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bob
bob.pad.base
Commits
4e89bcb3
Commit
4e89bcb3
authored
May 20, 2019
by
Anjith GEORGE
Browse files
Merge branch 'correct-apcer-calculation' into 'master'
Correct apcer calculation Closes
#31
See merge request
!60
parents
47b3f2d0
8f66c86e
Pipeline
#30336
passed with stages
in 12 minutes and 22 seconds
Changes
8
Pipelines
1
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bob/pad/base/algorithm/MLP.py
View file @
4e89bcb3
...
...
@@ -7,11 +7,8 @@ from bob.pad.base.algorithm import Algorithm
import
bob.learn.mlp
import
bob.io.base
from
bob.bio.video.utils
import
FrameContainer
from
bob.pad.base.utils
import
convert_frame_cont_to_array
from
bob.core.log
import
setup
logger
=
setup
(
"bob.pad.base"
)
import
logging
logger
=
logging
.
getLogger
(
__name__
)
class
MLP
(
Algorithm
):
...
...
@@ -42,11 +39,11 @@ class MLP(Algorithm):
criterion to stop the training: if the difference
between current and last loss is smaller than
this number, then stop training.
"""
Algorithm
.
__init__
(
self
,
Algorithm
.
__init__
(
self
,
performs_projection
=
True
,
requires_projector_training
=
True
,
requires_projector_training
=
True
,
**
kwargs
)
self
.
hidden_units
=
hidden_units
...
...
@@ -54,32 +51,31 @@ class MLP(Algorithm):
self
.
precision
=
precision
self
.
mlp
=
None
def
train_projector
(
self
,
training_features
,
projector_file
):
"""Trains the MLP
Parameters
----------
training_features : :any:`list` of :py:class:`numpy.ndarray`
training_features : :any:`list` of :py:class:`numpy.ndarray`
Data used to train the MLP. The real attempts are in training_features[0] and the attacks are in training_features[1]
projector_file : str
Filename where to save the trained model.
"""
# training is done in batch (i.e. using all training data)
batch_size
=
len
(
training_features
[
0
])
+
len
(
training_features
[
1
])
# The labels
label_real
=
numpy
.
zeros
((
len
(
training_features
[
0
]),
2
),
dtype
=
'float64'
)
label_real
[:,
0
]
=
1
label_attack
=
numpy
.
zeros
((
len
(
training_features
[
1
]),
2
),
dtype
=
'float64'
)
label_attack
[:,
1
]
=
0
real
=
numpy
.
array
(
training_features
[
0
])
attack
=
numpy
.
array
(
training_features
[
1
])
X
=
numpy
.
vstack
([
real
,
attack
])
Y
=
numpy
.
vstack
([
label_real
,
label_attack
])
# Building MLP architecture
input_dim
=
real
.
shape
[
1
]
shape
=
[]
...
...
@@ -89,16 +85,16 @@ class MLP(Algorithm):
# last layer contains two units: one for each class (i.e. real and attack)
shape
.
append
(
2
)
shape
=
tuple
(
shape
)
self
.
mlp
=
bob
.
learn
.
mlp
.
Machine
(
shape
)
self
.
mlp
.
output_activation
=
bob
.
learn
.
activation
.
Logistic
()
self
.
mlp
.
randomize
()
trainer
=
bob
.
learn
.
mlp
.
BackProp
(
batch_size
,
bob
.
learn
.
mlp
.
CrossEntropyLoss
(
self
.
mlp
.
output_activation
),
self
.
mlp
,
train_biases
=
True
)
n_iter
=
0
previous_cost
=
0
current_cost
=
1
while
(
n_iter
<
self
.
max_iter
)
and
(
abs
(
previous_cost
-
current_cost
)
>
self
.
precision
):
while
(
n_iter
<
self
.
max_iter
)
and
(
abs
(
previous_cost
-
current_cost
)
>
self
.
precision
):
previous_cost
=
current_cost
trainer
.
train
(
self
.
mlp
,
X
,
Y
)
current_cost
=
trainer
.
cost
(
self
.
mlp
,
X
,
Y
)
...
...
@@ -107,14 +103,13 @@ class MLP(Algorithm):
f
=
bob
.
io
.
base
.
HDF5File
(
projector_file
,
'w'
)
self
.
mlp
.
save
(
f
)
def
project
(
self
,
feature
):
"""Project the given feature
Parameters
----------
feature : :py:class:`numpy.ndarray`
feature : :py:class:`numpy.ndarray`
The feature to classify
Returns
...
