Skip to content
GitLab
Explore
Sign in
Register
Primary navigation
Search or go to…
Project
M
mu-map
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Wiki
Code
Merge requests
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Snippets
Build
Pipelines
Jobs
Pipeline schedules
Artifacts
Deploy
Releases
Package registry
Container Registry
Model registry
Operate
Environments
Terraform modules
Monitor
Incidents
Service Desk
Analyze
Value stream analytics
Contributor analytics
CI/CD analytics
Repository analytics
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
Community forum
Contribute to GitLab
Provide feedback
Terms and privacy
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
Tamino Huxohl
mu-map
Commits
89f91450
Commit
89f91450
authored
2 years ago
by
Tamino Huxohl
Browse files
Options
Downloads
Patches
Plain Diff
Add file for parameter evaluation in random search
parent
60138094
No related branches found
No related tags found
No related merge requests found
Changes
2
Hide whitespace changes
Inline
Side-by-side
Showing
2 changed files
mu_map/random_search/eval/label_outliers.py
+1
-2
1 addition, 2 deletions
mu_map/random_search/eval/label_outliers.py
mu_map/random_search/eval/params.py
+292
-0
292 additions, 0 deletions
mu_map/random_search/eval/params.py
with
293 additions
and
2 deletions
mu_map/random_search/eval/label_outliers.py
+
1
−
2
View file @
89f91450
...
@@ -9,7 +9,7 @@ from mu_map.dataset.default import MuMapDataset
...
@@ -9,7 +9,7 @@ from mu_map.dataset.default import MuMapDataset
from
mu_map.dataset.transform
import
SequenceTransform
,
PadCropTranform
from
mu_map.dataset.transform
import
SequenceTransform
,
PadCropTranform
from
mu_map.models.unet
import
UNet
from
mu_map.models.unet
import
UNet
from
mu_map.random_search.cgan
import
load_params
from
mu_map.random_search.cgan
import
load_params
from
mu_map.random_search.show_predictions
import
main
from
mu_map.random_search.
eval.
show_predictions
import
main
from
mu_map.random_search.eval.util
import
load_data
from
mu_map.random_search.eval.util
import
load_data
controls
=
"""
controls
=
"""
...
@@ -71,7 +71,6 @@ for i, run in enumerate(runs):
...
@@ -71,7 +71,6 @@ for i, run in enumerate(runs):
data
[
"
run
"
].
append
(
int
(
run
))
data
[
"
run
"
].
append
(
int
(
run
))
data
[
"
outlier
"
].
append
(
False
)
data
[
"
outlier
"
].
append
(
False
)
dir_run
=
os
.
path
.
join
(
args
.
random_search_dir
,
runs
[
run
][
"
dir
"
])
dir_run
=
os
.
path
.
join
(
args
.
random_search_dir
,
runs
[
run
][
"
dir
"
])
params
=
runs
[
run
][
"
params
"
]
params
=
runs
[
run
][
"
params
"
]
...
...
This diff is collapsed.
Click to expand it.
mu_map/random_search/eval/params.py
0 → 100644
+
292
−
0
View file @
89f91450
from
dataclasses
import
dataclass
import
itertools
import
os
from
typing
import
Any
,
Callable
,
Dict
,
List
,
Optional
import
matplotlib.pyplot
as
plt
import
numpy
as
np
from
scipy
import
stats
from
termcolor
import
colored
from
mu_map.dataset.normalization
import
(
GaussianNormTransform
,
MeanNormTransform
,
MaxNormTransform
,
)
from
mu_map.random_search.eval.util
import
(
ColorList
,
color_lists
,
load_data
,
remove_outliers
,
filter_by_params
,
jitter
,
TablePrinter
,
)
from
mu_map.training.loss
import
WeightedLoss
@dataclass
class
ParameterGroups
:
"""
Dataclass describing all manifestations of a parameter
to compare.
