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Weng
Random Survival Forest für BU
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af8e969b
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af8e969b
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Apr 01, 2022
by
Weng
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af8e969b
%% Cell type:markdown id:758926cd tags:
# Random Survival Trees für BU-Tafeln
%% Cell type:code id:df8cdf81 tags:
```
python
#Benötigte Bibliotheken
import
pandas
as
pd
import
numpy
as
np
from
random
import
randint
from
sklearn.model_selection
import
train_test_split
from
sksurv.tree
import
SurvivalTree
from
sksurv.tree
import
SurvivalTreeTruncated
from
sksurv.datasets.base
import
_get_x_y_survival
from
sksurv.datasets.base
import
_get_x_y_survival_truncated
from
sksurv.ensemble
import
RandomSurvivalForest
from
sksurv.ensemble
import
RandomSurvivalForestTruncated
from
export
import
print_tree
import
eli5
from
eli5.sklearn
import
PermutationImportance
```
%% Cell type:code id:78289b58 tags:
```
python
#Einlesen der Daten
path
=
"KleinerBUBestand_Test.csv"
data
=
pd
.
read_csv
(
path
)
data_train_ges
,
data_test_ges
=
train_test_split
(
data
,
test_size
=
0.2
,
random_state
=
20
)
del
data
[
'Unnamed: 0'
]
```
%% Cell type:code id:3ed487a8 tags:
```
python
data_ges
=
data
```
%% Cell type:code id:869c86e3 tags:
```
python
x_train_2
,
y_train_2
=
_get_x_y_survival
(
data_train_ges
,
"Ereignis"
,
"Austrittsalter"
,
1
)
x_train_2
.
drop
([
'Eintrittsalter'
],
axis
=
1
,
inplace
=
True
)
x_test_2
,
y_test_2
=
_get_x_y_survival
(
data_test_ges
,
"Ereignis"
,
"Austrittsalter"
,
1
)
x_test_2
.
drop
([
'Eintrittsalter'
],
axis
=
1
,
inplace
=
True
)
#links-abgeschnitten und rechts-zensiert
x_train_3
,
y_train_3
=
_get_x_y_survival_truncated
(
data_train_ges
,
"Ereignis"
,
"Eintrittsalter"
,
"Austrittsalter"
,
1
)
x_test_3
,
y_test_3
=
_get_x_y_survival_truncated
(
data_test_ges
,
"Ereignis"
,
"Eintrittsalter"
,
"Austrittsalter"
,
1
)
```
%% Cell type:code id:269f3b38 tags:
```
python
from
lifelines
import
NelsonAalenFitter
import
matplotlib.pyplot
as
plt
nf1
=
NelsonAalenFitter
(
nelson_aalen_smoothing
=
False
)
nf2
=
NelsonAalenFitter
(
nelson_aalen_smoothing
=
False
)
data_female
=
data_ges
[
data_ges
[
'Geschlecht'
]
==
1
]
data_male
=
data_ges
[
data_ges
[
'Geschlecht'
]
==
2
]
nf1
.
fit
(
data_male
[
'Austrittsalter'
],
data_male
[
'Ereignis'
],
entry
=
data_male
[
'Eintrittsalter'
])
nf2
.
fit
(
data_female
[
'Austrittsalter'
],
data_female
[
'Ereignis'
],
entry
=
data_female
[
'Eintrittsalter'
])
#in Hazardfunktion umrechnen
s1
=
nf1
.
cumulative_hazard_
.
to_numpy
().
reshape
(
43
)
t1
=
np
.
diff
(
s1
)
s2
=
nf2
.
cumulative_hazard_
.
to_numpy
().
reshape
(
43
)
t2
=
np
.
diff
(
s2
)
data_DAV
=
pd
.
read_csv
(
"Daten/DAV_Tafeln.csv"
,
sep
=
";"
,
decimal
=
","
)
p_f
=
data_DAV
[
'DAV Female'
].
to_numpy
(
dtype
=
'float'
)[
0
:
36
]
p_m
=
data_DAV
[
'DAV Male'
].
to_numpy
(
dtype
=
'float'
)[
0
:
36
]
plt
.
plot
(
t1
,
color
=
"red"
,
label
=
"Männer Daten"
)
plt
.
plot
(
p_m
,
color
=
"blue"
,
label
=
"Männer DAV"
)
plt
.
plot
(
t2
,
color
=
"green"
,
label
=
"Frauen Daten"
)
plt
.
plot
(
p_f
,
color
=
"orange"
,
label
=
"Frauen DAV"
)
plt
.
title
(
"Vergleich Daten mit DAV-Tafel"
)
plt
.
show
()
```
%%%% Output: display_data
%% Cell type:markdown id:b9997db0 tags:
## Survival Trees
%% Cell type:code id:e5896a4a tags:
```
python
estimator
=
SurvivalTreeTruncated
(
max_depth
=
5
).
fit
(
x_train_3
,
y_train_3
)
estimator
.
score
(
x_test_3
,
y_test_3
)
```
%%%% Output: execute_result
0.6693620440194961
%% Cell type:code id:9d625610 tags:
```
python
print_tree
(
estimator
)
```
%%%% Output: stream
The binary tree structure has 39 nodes and has the following tree structure:
node=0 is a split node: go to node 1 if X[:, 2] <= 1.5 else to node 12.
node=1 is a split node: go to node 2 if X[:, 0] <= 36.0 else to node 3.
node=2 is a leaf node.
node=3 is a split node: go to node 4 if X[:, 0] <= 19973.5 else to node 11.
node=4 is a split node: go to node 5 if X[:, 6] <= 6.5 else to node 8.
node=5 is a split node: go to node 6 if X[:, 0] <= 13581.0 else to node 7.
node=6 is a leaf node.
node=7 is a leaf node.
node=8 is a split node: go to node 9 if X[:, 0] <= 11261.0 else to node 10.
node=9 is a leaf node.
node=10 is a leaf node.
node=11 is a leaf node.
node=12 is a split node: go to node 13 if X[:, 5] <= 1.5 else to node 26.
node=13 is a split node: go to node 14 if X[:, 3] <= 1.5 else to node 21.
node=14 is a split node: go to node 15 if X[:, 0] <= 19525.5 else to node 18.
node=15 is a split node: go to node 16 if X[:, 0] <= 16218.5 else to node 17.
node=16 is a leaf node.
node=17 is a leaf node.
node=18 is a split node: go to node 19 if X[:, 0] <= 19537.0 else to node 20.
node=19 is a leaf node.
node=20 is a leaf node.
node=21 is a split node: go to node 22 if X[:, 0] <= 6871.5 else to node 23.
node=22 is a leaf node.
node=23 is a split node: go to node 24 if X[:, 2] <= 3.5 else to node 25.
node=24 is a leaf node.
node=25 is a leaf node.
node=26 is a split node: go to node 27 if X[:, 5] <= 2.5 else to node 34.
node=27 is a split node: go to node 28 if X[:, 1] <= 0.5 else to node 31.
node=28 is a split node: go to node 29 if X[:, 2] <= 2.5 else to node 30.
node=29 is a leaf node.
node=30 is a leaf node.
node=31 is a split node: go to node 32 if X[:, 1] <= 2.5 else to node 33.
node=32 is a leaf node.
node=33 is a leaf node.
node=34 is a split node: go to node 35 if X[:, 4] <= 3.5 else to node 38.
node=35 is a split node: go to node 36 if X[:, 1] <= 1.5 else to node 37.
node=36 is a leaf node.
node=37 is a leaf node.
node=38 is a leaf node.
%% Cell type:markdown id:8f961248 tags:
## Random Survival Forest
%% Cell type:code id:fed384e2 tags:
```
python
random_state
=
20
rsf2
=
RandomSurvivalForestTruncated
(
n_estimators
=
10
,
max_depth
=
5
,
max_features
=
"sqrt"
,
n_jobs
=
2
,
random_state
=
random_state
)
rsf2
.
fit
(
x_train_3
,
y_train_3
)
rsf2
.
score
(
x_test_3
,
y_test_3
)
#rsf2.score(x_train_3,y_train_3)
```
%%%% Output: execute_result
0.6730808979923856
%% Cell type:code id:8a0b4a40 tags:
```
python
#import joblib
#Nützliche Befehler, wenn man das Modell abspeichern bzw. laden möchte:
#joblib.dump(rsf2, "./random_forest_truncated.joblib")
#loaded_rf_truncated = joblib.load("./random_forest_truncated.joblib")
```
%% Cell type:code id:004eade0 tags:
```
python
perm
=
PermutationImportance
(
rsf2
,
n_iter
=
20
,
random_state
=
random_state
)
perm
.
fit
(
x_test_3
,
y_test_3
)
eli5
.
show_weights
(
perm
,
feature_names
=
x_test_3
.
columns
.
to_list
())
```
%%%% Output: execute_result
<IPython.core.display.HTML object>
%% Cell type:markdown id:dde0d3ce tags:
## Prognose
%% Cell type:code id:cb538b34 tags:
```
python
#Teilmenge definieren
subset
=
data_test_ges
data_subset_x
=
x_test_3
```
%% Cell type:code id:216929d3 tags:
```
python
#Modellprognose
table
=
rsf2
.
predict_cumulative_hazard_function
(
data_subset_x
,
return_array
=
True
)
curve
=
table
.
mean
(
axis
=
0
)
curve
=
np
.
diff
(
curve
)[
0
:
40
]
#Sterblichkeiten aus Nelson-Aalen-Estimator für Teilbestand
nf
=
NelsonAalenFitter
(
nelson_aalen_smoothing
=
False
)
nf
.
fit
(
subset
[
'Austrittsalter'
],
subset
[
'Ereignis'
],
entry
=
subset
[
'Eintrittsalter'
])
s2
=
nf
.
cumulative_hazard_
.
to_numpy
().
reshape
(
43
)
t2
=
np
.
diff
(
s2
)
```
%% Cell type:code id:fa3bebaf tags:
```
python
plt
.
plot
(
np
.
arange
(
20
,
55
),
curve
[
0
:
35
],
'b'
,
label
=
'Modell'
)
plt
.
plot
(
np
.
arange
(
20
,
55
),
t2
[
0
:
35
],
'r'
,
label
=
'Daten'
)
plt
.
legend
()
plt
.
title
(
"Inzidenzwahrscheinlichkeiten im Vergleich"
)
plt
.
show
()
```
%%%% Output: display_data
%% Cell type:code id:17ee73cb tags:
```
python
```
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