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Léo Schneider
LC-MS-RT-prediction
Commits
4b2bf25b
Commit
4b2bf25b
authored
4 months ago
by
Schneider Leo
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85e4a949
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RelectureBK2
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data_viz.py
+10
-13
10 additions, 13 deletions
data_viz.py
with
10 additions
and
13 deletions
data_viz.py
+
10
−
13
View file @
4b2bf25b
...
...
@@ -275,15 +275,12 @@ def add_length(dataframe):
dataframe
[
'
length
'
]
=
dataframe
[
'
seq
'
].
map
(
fonc
)
df
=
pd
.
read_csv
(
'
output/out_common_ISA_
ISA
_eval
_2
.csv
'
)
df
=
pd
.
read_csv
(
'
output/out_common_ISA_
augmented_10
_eval.csv
'
)
add_length
(
df
)
df
[
'
rt pred
'
]
=
0
for
seq
,
gr
in
df
.
groupby
(
'
seq
'
)
:
df
.
loc
[
df
[
'
seq
'
]
==
seq
,
'
rt pred
'
]
=
gr
[
'
true rt
'
].
mean
()
# df['abs_error'] = np.abs(df['rt pred']-df['true rt'])
# histo_abs_error(df, display=False, save=True, path='fig/custom model res/histo_ISA_ISA_eval.png')
scatter_rt
(
df
,
display
=
False
,
save
=
True
,
path
=
'
fig/custom model res/RT_pred_ISA_ISA_best_possible.png
'
,
color
=
True
)
# histo_length_by_error(df, bins=10, display=False, save=True, path='fig/custom model res/histo_length_ISA_ISA_eval.png')
df
[
'
abs_error
'
]
=
np
.
abs
(
df
[
'
rt pred
'
]
-
df
[
'
true rt
'
])
histo_abs_error
(
df
,
display
=
False
,
save
=
True
,
path
=
'
fig/custom model res/histo_SA_augmented_10_eval.png
'
)
scatter_rt
(
df
,
display
=
False
,
save
=
True
,
path
=
'
fig/custom model res/RT_pred_SA_augmented_10_eval.png
'
,
color
=
True
)
histo_length_by_error
(
df
,
bins
=
10
,
display
=
False
,
save
=
True
,
path
=
'
fig/custom model res/histo_length_SA_augmented_10_eval.png
'
)
#
# df = pd.read_csv('output/out_common_ISA_augmented_3_eval.csv')
# add_length(df)
...
...
@@ -308,10 +305,10 @@ scatter_rt(df, display=False, save=True, path='fig/custom model res/RT_pred_ISA_
## Compare error variation between run
## Prosit column changes affect some peptides more than others (but consistently)
df_1
=
pd
.
read_csv
(
'
output/out_common_ISA_prosit_eval.csv
'
)
df_2
=
pd
.
read_csv
(
'
output/out_common_ISA_prosit_eval_2.csv
'
)
df
=
select_best_data
(
df_1
,
df_2
,
7
)
df
.
to_pickle
(
'
database/data_prosit_threshold_7.pkl
'
)
#
df_1 = pd.read_csv('output/out_common_ISA_prosit_eval.csv')
#
df_2 = pd.read_csv('output/out_common_ISA_prosit_eval_2.csv')
#
#
df = select_best_data(df_1, df_2, 7)
#
df.to_pickle('database/data_prosit_threshold_7.pkl')
# compare_error(df_1,df_2,save=True,path='fig/custom model res/ISA_prosit_error_variation.png')
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