split everything into per-lab repos

lab5/Jenkinsfile

@@ -12,15 +12,15 @@ pipeline {
     stages {
         stage('Stage 1') {
             steps {
-				    sh 'chmod u+x ./process_dataset.py ./simple_regression.py'
+				    sh 'chmod u+x ./lab5/process_dataset.py ./lab5/simple_regression.py'
                     echo 'Processing dataset...'
-                    sh 'python3 process_dataset.py'
+                    sh 'python3 lab5/process_dataset.py'
 					echo 'Dataset processed'
 					echo 'Conducting simple regression model test'
-					sh 'python3 simple_regression.py $EPOCHS_NUM'
+					sh 'python3 lab5/simple_regression.py $EPOCHS_NUM'
 					echo 'Model predictions saved'
-                    sh 'head lego_reg_results.csv'
+                    sh 'head lab5/lego_reg_results.csv'
 				}
             }
         }		
-}
+}

lab5/Jenkinsfile1

@@ -5,12 +5,12 @@ pipeline {
     stages {
         stage('Stage 1') {
             steps {
-				    sh 'chmod u+x ./process_dataset.py ./simple_regression.py'
+				    sh 'chmod u+x ./lab5/process_dataset.py ./lab5/simple_regression.py'
                     echo 'Processing dataset...'
-                    sh 'python3 process_dataset.py'
+                    sh 'python3 lab5/process_dataset.py'
 					echo 'Dataset processed'
 					echo 'Conducting simple regression model test'
-					sh 'python3 simple_regression.py'
+					sh 'python3 lab5/simple_regression.py'
 					echo 'Model predictions saved'
 				}
             }

lab5/process_dataset.py

@@ -0,0 +1,30 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+# usuwamy przy okazji puste pola
+lego = pd.read_csv('lego_sets.csv', encoding='utf-8').dropna()
+
+# list_price moze byc do dwoch miejsc po przecinku
+lego['list_price'] = lego['list_price'].round(2)
+
+# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi
+lego['num_reviews'] = lego['num_reviews'].apply(np.int64)
+lego['piece_count'] = lego['piece_count'].apply(np.int64)
+lego['prod_id'] = lego['prod_id'].apply(np.int64)
+
+# wglad, statystyki
+print(lego)
+print(lego.describe(include='all'))
+
+# pierwszy podzial, wydzielamy zbior treningowy
+lego_train, lego_rem = train_test_split(lego, train_size=0.8)
+
+# drugi podział, wydzielamy walidacyjny i testowy
+lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5)
+
+# zapis
+lego.to_csv('lego_sets_clean.csv', index=None, header=True)
+lego_train.to_csv('lego_sets_clean_train.csv', index=None, header=True)
+lego_valid.to_csv('lego_sets_clean_valid.csv', index=None, header=True)
+lego_test.to_csv('lego_sets_clean_test.csv', index=None, header=True)

lab5/simple_regression.py

@@ -0,0 +1,69 @@
+import tensorflow as tf
+from keras import layers
+from keras.models import save_model
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+# Pobranie przykładowego argumentu trenowania
+EPOCHS_NUM = int(sys.argv[1])
+
+# Wczytanie danych
+data_train = pd.read_csv('lego_sets_clean_train.csv')
+data_test = pd.read_csv('lego_sets_clean_test.csv')
+
+# Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
+train_piece_counts = np.array(data_train['piece_count'])
+train_prices = np.array(data_train['list_price'])
+test_piece_counts = np.array(data_test['piece_count'])
+test_prices = np.array(data_test['list_price'])
+
+# Normalizacja
+normalizer = layers.Normalization(input_shape=[1, ], axis=None)
+normalizer.adapt(train_piece_counts)
+
+# Inicjalizacja
+model = tf.keras.Sequential([
+    normalizer,
+    layers.Dense(units=1)
+])
+
+# Kompilacja
+model.compile(
+    optimizer=tf.optimizers.Adam(learning_rate=0.1),
+    loss='mean_absolute_error'
+)
+
+# Trening
+history = model.fit(
+    train_piece_counts,
+    train_prices,
+    epochs=EPOCHS_NUM,
+    verbose=0,
+    validation_split=0.2
+)
+
+# Wykonanie predykcji na danych ze zbioru testującego
+y_pred = model.predict(test_piece_counts)
+
+# Zapis predykcji do pliku
+results = pd.DataFrame({'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
+results.to_csv('lego_reg_results.csv', index=False, header=True)
+
+# Zapis modelu do pliku
+model.save('lego_reg_model')
+
+# Opcjonalne statystyki, wykresy
+'''
+hist = pd.DataFrame(history.history)
+hist['epoch'] = history.epoch
+print(hist.tail())
+
+plt.scatter(train_piece_counts, train_prices, label='Data')
+plt.plot(x, y_pred, color='k', label='Predictions')
+plt.xlabel('pieces')
+plt.ylabel('price')
+plt.legend()
+plt.show()
+'''