Parameters
----------
groups: Dict[str, Any]
mappings of labels to manifestations of a parameter
keys: List[str]
keys to access to parameter manifestations from a parsed json file
"""
groups
:
Dict
[
str
,
Any
]
keys
:
List
[
str
]
"""
Definitions of parameters and their manifestations.
"""
parameter_groups
=
{
"
generator_depth
"
:
ParameterGroups
(
groups
=
{
"
Small
"
:
[
64
,
128
,
256
,
512
],
"
Large
"
:
[
32
,
64
,
128
,
256
,
512
]},
keys
=
[
"
generator_features
"
],
),
"
discriminator_depth
"
:
ParameterGroups
(
groups
=
{
"
Class S
"
:
(
"
class
"
,
[
32
,
64
,
128
]),
"
Class M
"
:
(
"
class
"
,
[
64
,
128
,
256
]),
"
Class L
"
:
(
"
class
"
,
[
32
,
64
,
128
,
256
]),
"
PatchGAN M
"
:
(
"
patch
"
,
[
32
,
64
,
128
,
256
]),
"
PatchGAN L
"
:
(
"
patch
"
,
[
64
,
128
,
256
,
512
]),
},
keys
=
[
"
discriminator_type
"
,
"
discriminator_conv_features
"
],
),
"
discriminator_type
"
:
ParameterGroups
(
groups
=
{
"
Class
"
:
"
class
"
,
"
PatchGAN
"
:
"
patch
"
},
keys
=
[
"
discriminator_type
"
]
),
"
distance_loss
"
:
ParameterGroups
(
groups
=
{
"
L1
"
:
WeightedLoss
.
from_str
(
"
L1
"
),
"
L2 + GDL
"
:
WeightedLoss
.
from_str
(
"
L2+GDL
"
),
},
keys
=
[
"
criterion_dist
"
],
),
"
loss_weights
"
:
ParameterGroups
(
groups
=
{
"
100:1
"
:
(
100
,
1
),
"
20:1
"
:
(
20
,
1
),
"
5:1
"
:
(
5
,
1
),
"
1:1
"
:
(
1
,
1
),
"
1:5
"
:
(
1
,
5
),
"
1:20
"
:
(
1
,
20
),
"
1:100
"
:
(
1
,
100
),
},
keys
=
[
"
weight_crit_dist
"
,
"
weight_crit_adv
"
],
),
"
patch_size
"
:
ParameterGroups
(
groups
=
{
"
32
"
:
32
,
"
64
"
:
64
},
keys
=
[
"
patch_size
"
]),
"
normalization
"
:
ParameterGroups
(
groups
=
{
"
Gaussian
"
:
GaussianNormTransform
(),
"
Mean
"
:
MeanNormTransform
(),
"
Max
"
:
MaxNormTransform
(),
},
keys
=
[
"
normalization
"
],
),
"
learning_rate_decay
"
:
ParameterGroups
(
groups
=
{
"
Decay
"
:
True
,
"
No Decay
"
:
False
},
keys
=
[
"
lr_decay
"
]
),
}
def
plot_param_groups
(
data
:
Dict
[
str
,
Dict
[
str
,
Any
]],
param_groups
:
ParameterGroups
,
measure
:
str
=
"
NMAE
"
,
title
:
Optional
[
str
]
=
None
,
colors
:
ColorList
=
color_lists
[
"
printer_friendly
"
],
):
"""
Create a plot to visually compare all manifestations of a parameter.
The plot is a bar plot of the mean with an indicator of the standard deviation.
In addition, all values are scattered on top.
