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evaluate/Jenkinsfile
vendored
4
evaluate/Jenkinsfile
vendored
@ -17,7 +17,7 @@ pipeline {
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steps {
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copyArtifacts fingerprintArtifacts: true, projectName: 'z-s464980-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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copyArtifacts fingerprintArtifacts: true, projectName: 's464980-training/master', selector: buildParameter('BUILD_SELECTOR')
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copyArtifacts fingerprintArtifacts: true, projectName: 's464980-evaluation/master', selector: buildParameter('BUILD_SELECTOR'), optional: true
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// copyArtifacts fingerprintArtifacts: true, projectName: 's464980-evaluation/master', selector: buildParameter('BUILD_SELECTOR'), optional: true
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}
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}
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stage("Prediction") {
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@ -31,7 +31,7 @@ pipeline {
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steps {
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sh "chmod +x ./predict.py"
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sh "python ./predict.py"
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archiveArtifacts artifacts: 'predictions.txt,accuracy.txt', onlyIfSuccessful: true
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archiveArtifacts artifacts: 'predictions.txt,accuracy.txt,rmse.jpg', onlyIfSuccessful: true
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}
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}
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}
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15
predict.py
15
predict.py
@ -2,6 +2,8 @@ import pandas as pd
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from tensorflow import keras
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import numpy as np
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from sklearn.metrics import root_mean_squared_error
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import matplotlib.pyplot as plt
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np.set_printoptions(threshold=np.inf)
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data = pd.read_csv("df_test.csv")
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@ -17,4 +19,15 @@ with open("predictions.txt", "w") as f:
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accuracy = root_mean_squared_error(y_test, predictions)
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with open("rmse.txt", 'a') as file:
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file.write(str(accuracy))
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file.write(str(accuracy)+"\n")
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with open("rmse.txt", 'r') as file:
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lines = file.readlines()
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num_lines = len(lines)
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lines = [float(line.replace("\n", "")) for line in lines]
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plt.plot(range(1, num_lines+1), lines)
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plt.xlabel("Build number")
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plt.ylabel("RMSE value")
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plt.title("RMSE")
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plt.savefig("rmse.jpg")
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@ -1,4 +1,5 @@
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pandas
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scikit-learn
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tensorflow
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numpy
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numpy
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matplotlib
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