Add plot for evaluation, add parameters to jenkins and fix mail sending
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parent
703c27c5f6
commit
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@ -7,23 +7,32 @@ pipeline {
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defaultSelector: lastSuccessful(),
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying artifacts',
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description: 'Which build to use for copying artifacts',
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name: 'WHICH_BUILD_DATA'
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name: 'WHICH_BUILD_DATA'
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)
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)
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buildSelector(
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buildSelector(
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defaultSelector: lastSuccessful(),
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying artifacts',
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description: 'Which build to use for copying artifacts',
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name: 'WHICH_BUILD_TRAIN'
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name: 'WHICH_BUILD_TRAIN'
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)
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)
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}
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying artifacts',
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name: 'WHICH_BUILD_EVAL'
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)
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gitParameter branchFilter: 'origin/(.*)', defaultValue: 'master', name: 'BRANCH', type: 'PT_BRANCH'
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}
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stages {
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stages {
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stage('checkout') {
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stage('copyArtifacts') {
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steps {
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steps {
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copyArtifacts fingerprintArtifacts: true, projectName: 's430705-create-dataset', selector: buildParameter('WHICH_BUILD_DATA')
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copyArtifacts fingerprintArtifacts: true, projectName: 's430705-create-dataset', selector: buildParameter('WHICH_BUILD_DATA')
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copyArtifacts fingerprintArtifacts: true, projectName: 's430705-training/master', selector: buildParameter('WHICH_BUILD_TRAIN')
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copyArtifacts optional: true, fingerprintArtifacts: true, projectName: 's430705-evaluation/master', selector: buildParameter('WHICH_BUILD_EVAL')
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}
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}
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}
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}
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stage('Docker'){
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stage('Evaluation){
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steps{
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steps{
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copyArtifacts fingerprintArtifacts: true, projectName: 's430705-training/master', selector: buildParameter('WHICH_BUILD_TRAIN')
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sh 'python3 "./lab06-eval.py ${WHICH_BUILD_TRAIN}"'
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sh 'python3 "./lab06-eval.py" >> eval.txt'
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sh 'python3 "./lab06-plot.py"'
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sh 'python3 "./lab07_sacred01.py"'
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sh 'python3 "./lab07_sacred01.py"'
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sh 'python3 "./lab07_sacred02.py"'
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sh 'python3 "./lab07_sacred02.py"'
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}
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}
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@ -32,14 +41,22 @@ pipeline {
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steps {
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steps {
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archiveArtifacts 'eval.txt'
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archiveArtifacts 'eval.txt'
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archiveArtifacts 'lab07/**'
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archiveArtifacts 'lab07/**'
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archiveArtifacts 'evaluation_plot.png'
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}
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}
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}
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}
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stage('sendMail') {
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}
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steps{
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post {
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emailext body: currentBuild.result ?: 'SUCCESS EVALUATION',
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success {
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subject: 's430705 evaluation',
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mail body: 'SUCCESS', subject: 's430705', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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}
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unstable {
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mail body: 'UNSTABLE', subject: 's430705', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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failure {
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mail body: 'FAILURE', subject: 's430705', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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changed {
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mail body: 'CHANGED', subject: 's430705', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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}
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}
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}
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}
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}
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@ -16,7 +16,7 @@ pipeline {
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stage('copyArtifacts') {
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stage('copyArtifacts') {
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steps {
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steps {
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copyArtifacts fingerprintArtifacts: true, projectName: 's430705-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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copyArtifacts fingerprintArtifacts: true, projectName: 's430705-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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sh 'python3 lab06_training.py $epochs'
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sh 'python3 lab06_training.py ${epochs}'
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}
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}
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}
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}
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@ -29,6 +29,8 @@ pipeline {
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post {
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post {
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success {
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success {
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build job: 's430705-training/evaluation', parameters: ]
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mail body: 'SUCCESS',
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mail body: 'SUCCESS',
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subject: 's430705',
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subject: 's430705',
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to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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@ -1,22 +1,22 @@
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from tensorflow.keras.models import Sequential
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import sys
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.optimizers import Adam
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from tensorflow.keras.layers import Dropout
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from tensorflow.keras.callbacks import EarlyStopping
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from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score
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from tensorflow.keras.models import load_model
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import pandas as pd
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test_df = pd.read_csv('test.csv')
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import pandas as pd
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from sklearn.metrics import mean_squared_error
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from tensorflow.keras.models import load_model
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test_df = pd.read_csv("test.csv")
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test_df.drop(test_df.columns[0], axis=1, inplace=True)
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test_df.drop(test_df.columns[0], axis=1, inplace=True)
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x_test = test_df.drop("rating", axis=1)
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x_test = test_df.drop("rating", axis=1)
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y_test = test_df["rating"]
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y_test = test_df["rating"]
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model = Sequential()
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model = load_model("model_movies")
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model = load_model('model_movies')
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y_pred = model.predict(x_test.values)
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y_pred = model.predict(x_test.values)
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rmse = mean_squared_error(y_test, y_pred)
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rmse = mean_squared_error(y_test, y_pred)
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build_number = sys.argv[1] if len(sys.argv) > 1 else 0
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print(f"RMSE: {rmse}")
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d = {"rmse": [rmse], "build": [build_number]}
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df = pd.DataFrame(data=d)
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with open("evaluation.csv", "a") as f:
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df.to_csv(f, header=f.tell() == 0, index=False)
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6
lab06-plot.py
Normal file
6
lab06-plot.py
Normal file
@ -0,0 +1,6 @@
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import pandas as pd
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import matplotlib.pyplot as plt
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df = pd.read_csv("evaluation.csv")
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df.plot(x="build", y="rmse")
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plt.savefig("evaluation_plot.png")
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@ -1,7 +1,7 @@
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import sys
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import sys
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import mlflow
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import mlflow
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import pandas as pd
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import pandas as pd
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from sklearn.metrics import mean_squared_error
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import train_test_split
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from tensorflow.keras.callbacks import EarlyStopping
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from tensorflow.keras.callbacks import EarlyStopping
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@ -9,4 +9,5 @@ tensorflow==2.0.0b1
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wget==3.2
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wget==3.2
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gast==0.3.3
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gast==0.3.3
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sacred==0.8.2
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sacred==0.8.2
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GitPython==3.1.14
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GitPython==3.1.14
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matplotlib==3.4.2
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