Parameters
----------
data: Dict[str, Dict[str, Any]]
data of a random search procedure as returned by the `load_data` method
in `mu_map.random_search.eval.util`
param_groups: ParameterGroups,
a parameter groups object defining all manifestations of a parameter as
well as how to access
measure: str,
the measure plotted
title: str, optional
the title given to the plot
colors: ColorList,
a color list object defining which colors to use for different parameter
manifestations
"""
fig_width
=
6
+
len
(
param_groups
.
groups
)
-
2
fig_height
=
4
fig
,
ax
=
plt
.
subplots
(
figsize
=
(
fig_width
,
fig_height
))
if
title
is
not
None
:
ax
.
set_title
(
title
)
y_max
=
0
for
i
,
(
label
,
value
)
in
enumerate
(
param_groups
.
groups
.
items
()):
_data
=
filter_by_params
(
data
,
value
,
param_groups
.
keys
)
ys
=
map
(
lambda
run
:
_data
[
run
][
"
measures
"
][
measure
].
mean
(),
_data
.
keys
())
ys
=
np
.
array
(
list
(
ys
))
y_max
=
max
(
y_max
,
ys
.
max
())
if
len
(
ys
)
==
0
:
continue
x
=
i
+
1
ax
.
bar
([
x
],
ys
.
mean
(),
yerr
=
ys
.
std
(),
color
=
colors
[
i
],
capsize
=
5.0
)
xs
=
jitter
(
np
.
full
(
ys
.
shape
,
x
),
amount
=
0.5
)
ax
.
scatter
(
xs
,
ys
,
color
=
colors
[
i
],
alpha
=
0.4
,
edgecolor
=
"
black
"
)
ax
.
set_ylabel
(
measure
)
ax
.
set_ylim
((
0
,
y_max
+
0.01
))
ax
.
grid
(
axis
=
"
y
"
,
alpha
=
0.3
)
ax
.
spines
[
"
left
"
].
set_visible
(
False
)
ax
.
spines
[
"
right
"
].
set_visible
(
False
)
ax
.
spines
[
"
top
"
].
set_visible
(
False
)
xticks
=
list
(
range
(
1
,
len
(
param_groups
.
groups
)
+
1
))
ax
.
set_xticks
(
xticks
)
ax
.
set_xticklabels
(
list
(
param_groups
.
groups
.
keys
()))
ax
.
set_xlim
(
xticks
[
0
]
-
0.5
,
xticks
[
-
1
]
+
0.5
)
def
analyse_stats
(
data
:
Dict
[
str
,
Dict
[
str
,
Any
]],
param_groups
:
ParameterGroups
,
measure
:
str
=
"
NMAE
"
,
):
"""
Perform a statistical analysis for the manifestation of a parameter.
Each manifestation is tested to be normally distributed and afterwards
pairs are compared to be different with the t-test.
Parameters
----------
data: Dict[str, Dict[str, Any]]
data of a random search procedure as returned by the `load_data` method
in `mu_map.random_search.eval.util`
param_groups: ParameterGroups,
a parameter groups object defining all manifestations of a parameter as
well as how to access
measure: str,
the measure analysed
"""
tp
=
TablePrinter
()
tp
.
color_formatter
[
"
Normal
"
]
=
lambda
_str
:
colored
(
_str
,
color
=
"
green
"
if
_str
.
strip
().
lower
()
==
"
yes
"
else
None
)
tp
.
color_formatter
[
"
Significant
"
]
=
lambda
_str
:
colored
(
_str
,
color
=
"
green
"
if
_str
.
strip
().
lower
()
==
"
yes
"
else
None
)
tp
.
color_formatter
[
"
P Value
"
]
=
lambda
_str
:
colored
(
_str
,
color
=
"
green
"
if
float
(
_str
)
<
0.05
else
None
)
ys
=
{}
for
label
,
param_value
in
param_groups
.
groups
.
items
():
_data
=
filter_by_params
(
data
,
param_value
,
param_groups
.
keys
)
_ys
=
map
(
lambda
run
:
_data
[
run
][
"
measures
"
][
measure
].
mean
(),
_data
.
keys
())
ys
[
label
]
=
np
.
