balls/xp_verif_screening.py b/experiments/SIAM/xp_0_balls/xp_verif_screening.py
deleted file mode 100644
index 0428919b8ecb42ea7b2a1037f4264d340543fac8..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/xp_verif_screening.py
+++ /dev/null
@@ -1,195 +0,0 @@
-# -*- coding: utf-8 -*-
-import time, argparse, sys
-from datetime import datetime
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-from src import __version__
-from src.parameters import SlopeParameters, EnumLipchitzOptions
-from src.slope import primal_func, dual_func, slope_gp
-from src.screening import SafeGapSphere, SafeGapSphereSingleTest, GeneralizedGapSphere, GeneralizedGapSphereV2, GeneralizedGapSphereV3
-from src.utils import get_lambda_max, gamma_sequence_generator, compute_coherence, compute_coherence_function
-from src.dictionaries import generate_dic
-
-from setup import Setup
-
-
-parser=argparse.ArgumentParser()
-parser.add_argument('--erase', help='save figure', action="store_true")
-parser.add_argument('--continue', help='save figure', action="store_true", dest="cont")
-parser.add_argument('--id', help='setup id', type=str,
- default=1)
-args=parser.parse_args()
-
-
-# -------------------------
-# Load Setup
-# -------------------------
-
-setup = Setup(args.id)
-out_file_name = f"results/xp_setup{args.id}.npz"
-
-try:
- if args.erase:
- raise FileNotFoundError
-
- # Try to load existing results
- load_results = np.load(out_file_name, allow_pickle=True)
- mat_seed = load_results["mat_seed"]
- mat_sol = load_results["mat_sol"]
-
- date_start_xp = load_results['date_start_xp']
-
-except FileNotFoundError:
-
- pass
-
-# -------------------------
-# Xp
-# -------------------------
-
-# For each trial
-# 1. Compute high accuracy solution
-# 2. Create ideal safe ball
-# 3. increse radius
-
- # When does the xp have started?
-print("#"*50)
-if type(date_start_xp) == np.ndarray:
- print("#\tdate and time =", date_start_xp)
-else:
- dt_string = date_start_xp.strftime("%d/%m/%Y %H:%M:%S")
- print("#\tdate and time =", dt_string)
-print(f"#\tXp setup{args.id}")
-print("#"*50)
-print("")
-
-def compute_quantities(vecy, matA, vecx, lbd, vec_gammas):
- """
- """
-
- # residual error
- vecu = vecy - matA @ vecx
-
- # dual scaling
- beta_dual = np.sort(np.abs(matA.T @ vecu))[::-1]
- beta_dual = np.cumsum(beta_dual) / np.cumsum(lbd * vec_gammas)
- vecu /= np.max(beta_dual)
-
- pval = primal_func(vecy, matA @ vecx, vecx, lbd, vec_gammas)
- dval = dual_func(vecy, np.linalg.norm(vecy, 2)**2, vecu)
-
- gap = np.abs(pval - dval)
-
- return vecu, gap
-
-
-nb_xp = setup.nb_dic * setup.n_rep * setup.nb_sequence * setup.nb_ratio_lbd
-i_xp = 0
-for i_dic in range(setup.nb_dic):
- for i_seq, seq in enumerate(setup.list_sequence):
- for rep in range(setup.n_rep):
- # if not np.any(np.isnan(mat_nb[1, :, i_dic, i_seq, :, rep])):
- # continue
-
- np.random.seed(mat_seed[i_dic, i_seq, rep])
-
- # ---- 1. Gen data ----
- matA = generate_dic(
- setup.list_dic[i_dic],
- setup.m,
- setup.n,
- setup.normalize
- )
-
- # Abs is to protect from complex numbers
- max_eig = np.linalg.norm(matA, ord=2)**2
-
- coherence = compute_coherence(matA)
- coherence_function = compute_coherence_function(matA)
-
- vecy = np.random.randn(setup.m)
-
-
- # ---- 2. Compute parameters ----
