diff --git a/EIVPackage/EIVData/cubic.py b/EIVPackage/EIVData/cubic.py
index dce6ea452fba08280d457e995295d5fe87d46d6d..9d5e3979db7af0e236536a9b4e8f6052fa43874e 100644
--- a/EIVPackage/EIVData/cubic.py
+++ b/EIVPackage/EIVData/cubic.py
@@ -9,8 +9,8 @@ total_number_of_datapoints = 1000
input_range = [-1,1]
slope = 1.0
intercept = 0.0
-x_noise_strength = 0.1
-y_noise_strength = 0.1
+x_noise_strength = 0.2
+y_noise_strength = 0.05
func = lambda true_x: slope * true_x**3 + intercept
def load_data(seed=0, splitting_part=0.8, normalize=True,
diff --git a/EIVPackage/EIVData/linear.py b/EIVPackage/EIVData/linear.py
index 97d2cd951e0d86650c9ab85af17d5ceb96312375..2a1b0d7a4da8e81db9c90376ec3386d2f466aec3 100644
--- a/EIVPackage/EIVData/linear.py
+++ b/EIVPackage/EIVData/linear.py
@@ -10,7 +10,7 @@ input_range = [-1,1]
slope = 1.0
intercept = 0.0
x_noise_strength = 0.1
-y_noise_strength = 0.1
+y_noise_strength = 0.2
func = lambda true_x: slope * true_x + intercept
def load_data(seed=0, splitting_part=0.8, normalize=True,
diff --git a/EIVPackage/EIVData/sine.py b/EIVPackage/EIVData/sine.py
index cde80d349eef58cadab44b79f0937d52a90e8d41..2e9eabc4d1a4f38b5c716f3bd05b28548faaafc1 100644
--- a/EIVPackage/EIVData/sine.py
+++ b/EIVPackage/EIVData/sine.py
@@ -8,8 +8,8 @@ from EIVGeneral.manipulate_tensors import add_noise, normalize_tensor,\
total_number_of_datapoints = 2000
input_range = [-0.2,0.8]
intercept = 0.0
-x_noise_strength = 0.02
-y_noise_strength = 0.02
+x_noise_strength = 0.04
+y_noise_strength = 0.01
func = lambda true_x: true_x +\
torch.sin(2 * torch.pi * true_x) +\
torch.sin(4 * torch.pi * true_x)
diff --git a/Experiments/configurations/eiv_cubic.json b/Experiments/configurations/eiv_cubic.json
index 0e9f9e88edb30a99bd73a2814a92c4ca504218fd..59c37c13abd44984283d2afb579296a18dec8a75 100644
--- a/Experiments/configurations/eiv_cubic.json
+++ b/Experiments/configurations/eiv_cubic.json
@@ -13,10 +13,10 @@
"std_y_update_points": [1,40],
"eiv_prediction_number_of_draws": [100,5],
"eiv_prediction_number_of_batches": 10,
- "init_std_y_list": [0.5],
+ "init_std_y_list": [0.05],
"gamma": 0.5,
"hidden_layers": [128, 128, 128, 128],
- "fixed_std_x": 0.10,
+ "fixed_std_x": 0.20,
"seed_range": [0,10],
"gpu_number": 1
}
diff --git a/Experiments/configurations/eiv_linear.json b/Experiments/configurations/eiv_linear.json
index 8b7ebe0767411186d51ca18dde9d2d6aeb92de06..ce84949e64c4edfc4f0f6b100742f2822ca32f1c 100644
--- a/Experiments/configurations/eiv_linear.json
+++ b/Experiments/configurations/eiv_linear.json
@@ -13,7 +13,7 @@
"std_y_update_points": [1,40],
"eiv_prediction_number_of_draws": [100,5],
"eiv_prediction_number_of_batches": 10,
- "init_std_y_list": [0.5],
+ "init_std_y_list": [0.1],
"gamma": 0.5,
"hidden_layers": [128, 128, 128, 128],
"fixed_std_x": 0.10,
diff --git a/Experiments/configurations/eiv_quadratic.json b/Experiments/configurations/eiv_quadratic.json
index c774bbed09beb17e65c475fdfedfca1c795682a7..dc8a9cd87c17b40cec38ee086d4d1954ade2c62c 100644
--- a/Experiments/configurations/eiv_quadratic.json
