work on lab6
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Kacper Dudzic 2022-04-25 22:07:19 +02:00
parent 76657a9957
commit 8b663f423d
3 changed files with 62 additions and 25 deletions

36
evaluate.py Normal file
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@ -0,0 +1,36 @@
import tensorflow as tf
from keras.models import load_model
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
# Załadowanie modelu z pliku
model = keras.models.load_model('lego_reg_model')
# Załadowanie zbioru testowego
test_piece_counts = np.array(data_test['piece_count'])
test_prices = np.array(data_test['list_price'])
# Prosta ewaluacja (mean absolute error)
test_results = model.evaluate(
test_piece_counts,
test_prices, verbose=0)
# Zapis wartości liczbowej metryki do pliku
with open('eval_results.txt', 'a+') as f:
f.write(test_results)
# Wygenerowanie i zapisanie do pliku wykresu
with open('eval_results.txt') as f:
scores = []
for line in f:
scores.append(float(line))
builds = list(range(1, len(scores) + 1))
plot = plt.plot(builds, scores)
plt.xlabel('Build number')
plt.xticks(range(1, len(scores) + 1))
plt.ylabel('Mean absolute error')
plt.title('Model error by build')
plt.savefig('error_plot.jpg')
plt.show()

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@ -2,44 +2,43 @@ pipeline {
agent {
dockerfile true
}
parameters {
string(
defaultValue: '100',
description: 'Example training parameter',
name: 'EPOCHS_NUM'
)
}
stages {
stage('Stage 1') {
steps {
sh 'chmod u+x ./simple_regression.py'
echo 'Copying datasets from create-dataset...'
copyArtifacts filter: '*', projectName: 's449288-create-dataset'
sh 'chmod u+x ./evaluate.py'
echo 'Copying datasets from the create-dataset job...'
copyArtifacts filter: 'lego_sets_clean_test.csv', projectName: 's449288-create-dataset'
echo 'Datasets copied'
echo 'Conducting simple regression model test'
sh 'python3 simple_regression.py $EPOCHS_NUM'
echo 'Model and predictions saved'
sh 'head lego_reg_results.csv'
sh 'ls -lh lego_reg_model'
echo 'Archiving model...'
sh 'tar -czf lego_reg_model.tar.gz lego_reg_model/'
archiveArtifacts 'lego_reg_model.tar.gz'
echo 'Model archived'
echo 'Copying model from the training job...'
copyArtifacts filter: 'lego_reg_model.tar.gz', projectName: 's449288-training'
echo 'Model copied'
sh 'tar xvzf lego_reg_model.tar.gz'
echo 'Optional copying of the metrics file from previous build...'
copyArtifacts filter: 'eval_results.txt', projectName: 's449288-evaluation', optional: 'True'
echo 'Metrics file copied if it didn't exist'
echo 'Evaluating model...'
sh 'python3 evaluate.py'
echo 'Model evaluated. Metrics saved. Plot saved.'
sh 'head eval_results.txt'
sh 'file error_plot.jpg'
echo 'Archiving metrics file...'
archiveArtifacts 'eval_results.txt'
echo 'File archived'
}
}
}
post {
success {
emailext body: 'SUCCESS', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
emailext body: 'SUCCESS', subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
}
failure {
emailext body: 'FAILURE', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
emailext body: 'FAILURE', subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
}
unstable {
emailext body: 'UNSTABLE', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
emailext body: 'UNSTABLE', subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
}
changed {
emailext body: 'CHANGED', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
emailext body: 'CHANGED', subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
}
}
}

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@ -25,6 +25,8 @@ pipeline {
sh 'tar -czf lego_reg_model.tar.gz lego_reg_model/'
archiveArtifacts 'lego_reg_model.tar.gz'
echo 'Model archived'
echo 'Launching the s449288-evaluation job...'
build job: 's449288-evaluation/master/'
}
}
}