- vec_gammas =gamma_sequence_generator(
- setup.m,
- setup.n,
- setup.list_sequence[i_seq],
- setup.m / (10 * setup.n)
- )
-
- lbd_max = get_lambda_max(vecy, matA, vec_gammas)
-
-
- # ---- 3. XP ----
- for i_ratio, ratio in enumerate(setup.list_ratio_lbd):
- i_xp += 1
- print(f"xp {i_xp} / {nb_xp}")
-
-
- vecu, gap = compute_quantities(
- vecy, matA, mat_sol[i_dic, i_seq, i_ratio, rep, :], ratio * lbd_max, vec_gammas
- )
-
- # if (gap_old <= setup.stopping_gap) and not np.any(np.isnan(mat_nb[:, :, i_dic, i_seq, i_ratio, rep])):
- # continue
- # else:
- if (gap > setup.stopping_gap):
- gap_old = gap
-
- # ---- 3a. Find solution ----
- params = SlopeParameters()
- params.vecx_init = mat_sol[i_dic, i_seq, i_ratio, rep, :]
- params.lipchitz_constant = max_eig
- params.lipchitz_update = EnumLipchitzOptions.GERSHGORIN
- params.max_it = 1e10
- params.gap_stopping = setup.stopping_gap
- params.time_stopping = np.inf
- params.screening1 = SafeGapSphereSingleTest()
- params.screening2 = SafeGapSphere(np.cumsum(vec_gammas), coherence_function)
- params.screening_it_div = 1.
- params.accelerated = True
- params.verbose = False
-
- out_slope = slope_gp(vecy, matA, ratio * lbd_max, vec_gammas, params)
-
- vecu, gap = compute_quantities(
- vecy, matA, out_slope["sol"], ratio * lbd_max, vec_gammas
- )
-
- if gap <= gap_old:
- mat_sol[i_dic, i_seq, i_ratio, rep, :] = out_slope["sol"]
-
-
- # ---- 3c. Testing sphere ----
- test1 = GeneralizedGapSphere(np.cumsum(vec_gammas))
- test2 = GeneralizedGapSphereV2(np.cumsum(vec_gammas))
- test3 = GeneralizedGapSphereV3(np.cumsum(vec_gammas))
- # test2 = GeneralizedCoherenceGapSphere(np.cumsum(vec_gammas), coherence_function)
-
- Atu = matA.T @ vecu
- rgap = np.sqrt(2 * gap)
-
- # 4. Computing duality when taking into account the output of screening rules
- print(f" old gap {gap}")
- delta = [0, 0, 0]
- sumzero = [0, 0, 0]
- for i_test, test in enumerate([test1, test2, test3]):
- t_1 = time.time()
- out = test.apply_test(Atu, gap, ratio * lbd_max, vec_gammas, 0.)
- delta[i_test] = time.time() - t_1
-
- sumzero[i_test] = np.sum(out)
-
- _, new_gap = compute_quantities(
- vecy, matA[:, np.invert(out)],
- mat_sol[i_dic, i_seq, i_ratio, rep, np.invert(out)],
- ratio * lbd_max, vec_gammas[:(setup.n - np.sum(out))]
- )
-
- print(f" new gap {i_test} {new_gap}")
- print(f" delta time {delta[0] / delta[1]}")
- print(f" delta time {delta[0] / delta[2]}")
- print(f" nb zero {sumzero[0]} - {sumzero[1]} - {sumzero[2]}")
diff --git a/experiments/SIAM/xp_0_balls/xp_viz_gen_sreening.py b/experiments/SIAM/xp_0_balls/xp_viz_gen_sreening.py
deleted file mode 100644
index 3e976ef53a58e773821ed98cf46bddc0c511669a..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/xp_viz_gen_sreening.py
+++ /dev/null
@@ -1,183 +0,0 @@
-# -*- coding: utf-8 -*-
-import time, argparse, sys
-from datetime import datetime
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-from src import __version__
-from src.screening import AbstractGapScreening
-from src.slope import primal_func, dual_func
-from src.utils import get_lambda_max, gamma_sequence_generator, compute_coherence, compute_coherence_function
-from src.dictionaries import generate_dic
-
-from setup import Setup
-
-
-parser=argparse.ArgumentParser()
-parser.add_argument('--noshow', help='save figure', action="store_true")