...
@@ -126,18 +121,17 @@ class MLP(Algorithm):
# feature = convert_frame_cont_to_array(feature)
return
self
.
mlp
(
feature
)
def
score
(
self
,
toscore
):
"""Returns the probability of the real class.
Parameters
----------
toscore : :py:class:`numpy.ndarray`
Returns
-------
float
probability of the authentication attempt to be real.
probability of the authentication attempt to be real.
"""
if
toscore
.
ndim
==
1
:
return
[
toscore
[
0
]]
...
...
bob/pad/base/script/error_utils.py
View file @
4e89bcb3
This diff is collapsed.
Click to expand it.
bob/pad/base/script/pad_commands.py
View file @
4e89bcb3
...
...
@@ -7,93 +7,235 @@ import bob.bio.base.script.gen as bio_gen
import
bob.measure.script.figure
as
measure_figure
from
bob.bio.base.score
import
load
from
.
import
pad_figure
as
figure
from
.error_utils
import
negatives_per_pai_and_positives
from
functools
import
partial
SCORE_FORMAT
=
(
"Files must be 4-col format, see "
":py:func:`bob.bio.base.score.load.four_column`."
)
CRITERIA
=
(
'eer'
,
'min-hter'
,
'bpcer20'
)
"Files must be 4-col format, see "
":py:func:`bob.bio.base.score.load.four_column`."
)
CRITERIA
=
(
"eer"
,
"min-hter"
,
"far"
,
"bpcer5000"
,
"bpcer2000"
,
"bpcer1000"
,
"bpcer500"
,
"bpcer200"
,
"bpcer100"
,
"bpcer50"
,
"bpcer20"
,
"bpcer10"
,
"bpcer5"
,
"bpcer2"
,
"bpcer1"
,
)
def
metrics_option
(
sname
=
"-m"
,
lname
=
"--metrics"
,
name
=
"metrics"
,
help
=
"List of metrics to print. Provide a string with comma separated metric "
"names. For possible values see the default value."
,
default
=
"apcer_pais,apcer,bpcer,acer,fta,fpr,fnr,hter,far,frr,precision,recall,f1_score"
,
**
kwargs
):
"""The metrics option"""
def
custom_metrics_option
(
func
):
def
callback
(
ctx
,
param
,
value
):
if
value
is
not
None
:
value
=
value
.
split
(
","
)
ctx
.
meta
[
name
]
=
value
return
value
return
click
.
option
(
sname
,
lname
,
default
=
default
,
help
=
help
,
show_default
=
True
,
callback
=
callback
,
**
kwargs
)(
func
)
return
custom_metrics_option
def
regexps_option
(
help
=
"A list of regular expressions (by repeating this option) to be used to "
"categorize PAIs. Each regexp must match one type of PAI."
,
**
kwargs
):
def
custom_regexps_option
(
func
):
def
callback
(
ctx
,
param
,
value
):
ctx
.
meta
[
"regexps"
]
=
value
return
value
return
click
.
option
(
"-r"
,
"--regexps"
,
default
=
None
,
multiple
=
True
,
help
=
help
,
callback
=
callback
,
**
kwargs
)(
func
)
return
custom_regexps_option
def
regexp_column_option
(
help
=
"The column in the score files to match the regular expressions against."
,
**
kwargs
):
def
custom_regexp_column_option
(
func
):
def
callback
(
ctx
,
param
,
value
):
ctx
.
meta
[
"regexp_column"
]
=
value
return
value
return
click
.
option
(
"-rc"
,
"--regexp-column"
,
default
=
"real_id"
,
type
=
click
.
Choice
((
"claimed_id"
,
"real_id"
,
"test_label"
)),
help
=
help
,
show_default
=
True
,
callback
=
callback
,
**
kwargs
)(
func
)
return
custom_regexp_column_option
@
click
.
command
()
@
click
.
argument
(
'
outdir
'
)
@
click
.
option
(
'
-mm
'
,
'
--mean-match
'
,
default
=
10
,
type
=
click
.
FLOAT
,
show_default
=
True
)
@
click
.
option
(
'
-mnm
'
,
'
--mean-non-match
'
,
default
=-
10
,
type
=
click
.
FLOAT
,
show_default
=
True
)
@
click
.
option
(
'
-n
'
,
'
--n-sys
'
,
default
=
1
,
type
=
click
.
