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gedin 2023-05-24 16:48:12 +02:00
parent 0d495bc36f
commit ac86a07ab8
2 changed files with 3 additions and 3 deletions
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2
JenkinsfileTrain
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@ -1,6 +1,6 @@
node { node {
checkout scm checkout scm
def dockerimage = docker.build("train-image", "./train-eval") def dockerimage = docker.build("train-image", "dockerfile_train")
dockerimage.inside { dockerimage.inside {
stage('Preparation') { stage('Preparation') {
properties([ properties([

4
train-eval/learning.py
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@ -3,7 +3,7 @@ import numpy as np
import torch import torch
from torch import nn from torch import nn
import pandas as pd import pandas as pd
import subprocess # import subprocess
import sys import sys
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
@ -47,7 +47,7 @@ if __name__ == "__main__":
Y = df[['Survived']] Y = df[['Survived']]
X.loc[:,('Sex')].replace(['female', 'male'], [0,1], inplace=True) #categorical data transformed to X.loc[:,('Sex')].replace(['female', 'male'], [0,1], inplace=True) #categorical data transformed to
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, random_state=45, test_size=0.2, shuffle=True) #split the date into train and test sets X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, shuffle=True) #split the date into train and test sets
testing_data = pd.concat([X_test, Y_test], axis=1) testing_data = pd.concat([X_test, Y_test], axis=1)
testing_data.to_csv('testing_data.csv', sep=',') testing_data.to_csv('testing_data.csv', sep=',')