-parser.add_argument('--save', help='save figure', action="store_true")
-parser.add_argument('--id', help='setup id', type=str,
- default=1)
-args=parser.parse_args()
-
-
-# -------------------------
-# Load Setup
-# -------------------------
-
-setup = Setup(args.id)
-out_file_name = f"results/xp_setup{args.id}.npz"
-
-try:
- load_results = np.load(out_file_name, allow_pickle=True)
- mat_seed = load_results["mat_seed"]
- mat_sol = load_results["mat_sol"]
-
-
-except FileNotFoundError:
- print("No experiment results found")
- exit()
-
-
-# -------------------------
-# Screening class
-# -------------------------
-
-class DebugTest_p(AbstractGapScreening):
- """ Generalized test
- """
- def __init__(self, vec_cumsum_gammas):
- super(AbstractGapScreening, self).__init__()
-
- # Adding zero at the beginning
- self.vec_cumsum_gammas = np.zeros(vec_cumsum_gammas.size+1)
- self.vec_cumsum_gammas[1:] = vec_cumsum_gammas
-
-
- def apply_test(self, Atc, gap, lbd, vec_gammas, offset_radius=0, index=None) -> np.ndarray:
- n = Atc.size
-
- radius = np.sqrt(2 * gap) + offset_radius
-
- # 1. Sort in descenting order
- if index is None:
- index = np.argsort(np.abs(Atc))[::-1]
-
- for l in range(n):
- # setdiff1d with "assume_unique" option keeps the ordering
- cumsum_Atc = np.zeros(n)
- cumsum_Atc[1:] = np.cumsum(np.abs(Atc[np.setdiff1d(index, [l], assume_unique=True)]))
-
- # row: q
- # column: p
- map_accept = np.zeros((n, n))
- for q in range(1, n+1):
- range_p = np.arange(q)
- vec_bounds_p = np.abs(Atc[l]) \
- + (cumsum_Atc[q-1] - cumsum_Atc[q - 1 - range_p]) \
- + (1. + range_p) * radius \
- + lbd * self.vec_cumsum_gammas[:q][::-1]
-
- # screening test
- map_accept[q-1, :q] += (vec_bounds_p < lbd * self.vec_cumsum_gammas[q])
-
- return map_accept / float(setup.n)
-
-
-# -------------------------
-# Hepers
-# -------------------------
-
-def compute_quantities(vecy, matA, vecx, lbd, vec_gammas):
- """
- """
-
- # residual error
- vecu = vecy - matA @ vecx
-
- # dual scaling
- beta_dual = np.sort(np.abs(matA.T @ vecu))[::-1]
- beta_dual = np.cumsum(beta_dual) / np.cumsum(lbd * vec_gammas)
- vecu /= np.max(beta_dual)
-
- pval = primal_func(vecy, matA @ vecx, vecx, lbd, vec_gammas)
- dval = dual_func(vecy, np.linalg.norm(vecy, 2)**2, vecu)
-
- gap = np.abs(pval - dval)
-
- return vecu, gap
-
-
-# -------------------------
-# Xps
-# -------------------------
-
-nb_xp = setup.nb_dic * setup.n_rep * setup.nb_sequence * setup.nb_ratio_lbd
-i_xp = 0
-for i_dic in range(setup.nb_dic):
- for i_seq, seq in enumerate(setup.list_sequence):
- for i_ratio, ratio in enumerate(setup.list_ratio_lbd):
-
- f, ax = plt.subplots(1, 1)
- mat_results = np.zeros((setup.n, setup.n))
-
- for rep in range(setup.n_rep):
- i_xp += 1
- print(f"xp {i_xp} / {nb_xp}")
-
- np.random.seed(mat_seed[i_dic, i_seq, rep])
-
- # ---- 1. Gen data ----
- matA = generate_dic(
- setup.list_dic[i_dic],
- setup.m,
- setup.n,
- setup.normalize
- )
-
- vecy = np.random.randn(setup.m)
-
-
- # ---- 2. Compute parameters ----
- vec_gammas = gamma_sequence_generator(
- setup.m,
- setup.n,
- setup.list_sequence[i_seq],
- setup.m / (10 * setup.n)
- )
-
- lbd_max = get_lambda_max(vecy, matA, vec_gammas)
-
-
- # ---- 3. XP ----
-
- vecu, gap = compute_quantities(
