iteration parameter
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@ -2,12 +2,19 @@ pipeline {
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agent {
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dockerfile true
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}
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parameters {
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string(
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defaultValue: '2',
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description: 'learning iterations',
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name: 'epoch'
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)
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}
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stages {
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stage('Stage 1') {
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steps {
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copyArtifacts filter: 'data.csv', fingerprintArtifacts: true, projectName: 's444386-create-dataset', selector: lastSuccessful()
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sh 'chmod u+x ./biblioteki_dl.py'
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sh 'python3 biblioteki_dl.py'
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sh 'python3 biblioteki_dl.py $epoch'
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sh 'tar -czf model.tar.gz model/'
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archiveArtifacts 'model.tar.gz'
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archiveArtifacts 'xtest.csv'
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@ -4,10 +4,13 @@ import pandas as pd
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import numpy as np
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import csv
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from sklearn.model_selection import train_test_split
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import sys
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# os.system("kaggle datasets download -d tamber/steam-video-games")
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# os.system("unzip -o steam-video-games.zip")
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epoch = int(sys.argv[1])
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steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
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steam.isnull().values.any()
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steam['userId'] = steam.userId.astype(str)
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@ -109,7 +112,7 @@ y_test = np.array(y_test).astype(np.float32)
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model.fit(x_train, y_train, epochs=2)
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model.fit(x_train, y_train, epochs=epoch)
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model.evaluate(x_test, y_test)
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prediction = model.predict(x_test)
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classes_x=np.argmax(prediction,axis=1)
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