lab6/Jenkinsfile_dataset_for_LAB6

@@ -5,16 +5,16 @@ pipeline {
     stages {
         stage('Stage 1') {
             steps {
-				    sh 'chmod u+x ./process_dataset.py'
+				    sh 'chmod u+x ./lab6/process_dataset.py'
                     echo 'Processing dataset...'
-                    sh 'python3 process_dataset.py'
+                    sh 'python3 lab6/process_dataset.py'
 					echo 'Dataset processed'
 					echo 'Archiving clean train and test datasets...' 
-					archiveArtifacts artifacts: 'lego_sets_clean_train.csv, lego_sets_clean_test.csv'
+					archiveArtifacts artifacts: 'lab6/lego_sets_clean_train.csv, lab6/lego_sets_clean_test.csv'
 					echo 'Datasets archived'
 					echo 'Launching the s449288-training job...'
 					build job: 's449288-training/master/'
 				}
             }
         }		
-}
+}

lab6/Jenkinsfile_eval

@@ -16,28 +16,28 @@ pipeline {
         stage('Stage 1') {
             steps {
                     git branch: "${params.BRANCH}", url: 'https://git.wmi.amu.edu.pl/s449288/ium_s449288.git'
-				    sh 'chmod u+x ./evaluate.py'
+				    sh 'chmod u+x ./lab6/evaluate.py'
 					echo 'Copying datasets from the create-dataset job...'
-					copyArtifacts filter: 'lego_sets_clean_test.csv', projectName: 's449288-create-dataset'
+					copyArtifacts filter: 'lab6/lego_sets_clean_test.csv', projectName: 's449288-create-dataset'
 					echo 'Datasets copied'					
 					echo 'Copying model from the training job...'
-					copyArtifacts filter: 'lego_reg_model.tar.gz', projectName: "s449288-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR') 				
+					copyArtifacts filter: 'lab6/lego_reg_model.tar.gz', projectName: "s449288-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR') 				
 					echo 'Model copied'
-					sh 'tar xvzf lego_reg_model.tar.gz'				
+					sh 'tar xvzf lab6/lego_reg_model.tar.gz'				
 					echo 'Optional copying of the metrics file from previous build...'
-					copyArtifacts filter: 'eval_results.txt', projectName: 's449288-evaluation/master/', optional: true
+					copyArtifacts filter: 'lab6/eval_results.txt', projectName: 's449288-evaluation/master/', optional: true
 					echo 'Metrics file copied if it did not exist'
 					echo 'Evaluating model...'
-					sh 'python3 evaluate.py'
+					sh 'python3 lab6/evaluate.py'
 					echo 'Model evaluated. Metrics saved. Plot saved.'
-                    sh 'head eval_results.txt'	
-					sh 'file error_plot.jpg'
+                    sh 'head lab6/eval_results.txt'	
+					sh 'file lab6/error_plot.jpg'
                     echo 'Archiving metrics file...'
-                    archiveArtifacts 'eval_results.txt'
+                    archiveArtifacts 'lab6/eval_results.txt'
                     echo 'File archived'
                     script {
                         LAST_MAE = sh (
-                        script: 'tail -1 eval_results.txt',
+                        script: 'tail -1 lab6/eval_results.txt',
                         returnStdout: true
                         ).trim()
                     }
@@ -58,4 +58,4 @@ pipeline {
             emailext body: "CHANGED - ${LAST_MAE} MAE", subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
         }		
     }
-}
+}

