Jenkins and docker intergration. Create model for lab 5.

CreateDataset.dockerfile

@@ -1,9 +0,0 @@
-FROM ubuntu:latest
-
-ADD get-data.sh /get-data.sh
-ADD prepare_dataset.py /prepare_dataset.py
-
-RUN apt-get update
-RUN apt-get install -y python3 python3-pip unzip
-RUN pip install pandas
-RUN pip install scikit-learn

DatasetStats.dockerfile

@@ -1 +0,0 @@
-FROM ubuntu:latest

JenkinsfileCreateDataset

@@ -1,6 +1,10 @@
 pipeline {
-   agent any
-   //Definijuemy parametry, które będzie można podać podczas wywoływania zadania
+   agent {
+      dockerfile {
+         filename 'Lab4.dockerfile'
+         reuseNode true
+      }
+   }
    parameters {
       string(
          defaultValue: '1000',
@@ -23,26 +27,23 @@ pipeline {
    stages {
       stage('Checkout') {
          steps {
-            sh 'rm -rf ium_z487183'
-            sh 'git clone https://git.wmi.amu.edu.pl/s487183/ium_z487183.git'
+            checkout scmGit(
+               branches: [[name: '*/master']],
+               extensions: [cleanBeforeCheckout()],
+               userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s487183/ium_z487183.git']]
+            )
          }
       }
       stage('Prepare data') {
          steps {
             withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}", "KAGGLE_KEY=${params.KAGGLE_KEY}"]) {
-               sh 'ium_z487183/get-data.sh'
-               sh 'python3 ium_z487183/prepare_dataset.py'
+               sh 'echo "{\"username\":\"$KAGGLE_USERNAME\",\"key\":\"$KAGGLE_KEY\"}" > /.kaggle/kaggle.json'
+               sh './get-data.sh'
+               sh 'python3 prepare_dataset.py'
             }
          }
       }
       stage('Archive artifacts') {
-         agent { 
-            dockerfile {
-               filename 'CreateDataset.dockerfile'
-               dir      'ium_z487183'
-               reuseNode true
-            }
-         }
          steps {
             withEnv(["CUTOFF=${params.CUTOFF}"]) {
                archiveArtifacts 'X_test.csv'

JenkinsfileDatasetStats

@@ -1,5 +1,6 @@
 pipeline {
    agent any
+
    parameters{
       buildSelector(
          defaultSelector: lastSuccessful(),
@@ -10,20 +11,22 @@ pipeline {
    stages {
       stage('Checkout') {
          steps {
-            sh 'rm -rf ium_z487183'
-            sh 'git clone https://git.wmi.amu.edu.pl/s487183/ium_z487183.git'
+            checkout scmGit(
+               branches: [[name: '*/master']],
+               extensions: [cleanBeforeCheckout()],
+               userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s487183/ium_z487183.git']]
+            )
          }
       }
       stage('Prepare stats') {
          agent { 
-            dockerfile {
-               filename 'DatasetStats.dockerfile'
-               dir      'ium_z487183'
+            docker {
+               image 'mmoryl/ium:latest'
                reuseNode true
             }
          }
          steps {
-            copyArtifacts filter: 'X_test.csv,X_val.csv,X_train.csv,Y_test.csv,Y_val.csv,Y_train.csv', fingerprintArtifacts: true, projectName: 'z487183-create-dataset', selector: workspace()
+            copyArtifacts projectName: 'z487183-create-dataset'
             sh './prepare-stats.sh'
             archiveArtifacts 'stats.txt'
          }

Lab4.dockerfile

@@ -0,0 +1,19 @@
+FROM ubuntu:latest
+
+RUN apt-get update
+RUN apt-get install -y python3 python3-pip unzip
+RUN pip install pandas
+RUN pip install scikit-learn
+RUN pip install kaggle
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+ADD get-data.sh /get-data.sh
+ADD prepare-stats.sh /prepare-stats.sh
+ADD prepare_dataset.py /prepare_dataset.py
+ADD property_model.py /property_model.py
+ADD predict_values.py /predict_values.py
+
+RUN mkdir /.kaggle
+RUN touch /.kaggle/kaggle.json
+RUN chmod 777 /.kaggle/kaggle.json

prepare_dataset.py

@@ -1,49 +1,67 @@
 import os
 import pandas as pd
 from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
 
 # get data
 sells = pd.read_csv('data/Property Sales of Melbourne City.csv')
 
-# delete unnecessary columns and drop rows with NaN values
-columns_to_drop = [
-  'Lattitude',
-  'Longtitude',
-  'CouncilArea',
-  'Propertycount',
-  'Method',
-  'SellerG',
-  'Date',
-  'Postcode',
-  'Bedroom2',
-  'Bathroom',
-  'Car',
-  'BuildingArea',
-  'Address'
-  ]
-sells = sells.drop(columns_to_drop, axis=1).dropna()
+# prepare column, which will be predicted
+price_median = sells['Price'].median()
 