+++ b/Experiments/configurations/eiv_quadratic.json
@@ -13,7 +13,7 @@
"std_y_update_points": [1,40],
"eiv_prediction_number_of_draws": [100,5],
"eiv_prediction_number_of_batches": 10,
- "init_std_y_list": [0.5],
+ "init_std_y_list": [0.1],
"gamma": 0.5,
"hidden_layers": [128, 128, 128, 128],
"fixed_std_x": 0.10,
diff --git a/Experiments/configurations/eiv_sine.json b/Experiments/configurations/eiv_sine.json
index 1195ea0891ab3dc49745b0c9b310416cc2ee0ba9..b6bf6d0d286644e120729899bc8b1c1a2854681e 100644
--- a/Experiments/configurations/eiv_sine.json
+++ b/Experiments/configurations/eiv_sine.json
@@ -14,10 +14,10 @@
"eiv_number_of_forward_draws": 10,
"eiv_prediction_number_of_draws": [100,5],
"eiv_prediction_number_of_batches": 10,
- "init_std_y_list": [0.1],
+ "init_std_y_list": [0.01],
"gamma": 0.5,
"hidden_layers": [128, 128, 128, 128],
- "fixed_std_x": 0.02,
+ "fixed_std_x": 0.04,
"seed_range": [0,10],
"gpu_number": 1
}
diff --git a/Experiments/configurations/noneiv_cubic.json b/Experiments/configurations/noneiv_cubic.json
index 6f71af1d77db1a357f0d68edfe192cc06578d3af..a325665d0c8e0bfe813abd2ae27ba02deaf4610c 100644
--- a/Experiments/configurations/noneiv_cubic.json
+++ b/Experiments/configurations/noneiv_cubic.json
@@ -13,7 +13,7 @@
"std_y_update_points": [1,40] ,
"noneiv_prediction_number_of_draws": 100,
"noneiv_prediction_number_of_batches": 10,
- "init_std_y_list": [0.5],
+ "init_std_y_list": [0.05],
"gamma": 0.5,
"hidden_layers": [128, 128, 128, 128],
"seed_range": [0,10],
diff --git a/Experiments/configurations/noneiv_linear.json b/Experiments/configurations/noneiv_linear.json
index ae3040e2b157bc7bd10cfd73d9f4f5448f4cf023..ff57b5686c5beeb2240c14bed889a3480d586429 100644
--- a/Experiments/configurations/noneiv_linear.json
+++ b/Experiments/configurations/noneiv_linear.json
@@ -13,7 +13,7 @@
"std_y_update_points": [1,40] ,
"noneiv_prediction_number_of_draws": 100,
"noneiv_prediction_number_of_batches": 10,
- "init_std_y_list": [0.5],
+ "init_std_y_list": [0.1],
"gamma": 0.5,
"hidden_layers": [128, 128, 128, 128],
"seed_range": [0,10],
diff --git a/Experiments/configurations/noneiv_quadratic.json b/Experiments/configurations/noneiv_quadratic.json
index 405263797ff8b9ffa3ef692540b0db5edc506cda..d858851a1dc0be2e52c3d12a579efe485aad7503 100644
--- a/Experiments/configurations/noneiv_quadratic.json
+++ b/Experiments/configurations/noneiv_quadratic.json
@@ -13,7 +13,7 @@
"std_y_update_points": [1,40] ,
"noneiv_prediction_number_of_draws": 100,
"noneiv_prediction_number_of_batches": 10,
- "init_std_y_list": [0.5],
+ "init_std_y_list": [0.1],
"gamma": 0.5,
"hidden_layers": [128, 128, 128, 128],
"seed_range": [0,10],
diff --git a/Experiments/configurations/noneiv_sine.json b/Experiments/configurations/noneiv_sine.json
index cc889194d725b1e167b1129d444e12720e2610a0..5191847de1cbf256a4b572a30a09f8dd22e0bea3 100644
--- a/Experiments/configurations/noneiv_sine.json
+++ b/Experiments/configurations/noneiv_sine.json
@@ -13,7 +13,7 @@
"std_y_update_points": [1,40] ,