lab6/Jenkinsfile_train

@@ -14,18 +14,18 @@ pipeline {
     stages {
         stage('Stage 1') {
             steps {
-				    sh 'chmod u+x ./simple_regression.py'
+				    sh 'chmod u+x ./lab6/simple_regression.py'
 					echo 'Copying datasets from create-dataset...'
 					copyArtifacts filter: '*', projectName: 's449288-create-dataset'
 					echo 'Datasets copied'
 					echo 'Conducting simple regression model test'
-					sh 'python3 simple_regression.py $EPOCHS_NUM'
+					sh 'python3 lab6/simple_regression.py $EPOCHS_NUM'
 					echo 'Model and predictions saved'
-                    sh 'head lego_reg_results.csv'
-					sh 'ls -lh lego_reg_model'
+                    sh 'head lab6/lego_reg_results.csv'
+					sh 'ls -lh lab6/lego_reg_model'
                     echo 'Archiving model...'
-                    sh 'tar -czf lego_reg_model.tar.gz lego_reg_model/'
-                    archiveArtifacts 'lego_reg_model.tar.gz'
+                    sh 'tar -czf lab6/lego_reg_model.tar.gz lab6/lego_reg_model/'
+                    archiveArtifacts 'lab6/lego_reg_model.tar.gz'
                     echo 'Model archived'
 					echo 'Launching the s449288-evaluation job...'
 					build job: 's449288-evaluation/master/'
@@ -46,4 +46,4 @@ pipeline {
             emailext body: 'CHANGED', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
         }		
     }
-}
+}

lab6/evaluate.py

@@ -0,0 +1,37 @@
+import tensorflow as tf
+from tensorflow import keras
+from matplotlib import pyplot as plt
+from matplotlib.ticker import MaxNLocator
+import numpy as np
+import pandas as pd
+
+# Załadowanie modelu z pliku
+model = keras.models.load_model('lego_reg_model')
+
+# Załadowanie zbioru testowego
+data_test = pd.read_csv('lego_sets_clean_test.csv')
+test_piece_counts = np.array(data_test['piece_count'])
+test_prices = np.array(data_test['list_price'])
+
+# Prosta ewaluacja (mean absolute error)
+test_results = model.evaluate(
+    test_piece_counts,
+    test_prices, verbose=0)
+
+# Zapis wartości liczbowej metryki do pliku
+with open('eval_results.txt', 'a+') as f:
+    f.write(str(test_results) + '\n')
+
+# Wygenerowanie i zapisanie do pliku wykresu
+with open('eval_results.txt') as f:
+    scores = [float(line) for line in f if line]
+    builds = list(range(1, len(scores) + 1))
+
+    plot = plt.plot(builds, scores)
+    plt.xlabel('Build number')
+    plt.xticks(range(1, len(scores) + 1))
+    plt.ylabel('Mean absolute error')
+    plt.title('Model error by build')
+    plt.savefig('error_plot.jpg')
+    plt.show()
+    

lab6/process_dataset.py

@@ -0,0 +1,30 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+# usuwamy przy okazji puste pola
+lego = pd.read_csv('lego_sets.csv', encoding='utf-8').dropna()
+
+# list_price moze byc do dwoch miejsc po przecinku
+lego['list_price'] = lego['list_price'].round(2)
+
+# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi
+lego['num_reviews'] = lego['num_reviews'].apply(np.int64)
+lego['piece_count'] = lego['piece_count'].apply(np.int64)
+lego['prod_id'] = lego['prod_id'].apply(np.int64)
+
+# wglad, statystyki
+print(lego)
+print(lego.describe(include='all'))
+
+# pierwszy podzial, wydzielamy zbior treningowy
+lego_train, lego_rem = train_test_split(lego, train_size=0.8)
+
+# drugi podział, wydzielamy walidacyjny i testowy
+lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5)
+
+# zapis
+lego.to_csv('lego_sets_clean.csv', index=None, header=True)
+lego_train.to_csv('lego_sets_clean_train.csv', index=None, header=True)
+lego_valid.to_csv('lego_sets_clean_valid.csv', index=None, header=True)
+lego_test.to_csv('lego_sets_clean_test.csv', index=None, header=True)

