Zadanie Domowe Zajęcia 5 (Jupyter + Docker)

Dockerfile

@@ -10,7 +10,10 @@ RUN pip3 install --user kaggle
 RUN pip3 install --user seaborn
 RUN pip3 install --user sklearn
 RUN pip3 install --user pandas
+RUN pip3 install --user numpy
 RUN pip3 install --user matplotlib
+RUN pip3 install --user tensorflow
+
 
 # Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)
 WORKDIR /app
@@ -18,6 +21,7 @@ WORKDIR /app
 # Skopiujmy nasz skrypt do katalogu /app w kontenerze
 COPY ./test.sh ./
 COPY ./Python_file.py ./
+COPY ./Zadanie_5_Docker.py ./
 
 # Domyślne polecenie, które zostanie uruchomione w kontenerze po jego starcie
 CMD ./test.sh

Zadanie_5_Docker.py

@@ -0,0 +1,47 @@
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import wget
+import numpy as np
+
+url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
+wget.download(url, out='winequality-red.csv', bar=None)
+
+wine=pd.read_csv('winequality-red.csv')
+wine
+
+y = wine.quality
+y.head()
+
+x = wine.drop(['quality'], axis= 1)
+x.head()
+
+x=((x-x.min())/(x.max()-x.min())) #Normalizacja
+
+x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
+
+def regression_model():
+    model = Sequential()
+    model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
+    model.add(Dense(64,activation = "relu"))
+    model.add(Dense(1,activation = "relu"))
+    
+    model.compile(optimizer = "adam", loss = "mean_squared_error")
+    return model
+
+model = regression_model()
+model.fit(x_train, y_train, epochs = 600, verbose = 1)
+
+y_pred = model.predict(x_test)
+
+y_pred[:5]
+
+y_pred = np.around(y_pred, decimals=0)
+
+y_pred[:5]
+
+print(accuracy_score(y_test, y_pred))
+
+print(classification_report(y_test,y_pred)) 

Zajęcia4_oraz_5/Dockerfile

@@ -10,7 +10,10 @@ RUN pip3 install --user kaggle
 RUN pip3 install --user seaborn
 RUN pip3 install --user sklearn
 RUN pip3 install --user pandas
+RUN pip3 install --user numpy
 RUN pip3 install --user matplotlib
+RUN pip3 install --user tensorflow
+
 
 # Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)
 WORKDIR /app
@@ -18,6 +21,7 @@ WORKDIR /app
 # Skopiujmy nasz skrypt do katalogu /app w kontenerze
 COPY ./test.sh ./
 COPY ./Python_file.py ./
+COPY ./Zadanie_5_Docker.py ./
 
 # Domyślne polecenie, które zostanie uruchomione w kontenerze po jego starcie
 CMD ./test.sh

Zajęcia4_oraz_5/Zadanie_5_Docker.py

@@ -0,0 +1,47 @@
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import wget
+import numpy as np
+
+url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
+wget.download(url, out='winequality-red.csv', bar=None)
+
+wine=pd.read_csv('winequality-red.csv')
+wine
+
+y = wine.quality
+y.head()
+
+x = wine.drop(['quality'], axis= 1)
+x.head()
+
+x=((x-x.min())/(x.max()-x.min())) #Normalizacja
+
+x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
+
+def regression_model():
+    model = Sequential()
+    model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
+    model.add(Dense(64,activation = "relu"))
+    model.add(Dense(1,activation = "relu"))
+    
+    model.compile(optimizer = "adam", loss = "mean_squared_error")
+    return model
+
+model = regression_model()
+model.fit(x_train, y_train, epochs = 600, verbose = 1)
+
+y_pred = model.predict(x_test)
+
+y_pred[:5]
+
+y_pred = np.around(y_pred, decimals=0)
+
+y_pred[:5]
+
+print(accuracy_score(y_test, y_pred))
+
+print(classification_report(y_test,y_pred)) 

Zajęcia4_oraz_5/test.sh

@@ -1,2 +1,2 @@
 #Uruchomienie skryptu i wyświetlenie 10 pierwszych wierszy wyjściowej tabeli
-python3 Python_file.py
+python3 Zadanie_5_Docker.py

zad8/Jenkinsfile

@@ -1,6 +1,6 @@
 pipeline {
     agent {
-        docker { image 'snowycocoon/ium_434788:1' }
+        docker { image 'snowycocoon/ium_434788:2' }
     }
     stages {
     stage('Test') {