updated data download job
This commit is contained in:
parent
b7b992cb8a
commit
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26
Dockerfile
26
Dockerfile
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@ -1,26 +0,0 @@
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FROM ubuntu:latest
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ENV KAGGLE_USERNAME=gulczas
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ENV KAGGLE_KEY=default_key
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RUN apt-get update && \
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apt-get install -y \
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python3 \
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python3-pip \
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wget \
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unzip \
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&& rm -rf /var/lib/apt/lists/*
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RUN pip3 install pandas scikit-learn requests kaggle numpy
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WORKDIR /app
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COPY model_creator.py /app/
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COPY use_model.py /app/
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COPY run_py_scripts.sh /app/
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RUN chmod +x model_creator.py
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RUN chmod +x use_model.py
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CMD ["bash", "run_py_scripts.sh"]
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@ -4,7 +4,6 @@ pipeline {
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parameters {
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string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
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password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
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string(name: 'CUTOFF', defaultValue: '90', description: 'Number of rows to cut')
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}
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stages {
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@ -27,7 +26,7 @@ pipeline {
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"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${env.KAGGLE_KEY}"])
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{
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sh "bash ./download_dataset.sh ${params.CUTOFF}"
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sh "bash ./download_dataset.sh"
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}
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}
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}
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@ -1,57 +0,0 @@
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pipeline {
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agent any
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parameters {
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string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
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password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
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}
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stages {
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stage('Clone Repository') {
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steps {
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git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
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}
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}
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stage('Stop and remove existing container') {
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steps {
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script {
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sh "docker stop s464953 || true"
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sh "docker rm s464953 || true"
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}
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}
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}
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stage('Build Docker image') {
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steps {
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script {
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withEnv([
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"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${env.KAGGLE_KEY}"
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]) {
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sh "docker build --build-arg KAGGLE_USERNAME=$KAGGLE_USERNAME --build-arg KAGGLE_KEY=$KAGGLE_KEY -t s464953 ."
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}
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}
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}
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}
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stage('Run Docker container') {
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steps {
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script {
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withEnv([
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"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${env.KAGGLE_KEY}"
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]) {
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sh "docker run --name s464953 -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v ${WORKSPACE}:/app s464953"
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}
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}
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}
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}
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stage('Archive stats.txt artifact') {
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steps {
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archiveArtifacts artifacts: 'stats.txt', allowEmptyArchive: true
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}
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}
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}
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}
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@ -1,44 +0,0 @@
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pipeline {
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agent any
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parameters {
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string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
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password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
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}
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stages {
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stage('Clone Repository') {
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steps {
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git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
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}
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}
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stage('Stop and remove existing container') {
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steps {
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script {
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sh "docker stop s464953 || true"
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sh "docker rm s464953 || true"
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}
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}
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}
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stage('Run Docker container') {
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steps {
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script {
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withEnv([
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"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${env.KAGGLE_KEY}"
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]) {
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sh "docker run --name s464953 -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v ${WORKSPACE}:/app michalgulczynski/ium_s464953:1.0"
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}
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}
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}
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}
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stage('Archive stats.txt artifact') {
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steps {
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archiveArtifacts artifacts: 'stats.txt', allowEmptyArchive: true
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}
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}
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}
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}
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@ -1,57 +0,0 @@
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pipeline {
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agent any
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parameters {
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string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
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password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
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}
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stages {
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stage('Clone Repository') {
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steps {
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git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
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}
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}
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stage('Stop and remove existing container') {
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steps {
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script {
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sh "docker stop s464953 || true"
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sh "docker rm s464953 || true"
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}
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}
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}
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stage('Build Docker image') {
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steps {
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script {
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withEnv([
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"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${env.KAGGLE_KEY}"
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]) {
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sh "docker build --build-arg KAGGLE_USERNAME=$KAGGLE_USERNAME --build-arg KAGGLE_KEY=$KAGGLE_KEY -t s464953 ."