INT
,
show_default
=
True
)
@
click
.
argument
(
"
outdir
"
)
@
click
.
option
(
"
-mm
"
,
"
--mean-match
"
,
default
=
10
,
type
=
click
.
FLOAT
,
show_default
=
True
)
@
click
.
option
(
"
-mnm
"
,
"
--mean-non-match
"
,
default
=-
10
,
type
=
click
.
FLOAT
,
show_default
=
True
)
@
click
.
option
(
"
-n
"
,
"
--n-sys
"
,
default
=
1
,
type
=
click
.
INT
,
show_default
=
True
)
@
verbosity_option
()
@
click
.
pass_context
def
gen
(
ctx
,
outdir
,
mean_match
,
mean_non_match
,
n_sys
,
**
kwargs
):
"""Generate random scores.
Generates random scores in 4col or 5col format. The scores are generated
using Gaussian distribution whose mean is an input
parameter. The generated scores can be used as hypothetical datasets.
Invokes :py:func:`bob.bio.base.script.commands.gen`.
"""
ctx
.
meta
[
'five_col'
]
=
False
ctx
.
forward
(
bio_gen
.
gen
)
@
common_options
.
metrics_command
(
common_options
.
METRICS_HELP
.
format
(
names
=
'FtA, APCER, BPCER, FAR, FRR, ACER'
,
criteria
=
CRITERIA
,
score_format
=
SCORE_FORMAT
,
hter_note
=
'Note that FAR = APCER * (1 - FtA), '
'FRR = FtA + BPCER * (1 - FtA) and ACER = (APCER + BPCER) / 2.'
,
command
=
'bob pad metrics'
),
criteria
=
CRITERIA
)
def
metrics
(
ctx
,
scores
,
evaluation
,
**
kwargs
):
process
=
figure
.
Metrics
(
ctx
,
scores
,
evaluation
,
load
.
split
)
process
.
run
()
"""Generate random scores.
Generates random scores in 4col or 5col format. The scores are generated
using Gaussian distribution whose mean is an input
parameter. The generated scores can be used as hypothetical datasets.
Invokes :py:func:`bob.bio.base.script.commands.gen`.
"""
ctx
.
meta
[
"five_col"
]
=
False
ctx
.
forward
(
bio_gen
.
gen
)
@
common_options
.
metrics_command
(
common_options
.
METRICS_HELP
.
format
(
names
=
"FtA, APCER, BPCER, FPR, FNR, FAR, FRR, ACER, HTER, precision, recall, f1_score"
,
criteria
=
CRITERIA
,
score_format
=
SCORE_FORMAT
,
hter_note
=
"Note that APCER = max(APCER_pais), BPCER=FNR, "
"FAR = FPR * (1 - FtA), "
"FRR = FtA + FNR * (1 - FtA), "
"ACER = (APCER + BPCER) / 2, "
"and HTER = (FPR + FNR) / 2. "
"You can control which metrics are printed using the --metrics option. "
"You can use --regexps and --regexp_column options to change the behavior "
"of finding Presentation Attack Instrument (PAI) types"
,
command
=
"bob pad metrics"
,
),
criteria
=
CRITERIA
,
epilog
=
"""
\b
More Examples:
\b
bob pad metrics -vvv -e -lg IQM,LBP -r print -r video -m fta,apcer_pais,apcer,bpcer,acer,hter
\
/scores/oulunpu/{qm-svm,lbp-svm}/Protocol_1/scores/scores-{dev,eval}
See also ``bob pad multi-metrics``.
"""
,
)
@
regexps_option
()
@
regexp_column_option
()
@
metrics_option
()
def
metrics
(
ctx
,
scores
,
evaluation
,
regexps
,
regexp_column
,
metrics
,
**
kwargs
):
load_fn
=
partial
(
negatives_per_pai_and_positives
,
regexps
=
regexps
,
regexp_column
=
regexp_column
)
process
=
figure
.
Metrics
(
ctx
,
scores
,
evaluation
,
load_fn
,
metrics
)
process
.
run
()
@
common_options
.
roc_command
(
common_options
.
ROC_HELP
.
format
(
score_format
=
SCORE_FORMAT
,
command
=
'bob pad roc'
)
)
common_options
.
ROC_HELP
.
format
(
score_format
=
SCORE_FORMAT
,
command
=
"bob pad roc"
)
)
def
roc
(
ctx
,
scores
,
evaluation
,
**
kwargs
):
process
=
figure
.
Roc
(
ctx
,
scores
,
evaluation
,
load
.
split
)
process
.
run
()
process
=
figure
.