-# normalize values
-sells["Price"] = sells["Price"] / sells["Price"].max()
-sells["Landsize"] = sells["Landsize"] / sells["Landsize"].max()
-sells["Distance"] = sells["Distance"] / sells["Distance"].max()
+def price_above_median(price):
+    if price > price_median:
+        return 1
+    else:
+        return 0
+    
+sells['PriceAboveMedian'] = sells['Price'].apply(price_above_median)
+
+# delete unnecessary columns and drop rows with NaN values
+columns_to_take = ['Rooms', 'Distance', 'Bedroom2', 'Bathroom', 'PriceAboveMedian']
+sells = sells[columns_to_take].dropna()
 
 # cut off dataset to fixed number of values
-cutoff = int(os.environ['CUTOFF'])
+cutoff = 1000
 sells = sells.sample(cutoff)
 
-# split to train/dev/test subsets
-X = sells
-Y = sells.pop('Price')
+# split dataset
+train_data, test_data = train_test_split(sells, test_size=0.2, random_state=42)
+train_data, dev_data = train_test_split(train_data, test_size=0.2, random_state=42)
 
-X_train, X_temp, Y_train, Y_temp = train_test_split(X, Y, test_size=0.3, random_state=1)
-X_val, X_test, Y_val, Y_test = train_test_split(X_temp, Y_temp, test_size=0.5, random_state=1)
+# prepare dataset
+features = ['Rooms', 'Distance', 'Bedroom2', 'Bathroom']
+target = 'PriceAboveMedian'
+
+X_train = train_data[features].values
+y_train = train_data[target].values
+
+X_dev = dev_data[features].values
+y_dev = dev_data[target].values
+
+X_test = test_data[features].values
+y_test = test_data[target].values
+
+# normalize values
+scaler = MinMaxScaler()
+features = ['Rooms', 'Distance', 'Bedroom2', 'Bathroom']
+for feature in features:
+    X_train = scaler.fit_transform(X_train)
+    X_dev = scaler.fit_transform(X_dev)
+    X_test = scaler.fit_transform(X_test)
 
 # save subsets to files
+X_train = pd.DataFrame(X_train, columns = ['Rooms', 'Distance', 'Bedroom2', 'Bathroom'])
+y_train = pd.DataFrame(y_train, columns = ['PriceAboveMedian'])
+X_dev = pd.DataFrame(X_dev, columns = ['Rooms', 'Distance', 'Bedroom2', 'Bathroom'])
+y_dev = pd.DataFrame(y_dev, columns = ['PriceAboveMedian'])
+X_test = pd.DataFrame(X_test, columns = ['Rooms', 'Distance', 'Bedroom2', 'Bathroom'])
+y_test = pd.DataFrame(y_test, columns = ['PriceAboveMedian'])
+
 X_train.to_csv('X_train.csv', index=False)
-X_val.to_csv('X_val.csv', index=False)
+X_dev.to_csv('X_val.csv', index=False)
 X_test.to_csv('X_test.csv', index=False)
 
-Y_train.to_csv('Y_train.csv', index=False)
-Y_val.to_csv('Y_val.csv', index=False)
-Y_test.to_csv('Y_test.csv', index=False)
+y_train.to_csv('Y_train.csv', index=False)
+y_dev.to_csv('Y_val.csv', index=False)
+y_test.to_csv('Y_test.csv', index=False)

property_model.py

@@ -0,0 +1,33 @@
+import pandas as pd
+from keras.models import Sequential
+from keras.layers import Dense
+
+# prepare dataset
+features = ['Rooms', 'Distance', 'Bedroom2', 'Bathroom']
+target = 'PriceAboveMedian'
+
+X_train = pd.read_csv('X_train.csv').values
+y_train = pd.read_csv('Y_train.csv').values
+
+X_dev = pd.read_csv('X_val.csv').values
+y_dev = pd.read_csv('Y_val.csv').values
+
+X_test = pd.read_csv('X_test.csv').values
+y_test = pd.read_csv('Y_test.csv').values
+
+# model definition
+model = Sequential([
+    Dense(32, activation='relu', input_shape=(len(features),)),
+    Dense(32, activation='relu'),
+    Dense(1, activation='sigmoid'),
+])
+
+#compile and train
+model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])
+hist = model.fit(X_train, y_train,
+          batch_size=32, epochs=100,
+          validation_data=(X_dev, y_dev))
+
+model.evaluate(X_test, y_test)[1]
+
+model.save('model.h5')