From abc9add5b370248ae3e388c02473d0bb31bc1eef Mon Sep 17 00:00:00 2001
From: Tizian Wenzel <wenzeltn@nbanm02.mathematik.uni-stuttgart.de>
Date: Fri, 9 Jun 2023 14:00:33 +0200
Subject: [PATCH] Some more fixes.
---
hahllo.py | 0
section_4.1_compute_visualize.py | 4 ++--
section_4.2_compute.py | 5 ++---
testfile.py | 0
utils_code/main_function.py | 8 ++++----
5 files changed, 8 insertions(+), 9 deletions(-)
create mode 100644 hahllo.py
create mode 100644 testfile.py
diff --git a/hahllo.py b/hahllo.py
new file mode 100644
index 0000000..e69de29
diff --git a/section_4.1_compute_visualize.py b/section_4.1_compute_visualize.py
index 6bcee1e..9628062 100644
--- a/section_4.1_compute_visualize.py
+++ b/section_4.1_compute_visualize.py
@@ -29,7 +29,7 @@ hyperparameter = dic_hyperparams[name_dataset]
## Run everything
A_start, A_optimized, model, model_vkoga1, model_vkoga2, data, \
- array_concatenate, _, dic_timings_2L = run_everything(
+ array_concatenate, _, list_timings_2L = run_everything(
name_dataset,
hyperparameter.maxIter_vkoga, hyperparameter.N_points,
hyperparameter.noise_level, hyperparameter.reg_para_optim, hyperparameter.reg_para_vkoga,
@@ -62,7 +62,7 @@ io.savemat(path_for_results + name_dataset + '.mat',
array_eps=array_eps,
array_cv_f=array_cv_f,
array_cv_f_val=array_cv_f_val,
- dic_timings_2L = dic_timings_2L,
+ list_timings_2L = list_timings_2L,
list_timings_1L = list_timings_1L))
# in Matlab:
diff --git a/section_4.2_compute.py b/section_4.2_compute.py
index a2054d6..bdfdd64 100644
--- a/section_4.2_compute.py
+++ b/section_4.2_compute.py
@@ -18,7 +18,6 @@ np.random.seed(1)
list_datasets = ['fried', 'sarcos', 'ct', 'diamonds', 'stock', 'kegg_undir_uci', 'online_video',
'wecs', 'mlr_knn_rng', 'query_agg_count', 'sgemm', 'road_network']
-
## Loop over reruns and datasets
for idx_indices in [0, 1, 2, 3, 4]:
for idx_dataset, name_dataset in enumerate(list_datasets):
@@ -29,7 +28,7 @@ for idx_indices in [0, 1, 2, 3, 4]:
## Run everything
A_start, A_optimized, model, model_vkoga1, model_vkoga2, data, \
- array_concatenate, array_test_rmse_deep, dic_timings_2L = run_everything(
+ array_concatenate, array_test_rmse_deep, list_timings_2L = run_everything(
name_dataset,
hyperparameter.maxIter_vkoga, hyperparameter.N_points,
hyperparameter.noise_level, hyperparameter.reg_para_optim, hyperparameter.reg_para_vkoga,
@@ -68,7 +67,7 @@ for idx_indices in [0, 1, 2, 3, 4]:
array_cv_f_val=array_cv_f_val,
array_test_rmse_deep=array_test_rmse_deep,
array_test_rmse_cv=array_test_rmse_cv,
- dic_timings_2L=dic_timings_2L,
+ list_timings_2L=list_timings_2L,
list_timings_1L=list_timings_1L))
# in Matlab:
diff --git a/testfile.py b/testfile.py
new file mode 100644
index 0000000..e69de29
diff --git a/utils_code/main_function.py b/utils_code/main_function.py
index dbc379f..687a663 100644
--- a/utils_code/main_function.py
+++ b/utils_code/main_function.py
@@ -29,7 +29,7 @@ def run_everything(name_dataset, maxIter_vkoga, N_points, noise_level, reg_para_
flag_vkoga_verbose=False, flag_plots=False, flag_optim_verbose=True,
flag_std_vkoga=True, idx_rerun=None):
- dic_timings = {}
+ list_timings = []
## Load dataset
dataset = Dataset(N_points=N_points)
@@ -82,7 +82,7 @@ def run_everything(name_dataset, maxIter_vkoga, N_points, noise_level, reg_para_
print(datetime.now().strftime("%H:%M:%S"), name_dataset, '2layered kernel optimization finished.')
t_optim_stop = time.time()
- dic_timings['2L_optim'] = t_optim_stop - t_optim_start
+ list_timings.append(t_optim_stop - t_optim_start)
## Application of VKOGA
# VKOGA with modified modified kernel, simply pre-apply the linear transformation
@@ -96,7 +96,7 @@ def run_everything(name_dataset, maxIter_vkoga, N_points, noise_level, reg_para_
print(datetime.now().strftime("%H:%M:%S"), name_dataset, '2layered VKOGA finished.')
t_vkoga1_stop = time.time()
- dic_timings['2L_vkoga'] = t_vkoga1_stop - t_vkoga1_start
+ list_timings.append(t_vkoga1_stop - t_vkoga1_start)
# VKOGA with standard kernel
model_vkoga2 = VKOGA(kernel=kernel, greedy_type='f_greedy',
@@ -219,7 +219,7 @@ def run_everything(name_dataset, maxIter_vkoga, N_points, noise_level, reg_para_
data = [X_train, X_test, y_train, y_test]
return A_start, model.A.detach().numpy(), model, model_vkoga1, model_vkoga2, \
- data, array_concatenate, array_test_rmse, dic_timings
+ data, array_concatenate, array_test_rmse, list_timings
--
GitLab