From ac86a07ab85a89e146cdfbf84b795accec8f0b8f Mon Sep 17 00:00:00 2001 From: gedin Date: Wed, 24 May 2023 16:48:12 +0200 Subject: [PATCH] files added --- JenkinsfileTrain | 2 +- train-eval/learning.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/JenkinsfileTrain b/JenkinsfileTrain index a3d613b..571b08c 100644 --- a/JenkinsfileTrain +++ b/JenkinsfileTrain @@ -1,6 +1,6 @@ node { checkout scm - def dockerimage = docker.build("train-image", "./train-eval") + def dockerimage = docker.build("train-image", "dockerfile_train") dockerimage.inside { stage('Preparation') { properties([ diff --git a/train-eval/learning.py b/train-eval/learning.py index 6ccaf74..8927fd5 100755 --- a/train-eval/learning.py +++ b/train-eval/learning.py @@ -3,7 +3,7 @@ import numpy as np import torch from torch import nn import pandas as pd -import subprocess +# import subprocess import sys from sklearn.model_selection import train_test_split @@ -47,7 +47,7 @@ if __name__ == "__main__": Y = df[['Survived']] 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.to_csv('testing_data.csv', sep=',')