Roc
(
ctx
,
scores
,
evaluation
,
load
.
split
)
process
.
run
()
@
common_options
.
det_command
(
common_options
.
DET_HELP
.
format
(
score_format
=
SCORE_FORMAT
,
command
=
'bob pad det'
)
)
common_options
.
DET_HELP
.
format
(
score_format
=
SCORE_FORMAT
,
command
=
"bob pad det"
)
)
def
det
(
ctx
,
scores
,
evaluation
,
**
kwargs
):
process
=
figure
.
Det
(
ctx
,
scores
,
evaluation
,
load
.
split
)
process
.
run
()
process
=
figure
.
Det
(
ctx
,
scores
,
evaluation
,
load
.
split
)
process
.
run
()
@
common_options
.
epc_command
(
common_options
.
EPC_HELP
.
format
(
score_format
=
SCORE_FORMAT
,
command
=
'bob pad epc'
)
)
common_options
.
EPC_HELP
.
format
(
score_format
=
SCORE_FORMAT
,
command
=
"bob pad epc"
)
)
def
epc
(
ctx
,
scores
,
**
kwargs
):
process
=
measure_figure
.
Epc
(
ctx
,
scores
,
True
,
load
.
split
,
hter
=
'
ACER
'
)
process
.
run
()
process
=
measure_figure
.
Epc
(
ctx
,
scores
,
True
,
load
.
split
,
hter
=
"
ACER
"
)
process
.
run
()
@
common_options
.
hist_command
(
common_options
.
HIST_HELP
.
format
(
score_format
=
SCORE_FORMAT
,
command
=
'bob pad hist'
)
)
common_options
.
HIST_HELP
.
format
(
score_format
=
SCORE_FORMAT
,
command
=
"bob pad hist"
)
)
def
hist
(
ctx
,
scores
,
evaluation
,
**
kwargs
):
process
=
figure
.
Hist
(
ctx
,
scores
,
evaluation
,
load
.
split
)
process
.
run
()
process
=
figure
.
Hist
(
ctx
,
scores
,
evaluation
,
load
.
split
)
process
.
run
()
@
common_options
.
evaluate_command
(
common_options
.
EVALUATE_HELP
.
format
(
score_format
=
SCORE_FORMAT
,
command
=
'bob pad evaluate'
),
criteria
=
CRITERIA
)
score_format
=
SCORE_FORMAT
,
command
=
"bob pad evaluate"
),
criteria
=
CRITERIA
,
)
def
evaluate
(
ctx
,
scores
,
evaluation
,
**
kwargs
):
common_options
.
evaluate_flow
(
ctx
,
scores
,
evaluation
,
metrics
,
roc
,
det
,
epc
,
hist
,
**
kwargs
)
common_options
.
evaluate_flow
(
ctx
,
scores
,
evaluation
,
metrics
,
roc
,
det
,
epc
,
hist
,
**
kwargs
)
@
common_options
.
multi_metrics_command
(
common_options
.
MULTI_METRICS_HELP
.
format
(
names
=
'FtA, APCER, BPCER, FAR, FRR, ACER'
,
criteria
=
CRITERIA
,
score_format
=
SCORE_FORMAT
,
command
=
'bob pad multi-metrics'
),
criteria
=
CRITERIA
)
def
multi_metrics
(
ctx
,
scores
,
evaluation
,
protocols_number
,
**
kwargs
):
ctx
.
meta
[
'min_arg'
]
=
protocols_number
*
(
2
if
evaluation
else
1
)
process
=
figure
.
MultiMetrics
(
ctx
,
scores
,
evaluation
,
load
.
split
)
process
.
run
()
names
=
"FtA, APCER, BPCER, FAR, FRR, ACER, HTER, precision, recall, f1_score"
,
criteria
=
CRITERIA
,
score_format
=
SCORE_FORMAT
,
command
=
"bob pad multi-metrics"
,
),
criteria
=
CRITERIA
,
epilog
=
"""
\b
More examples:
\b
bob pad multi-metrics -vvv -e -pn 6 -lg IQM,LBP -r print -r video
\
/scores/oulunpu/{qm-svm,lbp-svm}/Protocol_3_{1,2,3,4,5,6}/scores/scores-{dev,eval}
See also ``bob pad metrics``.
