download-dataset with docker

Dockerfile

@@ -0,0 +1,19 @@
+# Nasz obraz będzie dzidziczył z obrazu Ubuntu w wersji latest
+FROM ubuntu:latest
+
+# Instalujemy niezbędne zależności. Zwróć uwagę na flagę "-y" (assume yes)
+RUN apt update && apt install -y python3 \
+    python3-pip \
+    vim
+
+ENV CUTOFF=${CUTOFF}
+ENV KAGGLE_USERNAME=${KAGGLE_USERNAME}
+ENV KAGGLE_KEY=${KAGGLE_KEY}
+
+# 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
+
+# Skopiujmy nasz skrypt do katalogu /app w kontenerze
+COPY . /app/
+
+RUN python3 -m pip install -r requirements.txt

Jenkinsfile

@@ -1,4 +1,5 @@
 node {
+    docker.image('s444452/ium:1.0').inside {
         stage('Preparation') {
             properties([
                 parameters([
@@ -14,7 +15,7 @@ node {
                  name: 'KAGGLE_KEY'
                 ),
                 string(
-                defaultValue: "1000",
+                 defaultValue: "10000",
                  description: 'Determine the size of dataset',
                  name: 'CUTOFF'
                 )
@@ -28,11 +29,12 @@ node {
             withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
                 "KAGGLE_KEY=${params.KAGGLE_KEY}","CUTOFF=${params.CUTOFF}"]) {
                 sh 'echo KAGGLE_USERNAME: $KAGGLE_USERNAME'
-            sh "chmod u+x ./download_dataset.sh"
-            sh "./download_dataset.sh $CUTOFF"
+                sh "chmod u+x ./lab2_data.py"
+                sh "./lab2_data.py"
             }
         }
         stage('Archive artifacts') {
-        archiveArtifacts 'dataset.csv'
+            archiveArtifacts 'fake_job_postings.csv'
+        }
     }
 }

figlet-loop.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+while read line; do
+	figlet "$line"
+done

lab2_data.py

@@ -6,9 +6,7 @@ from sklearn.model_selection import train_test_split
 
 def download_and_save_dataset():
     api.authenticate() 
-    api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction',
-                               path='./data',
-                               unzip=True)
+    api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', unzip=True)
 
 
 def split_dataset(data: DataFrame):
@@ -26,7 +24,7 @@ def split_dataset(data: DataFrame):
 
 def main():
     # download_and_save_dataset()
-    df = read_csv('./data/fake_job_postings.csv')
+    df = read_csv('./fake_job_postings.csv')
     print(df.describe(include='all'))
     print(df.shape)
     x_train, x_val, x_test, y_train, y_val, y_test = split_dataset(df)

requirements.txt

@@ -0,0 +1,19 @@
+certifi==2021.10.8
+charset-normalizer==2.0.12
+idna==3.3
+joblib==1.1.0
+kaggle==1.5.12
+numpy==1.22.3
+pandas==1.4.1
+python-dateutil==2.8.2
+python-slugify==6.1.1
+pytz==2022.1
+requests==2.27.1
+scikit-learn==1.0.2
+scipy==1.8.0
+six==1.16.0
+sklearn==0.0
+text-unidecode==1.3
+threadpoolctl==3.1.0
+tqdm==4.63.1
+urllib3==1.26.9