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@ -1,6 +1,6 @@
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node {
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checkout scm
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def dockerimage = docker.build("train-image", "./train-eval")
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def dockerimage = docker.build("train-image", "dockerfile_train")
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dockerimage.inside {
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stage('Preparation') {
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properties([
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@ -3,7 +3,7 @@ import numpy as np
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import torch
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from torch import nn
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import pandas as pd
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import subprocess
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# import subprocess
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import sys
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from sklearn.model_selection import train_test_split
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@ -47,7 +47,7 @@ if __name__ == "__main__":
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Y = df[['Survived']]
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X.loc[:,('Sex')].replace(['female', 'male'], [0,1], inplace=True) #categorical data transformed to
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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
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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
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testing_data = pd.concat([X_test, Y_test], axis=1)
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testing_data.to_csv('testing_data.csv', sep=',')
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