-
Notifications
You must be signed in to change notification settings - Fork 0
/
ml_test_func.py
25 lines (24 loc) · 1.07 KB
/
ml_test_func.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
#In python version of machine learning, we will run our process based on sklearn
#Using this prebuild library, we do not need to do all the maths by ourselves
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.externals import joblib
def ml_test(X1, y1, X2, y2):
scaler = StandardScaler()
scaler.fit(X1)
#scaler.transform() is used to transform data from wide range to numbers close
#to 0 to make our later calculation a lot quicker
X1 = scaler.transform(X1)
X2 = scaler.transform(X2)
#set the number of hidden layers as 12 and number of iterations as 10000
mlp = MLPClassifier(hidden_layer_sizes=(12,12), max_iter=10000)
#run the machine learning training on X1 and y1
mlp.fit(X1, y1)
#get prediction values of X2
predictions = mlp.predict(X2)
#compare the value of X2 and y2 to get our accuracy
print(mlp.score(X2, y2))