"noneiv_prediction_number_of_draws": 100,
"noneiv_prediction_number_of_batches": 10,
- "init_std_y_list": [0.1],
+ "init_std_y_list": [0.01],
"gamma": 0.5,
"hidden_layers": [128, 128, 128, 128],
"seed_range": [0,10],
diff --git a/Experiments/plot_coverage_vs_q.py b/Experiments/plot_coverage_vs_q.py
index 9bc59a3eb4b15d86d9e2355264852712ef3985a3..5cb7f8f143f65524c22e28ecd6bcf388059f04dd 100644
--- a/Experiments/plot_coverage_vs_q.py
+++ b/Experiments/plot_coverage_vs_q.py
@@ -13,6 +13,7 @@ import torch.backends.cudnn
from torch.utils.data import DataLoader
from matplotlib.pyplot import cm
import matplotlib.pyplot as plt
+from tqdm import tqdm
from EIVArchitectures import Networks
from EIVTrainingRoutines import train_and_store
@@ -94,7 +95,7 @@ plt.ylabel('coverage')
# datasets to plot and their coloring
datasets = ['linear', 'quadratic','cubic','sine']
-colors = ['cyan', 'magenta', 'yellow', 'green']
+colors = ['#084519', '#7D098D', '#77050C', '#09017F']
def compute_coverages(data, eiv, number_of_draws):
"""
@@ -148,7 +149,6 @@ def compute_coverages(data, eiv, number_of_draws):
= load_data(seed=0, return_ground_truth=True,
normalize=normalize)
- print(f"Computing {'EiV' if eiv else 'non-EiV'} coverage for {long_dataname}")
# train_data only used for finding dimensions
input_dim = train_data[0][0].numel()
@@ -233,14 +233,13 @@ def compute_coverages(data, eiv, number_of_draws):
return numerical_coverage, theoretical_coverage
# loop through data
-for data, color in zip(datasets, colors):
+for data, color in tqdm(zip(datasets, colors)):
# compute coverages
eiv_coverages = compute_coverages(data=data, eiv=True,
number_of_draws=[100,5])
noneiv_coverages = compute_coverages(data=data, eiv=False,
number_of_draws=100)
# create plots
- plt.figure(1)
coverage_diagonal_plot(eiv_coverages, noneiv_coverages,
color=color, against_theoretical=False, label=data)
diff --git a/Experiments/plot_summary.py b/Experiments/plot_summary.py
index c3d8c0d284dcff754b9da4a29b9d767d587d930c..c0863c604ff618df5e45351576c493cece331f50 100644
--- a/Experiments/plot_summary.py
+++ b/Experiments/plot_summary.py
@@ -80,7 +80,7 @@ for i, ([(eiv_metric_mean, eiv_metric_std),
bottom = noneiv_metric_mean - k* noneiv_metric_std,
color=colors[1],
alpha=0.5)
-plt.ylim(bottom=0, top=y_max)
+plt.ylim(bottom=0, top=ymax)
ax = plt.gca()
ax.set_xticks(np.arange(1,len(data_list)+1))
ax.set_xticklabels(data_list, rotation='vertical')
@@ -127,7 +127,7 @@ for i, ([(eiv_metric_mean, eiv_metric_std),
color=colors[1],
alpha=0.5)
plt.axhline(0.95,0.0,1.0,color='k', linestyle='dashed')
-plt.ylim(bottom=0, top=y_max)
+plt.ylim(bottom=0, top=ymax)
ax = plt.gca()
ax.set_xticks(np.arange(1,len(data_list)+1))
ax.set_xticklabels(data_list, rotation='vertical')
@@ -173,7 +173,7 @@ for i, ([(eiv_metric_mean, eiv_metric_std),
bottom = noneiv_metric_mean - k* noneiv_metric_std,
color=colors[1],
alpha=0.5)
-plt.ylim(bottom=0, top=y_max)
+plt.ylim(bottom=0, top=ymax)
ax = plt.gca()
ax.set_xticks(np.arange(1,len(data_list)+1))
ax.set_xticklabels(data_list, rotation='vertical')