array
(
list
(
_ys
))
table
=
{
"
Label
"
:
[],
"
Normal
"
:
[],
"
P Value
"
:
[],
"
Stat
"
:
[],
"
Mean±Std
"
:
[]}
for
i
,
(
label
,
_ys
)
in
enumerate
(
ys
.
items
()):
if
len
(
_ys
)
<
3
:
print
(
f
"
Cannot evaluate
{
label
}
because of too little data n=
{
len
(
_ys
)
}
"
)
continue
stat
,
p_value
=
stats
.
shapiro
(
_ys
)
table
[
"
Label
"
].
append
(
label
)
table
[
"
Normal
"
].
append
(
"
YES
"
if
p_value
<
0.05
else
"
NO
"
)
table
[
"
Stat
"
].
append
(
stat
)
table
[
"
P Value
"
].
append
(
p_value
)
table
[
"
Mean±Std
"
].
append
(
f
"
{
_ys
.
mean
()
:
.
5
f
}
±
{
_ys
.
std
()
:
.
5
f
}
"
)
tp
.
print
(
table
)
print
()
table
=
{
"
Label 1
"
:
[],
"
Label 2
"
:
[],
"
Significant
"
:
[],
"
Stat
"
:
[],
"
P Value
"
:
[]}
for
label_1
,
label_2
in
itertools
.
combinations
(
ys
.
keys
(),
2
):
stat
,
p_value
=
stats
.
ttest_ind
(
ys
[
label_1
],
ys
[
label_2
])
table
[
"
Label 1
"
].
append
(
label_1
)
table
[
"
Label 2
"
].
append
(
label_2
)
table
[
"
Significant
"
].
append
(
"
YES
"
if
p_value
<
0.05
else
"
NO
"
)
table
[
"
Stat
"
].
append
(
stat
)
table
[
"
P Value
"
].
append
(
p_value
)
tp
.
print
(
table
)
if
__name__
==
"
__main__
"
:
import
argparse
parser
=
argparse
.
ArgumentParser
(
description
=
"
Compare different values for a parameter by plotting and performing statistical tests
"
,
formatter_class
=
argparse
.
ArgumentDefaultsHelpFormatter
,
)
parser
.
add_argument
(
"
--random_search_dir
"
,
type
=
str
,
default
=
"
cgan_random_search
"
,
help
=
"
the directory containing the random search data
"
,
)
parser
.
add_argument
(
"
--outliers_file
"
,
type
=
str
,
default
=
"
outliers.csv
"
,
help
=
"
optional file defining outliers / runs to be ignored for analysis
"
,
)
parser
.
add_argument
(
"
--param
"
,
choices
=
list
(
parameter_groups
.
keys
()),
help
=
"
the parameter to analyse
"
,
)
parser
.
add_argument
(
"
--measure
"
,
choices
=
[
"
NMAE
"
,
"
MSE
"
],
default
=
"
NMAE
"
,
help
=
"
the measure used for plotting and analysis
"
,
)
parser
.
add_argument
(
"
--colors
"
,
choices
=
list
(
color_lists
.
keys
()),
default
=
"
printer_friendly
"
,
help
=
"
chose which colors to use for plotting
"
,
)
args
=
parser
.
parse_args
()
data
=
load_data
(
args
.
random_search_dir
)
if
args
.
outliers_file
:
data
=
remove_outliers
(
data
,
os
.
path
.
join
(
args
.
random_search_dir
,
args
.
outliers_file
)
)
param_groups
=
parameter_groups
[
args
.
param
]
plot_title
=
"
"
.
join
(
map
(
lambda
_str
:
_str
[
0
].
upper
()
+
_str
[
1
:],
args
.
param
.
split
(
"
_
"
))
)
analyse_stats
(
data
,
param_groups
,
measure
=
args
.
measure
)
plot_param_groups
(
data
,
param_groups
,
measure
=
args
.
measure
,
title
=
plot_title
,
colors
=
color_lists
[
args
.
colors
],
)
plt
.
show
()
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment