-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathmetode.py
49 lines (38 loc) · 1.46 KB
/
metode.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
import joblib
import pandas as pd
# preprocessing
def normalisasi(x):
# import data test
cols = ['age','sex','BP','cholestrol']
df = pd.DataFrame([x],columns=cols)
data_test = pd.read_csv('model/data_test2.csv')
data_test = data_test.drop(data_test.columns[0],axis=1)
# memasukkan data kedalam data test
data_test = data_test.append(other=df,ignore_index=True)
# return data_test yang sudah dinormalisasi
return joblib.load('model/norm.sav').fit_transform(data_test)
# normal
def normal(x):
cols = ['age','sex','BP','cholestrol']
df = pd.DataFrame([x],columns=cols)
data_test = pd.read_csv('model/data_test2.csv')
data_test = data_test.drop(data_test.columns[0],axis=1)
# memasukkan data kedalam data test
data_test = data_test.append(other=df,ignore_index=True)
# return data_test yang sudah dinormalisasi
return (data_test)
# metode with normalization
def knn(x):
return joblib.load('model/modelKNN11.pkl').predict(x)
def bagging(x):
return joblib.load('model/bagginggaussian.pkl').predict(x)
def randomforest(x):
return joblib.load('model/randomforest.pkl').predict(x)
# metode without normalization
def knn_no_norm(x):
return joblib.load('model/modelKNN11_1.pkl').predict(x)
def bagging_no_norm(x):
return joblib.load('model/bagginggaussian_1.pkl').predict(x)
def randomforest_no_norm(x):
return joblib.load('model/randomforest_1.pkl').predict(x)
# print(normalisasi([50,1,120,200]))