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}
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}
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}
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}
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stage('Run Docker container') {
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steps {
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script {
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withEnv([
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"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${env.KAGGLE_KEY}"
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]) {
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sh "docker run --name s464953 -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v ${WORKSPACE}:/app s464953"
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}
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}
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}
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}
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stage('Archive stats.txt artifact') {
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steps {
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archiveArtifacts artifacts: 'model.pkl', allowEmptyArchive: true
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}
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}
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}
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}
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@ -1,42 +0,0 @@
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pipeline {
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agent any
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parameters {
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buildSelector( defaultSelector: lastSuccessful(), description: 'Build for copying artifacts', name: 'BUILD_SELECTOR')
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}
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stages {
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stage('Clone Repository') {
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steps {
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git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
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}
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}
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stage('Cleanup Artifacts') {
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steps {
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script {
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sh 'rm -rf artifacts'
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}
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}
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}
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stage('Copy Artifact') {
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steps {
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withEnv([
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"BUILD_SELECTOR=${params.BUILD_SELECTOR}"
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]) {
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copyArtifacts fingerprintArtifacts: true, projectName: 'z-s464953-create-dataset', selector: buildParameter('$BUILD_SELECTOR')}
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}
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}
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stage('Execute Shell Script') {
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steps {
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script {
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sh "bash ./dataset_stats.sh"
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}
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}
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}
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stage('Archive Results') {
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steps {
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archiveArtifacts artifacts: 'artifacts/*', onlyIfSuccessful: true
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}
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}
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}
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}
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#!/usr/bin/env python
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# Import bibliotek
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import os
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import shutil
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import requests
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from sklearn.preprocessing import MinMaxScaler
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from kaggle.api.kaggle_api_extended import KaggleApi
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#funkcja pobierająca plik
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def download_file(url, filename, destination_folder):
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# Wersja dla datasetów kaggle
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api = KaggleApi()
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api.authenticate()
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api.dataset_download_files('gulczas/spotify-dataset', path=destination_folder, unzip=True)
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# funkcja dzieląca zbiór
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def split_dataset(data, test_size=0.2, val_size=0.1, random_state=42):
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#Podział na test i trening
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train_data, test_data = train_test_split(data, test_size=test_size, random_state=random_state)
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#Podział na walidacje i trening
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train_data, val_data = train_test_split(train_data, test_size=val_size/(1-test_size), random_state=random_state)
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return train_data, val_data, test_data
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# Wyświetlanie statystyk zbioru
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def print_dataset_stats(data, subset_name):
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with open('stats.txt', 'a') as stats_file:
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print(f"Statystyki dla zbioru {subset_name}:", file=stats_file)
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print(f"Wielkość zbioru {subset_name}: {len(data)}", file=stats_file)
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print("\nStatystyki wartości poszczególnych parametrów:", file=stats_file)
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print(data.describe(), file=stats_file)
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for column in data.columns:
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print(f"Rozkład częstości dla kolumny '{column}':", file=stats_file)
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print(data[column].value_counts(), file=stats_file)
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print("\n", file=stats_file)
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# Normalizacja danych
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def normalize_data(data):
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scaler = MinMaxScaler()
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numeric_columns = data.select_dtypes(include=['int', 'float']).columns
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scaler.fit(data[numeric_columns])
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df_normalized = data.copy()
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df_normalized[numeric_columns] = scaler.transform(df_normalized[numeric_columns])
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return df_normalized
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#Czyszczenie danych
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def clean_dataset(data):
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data.dropna(inplace=True)
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data.drop_duplicates(inplace=True)
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return data
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# main
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url = "https://www.kaggle.com/datasets/gulczas/spotify-dataset?select=Spotify_Dataset.csv"
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filename = "Spotify_Dataset.csv"
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destination_folder = "datasets"
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# Pobieranie jeśli nie ma już pobranego pliku
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if not os.path.exists(destination_folder):
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os.makedirs(destination_folder)
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print(f"Utworzono folder: {destination_folder}")
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else:
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print(f"Folder {destination_folder} już istnieje.")