lab6/simple_regression.py

@@ -0,0 +1,69 @@
+import tensorflow as tf
+from keras import layers
+from keras.models import save_model
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+# Pobranie przykładowego argumentu trenowania
+EPOCHS_NUM = int(sys.argv[1])
+
+# Wczytanie danych
+data_train = pd.read_csv('lego_sets_clean_train.csv')
+data_test = pd.read_csv('lego_sets_clean_test.csv')
+
+# Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
+train_piece_counts = np.array(data_train['piece_count'])
+train_prices = np.array(data_train['list_price'])
+test_piece_counts = np.array(data_test['piece_count'])
+test_prices = np.array(data_test['list_price'])
+
+# Normalizacja
+normalizer = layers.Normalization(input_shape=[1, ], axis=None)
+normalizer.adapt(train_piece_counts)
+
+# Inicjalizacja
+model = tf.keras.Sequential([
+    normalizer,
+    layers.Dense(units=1)
+])
+
+# Kompilacja
+model.compile(
+    optimizer=tf.optimizers.Adam(learning_rate=0.1),
+    loss='mean_absolute_error'
+)
+
+# Trening
+history = model.fit(
+    train_piece_counts,
+    train_prices,
+    epochs=EPOCHS_NUM,
+    verbose=0,
+    validation_split=0.2
+)
+
+# Wykonanie predykcji na danych ze zbioru testującego
+y_pred = model.predict(test_piece_counts)
+
+# Zapis predykcji do pliku
+results = pd.DataFrame({'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
+results.to_csv('lego_reg_results.csv', index=False, header=True)
+
+# Zapis modelu do pliku
+model.save('lego_reg_model')
+
+# Opcjonalne statystyki, wykresy
+'''
+hist = pd.DataFrame(history.history)
+hist['epoch'] = history.epoch
+print(hist.tail())
+
+plt.scatter(train_piece_counts, train_prices, label='Data')
+plt.plot(x, y_pred, color='k', label='Predictions')
+plt.xlabel('pieces')
+plt.ylabel('price')
+plt.legend()
+plt.show()
+'''

lab7/Jenkinsfile_eval

@@ -16,28 +16,28 @@ pipeline {
         stage('Stage 1') {
             steps {
                     git branch: "${params.BRANCH}", url: 'https://git.wmi.amu.edu.pl/s449288/ium_s449288.git'
-				    sh 'chmod u+x ./evaluate.py'
+				    sh 'chmod u+x ./lab7/evaluate.py'
 					echo 'Copying datasets from the create-dataset job...'
-					copyArtifacts filter: 'lego_sets_clean_test.csv', projectName: 's449288-create-dataset'
+					copyArtifacts filter: 'lab7/lego_sets_clean_test.csv', projectName: 's449288-create-dataset'
 					echo 'Datasets copied'					
 					echo 'Copying model from the training job...'
-					copyArtifacts filter: 'lego_reg_model.tar.gz', projectName: "s449288-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR') 				
+					copyArtifacts filter: 'lab7/lego_reg_model.tar.gz', projectName: "s449288-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR') 				
 					echo 'Model copied'
-					sh 'tar xvzf lego_reg_model.tar.gz'				
+					sh 'tar xvzf lab7/lego_reg_model.tar.gz'				
 					echo 'Optional copying of the metrics file from previous build...'
-					copyArtifacts filter: 'eval_results.txt', projectName: 's449288-evaluation/master/', optional: true
+					copyArtifacts filter: 'lab7/eval_results.txt', projectName: 's449288-evaluation/master/', optional: true
 					echo 'Metrics file copied if it did not exist'
 					echo 'Evaluating model...'
-					sh 'python3 evaluate.py'
+					sh 'python3 lab7/evaluate.py'
 					echo 'Model evaluated. Metrics saved. Plot saved.'
-                    sh 'head eval_results.txt'	
-					sh 'file error_plot.jpg'
+                    sh 'head lab7/eval_results.txt'	
+					sh 'file lab7/error_plot.jpg'
                     echo 'Archiving metrics file...'
-                    archiveArtifacts 'eval_results.txt'
+                    archiveArtifacts 'lab7/eval_results.txt'
                     echo 'File archived'
                     script {
                         LAST_MAE = sh (
-                        script: 'tail -1 eval_results.txt',
+                        script: 'tail -1 lab7/eval_results.txt',
                         returnStdout: true
                         ).trim()
                     }
@@ -58,4 +58,4 @@ pipeline {
             emailext body: "CHANGED - ${LAST_MAE} MAE", subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
         }		
     }
-}
+}

