evaluation (first attempt)

.gitignore

@@ -63,4 +63,5 @@ train.csv
 dev.csv
 *.txt
 .venv/
-model.h5
+model.h5
+evaluation.png

Dockerfile

@@ -8,4 +8,5 @@ RUN pip3 install -r requirements.txt
 
 COPY ["Zadanie 1.py", "."]
 COPY ["stats.py", "."]
-COPY ["train.py", "."]
+COPY ["train.py", "."]
+COPY ["evaluate.py", "."]

Jenkinsfile-evaluation

@@ -0,0 +1,54 @@
+pipeline {
+    agent {
+        docker { image 'adnovac/ium_s434760:1.1' }
+    }
+    parameters{
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying data artifacts',
+            name: 'WHICH_BUILD_DATA'
+        )
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying train artifacts',
+            name: 'WHICH_BUILD_TRAIN'
+        )
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying current project artifacts',
+            name: 'WHICH_BUILD_THIS'
+        )
+    }
+    stages {
+            stage('copy artifacts')
+            {
+                steps
+                {
+                    copyArtifacts(fingerprintArtifacts: true, projectName: 's434760-create-dataset', selector: buildParameter('WHICH_BUILD_DATA'))
+                    copyArtifacts(fingerprintArtifacts: true, projectName: 's434760-training', selector: buildParameter('WHICH_BUILD_TRAIN'))
+                    copyArtifacts(fingerprintArtifacts: true, projectName: 's434760-evaluation', selector: buildParameter('WHICH_BUILD_THIS'))
+                }
+            }
+            stage('train')
+            {
+                steps
+                {
+                    catchError {
+                        sh 'python3.8 evaluate.py ${BATCH_SIZE} ${EPOCHS}'
+                    }
+                }
+            }
+            stage('archive artifacts') {
+                steps {
+                    archiveArtifacts 'results.txt,evaluation.png'
+                }
+            }
+            stage('send email') {
+                steps {
+                    emailext body: currentBuild.result ?: 'SUCCESS',
+                    subject: 's434760 - validation',
+                    to: 'annnow19@st.amu.edu.pl'
+                }
+            }
+    }
+}

Jenkinsfile-train

@@ -45,7 +45,7 @@ pipeline {
                 steps {
                     emailext body: currentBuild.result ?: 'SUCCESS',
                     subject: 's434760 - train',
-                    to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+                    to: 'annnow19@st.amu.edu.pl'
                 }
             }
     }

evaluate.py

@@ -0,0 +1,35 @@
+import pandas as pd
+import numpy as np
+from os import path
+from tensorflow import keras
+import sys
+import matplotlib.pyplot as plt
+
+model_name = "model.h5"
+
+input_columns=["Age","Nationality","Position","Club"]
+model = keras.models.load_model(model_name)
+
+test_data=pd.read_csv('test.csv')
+X_test=test_data[input_columns].to_numpy()
+Y_test=test_data[["Overall"]].to_numpy()
+
+#MeanSquaredError
+results_test = model.evaluate(X_test, Y_test, batch_size=128)
+with open('results.txt', 'a+', encoding="UTF-8") as f:
+    f.write(str(results_test) +"\n")
+
+with open('results.txt', 'r', encoding="UTF-8") as f:
+    lines = f.readlines()
+
+
+fig = plt.figure(figsize=(5,5))
+
+chart = fig.add_subplot()
+chart.set_ylabel("Mean squared error")
+chart.set_xlabel("Number of build")
+x = np.arange(0, len(lines), 1)
+y = [float(x) for x in lines]
+print(y)
+plt.plot(x,y,"ro")
+plt.savefig("evaluation.png")

requirements.txt

@@ -3,4 +3,5 @@ pandas==1.2.4
 numpy==1.19.2
 sklearn
 tensorflow==2.4.1
-jinja2==2.11.3
+jinja2==2.11.3
+matplotlib