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machine-learning
MLAir
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!318
Resolve "release v1.4.0"
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Resolve "release v1.4.0"
release_v1.4.0
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Ghost User
requested to merge
release_v1.4.0
into
master
3 years ago
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#317 (closed)
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3 years ago
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Ghost User
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8ea88529
cleanup, remove outdated validate_station_old method
· 8ea88529
leufen1
authored
3 years ago
mlair/run_modules/pre_processing.py
+
0
−
32
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@@ -285,38 +285,6 @@ class PreProcessing(RunEnvironment):
for
k
,
v
in
attrs
.
items
():
self
.
data_store
.
set
(
k
,
v
)
def
validate_station_old
(
self
,
data_handler
:
AbstractDataHandler
,
set_stations
,
set_name
=
None
,
store_processed_data
=
True
):
"""
Check if all given stations in `all_stations` are valid.
Valid means, that there is data available for the given time range (is included in `kwargs`). The shape and the
loading time are logged in debug mode.
:return: Corrected list containing only valid station IDs.
"""
t_outer
=
TimeTracking
()
logging
.
info
(
f
"
check valid stations started
{
'
(%s)
'
%
(
set_name
if
set_name
is
not
None
else
'
all
'
)
}
"
)
# calculate transformation using train data
if
set_name
==
"
train
"
:
logging
.
info
(
"
setup transformation using train data exclusively
"
)
self
.
transformation
(
data_handler
,
set_stations
)
# start station check
collection
=
DataCollection
()
valid_stations
=
[]
kwargs
=
self
.
data_store
.
create_args_dict
(
data_handler
.
requirements
(),
scope
=
set_name
)
for
station
in
set_stations
:
try
:
dp
=
data_handler
.
build
(
station
,
name_affix
=
set_name
,
store_processed_data
=
store_processed_data
,
**
kwargs
)
collection
.
add
(
dp
)
valid_stations
.
append
(
station
)
except
(
AttributeError
,
EmptyQueryResult
):
continue
logging
.
info
(
f
"
run for
{
t_outer
}
to check
{
len
(
set_stations
)
}
station(s). Found
{
len
(
collection
)
}
/
"
f
"
{
len
(
set_stations
)
}
valid stations.
"
)
return
collection
,
valid_stations
def
transformation
(
self
,
data_handler
:
AbstractDataHandler
,
stations
):
if
hasattr
(
data_handler
,
"
transformation
"
):
kwargs
=
self
.
data_store
.
create_args_dict
(
data_handler
.
requirements
(),
scope
=
"
train
"
)
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