"""
,
)
@
regexps_option
()
@
regexp_column_option
()
@
metrics_option
(
default
=
"fta,apcer_pais,apcer,bpcer,acer,hter"
)
def
multi_metrics
(
ctx
,
scores
,
evaluation
,
protocols_number
,
regexps
,
regexp_column
,
metrics
,
**
kwargs
):
ctx
.
meta
[
"min_arg"
]
=
protocols_number
*
(
2
if
evaluation
else
1
)
load_fn
=
partial
(
negatives_per_pai_and_positives
,
regexps
=
regexps
,
regexp_column
=
regexp_column
)
process
=
figure
.
MultiMetrics
(
ctx
,
scores
,
evaluation
,
load_fn
,
metrics
)
process
.
run
()
bob/pad/base/script/pad_figure.py
View file @
4e89bcb3
'''Runs error analysis on score sets, outputs metrics and plots'''
"""Runs error analysis on score sets, outputs metrics and plots"""
import
bob.measure.script.figure
as
measure_figure
from
bob.measure.utils
import
get_fta_list
from
bob.measure
import
farfrr
,
precision_recall
,
f_score
import
bob.bio.base.script.figure
as
bio_figure
from
.error_utils
import
calc_threshold
from
.error_utils
import
calc_threshold
,
apcer_bpcer
import
click
from
tabulate
import
tabulate
import
numpy
as
np
ALL_CRITERIA
=
(
'bpcer20'
,
'eer'
,
'min-hter'
)
def
_normalize_input_scores
(
input_score
,
input_name
):
pos
,
negs
=
input_score
# convert scores to sorted numpy arrays and keep a copy of all negatives
pos
=
np
.
ascontiguousarray
(
pos
)
pos
.
sort
()
all_negs
=
np
.
ascontiguousarray
([
s
for
neg
in
negs
.
values
()
for
s
in
neg
])
all_negs
.
sort
()
# FTA is calculated on pos and all_negs so we remove nans from negs
for
k
,
v
in
negs
.
items
():
v
=
np
.
ascontiguousarray
(
v
)
v
.
sort
()
negs
[
k
]
=
v
[
~
np
.
isnan
(
v
)]
neg_list
,
pos_list
,
fta_list
=
get_fta_list
([(
all_negs
,
pos
)])
all_negs
,
pos
,
fta
=
neg_list
[
0
],
pos_list
[
0
],
fta_list
[
0
]
return
input_name
,
pos
,
negs
,
all_negs
,
fta
class
Metrics
(
bio_figure
.
Metrics
):
'''Compute metrics from score files'''
"""Compute metrics from score files"""
def
__init__
(
self
,
ctx
,
scores
,
evaluation
,
func_load
,
names
):
if
isinstance
(
names
,
str
):
names
=
names
.
split
(
","
)
super
(
Metrics
,
self
).
__init__
(
ctx
,
scores
,
evaluation
,
func_load
,
names
)
def
get_thres
(
self
,
criterion
,
pos
,
negs
,
all_negs
,
far_value
):
return
calc_threshold
(
criterion
,
pos
=
pos
,
negs
=
negs
.
values
(),
all_negs
=
all_negs
,
far_value
=
far_value
,
is_sorted
=
True
,
)
def
_numbers
(
self
,
threshold
,
pos
,
negs
,
all_negs
,
fta
):
pais
=
list
(
negs
.
keys
())
apcer_pais
,
apcer
,
bpcer
=
apcer_bpcer
(
threshold
,
pos
,
*
[
negs
[
k
]
for
k
in
pais
])
apcer_pais
=
{
k
:
apcer_pais
[
i
]
for
i
,
k
in
enumerate
(
pais
)}
acer
=
(
apcer
+
bpcer
)
/
2.0
fpr
,
fnr
=
farfrr
(
all_negs
,
pos
,
threshold
)
hter
=
(
fpr
+
fnr
)
/
2.0
far
=
fpr
*
(
1
-
fta
)
frr
=
fta
+
fnr
*
(
1
-
fta
)
nn
=
all_negs
.
shape
[
0
]
# number of attack
fp
=
int
(
round
(
fpr
*
nn
))
# number of false positives
np
=
pos
.
shape
[
0
]
# number of bonafide
fn
=
int
(
round
(
fnr
*
np
))
# number of false negatives
# precision and recall
precision
,
recall
=
precision_recall
(
all_negs
,
pos
,
threshold
)
# f_score
f1_score
=
f_score
(
all_negs
,
pos
,
threshold
,
1
)
metrics
=
dict
(
apcer_pais
=
apcer_pais
,
apcer
=
apcer
,
bpcer
=
bpcer
,
acer
=
acer
,
fta
=
fta
,
fpr
=
fpr
,
fnr
=
fnr
,
hter
=
hter
,
far
=
far
,
frr
=
frr
,
fp
=
fp
,
nn
=
nn
,
fn
=
fn
,
np
=
np
,
precision
=
precision
,
recall
=
recall
,
f1_score
=
f1_score
,
)
return
metrics
def
__init__
(
self
,
ctx
,
scores
,
evaluation
,
func_load
,
names
=
(
'FtA'
,
'APCER'
,
'BPCER'
,
'FAR'
,
'FRR'
,
'HTER'
)):
super
(
Metrics
,
self
).
