5_jenkins_predict

Dockerfile

@@ -14,6 +14,7 @@ RUN pip3 install keras
 RUN pip3 install tensorflow
 RUN pip3 install scikit-learn
 RUN pip3 install argparse
+RUN pip3 install matplotlib
 
 
 WORKDIR /app

Jenkinsfile_evaluation

@@ -0,0 +1,36 @@
+node {
+ stage('Preparation') { 
+     properties([
+         parameters([
+                string(
+                    defaultValue: '1', 
+                    description: 'Build number',
+                    name: 'build'),
+				gitParameter(
+                    branchFilter: 'origin/(.*)',
+                    defaultValue: 'master',
+                    name: 'BRANCH',
+                    type: 'PT_BRANCH'
+                ),
+         ])
+     ])
+ }
+ 
+      stage('checkout: Check out from version control') {
+     checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's487197', url: 'https://git.wmi.amu.edu.pl/s487197/ium_487197']]])
+ }
+ stage('Dockerfile'){
+    def testImage = docker.image('s487197/ium:37')
+	testImage.inside{ 
+         copyArtifacts filter: 'baltimore_train.csv', projectName: 's487197-create-dataset'
+		 copyArtifacts filter: 'baltimore_test.csv', projectName: 's487197-create-dataset'
+         copyArtifacts filter: 'baltimore_model.h5', fingerprintArtifacts: true, projectName: "s487197-training/$BRANCH", selector: buildParameter(lastSuccessful())
+		 copyArtifacts filter: 'metrics.csv', projectName: "s487197-evaluation.eg/$BRANCH", optional: true
+		 sh "python3 ium_predict.py -build $BUILD"
+		 archiveArtifacts artifacts: 'metrics.csv, listfile.txt, metrics_img.png', onlyIfSuccessful: true
+     
+	 
+    }
+   
+ }
+}

Jenkinsfile_train

@@ -25,12 +25,14 @@ node {
      checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's487197', url: 'https://git.wmi.amu.edu.pl/s487197/ium_487197']]])
  }
  stage('Dockerfile'){
-    def testImage = docker.image('s487197/ium:36')
+    def testImage = docker.image('s487197/ium:37')
 	testImage.inside{ 
          copyArtifacts filter: 'baltimore_train.csv', projectName: 's487197-create-dataset'
 		 sh "python3 ium_train.py -epochs $EPOCHS -lr $LR -validation_split $VALIDATION_SPLIT"
-	     archiveArtifacts artifacts: 'baltimore_model.h5'
+	     archiveArtifacts artifacts: 'baltimore.zip'
-     
+	 stage('Buil'){
+     build job: 's449312-evaluation.eg/master', parameters: [string(name: 'build', value: "${currentBuild.number}")], wait:false
+	 }
 	 
     }
    

ium_predict.py

@@ -1,10 +1,13 @@
-from keras.models import Sequential
+from keras.models import load_model
 import pandas as pd
 from sklearn.preprocessing import LabelEncoder
 from sklearn import metrics
 import math
 import numpy as np
-
+import os.path
+import argparse
+import matplotlib.pyplot as plt
+import shutil
 
 def write_list(names):
     with open('listfile.txt', 'w') as fp:
@@ -26,7 +29,11 @@ def get_x_y(data):
 
 
 def predict():
-    model = load_model('baltimore_model.h5')
+    parser = argparse.ArgumentParser(description='Pred')
+    parser.add_argument('-build', type=int, default=1)
+    args = parser.parse_args()
+    shutil.unpack_archive('baltimore.zip', 'baltimore_model', 'zip')
+    model = load_model('baltimore_model')
 
     train = pd.read_csv('baltimore_train.csv')
     baltimore_data_test = pd.read_csv('baltimore_test.csv')
@@ -42,5 +49,40 @@ def predict():
         x_test,
         y_test, verbose=0)
     write_list(y_predicted)
+    print('Accuracy : ', scores[1] * 100)
+    print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
+    print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
+    if os.path.exists("metrics.csv"):
+        df = pd.read_csv('metrics.csv')
+        data = {
+            'build': [args.build],
+            'mse': metrics.mean_squared_error(y_test, y_predicted),
+            'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
+            'accuracy': scores[1] * 100
+        }
+        row = pd.DataFrame([data])
+        if df['build'].isin([int(args.build)]).any():
+            df[df['build'] == args.build] = row.iloc[0]
+        else:
+            df = pd.concat([df, row])
+            df['build'] = df['build'].astype('int')
+            df.to_csv('metrics.csv', index=False)
+    else:
+        data = {
+            'build': [args.build],
+            'mse': metrics.mean_squared_error(y_test, y_predicted),
+            'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
+            'accuracy': scores[1] * 100
+        }
+        df = pd.DataFrame(data)
+        df['build'] = df['build'].astype('int')
+        df.to_csv('metrics.csv', index=False)
+    plt.plot(df['build'], df['mse'], label="mse")
+    plt.plot(df['build'], df['rmse'], label="rmse")
+    plt.plot(df['build'], df['accuracy'], label="accuracy")
+    plt.legend()
+    plt.show()
+    plt.savefig('metrics_img.png')
+
 
 predict()

ium_train.py

@@ -10,6 +10,7 @@ import tensorflow as tf
 import numpy as np
 from sklearn.preprocessing import LabelEncoder
 import argparse
+import shutil
 
 
 def get_x_y(data):
@@ -57,7 +58,8 @@ def train_model():
         validation_split=args.validation_split)
     hist = pd.DataFrame(history.history)
     hist['epoch'] = history.epoch
-    model.save('baltimore_model.h5')
+    model.save('baltimore_model')
+    shutil.make_archive('baltimore', 'zip', 'baltimore_model')
 
 
 train_model()