lab7/Jenkinsfile_train

@@ -7,23 +7,23 @@ pipeline {
     stages {
         stage('Stage 1') {
             steps {
-				    sh 'chmod u+x ./simple_regression_lab7.py'
+				    sh 'chmod u+x ./lab7/simple_regression_lab7.py'
 					echo 'Copying datasets from create-dataset...'
 					copyArtifacts filter: '*', projectName: 's449288-create-dataset'
 					echo 'Datasets copied'
 					echo 'Conducting simple regression model test'
-					sh 'python3 simple_regression_lab7.py'
+					sh 'python3 lab7/simple_regression_lab7.py'
 					echo 'Model and predictions saved'
-                    sh 'head lego_reg_results.csv'
+                    sh 'head lab7/lego_reg_results.csv'
                     echo 'Archiving model...'
-                    sh 'ls -lh lego_reg_model'
-                    sh 'tar -czf lego_reg_model.tar.gz lego_reg_model/'
-                    archiveArtifacts 'lego_reg_model.tar.gz'
+                    sh 'ls -lh lab7/lego_reg_model'
+                    sh 'tar -czf lab7/lego_reg_model.tar.gz lab7/lego_reg_model/'
+                    archiveArtifacts 'lab7/lego_reg_model.tar.gz'
                     echo 'Model archived'
 					echo 'Archiving Sacreds output repo...'
-					sh 'ls -lh runs/*/'
-					sh 'tar -czf sacred_runs.tar.gz runs/'
-                    archiveArtifacts 'sacred_runs.tar.gz'
+					sh 'ls -lh lab7/runs/*/'
+					sh 'tar -czf lab7/sacred_runs.tar.gz lab7/runs/'
+                    archiveArtifacts 'lab7/sacred_runs.tar.gz'
 					echo 'Sacreds repo archived'
 					echo 'Launching the s449288-evaluation job...'
 					build job: 's449288-evaluation/master/'
@@ -44,4 +44,4 @@ pipeline {
             emailext body: 'CHANGED', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
         }		
     }
-}
+}

lab7/evaluate.py

@@ -0,0 +1,37 @@
+import tensorflow as tf
+from tensorflow import keras
+from matplotlib import pyplot as plt
+from matplotlib.ticker import MaxNLocator
+import numpy as np
+import pandas as pd
+
+# Załadowanie modelu z pliku
+model = keras.models.load_model('lego_reg_model')
+
+# Załadowanie zbioru testowego
+data_test = pd.read_csv('lego_sets_clean_test.csv')
+test_piece_counts = np.array(data_test['piece_count'])
+test_prices = np.array(data_test['list_price'])
+
+# Prosta ewaluacja (mean absolute error)
+test_results = model.evaluate(
+    test_piece_counts,
+    test_prices, verbose=0)
+
+# Zapis wartości liczbowej metryki do pliku
+with open('eval_results.txt', 'a+') as f:
+    f.write(str(test_results) + '\n')
+
+# Wygenerowanie i zapisanie do pliku wykresu
+with open('eval_results.txt') as f:
+    scores = [float(line) for line in f if line]
+    builds = list(range(1, len(scores) + 1))
+
+    plot = plt.plot(builds, scores)
+    plt.xlabel('Build number')
+    plt.xticks(range(1, len(scores) + 1))
+    plt.ylabel('Mean absolute error')
+    plt.title('Model error by build')
+    plt.savefig('error_plot.jpg')
+    plt.show()
+    

lab7/process_dataset.py

@@ -0,0 +1,30 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+# usuwamy przy okazji puste pola
+lego = pd.read_csv('lego_sets.csv', encoding='utf-8').dropna()
+
+# list_price moze byc do dwoch miejsc po przecinku
+lego['list_price'] = lego['list_price'].round(2)
+
+# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi
+lego['num_reviews'] = lego['num_reviews'].apply(np.int64)
+lego['piece_count'] = lego['piece_count'].apply(np.int64)
+lego['prod_id'] = lego['prod_id'].apply(np.int64)
+
+# wglad, statystyki
+print(lego)
+print(lego.describe(include='all'))
+
+# pierwszy podzial, wydzielamy zbior treningowy
+lego_train, lego_rem = train_test_split(lego, train_size=0.8)
+
+# drugi podział, wydzielamy walidacyjny i testowy
+lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5)
+
+# zapis
+lego.to_csv('lego_sets_clean.csv', index=None, header=True)
+lego_train.to_csv('lego_sets_clean_train.csv', index=None, header=True)
+lego_valid.to_csv('lego_sets_clean_valid.csv', index=None, header=True)
+lego_test.to_csv('lego_sets_clean_test.csv', index=None, header=True)

