fix jenkinsfile-create-dataset
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5370f573fa
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75da800223
@ -1,28 +1,58 @@
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pipeline {
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agent any
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parameters {
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agent none
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/* parameters {
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string(defaultValue: '6000',
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description: 'numbers of data entries to keep in train.csv',
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name: 'CUTOFF',
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trim: true)
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}
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*/
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stages {
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stage('sh: Shell Script') {
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stage('copy files') {
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agent any
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steps {
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sh '''
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cp ./lab1/script.sh .
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cp ./lab1/python_script.py .
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cp ./lab3/Dockerfile .
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cp ./lab3/requirements.txt .
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'''
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}
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}
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/* stage('sh: Shell Script') {
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steps {
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}" ]) {
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sh 'chmod +x ./lab2/script-zadanie-2-4.sh'
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sh './lab2/script-zadanie-2-4.sh'
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sh 'chmod +x ./lab2/script-zadanie-2-4-cutoff.sh'
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sh ''' chmod +x ./lab2/script-zadanie-2-4.sh
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./lab2/script-zadanie-2-4.sh
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chmod +x ./lab2/script-zadanie-2-4-cutoff.sh'''
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sh "./lab2/script-zadanie-2-4-cutoff.sh ${params.CUTOFF}"
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}
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}
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}
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stage('archive artifacts') {
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steps {
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archiveArtifacts 'train.csv'
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archiveArtifacts 'test.csv'
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archiveArtifacts 'valid.csv'
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*/
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stage('docker') {
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agent {
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dockerfile true
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}
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stages {
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stage('test') {
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steps {
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sh 'cat /etc/issue'
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}
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}
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stage('actual') {
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steps {
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sh './script.sh'
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}
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}
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stage('archive artifacts') {
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steps {
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archiveArtifacts 'train.csv'
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archiveArtifacts 'test.csv'
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archiveArtifacts 'valid.csv'
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}
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}
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}
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}
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}
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19
lab3/Dockerfile
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19
lab3/Dockerfile
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@ -0,0 +1,19 @@
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FROM ubuntu:latest
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RUN apt update >>/dev/null
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RUN apt install -y apt-utils >>/dev/null
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RUN apt install -y python3.8 >>/dev/null
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RUN apt install -y python3-pip >>/dev/null
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RUN apt install -y unzip >>/dev/null
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WORKDIR /app
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COPY ./python_script.py ./
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COPY ./script.sh ./
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RUN chmod +x script.sh
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COPY ./requirements.txt ./
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RUN pip3 install -r requirements.txt >>/dev/null
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CMD ./script.sh
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37
lab3/python_script.py
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37
lab3/python_script.py
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import pandas as pd
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from sklearn import preprocessing
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from sklearn.model_selection import train_test_split
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df = pd.read_csv('smart_grid_stability_augmented.csv')
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scaler = preprocessing.StandardScaler().fit(df.iloc[:, 0:-1])
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df_norm_array = scaler.transform(df.iloc[:, 0:-1])
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df_norm = pd.DataFrame(data=df_norm_array,
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columns=df.columns[:-1])
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df_norm['stabf'] = df['stabf']
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train, testAndValid = train_test_split(
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df_norm,
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test_size=0.2,
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random_state=42,
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stratify=df_norm['stabf'])
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test, valid = train_test_split(
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testAndValid,
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test_size=0.5,
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random_state=42,
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stratify=testAndValid['stabf'])
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def namestr(obj, namespace):
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return [name for name in namespace if namespace[name] is obj]
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dataset = df_norm
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for x in [dataset, train, test, valid]:
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print([q for q in namestr(x, globals()) if len(q) == max([len(w) for w in namestr(x, globals())])][-1])
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print("size:", len(x))
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print(x.describe(include='all'))
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print("class distribution", x.value_counts('stabf'))
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print('===============================================================')
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3
lab3/requirements.txt
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3
lab3/requirements.txt
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kaggle==1.5.12
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pandas==1.1.2
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sklearn==0.0
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6
lab3/script.sh
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6
lab3/script.sh
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#!/bin/bash
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kaggle datasets download -d 'pcbreviglieri/smart-grid-stability' >>/dev/null 2>&1
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unzip smart-grid-stability.zip >>/dev/null 2>&1
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python3 python_script.py
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