Added evaluation

Dockerfile

@@ -17,5 +17,6 @@ WORKDIR /app
 COPY ./preparations.sh ./
 COPY ./preprocesing.py ./
 COPY ./training.py ./
+COPY ./evaluation.py ./
 
 # CMD ./preparations.sh

eval.Jenkinsfile

@@ -0,0 +1,40 @@
+pipeline {
+    agent any;
+
+	parameters {
+		buildSelector(
+					defaultSelector: lastSuccessful(),
+					description: 'Which build to use for copying data artifacts',
+					name: 'BUILD_SELECTOR_DATASET'
+        )
+
+        buildSelector(
+					defaultSelector: lastSuccessful(),
+					description: 'Which build to use for copying training artifacts',
+					name: 'BUILD_SELECTOR_TRAINING'
+        )
+	}
+
+    stages {
+    
+        stage('copy-artifacts')
+                {
+                    steps
+                    {
+                        copyArtifacts(fingerprintArtifacts: true, projectName: 's434742-create-dataset', selector: buildParameter('BUILD_SELECTOR_DATASET'))
+                        copyArtifacts(fingerprintArtifacts: true, projectName: 's434742-training/${BRANCH}', selector: buildParameter('BUILD_SELECTOR_TRAINING'))
+                    }
+                }
+
+        stage('evaluation') {
+			steps {
+                 script {
+                    def image = docker.build('dock')
+                        image.inside{
+                            sh 'chmod +x evaluation.py'
+                            sh 'python3 evaluation.py'
+                }
+			}
+		}
+    }
+}

evaluation.py

@@ -0,0 +1,26 @@
+import pandas as pd
+import numpy as np
+from tensorflow import keras
+import matplotlib.pyplot as plt
+from keras import backend as K
+from sklearn.metrics import mean_squared_error
+
+model = 'suicide_model.h5'
+model = keras.models.load_model(model)
+
+train = pd.read_csv('train.csv')
+test = pd.read_csv('test.csv')
+validate = pd.read_csv('validate.csv')
+
+# podział train set
+X_train = train.loc[:, train.columns != 'suicides_no']
+y_train = train[['suicides_no']]
+X_test = test.loc[:, train.columns != 'suicides_no']
+y_test = test[['suicides_no']]
+
+predictions = model.predict(X_test)
+
+error = mean_squared_error(y_test, predictions)
+
+with open('eval_results.txt', 'a') as f:
+    f.write(str(error) + "\n")