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Jörg Martin
journal_eiv
Commits
5660f28b
Commit
5660f28b
authored
2 years ago
by
Jörg Martin
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Updated plot_diagonal_uncertainties
parent
407970a7
Branches
dev
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Experiments/create_diagonal_uncertainties_plots.sh
+6
-0
6 additions, 0 deletions
Experiments/create_diagonal_uncertainties_plots.sh
Experiments/plot_diagonal_uncertainties.py
+22
-15
22 additions, 15 deletions
Experiments/plot_diagonal_uncertainties.py
with
28 additions
and
15 deletions
Experiments/create_diagonal_uncertainties_plots.sh
0 → 100644
+
6
−
0
View file @
5660f28b
#!/bin/bash
for
data
in
{
yacht,naval,linear,protein,concrete,kin8nm,wine,quadratic,power,sine,energy,california,cubic
}
;
do
echo
Plotting diagonal uncertainties
for
$data
python plot_diagonal_uncertainties.py
--data
$data
echo
done
!
done
This diff is collapsed.
Click to expand it.
Experiments/plot_diagonal_uncertainties.py
+
22
−
15
View file @
5660f28b
...
@@ -12,6 +12,7 @@ import torch
...
@@ -12,6 +12,7 @@ import torch
import
torch.backends.cudnn
import
torch.backends.cudnn
from
torch.utils.data
import
DataLoader
from
torch.utils.data
import
DataLoader
from
tqdm
import
tqdm
from
tqdm
import
tqdm
import
matplotlib
import
matplotlib.pyplot
as
plt
import
matplotlib.pyplot
as
plt
from
EIVArchitectures
import
Networks
from
EIVArchitectures
import
Networks
...
@@ -21,6 +22,12 @@ from EIVGeneral.coverage_metrics import epistemic_coverage, normalized_std,\
...
@@ -21,6 +22,12 @@ from EIVGeneral.coverage_metrics import epistemic_coverage, normalized_std,\
from
EIVData.repeated_sampling
import
repeated_sampling
from
EIVData.repeated_sampling
import
repeated_sampling
from
EIVGeneral.linear_evaluation
import
linear_pred_unc
,
linear_coverage
,
compute_par_est_var
from
EIVGeneral.linear_evaluation
import
linear_pred_unc
,
linear_coverage
,
compute_par_est_var
font
=
{
'
family
'
:
'
DejaVu Sans
'
,
'
weight
'
:
'
normal
'
,
'
size
'
:
20
}
matplotlib
.
rc
(
'
font
'
,
**
font
)
# read in data via --data option
# read in data via --data option
parser
=
argparse
.
ArgumentParser
()
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"
--data
"
,
help
=
"
Loads data
"
,
default
=
'
yacht
'
)
parser
.
add_argument
(
"
--data
"
,
help
=
"
Loads data
"
,
default
=
'
yacht
'
)
...
@@ -36,31 +43,30 @@ with open(os.path.join('configurations',f'noneiv_{data}.json'),'r') as conf_file
...
@@ -36,31 +43,30 @@ with open(os.path.join('configurations',f'noneiv_{data}.json'),'r') as conf_file
noneiv_conf_dict
=
json
.
load
(
conf_file
)
noneiv_conf_dict
=
json
.
load
(
conf_file
)
# assuming normalized data was used
try
:
assert
eiv_conf_dict
[
'
normalize
'
]
assert
noneiv_conf_dict
[
'
normalize
'
]
except
KeyError
:
pass
normalize
=
True
long_dataname
=
eiv_conf_dict
[
"
long_dataname
"
]
long_dataname
=
eiv_conf_dict
[
"
long_dataname
"
]
short_dataname
=
eiv_conf_dict
[
"
short_dataname
"
]
short_dataname
=
eiv_conf_dict
[
"
short_dataname
"
]
print
(
f
"
Evaluating
{
long_dataname
}
"
)
print
(
f
"
Evaluating
{
long_dataname
}
"
)
scale_outputs
=
False
scale_outputs
=
False
load_data
=
importlib
.
import_module
(
f
'
EIVData.
{
long_dataname
}
'
).
load_data
load_data
=
importlib
.
import_module
(
f
'
EIVData.
{
long_dataname
}
'
).
load_data
train_data
,
_
=
load_data
()
input_dim
=
train_data
[
0
][
0
].
numel
()
output_dim
=
train_data
[
0
][
1
].
numel
()
try
:
min_x
,
max_x
=
importlib
.
import_module
(
f
'
EIVData.
{
long_dataname
}
'
).
input_range
except
AttributeError
:
max_x
=
torch
.
ones
((
1
,
input_dim
))
min_x
=
-
max_x
try
:
try
:
sigma_y
=
importlib
.
import_module
(
f
'
EIVData.