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if 'Spotify_Dataset.csv' not in os.listdir(destination_folder):
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# Pobranie pliku
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filepath = download_file(url, filename, destination_folder)
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# Wczytanie danych z pliku CSV
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data = pd.read_csv("datasets/Spotify_Dataset.csv", sep=";")
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# Podział datasetu na zbiory treningowy, walidacyjny i testowy
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train_data, val_data, test_data = split_dataset(data)
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# Zapisanie podzielonych zbiorów danych do osobnych plików CSV
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train_data.to_csv("datasets/train.csv", index=False)
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val_data.to_csv("datasets/val.csv", index=False)
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test_data.to_csv("datasets/test.csv", index=False)
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# Wydrukowanie statystyk dla zbiorów
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print_dataset_stats(train_data, "treningowego")
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print("\n")
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print_dataset_stats(val_data, "walidacyjnego")
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print("\n")
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print_dataset_stats(test_data, "testowego")
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# Normalizacja i czyszczenie zbirów
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train_data = normalize_data(train_data)
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train_data = clean_dataset(train_data)
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val_data = normalize_data(train_data)
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val_data = clean_dataset(train_data)
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test_data = normalize_data(train_data)
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test_data = clean_dataset(train_data)
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#!/bin/bash
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echo "------------------ Train dataset stats ------------------"
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wc -l artifacts/train.csv > stats_train.txt
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echo "------------------ Validation dataset stats ------------------"
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wc -l artifacts/validation.csv > stats_validation.txt
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echo "------------------ Test dataset stats ------------------"
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wc -l artifacts/test.csv > stats_test.txt
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mkdir -p data
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mv stats_train.txt stats_validation.txt stats_test.txt artifacts/
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File diff suppressed because it is too large
Load Diff
130389
datasets/test.csv
130389
datasets/test.csv
File diff suppressed because it is too large
Load Diff
456355
datasets/train.csv
456355
datasets/train.csv
File diff suppressed because it is too large
Load Diff
65195
datasets/val.csv
65195
datasets/val.csv
File diff suppressed because it is too large
Load Diff
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#!/bin/bash
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pip install kaggle --upgrade
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kaggle datasets download -d gulczas/spotify-dataset
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unzip -o spotify-dataset.zip
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echo "------------------ Shufle ------------------"
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shuf Spotify_Dataset.csv -o shuffled_spotify.csv
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echo "------------------ Cut off to top $1 rows ------------------"
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head -n $1 shuffled_spotify.csv > cutoff_spotify.csv
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echo "------------------ Split ------------------"
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total_lines=$(wc -l < cutoff_spotify.csv)
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num_test=$((total_lines / 10))
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num_train=$((total_lines - (num_test * 2)))
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num_validation=$num_test
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head -n $num_train cutoff_spotify.csv > train.csv
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tail -n $((num_test+num_validation)) cutoff_spotify.csv | head -n $num_test > test.csv
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tail -n $num_validation cutoff_spotify.csv > validation.csv
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mkdir -p artifacts
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mv Spotify_Dataset.csv cutoff_spotify.csv train.csv validation.csv test.csv artifacts/
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#!/bin/bash
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pip install kaggle --upgrade
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kaggle datasets download -d gulczas/spotify-dataset --unzip
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kaggle datasets download -d joebeachcapital/30000-spotify-songs --unzip
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mkdir -p artifacts
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mv Spotify_Dataset.csv spotify_songs.csv artifacts/
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127
model_creator.py
127
model_creator.py
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import pandas as pd
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import os
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import numpy as np
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from kaggle.api.kaggle_api_extended import KaggleApi
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import accuracy_score
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from sklearn.preprocessing import StandardScaler
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from sklearn. preprocessing import LabelEncoder
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import pickle
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def download_dataset(dataset_address, destination_folder):
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api = KaggleApi()
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api.authenticate()
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api.dataset_download_files(dataset_address, path=destination_folder, unzip=True)
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def check_datasets_presence():
