Update for Zadanie 6.2 - evaluation

Jenkinsfile_evaluation

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+pipeline {
+    agent {dockerfile true}	
+	parameters {
+		buildSelector(
+					defaultSelector: lastSuccessful(),
+					description: 'Which build to use for copying test data',
+					name: 'BUILD_SELECTOR_CREATE_DATASET')
+		buildSelector(
+					defaultSelector: upstream(),
+					description: 'Which build to use for copying trained model',
+					name: 'BUILD_SELECTOR_TRAINING')	
+		gitParameter branchFilter: 'origin/(.*)', defaultValue: 'master', name: 'BRANCH', type: 'PT_BRANCH'
+	}
+    stages {
+		stage('copyArtifacts') {
+            steps {
+                copyArtifacts fingerprintArtifacts: true, projectName: 's434684-create-dataset', selector: buildParameter('BUILD_SELECTOR_CREATE_DATASET')
+				copyArtifacts fingerprintArtifacts: true, projectName: 's434684-training/${params.BRANCH}', selector: buildParameter('BUILD_SELECTOR_TRAINING')
+				copyArtifacts fingerprintArtifacts: true, projectName: 's434684-evaluation/${params.BRANCH}', selector: lastSuccessful(), optional: True
+            }
+        }
+		stage('Sh script') {
+			steps {
+                sh 'chmod +x run_evaluation.sh'
+				sh './run_evaluation.sh'
+                
+			
+			}
+		}
+		stage('Archive artifacts') {
+			steps{
+				archiveArtifacts artifacts: 'evaluation.txt', 'mean_square_error.png'
+			}
+		}
+    }
+}

ium_zadanie6_evaluation.py

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+import tensorflow as tf
+from tf.keras.models import Sequential
+from tf.keras import layers
+# from keras.layers import Flatten,Dense,Dropout, GlobalAveragePooling2D
+from tf.keras.optimizers import Adam
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_squared_error
+import matplotlib.pyplot as plt
+
+movies_test = pd.read_csv('movies_test.csv')
+
+x_test = movies_test.copy()
+y_test = x_test.pop('rottentomatoes_audience_score')
+x_test.pop('Unnamed: 0')
+
+model = keras.models.load_model('model_movies')
+
+y_predicted = model.predict(x_test, batch_size=64)
+
+error = mean_squared_error(y_test, y_predicted)
+
+with open('evaluation.txt', 'a+') as f:
+  f.write('%d\n' % error)
+  
+errors = np.genfromtxt('evaluation.txt')
+
+fig = plt.figure()
+plt.plot(errors)
+plt.title('Evaluation of trained models')
+plt.ylabel('Mean squared error')
+fig.savefig('mean_squared_error.png')

run_evaluation.sh

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+#!/bin/bash
+python3 ium_zadanie6_evaluation.py