{
long_dataname
}
'
).
y_noise_strength
sigma_y
=
importlib
.
import_module
(
f
'
EIVData.
{
long_dataname
}
'
).
y_noise_strength
design_matrix
=
importlib
.
import_module
(
f
'
EIVData.
{
long_dataname
}
'
).
design_matrix
design_matrix
=
importlib
.
import_module
(
f
'
EIVData.
{
long_dataname
}
'
).
design_matrix
except
AttributeError
:
except
AttributeError
:
sigma_y
=
None
sigma_y
=
None
train_data
,
test_data
=
load_data
(
normalize
=
normalize
)
input_dim
=
train_data
[
0
][
0
].
numel
()
output_dim
=
train_data
[
0
][
1
].
numel
()
# do computations on cpu
# do computations on cpu
device
=
torch
.
device
(
'
cpu
'
)
device
=
torch
.
device
(
'
cpu
'
)
...
@@ -136,13 +142,12 @@ def collect_predictions(x, seed=0,
...
@@ -136,13 +142,12 @@ def collect_predictions(x, seed=0,
def
create_diagonal
(
train
,
number_of_steps
=
100
):
def
create_diagonal
(
train
,
min_x
,
max_x
,
number_of_steps
=
100
):
input_shape
=
train
[
0
][
0
].
shape
input_shape
=
train
[
0
][
0
].
shape
assert
len
(
input_shape
)
==
1
assert
len
(
input_shape
)
==
1
input_dim
=
input_shape
[
0
]
input_dim
=
input_shape
[
0
]
ones
=
0.75
*
torch
.
ones
((
1
,
input_dim
))
t
=
torch
.
linspace
(
start
=
0
,
end
=
1
,
steps
=
number_of_steps
)[...,
None
]
t
=
torch
.
linspace
(
start
=
0
,
end
=
1
,
steps
=
number_of_steps
)[...,
None
]
return
(
1
-
t
)
*
ones
-
t
*
ones
return
(
1
-
t
)
*
min_x
+
t
*
max_x
...
@@ -154,7 +159,8 @@ eiv_uncertainties = 0
...
@@ -154,7 +159,8 @@ eiv_uncertainties = 0
number_of_seeds
=
len
(
seed_list
)
number_of_seeds
=
len
(
seed_list
)
number_of_steps
=
100
number_of_steps
=
100
for
seed
in
tqdm
(
seed_list
):
for
seed
in
tqdm
(
seed_list
):
x_diagonal
=
create_diagonal
(
train
=
train_data
,
number_of_steps
=
number_of_steps
)
x_diagonal
=
create_diagonal
(
train
=
train_data
,
min_x
=
min_x
,
max_x
=
max_x
,
number_of_steps
=
number_of_steps
)
results
=
collect_predictions
(
x_diagonal
,
seed
=
seed
)
results
=
collect_predictions
(
x_diagonal
,
seed
=
seed
)
noneiv_uncertainties
+=
1
/
number_of_seeds
*
results
[
'
noneiv
'
][
'
uncertainties
'
].
mean
(
dim
=-
1
)
noneiv_uncertainties
+=
1
/
number_of_seeds
*
results
[
'
noneiv
'
][
'
uncertainties
'
].
mean
(
dim
=-
1
)
eiv_uncertainties
+=
1
/
number_of_seeds
*
results
[
'
eiv
'
][
'
uncertainties
'
].
mean
(
dim
=-
1
)
eiv_uncertainties
+=
1
/
number_of_seeds
*
results
[
'
eiv
'
][
'
uncertainties
'
].
mean
(
dim
=-
1
)
...
@@ -167,4 +173,5 @@ plt.fill_between(plot_x, noneiv_uncertainties, color='b', alpha=0.5)
...
@@ -167,4 +173,5 @@ plt.fill_between(plot_x, noneiv_uncertainties, color='b', alpha=0.5)
plt
.
fill_between
(
plot_x
,
eiv_uncertainties
,
color
=
'
r
'
,
alpha
=
0.5
)
plt
.
fill_between
(
plot_x
,
eiv_uncertainties
,
color
=
'
r
'
,
alpha
=
0.5
)
plt
.
xlabel
(
r
'
$\lambda$
'
)
plt
.
xlabel
(
r
'
$\lambda$
'
)
plt
.
ylabel
(
r
'
$u$
'
)
plt
.
ylabel
(
r
'
$u$
'
)
plt
.
tight_layout
()
plt
.
savefig
(
f
'
results/figures/diagonal_uncertainties_
{
data
}
.pdf
'
)
plt
.
savefig
(
f
'
results/figures/diagonal_uncertainties_
{
data
}
.pdf
'
)
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