download-dataset with docker
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19
Dockerfile
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19
Dockerfile
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# Nasz obraz będzie dzidziczył z obrazu Ubuntu w wersji latest
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FROM ubuntu:latest
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# Instalujemy niezbędne zależności. Zwróć uwagę na flagę "-y" (assume yes)
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RUN apt update && apt install -y python3 \
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python3-pip \
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vim
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ENV CUTOFF=${CUTOFF}
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ENV KAGGLE_USERNAME=${KAGGLE_USERNAME}
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ENV KAGGLE_KEY=${KAGGLE_KEY}
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# 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)
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WORKDIR /app
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# Skopiujmy nasz skrypt do katalogu /app w kontenerze
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COPY . /app/
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RUN python3 -m pip install -r requirements.txt
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56
Jenkinsfile
vendored
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node {
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stage('Preparation') {
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properties([
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parameters([
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string(
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docker.image('s444452/ium:1.0').inside {
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stage('Preparation') {
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properties([
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parameters([
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string(
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defaultValue: 'adamosiowy',
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description: 'Kaggle username',
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name: 'KAGGLE_USERNAME',
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trim: false
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),
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password(
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),
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password(
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defaultValue: '',
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description: 'Kaggle token taken from kaggle.json file, as described in https://github.com/Kaggle/kaggle-api#api-credentials',
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name: 'KAGGLE_KEY'
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),
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string(
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defaultValue: "1000",
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description: 'Determine the size of dataset',
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name: 'CUTOFF'
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)
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])
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])
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}
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stage('Clone repository') {
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checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: '5e0a58a0-03ad-41dd-beff-7b8a07c7fe0c', url: 'https://git.wmi.amu.edu.pl/s444452/ium_444452.git']]])
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}
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stage('Run script') {
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}","CUTOFF=${params.CUTOFF}"]) {
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sh 'echo KAGGLE_USERNAME: $KAGGLE_USERNAME'
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sh "chmod u+x ./download_dataset.sh"
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sh "./download_dataset.sh $CUTOFF"
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),
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string(
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defaultValue: "10000",
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description: 'Determine the size of dataset',
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name: 'CUTOFF'
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)
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])
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])
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}
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stage('Clone repository') {
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checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: '5e0a58a0-03ad-41dd-beff-7b8a07c7fe0c', url: 'https://git.wmi.amu.edu.pl/s444452/ium_444452.git']]])
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}
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stage('Run script') {
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}","CUTOFF=${params.CUTOFF}"]) {
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sh 'echo KAGGLE_USERNAME: $KAGGLE_USERNAME'
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sh "chmod u+x ./lab2_data.py"
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sh "./lab2_data.py"
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}
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}
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stage('Archive artifacts') {
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archiveArtifacts 'fake_job_postings.csv'
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}
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}
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stage('Archive artifacts') {
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archiveArtifacts 'dataset.csv'
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}
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}
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4
figlet-loop.sh
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figlet-loop.sh
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#!/bin/bash
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while read line; do
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figlet "$line"
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done
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@ -6,9 +6,7 @@ from sklearn.model_selection import train_test_split
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def download_and_save_dataset():
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api.authenticate()
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api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction',
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path='./data',
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unzip=True)
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api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', unzip=True)
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def split_dataset(data: DataFrame):
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@ -26,7 +24,7 @@ def split_dataset(data: DataFrame):
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def main():
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# download_and_save_dataset()
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df = read_csv('./data/fake_job_postings.csv')
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df = read_csv('./fake_job_postings.csv')
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print(df.describe(include='all'))
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print(df.shape)
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x_train, x_val, x_test, y_train, y_val, y_test = split_dataset(df)
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19
requirements.txt
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requirements.txt
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certifi==2021.10.8
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charset-normalizer==2.0.12
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idna==3.3
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joblib==1.1.0
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kaggle==1.5.12
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numpy==1.22.3
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pandas==1.4.1
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python-dateutil==2.8.2
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python-slugify==6.1.1
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pytz==2022.1
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requests==2.27.1
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scikit-learn==1.0.2
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scipy==1.8.0
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six==1.16.0
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sklearn==0.0
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text-unidecode==1.3
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threadpoolctl==3.1.0
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tqdm==4.63.1
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urllib3==1.26.9
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