lab7/simple_regression_lab7.py

@@ -0,0 +1,86 @@
+import tensorflow as tf
+from keras import layers
+from keras.models import save_model
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+from sacred.observers import MongoObserver
+
+# Stworzenie obiektu klasy Experiment do śledzenia przebiegu regresji narzędziem Sacred
+ex = Experiment(save_git_info=False)
+
+# Dodanie obserwatora FileObserver
+ex.observers.append(FileStorageObserver('runs'))
+
+#Dodanie obserwatora Mongo
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
+
+# Przykładowa modyfikowalna z Sacred konfiguracja wybranych parametrów treningu
+@ex.config
+def config():
+    epochs = 100
+    units = 1
+    learning_rate = 0.1
+
+
+# Reszta kodu wrzucona do udekorowanej funkcji train do wywołania przez Sacred, żeby coś było capture'owane
+@ex.capture
+def train(epochs, units, learning_rate, _run):
+
+    # Wczytanie danych
+    data_train = pd.read_csv('lego_sets_clean_train.csv')
+    data_test = pd.read_csv('lego_sets_clean_test.csv')
+
+    # Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
+    train_piece_counts = np.array(data_train['piece_count'])
+    train_prices = np.array(data_train['list_price'])
+    test_piece_counts = np.array(data_test['piece_count'])
+    test_prices = np.array(data_test['list_price'])
+
+    # Normalizacja
+    normalizer = layers.Normalization(input_shape=[1, ], axis=None)
+    normalizer.adapt(train_piece_counts)
+
+    # Inicjalizacja
+    model = tf.keras.Sequential([
+        normalizer,
+        layers.Dense(units=units)
+    ])
+
+    # Kompilacja
+    model.compile(
+        optimizer=tf.optimizers.Adam(learning_rate=learning_rate),
+        loss='mean_absolute_error'
+    )
+
+    # Trening
+    history = model.fit(
+        train_piece_counts,
+        train_prices,
+        epochs=epochs,
+        verbose=0,
+        validation_split=0.2
+    )
+
+    # Wykonanie predykcji na danych ze zbioru testującego
+    y_pred = model.predict(test_piece_counts)
+
+    # Zapis predykcji do pliku
+    results = pd.DataFrame(
+        {'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
+    results.to_csv('lego_reg_results.csv', index=False, header=True)
+
+    # Zapis modelu do pliku standardowo poprzez metodę kerasa i poprzez metodę obiektu Experiment z Sacred
+    model.save('lego_reg_model')
+    ex.add_artifact('lego_reg_model/saved_model.pb')
+
+    # Przykładowo zwracamy loss ostatniej epoki w charakterze wyników, żeby było widoczne w plikach zapisanych przez obserwator
+    hist = pd.DataFrame(history.history)
+    hist['epoch'] = history.epoch
+    _run.log_scalar('final.training.loss', hist['loss'].iloc[-1])
+
+@ex.automain
+def main(units, learning_rate):
+    train()

lab8/process_dataset.py

@@ -0,0 +1,30 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+# usuwamy przy okazji puste pola
+lego = pd.read_csv('lego_sets.csv', encoding='utf-8').dropna()
+
+# list_price moze byc do dwoch miejsc po przecinku
+lego['list_price'] = lego['list_price'].round(2)
+
+# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi
+lego['num_reviews'] = lego['num_reviews'].apply(np.int64)
+lego['piece_count'] = lego['piece_count'].apply(np.int64)
+lego['prod_id'] = lego['prod_id'].apply(np.int64)
+
+# wglad, statystyki
+print(lego)
+print(lego.describe(include='all'))
+
+# pierwszy podzial, wydzielamy zbior treningowy
+lego_train, lego_rem = train_test_split(lego, train_size=0.8)
+
+# drugi podział, wydzielamy walidacyjny i testowy
+lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5)
+
+# zapis
+lego.to_csv('lego_sets_clean.csv', index=None, header=True)
+lego_train.to_csv('lego_sets_clean_train.csv', index=None, header=True)
+lego_valid.to_csv('lego_sets_clean_valid.csv', index=None, header=True)
+lego_test.to_csv('lego_sets_clean_test.csv', index=None, header=True)

