evaluation added

Dockerfile

@@ -18,10 +18,12 @@ WORKDIR /app
 COPY ./requirements.txt ./
 COPY ./avocado-preprocessing.py ./
 COPY ./avocado-training.py ./
+COPY ./avocado-evaluation.py ./
 
 RUN chmod +x ./requirements.txt
 RUN chmod +x ./avocado-preprocessing.py 
 RUN chmod +x ./avocado-training.py
+RUN chmod +x ./avocado-evaluation.py
 
 RUN pip3 install -r ./requirements.txt
 # CMD python3 avocado-preprocessing.py

Jenkinsfile-evaluation

@@ -0,0 +1,56 @@
+pipeline {
+    agent any
+
+	parameters {
+		buildSelector(
+					defaultSelector: lastSuccessful(),
+					description: 'Which build to use for copying artifacts',
+					name: 'BUILD_SELECTOR'
+        )
+
+        buildSelector(
+					defaultSelector: lastSuccessful(),
+					description: 'Which build to use for copying artifacts',
+					name: 'BUILD_SELECTOR_DATASET'
+        )
+       
+       buildSelector(
+					defaultSelector: lastSuccessful(),
+					description: 'Which build to use for copying artifacts',
+					name: 'BUILD_SELECTOR_TRAINING'
+        )
+	}
+
+    
+        stage('copy artifacts')
+                {
+                    steps
+                    {
+                        copyArtifacts(fingerprintArtifacts: true, projectName: 's434742-create-dataset', selector: buildParameter('WHICH_BUILD_DATASET'))
+                        copyArtifacts(fingerprintArtifacts: true, projectName: 's434742-training', selector: buildParameter('WHICH_BUILD_TRAINING'))
+                    }
+                }
+
+        stage('docker-training') {
+			steps {
+                script {
+                    def img = docker.build('patlaz/ium:1.0')
+                    img.inside {
+                        sh 'chmod +x avocado-evaluation.py'
+                        sh 'python3 avocado-evaluation.py'
+                 
+                    }
+                }
+			}
+		}
+
+
+        stage('sendMail') {
+            steps{
+                emailext body: currentBuild.result ?: 'SUCCESS',
+                subject: 's434742 evaluation',
+                to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+            }
+        }
+    }
+}

Jenkinsfile-training

@@ -42,14 +42,14 @@ pipeline {
 
 		stage('archiveArtifacts') {
 			steps{
-				archiveArtifacts 'avocado-model.h5'
+				archiveArtifacts 'avocado_model.h5'
 			}
 		}
 
         stage('sendMail') {
             steps{
                 emailext body: currentBuild.result ?: 'SUCCESS',
-                subject: 's434742',
+                subject: 's434742 training',
                 to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
             }
         }

avocado-evaluation.py

@@ -0,0 +1,42 @@
+import pandas as pd
+import numpy as np
+from tensorflow import keras
+import matplotlib.pyplot as plt
+from keras import backend as K
+
+def recall_m(y_true, y_pred):
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+    recall = true_positives / (possible_positives + K.epsilon())
+    return recall
+
+def precision_m(y_true, y_pred):
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+    precision = true_positives / (predicted_positives + K.epsilon())
+    return precision
+
+def f1_m(y_true, y_pred):
+    precision = precision_m(y_true, y_pred)
+    recall = recall_m(y_true, y_pred)
+    return 2*((precision*recall)/(precision+recall+K.epsilon()))
+
+
+# zaladowanie modelu
+avocado_model = 'avocado-model.h5'
+model = keras.models.load_model(avocado_model)
+
+# odczytanie danych z plikow
+avocado_train = pd.read_csv('avocado_train.csv')
+avocado_test = pd.read_csv('avocado_test.csv')
+avocado_validate = pd.read_csv('avocado_validate.csv')
+
+# podzial na X i y
+X_train = avocado_train[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
+y_train = avocado_train[['type']]
+X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
+y_test = avocado_test[['type']]
+
+# ewaluacja
+loss, accuracy, f1_score, precision, recall = model.evaluate(X_test, y_test, verbose=0)
+with open('')

avocado-training.py

@@ -11,6 +11,7 @@ from tensorflow.keras.models import Model
 from tensorflow.keras.callbacks import EarlyStopping
 from keras.models import Sequential
 
+# odczytanie danych z plików
 avocado_train = pd.read_csv('avocado_train.csv')
 avocado_test = pd.read_csv('avocado_test.csv')
 avocado_validate = pd.read_csv('avocado_validate.csv')
@@ -37,8 +38,11 @@ model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']
 epochs = int(sys.argv[1])
 batch_size = int(sys.argv[2])
 
+# trenowanie modelu
 model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
-model.save('avocado-model.h5')
+
+# zapisanie modelu
+model.save('avocado_model.h5')
 
 # predict
 predictions = model.predict(X_test)