- vecy, matA, mat_sol[i_dic, i_seq, i_ratio, rep, :], ratio * lbd_max, vec_gammas
- )
-
- # ---- 3c. Testing sphere ----
- test = DebugTest_p(np.cumsum(vec_gammas))
-
- Atu = matA.T @ vecu
- rgap = np.sqrt(2 * gap)
-
- # 4. Computing duality when taking into account the output of screening rules
- mat_results += test.apply_test(Atu, gap, ratio * lbd_max, vec_gammas, 0.) / setup.n_rep
-
- # [i_ratio, i_seq]
- heatmap = ax.pcolor(mat_results, cmap='viridis')
- # ax.imshow(mat_results, cmap='viridis')
- plt.colorbar(heatmap)
- ax.set_title(f"{setup.list_dic[i_dic]} -- {setup.list_sequence[i_seq]} -- lbd/lbd_max={ratio}")
-
- if not args.noshow:
- plt.draw()
- plt.pause(0.001)
-
- if args.save:
- filename = f"figs/viztestp/setup{args.id}_{setup.list_dic[i_dic]}_seq{i_seq}_lbd{i_ratio}.pdf"
- plt.savefig(filename, bbox_inches='tight')
-
-if not args.noshow:
- plt.show()
diff --git a/experiments/SIAM/xp_0_balls/xpparams.py b/experiments/SIAM/xp_0_balls/xpparams.py
deleted file mode 100644
index 0402fc413f9ee508fbb570ae8845fe0448cfdd9f..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_0_balls/xpparams.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import numpy as np
-
-# Algorithmic parameters
-stopping_gap = 1e-14
-
-# -- Screening parameter --
-# vec_offsets = np.linspace(0, setup.max_offset, setup.nb_point)
-nb_point = 2000
-max_offset = 2
-vec_offsets = max_offset * np.logspace(-7., .0, num=nb_point)
\ No newline at end of file
diff --git a/experiments/SIAM/xp_1_balls/figs/xp0_gaussian 0..eps b/experiments/SIAM/xp_1_balls/figs/xp0_gaussian 0..eps
deleted file mode 100644
index f6dc3de1fcf9dc961a2982e9970265f1abef7242..0000000000000000000000000000000000000000
--- a/experiments/SIAM/xp_1_balls/figs/xp0_gaussian 0..eps
+++ /dev/null
@@ -1,11905 +0,0 @@
-%!PS-Adobe-3.0 EPSF-3.0
-%%Title: xp0_gaussian 0..eps
-%%Creator: Matplotlib v3.4.3, https://matplotlib.org/
-%%CreationDate: Mon Sep 6 16:59:19 2021
-%%Orientation: portrait
-%%BoundingBox: -337 165 950 627
-%%HiResBoundingBox: -337.720313 165.787500 949.720313 626.212500
-%%EndComments
-%%BeginProlog
-/mpldict 14 dict def
-mpldict begin
-/_d { bind def } bind def
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-/l { lineto } _d
-/r { rlineto } _d
-/c { curveto } _d
-/cl { closepath } _d
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-/box {
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- 1 index 0 r
- 0 exch r
- neg 0 r
- cl
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diff --git a/experiments/SIAM/xp_2_bench_time/launcher.sh b/experiments/SIAM/xp_2_bench_time/launcher.sh
index 2f4b2ab23d5158d0f18209c9830a0ac95775ac5f..285d856de24d4217dc364ecd457466733c06bacb 100755
--- a/experiments/SIAM/xp_2_bench_time/launcher.sh
+++ b/experiments/SIAM/xp_2_bench_time/launcher.sh
@@ -3,7 +3,4 @@ python xp_b_get_budget.py --id 1a --precision 8
python xp_c_results_time.py --id 1a --precision 8
python xp_d_results_it.py --id 1a --precision 8
-python xp_a_get_state.py --id 1b --precision 8
-python xp_b_get_budget.py --id 1b --precision 8
-python xp_c_results_time.py --id 1b --precision 8
-python xp_d_results_it.py --id 1b --precision 8
\ No newline at end of file
+python xp_e_fig.py --id 1a --precision 8 --noshow --save
\ No newline at end of file