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dataset_1 = "Spotify_Dataset.csv"
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dataset_2 = "spotify_songs.csv"
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destination_folder = "datasets"
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if not os.path.exists(destination_folder):
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os.makedirs(destination_folder)
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print(f"Utworzono folder: {destination_folder}")
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else:
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print(f"Folder {destination_folder} już istnieje.")
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if dataset_1 not in os.listdir(destination_folder):
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download_dataset('gulczas/spotify-dataset', destination_folder)
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if dataset_2 not in os.listdir(destination_folder):
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download_dataset('joebeachcapital/30000-spotify-songs', destination_folder)
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def datasets_preparation():
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df_1 = pd.read_csv("datasets/spotify_songs.csv")
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df_2 = pd.read_csv("datasets/Spotify_Dataset.csv", sep=";")
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df_1 = df_1.dropna()
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df_2 = df_2.dropna()
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df_2 = df_2.rename(columns={'Title': 'track_name'})
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columns_to_remove_df_1 = ['track_id', 'track_album_id', 'track_album_name', 'track_album_release_date',
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'playlist_id', 'playlist_subgenre']
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columns_to_remove_df_2 = ['Date','# of Artist', 'Artist (Ind.)', '# of Nationality',
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'Nationality', 'Continent', 'Points (Total)',
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'Points (Ind for each Artist/Nat)', 'id', 'Song URL']
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df_1 = df_1.drop(columns=columns_to_remove_df_1)
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df_2 = df_2.drop(columns=columns_to_remove_df_2)
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df_1 = df_1.drop_duplicates(subset=['track_name'])
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df_2 = df_2.drop_duplicates(subset=['track_name'])
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le = LabelEncoder()
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unique_names_df2 = df_2['track_name'].unique()
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diff_df = df_1[~df_1['track_name'].isin(unique_names_df2)]
|
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diff_df = diff_df.iloc[:10000]
|
||||
|
||||
#diff_df = pd.concat([diff_df, df_1.iloc[:20]], ignore_index=True)
|
||||
diff_df['track_artist'] = le.fit_transform(diff_df.track_artist)
|
||||
diff_df['playlist_name'] = le.fit_transform(diff_df.playlist_name)
|
||||
diff_df['playlist_genre'] = le.fit_transform(diff_df.playlist_genre)
|
||||
|
||||
#df_1 = df_1.iloc[20:]
|
||||
|
||||
if "docker_test_dataset.csv" not in os.listdir("datasets"):
|
||||
diff_df.to_csv("datasets/docker_test_dataset.csv", index=False)
|
||||
|
||||
result_df = pd.merge(df_1, df_2, on='track_name', how='inner')
|
||||
result_df = result_df.drop_duplicates(subset=['track_name'])
|
||||
columns_to_remove_result_df = ['Rank', 'Artists', 'Danceability', 'Energy', 'Loudness',
|
||||
'Speechiness', 'Acousticness', 'Instrumentalness', 'Valence']
|
||||
result_df = result_df.drop(columns=columns_to_remove_result_df)
|
||||
|
||||
result_df['track_artist'] = le.fit_transform(result_df.track_artist)
|
||||
result_df['playlist_name'] = le.fit_transform(result_df.playlist_name)
|
||||
result_df['playlist_genre'] = le.fit_transform(result_df.playlist_genre)
|
||||
|
||||
return result_df
|
||||
|
||||
|
||||
check_datasets_presence()
|
||||
result_df = datasets_preparation()
|
||||
Y = result_df[['playlist_genre']]
|
||||
X = result_df.drop(columns='playlist_genre')
|
||||
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.10, random_state=42)
|
||||
|
||||
|
||||
Y_train = np.ravel(Y_train)
|
||||
Y_test = np.ravel(Y_test)
|
||||
|
||||
scaler = StandardScaler()
|
||||
numeric_columns = X_train.select_dtypes(include=['int', 'float']).columns
|
||||
X_train_scaled = scaler.fit_transform(X_train[numeric_columns])
|
||||
X_test_scaled = scaler.transform(X_test[numeric_columns])
|
||||
|
||||
model = LogisticRegression(max_iter=1000)
|
||||
model.fit(X_train_scaled, Y_train)
|
||||
|
||||
|
||||
Y_pred = model.predict(X_test_scaled)
|
||||
|
||||
accuracy = accuracy_score(Y_test, Y_pred)
|
||||
print("Accuracy:", accuracy)
|
||||
|
||||
file_path = 'model.pkl'
|
||||
|
||||
if os.path.exists(file_path):
|
||||
os.remove(file_path)
|
||||
|
||||
if file_path not in os.listdir("./"):
|
||||
with open(file_path, 'wb') as file:
|
||||
pickle.dump(model, file)
|
||||
|
||||
print("Model został zapisany do pliku:", file_path)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
Real:['edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm']
|
||||
Predicted: ['pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop']
|
||||
Accuracy:0.1521
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
python3 model_creator.py
|
||||
36
use_model.py
36
use_model.py
|
|
@ -1,36 +0,0 @@
|
|||
import pickle
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.metrics import accuracy_score
|
||||
|
||||
np.set_printoptions(threshold=20)
|
||||
|
||||
file_path = 'model.pkl'
|
||||
with open(file_path, 'rb') as file:
|
||||
model = pickle.load(file)
|
||||
print("Model został wczytany z pliku:", file_path)
|
||||
|
||||
test_df = pd.read_csv("datasets/docker_test_dataset.csv")
|
||||
|
||||
Y_test = test_df[['playlist_genre']]
|
||||
X_test = test_df.drop(columns='playlist_genre')
|
||||
Y_test = np.ravel(Y_test)
|
||||
|
||||
scaler = StandardScaler()
|
||||
numeric_columns = X_test.select_dtypes(include=['int', 'float']).columns
|
||||
X_test_scaled = scaler.fit_transform(X_test[numeric_columns])
|
||||
|
||||
Y_pred = model.predict(X_test_scaled)
|
||||
|
||||
with open('model_predictions.txt', 'w') as f:
|
||||
pass
|
||||
|
||||
with open('model_predictions.txt', 'a') as f:
|
||||
labels_dict = {0: 'edm', 1 : 'latin', 2 : 'pop', 3 : 'r&b', 4 : 'rap', 5 :'rock'}
|
||||
Y_test_labels = [labels_dict[number] for number in Y_test]
|
||||
Y_pred_labels = [labels_dict[number] for number in Y_pred]
|
||||
f.write("Real:" + str(Y_test_labels[:20])+ " \nPredicted: "+ str(Y_pred_labels[:20]))
|
||||
accuracy = accuracy_score(Y_test, Y_pred)
|
||||
f.write("\nAccuracy:" + str(accuracy))
|
||||
|
||||
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Reference in New Issue