fix

Dockerfile

@@ -3,4 +3,6 @@ FROM python:latest
 RUN apt-get update && apt-get install -y
 
 RUN pip install pandas
+RUN pip install tensorflow
+RUN pip install matplotlib
 RUN pip install scikit-learn

Jenkinsfile

@@ -9,13 +9,13 @@ pipeline {
              )
          }
    stages {
-      stage('clear') {
+      stage('Clear_Before') {
          steps {
             sh 'rm -rf *'
          }
       }
 
-      stage('Build') {
+      stage('Clone_and_Build') {
          steps {
             sh 'git clone https://git.wmi.amu.edu.pl/s444439/ium_z444439'
             sh 'curl -O https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'
@@ -38,9 +38,17 @@ pipeline {
                sh 'ls -a'
                sh 'python ./ium_z444439/create-dataset.py'
                echo 'process finish'
-               archiveArtifacts 'adult_test.csv'
+               archiveArtifacts 'X_test.csv'
-               archiveArtifacts 'adult_dev.csv'
+               archiveArtifacts 'X_dev.csv'
-               archiveArtifacts 'adult_train.csv'
+               archiveArtifacts 'X_train.csv'
+               archiveArtifacts 'Y_test.csv'
+               archiveArtifacts 'Y_dev.csv'
+               archiveArtifacts 'Y_train.csv'
+        }
+      }
+      stage('Clear_After') {
+         steps {
+            sh 'rm -rf *'
          }
       }
    }

Jenkinsfile_stats

@@ -33,9 +33,9 @@ pipeline {
                sh 'ls -a'
                sh 'python ./ium_z444439/stats.py'
                echo 'process finish'
-               archiveArtifacts 'adult_test_stats.csv'
+               archiveArtifacts 'X_test_stats.csv'
-               archiveArtifacts 'adult_dev_stats.csv'
+               archiveArtifacts 'X_dev_stats.csv'
-               archiveArtifacts 'adult_train_stats.csv'
+               archiveArtifacts 'X_train_stats.csv'
          }
       }
       stage('Goodbye!') {

create-dataset.py

@@ -8,12 +8,15 @@ adults = adults.dropna()
 
 adults = adults.sample(CUTOFF)
 
-adult_X, adult_Y = adults, adults
+X = adults.copy()
-adult_X_train, adult_X_temp, adult_Y_train, adult_Y_temp = train_test_split(adult_X, adult_Y, test_size=0.3,
+Y = pd.DataFrame(adults.pop('age'))
-																			random_state=1)
-adult_X_dev, adult_X_test, adult_Y_dev, adult_Y_test = train_test_split(adult_X_temp, adult_Y_temp, test_size=0.3,
-																		random_state=1)
 
-adult_X_train.to_csv('adult_train.csv', index=False)
+X_train, X_temp, Y_train, Y_temp = train_test_split(X, Y, test_size=0.3, random_state=1)
-adult_X_dev.to_csv('adult_dev.csv', index=False)
+X_dev, X_test, Y_dev, Y_test = train_test_split(X_temp, Y_temp, test_size=0.3, random_state=1)
-adult_X_test.to_csv('adult_test.csv', index=False)
+
+X_train.to_csv('X_train.csv', index=False)
+X_dev.to_csv('X_dev.csv', index=False)
+X_test.to_csv('X_test.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_dev.csv', index=False)

script.py

@@ -2,12 +2,8 @@ import os
 import urllib.request
 from os.path import exists
 
-import pandas
-from keras.layers import Dense
-from keras.models import Sequential
 import pandas as pd
 import numpy as np
-from keras.utils import to_categorical
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 
@@ -117,22 +113,6 @@ def train_dev_test(data):
 	return train_data, dev_data, test_data
 
 
-def create_model():
-	data = pd.read_csv('adult_train.csv')
-	X = data.copy()
-	y = data["education-num"]
-	X_train_encoded = pd.get_dummies(X)
-	y_train_cat = to_categorical(y)
-	model = Sequential()
-	model.add(Dense(64, activation='relu', input_dim=X_train_encoded.shape[1]))
-	model.add(Dense(17, activation='sigmoid'))
-	model.compile(optimizer='adam',
-				  loss='binary_crossentropy',
-				  metrics=['accuracy'])
-	model.fit(X_train_encoded, y_train_cat, epochs=10, batch_size=32, validation_data=(X_train_encoded, y_train_cat))
-	model.save('model.joblib')
-
-
 if __name__ == '__main__':
 	download_file()
 	csv_file_name = 'adult.csv'
@@ -141,4 +121,3 @@ if __name__ == '__main__':
 	get_statistics(data)
 	normalization(data)
 	clean(data)
-	create_model()

stats.py

@@ -1,9 +1,8 @@
 import pandas
 
-adult_dev = pandas.read_csv('adult_dev.csv', engine='python', encoding='ISO-8859-1', sep=',')
+adult_dev = pandas.read_csv('X_dev.csv', engine='python', encoding='ISO-8859-1', sep=',')
-adult_train = pandas.read_csv('adult_train.csv', engine='python', encoding='ISO-8859-1', sep=',')
+adult_train = pandas.read_csv('X_train.csv', engine='python', encoding='ISO-8859-1', sep=',')
-
+adult_test = pandas.read_csv('X_test.csv', engine='python', encoding='ISO-8859-1', sep=',')
-adult_test = pandas.read_csv('adult_test.csv', engine='python', encoding='ISO-8859-1', sep=',')
 
 adult_dev.describe(include='all').to_csv('adult_dev_stats.csv', index=True)
 adult_train.describe(include='all').to_csv('adult_train_stats.csv', index=True)

train.py

@@ -0,0 +1,24 @@
+import pandas as pd
+import tensorflow
+from keras.applications.densenet import layers
+
+train_data_x = pd.read_csv('./X_train.csv')
+
+adults_train = train_data_x.copy()
+adults_predict = train_data_x.pop('age')
+normalize = layers.Normalization()
+normalize.adapt(adults_train)
+
+adult_model = tensorflow.keras.Sequential([
+  normalize,
+  layers.Dense(64),
+  layers.Dense(1)
+])
+
+adult_model.compile(
+    loss=tensorflow.keras.losses.MeanSquaredError(),
+    optimizer=tensorflow.keras.optimizers.Adam())
+
+adult_model.fit(adults_train, adults_predict, epochs=500)
+
+adult_model.save('model')