lab8/simple_regression_lab8.py

@@ -0,0 +1,118 @@
+import tensorflow as tf
+from keras import layers
+from keras.models import save_model
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+from sacred.observers import MongoObserver
+import mlflow
+from urllib.parse import urlparse
+
+# Konfiguracja serwera i nazwy eksperymentu MLflow
+mlflow.set_tracking_uri('http://tzietkiewicz.vm.wmi.amu.edu.pl:5000/#/')
+mlflow.set_experiment('s449288')
+
+# Stworzenie obiektu klasy Experiment do śledzenia przebiegu regresji narzędziem Sacred
+ex = Experiment(save_git_info=False)
+
+# Dodanie obserwatora FileObserver
+ex.observers.append(FileStorageObserver('runs'))
+
+#Dodanie obserwatora Mongo
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
+
+# Przykładowa modyfikowalna z Sacred konfiguracja wybranych parametrów treningu
+@ex.config
+def config():
+    epochs = 100
+    units = 1
+    learning_rate = 0.1
+
+
+# Reszta kodu wrzucona do udekorowanej funkcji train do wywołania przez Sacred, żeby coś było capture'owane
+@ex.capture
+def train(epochs, units, learning_rate, _run):
+
+    # Podpięcie treningu do MLflow
+    with mlflow.start_run() as run:
+        print('MLflow run experiment_id: {0}'.format(run.info.experiment_id))
+        print('MLflow run artifact_uri: {0}'.format(run.info.artifact_uri))
+
+        # Wczytanie danych
+        data_train = pd.read_csv('lego_sets_clean_train.csv')
+        data_test = pd.read_csv('lego_sets_clean_test.csv')
+
+        # Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
+        train_piece_counts = np.array(data_train['piece_count'])
+        train_prices = np.array(data_train['list_price'])
+        test_piece_counts = np.array(data_test['piece_count'])
+        test_prices = np.array(data_test['list_price'])
+
+        # Normalizacja
+        normalizer = layers.Normalization(input_shape=[1, ], axis=None)
+        normalizer.adapt(train_piece_counts)
+
+        # Inicjalizacja
+        model = tf.keras.Sequential([
+            normalizer,
+            layers.Dense(units=units)
+        ])
+
+        # Kompilacja
+        model.compile(
+            optimizer=tf.optimizers.Adam(learning_rate=learning_rate),
+            loss='mean_absolute_error'
+        )
+
+        # Trening
+        history = model.fit(
+            train_piece_counts,
+            train_prices,
+            epochs=epochs,
+            verbose=0,
+            validation_split=0.2
+        )
+
+        # Wykonanie predykcji na danych ze zbioru testującego
+        y_pred = model.predict(test_piece_counts)
+
+        # Zapis predykcji do pliku
+        results = pd.DataFrame(
+            {'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
+        results.to_csv('lego_reg_results.csv', index=False, header=True)
+
+        # Zapis modelu do pliku standardowo poprzez metodę kerasa i poprzez metodę obiektu Experiment z Sacred
+        model.save('lego_reg_model')
+        ex.add_artifact('lego_reg_model/saved_model.pb')
+
+        # Przykładowo zwracamy loss ostatniej epoki w charakterze wyników, żeby było widoczne w plikach zapisanych przez obserwator
+        hist = pd.DataFrame(history.history)
+        hist['epoch'] = history.epoch
+        _run.log_scalar('final.training.loss', hist['loss'].iloc[-1])
+
+        # Ewaluacja MAE na potrzeby MLflow (kopia z evaluate.py)
+        mae = model.evaluate(
+            test_piece_counts,
+            test_prices, verbose=0)
+
+        # Zapis parametrów i metryk dla MLflow
+        mlflow.log_param('epochs', epochs)
+        mlflow.log_param('units', units)
+        mlflow.log_param('learning_rate', learning_rate)
+        mlflow.log_metric("mae", mae)
+
+        # Logowanie i zapis modelu dla Mlflow
+        signature = mlflow.models.signature.infer_signature(train_piece_counts, model.predict(train_piece_counts))
+        tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
+        if tracking_url_type_store != 'file':
+            mlflow.keras.log_model(model, 'lego-model', registered_model_name='TFLegoModel',
+                                     signature=signature)
+        else:
+            mlflow.keras.log_model(model, 'model', signature=signature, input_example=500)
+
+
+@ex.automain
+def main(epochs, units, learning_rate):
+    train()