Fixes for lab5, lab6
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@ -11,13 +11,14 @@ stages {
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}", "CUTOFF=${params.CUTOFF}" ]) {
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"KAGGLE_KEY=${params.KAGGLE_KEY}", "CUTOFF=${params.CUTOFF}" ]) {
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// Skrypt z lab 5 - prosta sieć neuronowa
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// Skrypt z lab 5 - prosta sieć neuronowa
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// sh 'python3 linear_regression.py'
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sh 'python3 linear_regression.py'
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// Wygenerowanie podziału danych
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// Wygenerowanie podziału danych
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sh 'python3 script.py'
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sh 'python3 script.py'
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// Stare skrypty bashowe do podziału zbioru
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// Stare skrypty bashowe do podziału zbioru
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// sh "chmod 777 ./data_download.sh"
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// sh "chmod 777 ./data_download.sh"
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// sh "./data_download.sh"
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// sh "./data_download.sh"
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// Zapisanie artefaktów
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// Zapisanie artefaktów
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archiveArtifacts "results.txt"
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archiveArtifacts "MoviesOnStreamingPlatforms_updated.dev"
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archiveArtifacts "MoviesOnStreamingPlatforms_updated.dev"
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archiveArtifacts "MoviesOnStreamingPlatforms_updated.test"
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archiveArtifacts "MoviesOnStreamingPlatforms_updated.test"
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archiveArtifacts "MoviesOnStreamingPlatforms_updated.train"
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archiveArtifacts "MoviesOnStreamingPlatforms_updated.train"
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@ -5,7 +5,7 @@ pipeline {
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parameters {
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parameters {
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gitParameter branchFilter: 'origin/(.*)', defaultValue: 'master', name: 'BRANCH', type: 'PT_BRANCH'
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gitParameter branchFilter: 'origin/(.*)', defaultValue: 'master', name: 'BRANCH', type: 'PT_BRANCH'
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string(name: 'epochs', description: 'epochs number', defaultValue: '100')
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string(name: 'epochs', description: 'epochs number', defaultValue: '100')
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string(name: 'verbose', description: 'verbose number' defaultValue: '0')
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string(name: 'verbose', description: 'verbose number', defaultValue: '0')
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buildSelector(defaultSelector: lastSuccessful(), description: 'Use latest build', name: 'BUILD_SELECTOR')
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buildSelector(defaultSelector: lastSuccessful(), description: 'Use latest build', name: 'BUILD_SELECTOR')
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}
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}
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stages {
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stages {
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@ -46,6 +46,6 @@ else:
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results = model.predict(test_X)
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results = model.predict(test_X)
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# Zapis danych do pliku
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# Zapis danych do pliku
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with open("results.txt", 'w') as file:
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with open("results_lab5.txt", 'w') as file:
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for result in results:
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for result in results:
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file.writelines(str(result[0]) + "\n")
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file.writelines(str(result[0]) + "\n")
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