__init__
(
ctx
,
scores
,
evaluation
,
func_load
,
names
def
_strings
(
self
,
metrics
):
n_dec
=
".%df"
%
self
.
_decimal
for
k
,
v
in
metrics
.
items
():
if
k
in
(
"precision"
,
"recall"
,
"f1_score"
):
metrics
[
k
]
=
"%s"
%
format
(
v
,
n_dec
)
elif
k
in
(
"np"
,
"nn"
,
"fp"
,
"fn"
):
continue
elif
k
in
(
"fpr"
,
"fnr"
):
if
"fp"
in
metrics
:
metrics
[
k
]
=
"%s%% (%d/%d)"
%
(
format
(
100
*
v
,
n_dec
),
metrics
[
"fp"
if
k
==
"fpr"
else
"fn"
],
metrics
[
"np"
if
k
==
"fpr"
else
"nn"
],
)
else
:
metrics
[
k
]
=
"%s%%"
%
format
(
100
*
v
,
n_dec
)
elif
k
==
"apcer_pais"
:
metrics
[
k
]
=
{
k1
:
"%s%%"
%
format
(
100
*
v1
,
n_dec
)
for
k1
,
v1
in
v
.
items
()
}
else
:
metrics
[
k
]
=
"%s%%"
%
format
(
100
*
v
,
n_dec
)
return
metrics
def
_get_all_metrics
(
self
,
idx
,
input_scores
,
input_names
):
""" Compute all metrics for dev and eval scores"""
for
i
,
(
score
,
name
)
in
enumerate
(
zip
(
input_scores
,
input_names
)):
input_scores
[
i
]
=
_normalize_input_scores
(
score
,
name
)
dev_file
,
dev_pos
,
dev_negs
,
dev_all_negs
,
dev_fta
=
input_scores
[
0
]
if
self
.
_eval
:
eval_file
,
eval_pos
,
eval_negs
,
eval_all_negs
,
eval_fta
=
input_scores
[
1
]
threshold
=
(
self
.
get_thres
(
self
.
_criterion
,
dev_pos
,
dev_negs
,
dev_all_negs
,
self
.
_far
)
if
self
.
_thres
is
None
else
self
.
_thres
[
idx
]
)
title
=
self
.
_legends
[
idx
]
if
self
.
_legends
is
not
None
else
None
if
self
.
_thres
is
None
:
far_str
=
""
if
self
.
_criterion
==
"far"
and
self
.
_far
is
not
None
:
far_str
=
str
(
self
.
_far
)
click
.
echo
(
"[Min. criterion: %s %s] Threshold on Development set `%s`: %e"
%
(
self
.
_criterion
.
upper
(),
far_str
,
title
or
dev_file
,
threshold
),
file
=
self
.
log_file
,
)
else
:
click
.
echo
(
"[Min. criterion: user provided] Threshold on "
"Development set `%s`: %e"
%
(
dev_file
or
title
,
threshold
),
file
=
self
.
log_file
,
)
res
=
[]
res
.
append
(
self
.
_strings
(
self
.
_numbers
(
threshold
,
dev_pos
,
dev_negs
,
dev_all_negs
,
dev_fta
)
)
)
def
get_thres
(
self
,
criterion
,
dev_neg
,
dev_pos
,
far
):
if
self
.
_criterion
==
'bpcer20'
:
return
calc_threshold
(
'bpcer20'
,
dev_neg
,
dev_pos
)
if
self
.
_eval
:
# computes statistics for the eval set based on the threshold a priori
res
.
append
(
self
.
_strings
(
self
.
_numbers
(
threshold
,
eval_pos
,
eval_negs
,
eval_all_negs
,
eval_fta
)
)
)
else
:
return
super
(
Metrics
,
self
).
get_thres
(
criterion
,
dev_neg
,
dev_pos
,
far
)
res
.
append
(
None
)
return
res